TM Forum Catalyst Whitepaper Ghost in the shell

ãTM Forum 2020. All Rights Reserved.

TM Forum Catalyst Whitepaper Ghost in the shell

Borderless Remote Health Care Counselling: a Reality with 5G

Team Approved Date: 07/10/2020

Release Status: Released Approval Status: Team Approved Version 8 IPR Mode: RAND

Whitepaper - DTW 2020

© TM Forum 2020. All Rights Reserved. Page 2 of 47

Notice

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Table of Contents

Notice .................................................................................................................................... 2Table of Contents .................................................................................................................. 3

1.Document Information ...................................................................................................... 5

1.1. Key Contributors/Reviewers ........................................................................... 5

1.2. Document History ........................................................................................... 6

1.3. About the Document ...................................................................................... 6

2.Executive Summary ........................................................................................................... 6

3.Business Scenario .............................................................................................................. 8

3.1. Scenario Description ....................................................................................... 8

e-health & evolved training with 5G .......................................................... 8

3.2. Problem Statement ......................................................................................... 9

Technical Challenges ................................................................................ 10

Business Challenges .................................................................................. 11

3.3. Business Ecosystem ...................................................................................... 113.4. Social Impact ................................................................................................. 12

3.5. Business Impact ............................................................................................ 12

4.Architecture ..................................................................................................................... 13

4.1. Business Architecture ................................................................................... 13

Value Chain in the Health Conference Application .................................. 13

Generalised Representation of Inter-Provider Eco-system ...................... 14

Assurance++ ............................................................................................. 16

4.2. High Level Solution Architecture .................................................................. 17

Catalyst Evolution ..................................................................................... 17

The Challenge ........................................................................................... 17

Architecture Overview ............................................................................. 18

API Selection ............................................................................................. 18

Use Cases .................................................................................................. 19 Areas for Further Study ............................................................................ 25

5.Use Cases ......................................................................................................................... 25

5.1. Medical training simulation .......................................................................... 25

5.2. AR/VR assisted 3D Organ service .................................................................. 27

5.3. Face detection and emotional recognition as a service ............................... 28



5.4. AI Counselor service ..................................................................................... 29

5.5. The Composition scenario ............................................................................ 32

5.6. The prospective vision .................................................................................. 34

6.Ghost in the Shell Contributions ...................................................................................... 35

7.Ghost in the Shell Catalyst Participants ........................................................................... 36

7.1. Champions .................................................................................................... 36

Chunghwa Telecom .................................................................................. 36 Meiji University ........................................................................................ 37

NTT ........................................................................................................... 37

Orange ...................................................................................................... 38

SYBICA Inc. ................................................................................................ 38

Sparkle ...................................................................................................... 38

7.1.1. TIM ........................................................................................................... 39

Università degli Studi di Milano ............................................................... 39

PCCW - HKT .............................................................................................. 40

Université de Paris : iLUMENS medical training and simulation center ... 40

7.2. Partners ........................................................................................................ 40

Amartus .................................................................................................... 40

Ericsson .................................................................................................... 41

EXFO ......................................................................................................... 41 IOTA .......................................................................................................... 42

8.Conclusions and Next Steps ............................................................................................. 42

9.References ....................................................................................................................... 42

10. Appendix – “Deep dive in VICA” ............................................................................. 43

10.1. VICA: Virtual Intelligent Counseling Agent ................................................... 43

VICA Counselling scenario ....................................................................... 43

VICA Counseling Methodology ............................................................... 43

VICA Feature and Mechanism ................................................................. 44

VICA Uniqueness ..................................................................................... 44

10.2. VICA basic (voice version): System Architecture .......................................... 45

10.3. VICA advanced for ultra-reliability ................................................................ 45

VICA advanced (new version): System Architecture ............................... 46

VICA advanced: Network (Slicing) Architecture ...................................... 46



1. DocumentInformation

1.1. Key Contributors/Reviewers

Name (Contributors) Company

Alessandro Dandreamatteo Ericsson Chao Wu NTT

Dario Serra TIM Sparkle Frederic Desnoes Orange Ihab Mahna EXFO

Konrad Herba Amartus

Marco Anisetti Università degli Studi di Milano Maurizio Vertemati Università degli Studi di Milano Massimo Banzi TIM Marco Gatti Ericsson

Miho Toyama NTT Pierre-François Ceccaldi Université de Paris Russell Harrison Ericsson

Sophie Nachman Orange Setsuo Tsuruta Meiji University Shingo Horiuchi NTT

Takayuki Nakamura NTT

Tse-Han Wang Chunghwa Telecom Yogesh Pathare Sybica

Name(Reviewers) CompanyMassimoBanzi TIMRussellHarrison Ericsson



1.2. Document History

Version Date Author Change Summary 1.0 June 22nd, 2020 Ghost in the Shell Catalyst Team Initial Version 2.0 October 1st, 2020 Ghost in the Shell Catalyst Team Enhanced version for DTW 2020

1.3. About the Document

This document captures the work done as part of Ghost in the Shell catalyst, contributions of various participants, and learnings from the catalyst implementation. This document also provides feedback to various industry forums for further evolution of the business model, architecture and standards to facilitate adoption and automation of various industry vertical services on top of 5G networks and promote seamless collaboration across diverse partners in this digital ecosystem.

2. ExecutiveSummaryHealthcare is a priority, and recent tragic global events are proving it now more than ever. “Ghost in the Shell” is the third iteration of a successful Catalyst series focusing on the understanding of the Healthcare industry needs and the related actions and improvements required from the Telecom industry. The previous two iterations of the Catalyst, codenamed “Blade Runner” and “Skynet”, focused on enabling global collaboration using a diverse ecosystem of partners, including multiple Service providers located on different geographies, to seamlessly provide a set of end-to-end hybrid services, based on virtualized and physical networks as well as wireless and fixed networks. We believe we proved that this collaborative vision is not only feasible but it will be extremely beneficial for the Healthcare users (hospitals, universities, doctors, medical equipment and services vendors) and for the Service Providers by pushing them to achieve a more dynamic application architecture enabling optimal usage of resources and extreme automation, increasing service management speed and service reliability at the same time. The vision is constantly reviewed and confirmed with all the Catalyst members so that we can bring a contribution that is potentially industry wide to the standard bodies that are relevant for our scope. The previous iterations proved how the fulfilment, assurance and intercarrier settlement can be managed at OSS/BSS layer for complex services in this global multi-carrier multi-technology scenarios. This third iteration is focused on analyzing more in depth what such a scenario would mean for:

• the assurance components, how closed loop and preventive analysis and monitoring can help achieving the reliability required for critical services running on the depicted geographically distributed Service Providers landscape;

• the inter-carrier fulfilment and settlement aspects of what was analyzed in Skynet. One of the goals is to enable operational automation and dynamic scalability of network resources;



• the creation of architecture blueprints, expanding the previous work of mapping all the components in TM Forum’s Open Digital Architecture (ODA) using Open APIs.

To achieve our demonstrating and analysis purpose, in addition to the traditional Catalyst team of Service Providers and Vendors, we have added medical and technical Universities located in Milan, Paris and Tokyo, and leading medical equipment vendors. This allows us to create the connection between the final users and customers of these technologies and the Service Providers building it, ensuring the industry impact. The team’s work is focusing on delivering services to the medical and academic sectors via 5G network slices for use cases related to medical simulation, artificial intelligence and virtual reality, introducing the service and network assurance aspects of the solution. We are splitting what is traditionally a local use case of medical training to multiple locations, and we are enriching it with advanced services like AI consultation for distressed patients and personnel, VR Human Organs libraries, and operations on robotic mannequins. This will give a view and open possibilities to what will be the future of the medical industry, heavily using the capabilities delivered by new network technologies such as 5G. Network assurance is combined with the remote medical use case in order to prioritize and secure mission critical traffic based on external social information at the time of an epidemic or during disaster situations. This is called Assurance++. In the business layer, the Catalyst designs the business scenario in CurateFx and proposes a model for the deployment of the APIs in the business applications themselves in order to support, for example, the remote management of medical mannequins. The partners working on this 2020 catalyst iteration are (in alphabetical order): Service Providers:

• Chunghwa Telecom • NTT • Orange • HKT(PCCW) • Sparkle • TIM

Universities:

• University of Meiji (Tokyo) • Università degli studi di Milano • University of Paris: iLUMENS medical training and simulation center

Vendors:

• Amartus • Ericsson • EXFO • IOTA • Sybica

Due to the Covid-19 situation, we were not able to set up a live demo showing end to end service. However, this white paper describes the feasibility of such a live scenario. Instead, we have decided to perform recorded demos in labs focusing on 5G and network and service assurance based on closed loops and problem management. The work done in "Ghost in the Shell" should open opportunities for live POCs with the same stakeholders in 2021 in order to propose a deployable solution to the healthcare vertical.



3. BusinessScenario

3.1. Scenario Description

e-health & evolved training with 5G According to the World Health Organization (WHO), e-health is the transfer of health resources and health care by electronic means. It encompasses three main areas:

• The delivery of health information, for health professionals and health consumers, through the Internet and telecommunications.

• Using the power of IT and e-commerce to improve public health services, e.g. through the education and training of health workers.

• The use of e-commerce and e-business practices in health systems management. So one of the main areas where this can be accomplished is exactly the use of new technologies, IT and communication technologies for the education and training of health workers. Over the years, universities work to build up consortia where experiences are shared and leverage on center of excellence for providing the best training services they can. Technology was a constraint and now 5G can enable it and not just for training and counselling. If you consider that the growth of 5G, opportunities for business in e-health is amazing. A Study from Ericsson reveals potential revenues of up to $75 billion addressing healthcare industry with 5G [1]

Picture 3.1 - Potential revenue for Telco Industry in Healthcare market Potential savings coming from the use of 5G technologies in eHealth is impressive: in a research study involving hundreds of executives from the healthcare and telecoms sectors, STL Partners forecasts that, at an aggregated global level, 5G could bring cost savings of approximately 94 billion USD to the healthcare industry in 2030 [4].



Picture 3.2 - Cost Savings for 5G in HealthCare industry according to STL Partners This is a huge market to be addressed.

3.2. Problem Statement

The main idea behind the project is the evolution of the model of training complex topics such as medicine: the current approach is similar to the one used in the 18th century. At that time, like now, the physical co-presence of doctors and students was necessary, and cadavers were used for train new doctors. Today, new technologies can provide a variety of pedagogical aids: manikins, probes, tools for modelling organs, and use of Augmented Reality in this market. Supported by a platform for sharing images, voice and video as proposed by the catalyst, all this allow medical teachers to perform their own lessons to classes remotely, controlling facilities from their own office, and sharing resources from where these are already available, in laboratories spread in the various universities or research centers with highly specialized skills. Students can be in the laboratory or in the classrooms while others can attend the class remotely. This model of teaching stems from the current trend towards university consortia, and rather than duplicating the specialized knowledge, promotes the constitution of highly specialized hubs from where the skills can be provided to everyone that requires them. This scenario is possible right now, when CSP can locate 5G antennas and devices locally in laboratories. In the near future, when the coverage of 5G will be more ubiquitous in all its features, we can imagine to transport the service to remote territories, where medical operators can help patients, supported by remote specialists, Artificial intelligence and, if needed, by the use of enhanced online translators. Of course, this is dependent on 5G coverage allowing the use of remote teaching techniques in support to Emergency operators. The full deployment of the platform could also enable remote surgery.



Picture 3.3 - The evolution of medical teaching

The solution also aims at being modular: a teaching session can be simply a traditional remote class, but according to the needs, can be integrated by facilities provided by the various members of the teaching consortium: teaching modules aggregated in a sort of marketplace from where courses can be suitably modelled by the customer university or teaching institute. The Marketplace is provided and supported by a Lead Service Provider that manages the provisioning and assurance of a service with the necessary quality required by each module offered by the specialized institution. We are also working on Assurance++: an evolution of the traditional assurance that leverages on Artificial Intelligence to provide a proactive sort of assurance that identifies and reacts to possible issues affecting the service before it actually happens, enabling compliance to KPIs necessary for healthcare scenarios.

Technical Challenges In Picture 3.4 there is a representation of a teaching session, where the complexity of the interactions can be fully perceived. The Lead service provider needs the support of other telco operators to provide the connectivity. The lead provider orchestrates it among all impacted nodes, dynamically, on demand and according to the specific required KPIs. A telco could also have a central role in the provisioning of the service itself. An operator can, for instance, provide the “courses marketplace” with all the modules coming from the different universities and serving the aggregated course as a whole. However, the availability of a course in the marketplace does not ensure the possibility for all possible customers to access it. Before offering the service, it is necessary to ensure the connectivity between the nodes used by the service with the required level of performance between the organization providing the teaching module(s) and the course consumer. In certain cases it is also necessary to ensure edge facilities supporting the course when highly demanding (eg. high resolution organs images rendering) computational requirements are requested. So before allowing the hiring of a course module it is mandatory to identify and orchestrate all necessary connectivity providers for ensuring the fulfillment of the necessary quality parameters. This aspect was also faced in Skynet in the past year. The Focus this year is on assurance.



Picture 3.4 - representation of the ecosystem fora a teaching session

Another aspect is the possibility to allow services to integrate each other. This requires standardized interfaces that are present in the telco world (or at least their need has been clearly recognized) but are missing between different medical facilities (Mannequins manufacturers, Augmented reality solutions, ...) that need to be integrated.

Business Challenges Complex revenues flows complete the picture of interactions between providers and consumers of the portfolio of courses. A provider of a service can also be the consumer of its own service aggregated through others. Catalogue synchronization is something faced in the Skynet catalyst last year, but it must be said that the phase of identifying the actual communication providers able to fulfill the necessary requirements and signing a suitable agreement for service assurance is a challenging issue since the lead service provider is to be the only interface to the customer. Moreover, in case of certain customers (universities for instance) they need a unique reference provider with whom a previous agreement and contract has been signed. This acts as an interface to the others that are the actual providers of the modules and respond for the fulfilment of the SLAs. Finally all this has also to face the issue of cross-national regulations that maybe inhibit the possibility of foreign companies providing certain kind of services; this is more true when dealing with direct medical services instead of teaching. Such courses can also be stored in the cloud to be consumed offline by everyone, with different business models.

3.3. Business Ecosystem

The Actors in the current ecosystem are: • The Application Provider delivers the teaching / counselling service



• The Lead Service Provider delivers the overall connectivity service, and can be a CSP also providing digital services

• Universities and Research Centers are necessary for the knowledge they can provide • Skilled Professionals (independent teachers) may be from universities or hired on need • Medical Solutions Providers develop and deliver the technology supporting the

teaching/counselling activity • Last-Mile Telco Operators provide local connectivity and ensure the fulfillment of the

quality parameters in the transmission necessary for this specialized services • Carriers transmit the signal long distances the signal without loss of quality.

3.4. Social Impact

This project enables the possibility of leveraging on Centers of Excellence in the domain of specialized teaching/counselling. For remote students and patients, it enables the possibility of offering highly specialized health care in emergency situations without the need of having a specialist physically on the ambulance. Best of breed counselling service and remote intervention can be provided to remote areas, with the only constraint being network coverage. All this is not just applicable to healthcare, but also to many other areas: firefighting, security, territory control, social events. All these can benefit from remote intervention of one or more teams of professionals, all of them coordinated by a control room and empowered by IoT devices such as drones, VR/AR visors, and other specialized hardware components.

3.5. Business Impact

The e-health business scenario, thanks to the scalability of the architecture, modularity of the solution, high level quality of service and low latencies, can be extended quickly to remote consultancy and real time surgery and, as described above, to several other domains with benefits to the customers. But in a wider scenario this kind of counselling/remote support can be extended also to other environments such as security police interventions, fire department operations, local military ops or other territory operations that require, on demand, the collaboration of other dedicated teams or specialized professionals that are available in such an integrated service platform. For example, during a rescue operation after a building collapse, it could be necessary to support the firemen and ambulance teams with the collaboration of a professional due to the presence of specific toxic elements. From the control room the coordinator requires the counselling of an expert of explosives/toxic elements, adjuvated by VR visors and IoT drones that are automatically sent from the control room to the staged area to support the consultant. The consultancy required (toxic substance specialists, drone operators) is quickly and simply activated adding the "modules" on the integrated service platform. Moreover the platform, with its characteristics of enhanced mobile broadband and extremely low latency, is suitable for other markets as is such as the gaming on-line sector. Many of these markets are currently expanding and returning high profits.



4. Architecture

4.1. Business Architecture

Value Chain in the Health Conference Application In the scenario for this catalyst, services are provided at several levels between parties that culminate in the health services being provided to multiple parties via videoconference. From an ordering perspective, the customer uses a healthcare application, which deals with the local telecoms provider who becomes the lead operator. In the egalitarian spirit of this collaboration, any of the participating operators may take the role of lead provider depending on the customer who initiates the service. There are effectively two levels of service provided. The customer is interested in the end applications namely health and conferencing. These in turn rely on a connectivity service, which is itself composed of multiple access and inter-carrier links. So, at fulfilment time, the application provider composes the health, conference and connectivity services, and in turn the lead connectivity service provider combines individual services from multiple providers to stitch together the connectivity service. From a bottom-up perspective, multiple local and international operators provide services that are composed into a wide-area networking service, that in turn supports the health and videoconference applications. These applications are then in turn provided by the "lead" operator to the end customers.

The diagram above shows the value chains and services provided amongst the parties. Note that a single entity such as a telecom operator may take mutiple roles in this ecosystem. Each of the orange arrows represents a service, which can be fulfilled, assured and billed between the provider and consumer. So, every user has a commercial relationship with their local operator who provides the access service.



When a conference is organised, the organiser's local operator also becomes the lead service provider responsible for end-to-end connectivity supporting the conference. This operator in turn needs to procure transit connections and connect to the other local operators who provide the last-mile connection. Each of these services is managed via Open APIs and represents an inter-provider relationship in the MEF LSO architecture as described further below. This means that there are API flows related to each of the orange arrows, that implement all of the requirements of the respective MEF integration reference points (Cantata, Sonata and Interlude, as described below). Note that these may not mirror the user data flows.

Generalised Representation of Inter-Provider Eco-system In today’s environment Providers are both competitors and partners (“coopetition”), and business is conducted based on bilateral agreements that are painstakingly thrashed out between the parties. Within an ecosystem of such Providers a supply chain can be formed to address particular Customer’s desire. It consists, at minimum, of two parties: Customer and a Service Provider.

Customer act as a buyer, whilst Service Provider act as a seller, and holds responsibility to the Customer for successful product or service delivery. Service Provider can engage partners, whenever not able to fulfil Customer’s request in own network. Those can engage own suppliers, so that at the end a supply chain can be composed of multiple parties, which act as down- or up-stream Partner or both.



The Inter-Provider ecosystem which allows for automated creation of such supply chains, must adhere to Core principles for business engagement: • Truly Decentralized – No Central Authority who can control or see the other members or

their data without their permission – equal standing • Democratic – Any provider can join as a member but to engage with other members they

must first agree with each member • Bi-Lateral – Supports the current way of working where two members can conduct their

business in complete privacy without others knowing • Private – All communication between partners is guaranteed private and confidential, no

other partners or central authority has any visibility • Controlled – Each member has full control of what they share with other members and only

they decide this • Open – All aspect of the solution must be fully open, where processes and mechanisms are

fully transparent at each step (no black-box) • Extensible – must be open for any member, vendor or system integrator to develop their own

use-case specific extensions and plug-ins without lock-in The MEF 55 has developed an LSO Reference Architecture which defines the roles and a reference points (addressed through APIs) that supports implementation of Inter-Provider ecosystem.

Probably the most important of the IRPs (Interface Reference Points) is LSO Sonata. It is the Management Interface Reference Point supporting the management and operations interactions (e.g., ordering, billing, trouble management, etc.) between two Partners. The LSO Sonata uses concepts and APIs developed by TMF, extends those APIs with specific constructs and bundles them, so that the above declared objectives are met. In this catalyst we also consider the Interlude IRP, defined by MEF as "the Management Interface Reference Point that provides for the coordination of a portion of LSO services within the partner domain that are managed by a Service Provider’s Service Orchestration Functionality within the bounds and policies defined for the service". Therefore it appropriately covers service problem management and service updates where the changes are commercially within the bounds of an existing contract. Note that these changes may still have financial implications but do not require establishment of a new service contract. MEF does not define an IRP at the infrastructure control and management layer. This enables separation of concerns where each operator manages its own resources to deliver the service, and is not responsible for propagating low-level infrastructure data amongst its partners. However, there is still a need for operators to have a common understanding of the boundaries and presence



of their peers. This is enabled through the abstraction of Network Interfaces including ENNI (External Network-Network Interface) and UNI (User-Network Interface). The following table summarises the key Open APIs applicable to the Sonata and Interlude reference points. The APIs in bold are used in the scenario descriptions below. MEF Interface Reference Point TM Forum Open API Sonata TMF673 Geographic Address Management

TMF674 Geographic Site Management TMF679 Product Offering Qualification TMF648 Quote Management TMF622 Product Order Management TMF623 Service Level Agreement Management TMF637 Product Inventory Management

Interlude* TMF656 Service Problem Management TMF640 Configuration and Activation

* Interlude has not yet been mapped to TMF Open APIs, but the listed APIs are considered relevant. The LSO Sonata bundle is constantly being developed (current latest version is R5, with R6 planned in November 2020). As it can be observed above currently LSO Sonata does not address the Trouble Ticketing (TMF621), nor Billing and Settlement. However, MEF with cooperation with GLF CBAN evaluates the application of the Distributed Ledger Technology for the latter. GLF CBAN developed a reference architecture of the DLT enabled platform and defined a set of common (core) services used by applications leveraging underlying DLT capabilities.[6] MEF in turn focuses on Distributed Ledger Technology (DLT)-based Billing (Rating, Invoicing, Reconciliation and Settlement) for use by service providers (e.g. telecoms, cloud providers, mobile operators, IoT etc.) transacting services between them. All that work combined aims in enabling an agile and highly automated ecosystem of ICT providers, which in the medium to long term, allows addressing markets that require hyper-automation – for example for IoT, Industry 4.0, autonomous driving, and smart cities. These trends are evidenced in the analyst reports from Allied Market Research and Global Market Insights among many others.

Assurance++ Mission-critical services such as remote medical care and surgeries demands extremely high service availability and service quality assurance even if in times of stress such as natural disasters. To achieve that, NTT has introduced an enhanced version of assurance, Assurance++. Assurance++ aggregates and utilizes a higher variety of information and takes full advantages of intent management and AI. The information includes not only multi-layer, E2E NW configurations information but also social information concerning epidemic, traffic, weather etc. Thus Assurance++ has the capability to proactively adjust the communication service and resources, as well as provide valuable information or recommendations to the third party in accordance to various social events. In Assurance++, Intelligence management aggregates social information. If Intelligence Management make predictions from the information that there is connectivity will violate SLA in near future, it will retrieve NW topology information from ODA-Production domain, calculate optimized alternative RFS and path, and then request ODA-Production domain to change them.



A specific set of such capabilities can be branded as "Assurance++ compliant" so that operators can collaborate easily with common expectations about how to handle inter-carrier problems.

4.2. High Level Solution Architecture

Catalyst Evolution Fulfilment scenarios were examined in some detail in the previous Skynet catalyst. So, this year's catalyst assume that the services have previously been instantiated, and concentrates on Assurance scenarios covering multiple operators. The information model, including product model and service model, is also carried over from Skynet. This catalyst introduces two innovations to the handling of multi-party assurance scenarios: 1. Problem prediction and proactive action (Assurance++) enabling the supply chain to adapt to

the problem before it occurs. 2. Involving multiple parties in decisions about the actions taken to avoid problem impact.

The Challenge The current models like MEF LSO Reference Architecture promote direct operator responsibility for the segment under their control. Each “segment” operator is free to apply any assurance solution, for as long as it allows to fulfill SLA requirements. In case of violations penalty payments may be applied. It results in the fact that intercarrier service is delivered by the supply chain of operators, and a resiliency of such is determined by the “weakest link”. Introducing prescriptive service protection (Assurance++) in own network allows an operator to maintain a reputation of reliable partner capable to meet harsh requirements for mission critical applications. The next step is to expand Assurance++ capabilities to span across all the segments of the end-to-end connectivity service. This in turn will allow to more efficiently and effectively address the requirements of mission critical application (through proactive mitigation of SLA violations). This will vastly contribute to increasing the reliability of such services and provide required support for mission critical applications like i.e. medical applications.



Architecture Overview The application of Assurance++ mechanisms in the inter-carrier context means: 1. At minimum, enabling partner to obtain information about the list of own(bought) services

and associated quantified impacts caused by network elements outage (expected outcome of predicted disaster).

2. At best, allow partner to request service(s) topology modifications, to bypass the geographical locations at risk, and thus mitigate the risk of service impacts (due to expected outcome of predicted disaster).

For above defined requirements, the solution should cover: 1. Ability to assess the impact and scope (geographical, topological etc.) of a problem to assess

the impacts on services provided to the upper layers, 2. API for notifying consumers of impacted service information based on the predicted disaster

geographical range, 3. Optionally an API for enforcing topology changes in the Partner domain for mission critical

services. Within each operator, the MEF Sonata and Interlude integration reference points connect to relevant BSS and OSS. Intelligent problem prediction and reaction is managed within each operator, but they collaborate via the inter-carrier interfaces to determine the full problem impact and response.

API Selection In this catalyst we primarily consider the use of TMF APIs between the ecosystem participants. Of course, APIs are also used between systems within each operator, and standardization brings benefits to these interfaces as well. As a prerequisite, we assume ordering and fulfilment have already taken place. This stage might have included SLA negotiation using the TMF 623 SLA Management API. Each of the two use cases is broken into two main phases. Firstly, a problem is predicted or detected. Then, action is taken to avoid or resolve the problem. Problem Communication



To communicate the problem between operators, we concentrate on the service layer. The consumer is not interested in a problem with a particular device (resource) and in fact would not necessarily have the topological information to work out the consequences of such a problem. Rather, they are interested in the direct effect of the problem on the service, i.e. the utility being provided. From ODA perspective, the service is managed in the Production layer. This raises the question of whether it is appropriate for the Production layer to directly communicate with external partners. In the ODA diagram this is suggested by the placement of the Engagement Management box spanning all three layers. Likewise, MEF also anticipates this pattern through the inclusion of the Interlude reference point. In these use cases, we have chosen to use the TMF 656 Service Problem Management API to notify partners of problems. This is assumed to be via the Engagement Management layer to provide B2B exposure, but this is independent of the functional payload and for simplicity is omitted from the sequence diagrams below. Separately, reporting on SLA compliance could take place via the TMF623 SLA Management API which might lead to commercial penalties. In summary, in this architecture we have chosen to keep the problem propagation path as simple as possible to enable rapid resolution of time-critical problems. However, in some architectures, it may be preferable that all interaction should pass via Commerce Management. Note that the TMF 656 Service Problem Management API is used at multiple levels of the value chain. A problem in an access network provider is communicated to the composite WAN provider, which in turn interacts with the application service provider. Further, the service instances are separate at these two steps in the value chain, so two different service identifiers are used for these two levels: an access service id and the overall WAN service id. Problem Remediation In the second part of each use case, actions are requested from one party to another. In Use Case 1, the lead network operator needs to order a completely new service from a different provider. This is beyond the level of delegation allowed to the service orchestrator, as it requires commercial procurement and fulfilment. Therefore in this case the TMF622 Product Ordering API is used. Because the issue originated from the service layer, a kind of "reverse decomposition" is required to identify suitable products that could be ordered (from one or more providers) to deliver the required service capability. In Use Case 2, the request is within the boundaries of the service contract originally struck between the provider and consumer. As described above, this corresponds to the Interlude reference point defined by MEF, between service orchestrators. In this scenario we use the TMF 640 Activation and Configuration API allowing one orchestrator to request a change from the orchestrator in the other operator.

Use Cases

Use Case 1: Inter-Carrier Reactive Assurance with Service Provider Decision and New Service Contract

In this scenario, a submarine cable cut causes a problem in a transit link provided by PCCW, that forms part of an end-to-end communications service managed by TIM. In response, TIM seeks alternate international connectivity by issuing a query to another international transit provider, Sparkle. Upon positive confirmation that the service can be provided, a new product is ordered to procure this connectivity. This service order is executed at the commerce management layer and results in a completely new contract with a different provider. The following TMF Open APIs are used:



• TMF623 SLA Management (described above but not shown) • TMF656 Service Problem Management • TMF679 Product Offer Qualification • TMF622 Product Ordering

The steps in the scenario are as follows: 1. PCCW's assurance system detects a problem in an international link and raises the problem

in the internal fault management system. 2. This fault management system determines the impacted services and their associated

customers, and creates associated service problems. 3. The creation of the service problem triggers a ServiceProblemCreateEvent to the consuming

operator (TIM) according to the TMF 656 API. 4. TIM, as the lead communications provider, records this problem in its own problem

management system as a fault in an underlying connection. 5. TIM's system determines the impact of this underlying problem on services that it provides,

in this case to the health application provided by the University of Milan. 6. This service problem is notified to the University of Milan via TMF 656 API. 7. TIM, as lead communications provider, looks for alternate connectivity. It queries Sparkle

to find out whether Sparkle could provide a suitable transit service. 8. Sparkle responds with an appropriate product and associated pricing. 9. TIM's automated assurance system decides that price is acceptable and proceeds to order this

service. 10. Sparkle confirms the order and indicates the expected connection date. 11. This is then noted and communicated as the resolution date for the problem. 12. Sparkle completes connection of the new service and TIM integrates it into the end-to-end

communications service. 13. TIM indicates to the consumer, University of Milan, that the service is working again.



Use Case 2: Inter-Carrier Prescriptive Assurance with Service Provider Decision Within Service Contract

In this scenario, intelligent prediction by Assurance++ detects an imminent problem within the NTT fixed-line network connecting one of the participants in an interactive health conference. NTT notifies the lead communications provider, Orange, which in turn notifies the application provider, University of Paris. The problem is flagged as critical, triggering a request back to NTT to switch to a different access path. As explained above, because this change is already anticipated by the service contract, a service configuration API is used. The following TMF Open APIs are used: • TMF656 Service Problem Management



• TMF640 Activation and Configuration The figure below shows the scenario topology. Assurance++ provides proactive network assurance to connectivity which is used for remote medical service between hospitals in Japan and France. NW Operator’s platform receives flood alerts and traffic information of the area, and then it finds out that this remote medical service will be affected by the flood. Therefore, the lead operator requests a different access path which is used to provide service continuity in anticipation of the natural disaster.

The steps in the scenario are as follows: 1. A problem is detected in the NTT fixed line network. As shown below, problem detection

and impact analysis is facilitated by a proposed Extended Topology API, which provide multi-layer topology information to Intelligence Management as a mechanism to enable proactive network assurance. API requirements are currently being defined in ODA-443.



2. The fault management system determines which services are affected by the problem. 3. Service problems are created, resulting in TMF 656 notification to the service consumers. 4. Orange, which uses the access service as part of an end-to-end connectivity service, receives

the notification and creates a service problem in its own system against the underlying service.

5. Orange works out which of its services would be affected if the predicted problem occurs, and creates service problems for those.

6. As a result, the University of Paris is notified of the potential problem. 7. University of Paris sends an update to mark the problem severity as Critical, because a remote

surgery is in progress. 8. Orange as the lead communications provider responds to the problem severity by triggering

a request to NTT to change the access path. 9. The updated service is tested. 10. Orange as the communications provider notifies University of Paris that the problem is

resolved by an interim solution. Note: The lab demo ends at this point. The scenario can be extended to restore the original service configuration:

11. NTT notifies Orange that the original problem has been resolved. 12. Orange requests that the underlying service is updated to use fixed access once again. 13. Once this is done, the service is tested and the end-to-end service problem is marked as

resolved. 14. The application provider, University of Paris, is notified that the problem is permanently

resolved.



sd Prescriptive Assurance B2B

NTT: AccessProvider

CHT: AccessProvider

Orange: LeadCommunications

Provider

Uni de Paris:Application

Provider

test service

POST ServiceProblemCreateEvent(id = ntt_sp001,affectedService, future date, probability))

completed

create predictedresource problem

assess service impact

PATCH ServiceProblem(severity = critical)

mark service problem as resolved

POST ServiceProblemStateChange(status = Resolved)

create service problem(id = ntt_sp001)

PATCH Service(use primary path)

POST ServiceProblemStateChange(status = Resolved)

POSTServiceProblemCreateEvent

(affectedService)

PATCH Service(use alternate path)

create service problem(id = ntt_sp001)

POST ServiceProblemStateChange(status = Interim Resolved)

create service problem(id = or_sp001)

test service

assess problem impact



Areas for Further Study TMF656 Problem Management API - Although the basic service problem entity is fairly well-suited to predicted problems, a few extensions are needed to handle this scenario. There needs to be a way to indicate the timeframe of the predicted problem, whether it is in a few minutes or next week. This could also be applicable to communicating planned outages. Secondly, the probability of the problem occurring would also be useful information. It would also be useful to add a status for "Interim Resolved" where a temporary solution has restored the service, but it is still pending a permanent fix to return to the original configuration. Real-time Inter-Operator Performance Reporting – There is a need for assurance systems (in Production and Intelligence Management domains of ODA) to interoperate across carriers to improve their analysis and prediction of problems. Antifragility - Nassim Nicholas Taleb introduced the concept of "antifragile" systems in his book of the same name. When we exercise, the muscles may be tired afterwards, but they recover to be stronger than before. Likewise, a learning assurance system would recover from a problem situation to be more robust than it was before the problem occurred. The problem episode allows the system to learn how to better interpret pre-indicative signals, and to study the effectiveness of the response. In this way, we can create a system that "learns from its mistakes".

5. UseCasesGhost in the Shell, GITS, catalyst will unleash the potential of next generation microservices that will be designed to work in an ecosystem where a reliable networking and edge computation capabilities can be accessed ‘as a service’ providing advanced and trustworthy SLA. These new generation microservices can be combined in orchestrations in a much simpler way than the actual ones leading to fully tailored and advanced composed applications. This new scenario will extensively enlarge the business potential of microservices and the relative marketplaces. In this section we will present four application level services and examples of how they can be orchestrated to show the potentiality of the Ghost in the Shell catalyst. The application level services presented targeted the medical sector, and specifically training and emergency support. In medical training sector, our services address the need to access training facilities with the same effectiveness when connected from remote. In the medical emergency sector, we propose a service that is capable to support the health system in case of emergency crisis requests flooding with an AI filtering.

5.1. Medical training simulation

iLUMENS ‘Médecine Paris Nord’ university was founded in 2017 to pioneer medical training simulation for healthcare professionals. There are 10 simulation rooms available, and an emergency vehicle and an Internet of Things (IoT) room, iLUMENS targets finally as many as 8000 medicine students. The current services providing remote teleconferences are more suitable for meetings than for training and teaching purposes, especially in some sectors. In the medical sector for instance, the possibility to do practical face to face exercises is fundamental but, in some situations, not



compatible with the need to be remote. In addition, it is a huge sector and specific competences cannot be easily found in a single Institution or even country or continent. In this scenario we designed a three-legged remote training architecture potentially distributed worldwide, where there are: 1. a control room where an expert drives the simulation for teaching purposes, 2. a teaching room where learners learn how to address the simulation, 3. debriefing room online or offline to follow the simulation and ask questions. More in details our three-legged remote training service will allow to book for and expert (e.g., pneumonia expert) for a specific training section (e.g., virus pneumonia) on a specific time schedule, to setup reliable audio and video channels between the three sites, and to virtualize teaching room with the help of VR/AR devices if needed. The expert in the control room provides a simulation exercise of a specific pathology to the learners in the teaching room and l follow them changing the simulation (e.g., increasing/decrease the severity of the pneumonia or other parameters). A third party can be connected as active auditor of a medical training event. It can ask questions at the end of the event only, while the learners in the teaching room actively do the simulation. Note that room can be either physical one or virtual one where learners and teachers are connected from remote. We also note that for this mission-critical services extremely high service availability and service quality assurance is needed. Decoupling between simulation, control and debrief rooms is useful to enlarge the potential remote audience for the training session. This service will be adopted for international cooperation projects in the Healthcare medical training and simulation area, for on the field training to emergency so called in France ‘White Plan’ in case of sanitary crisis, for on-boarding Western African countries to the medical training and simulation center iLUMENS of Medicine Paris Nord University.



5.2. AR/VR assisted 3D Organ service

Medical image interpretation requires an overall vision of the human anatomy to become adept and accurate in interpreting radiographic images. Many studies have investigated the role and the impact of three-dimensional models on understanding of anatomy structure and relationship among organs, but the results are often inconclusive, because of differences in course structure and organization and also in research quality[5]. One of the main criticisms of these tools is that they do not encourage critical thinking but they expose students to a passive learning, where the information flow one-sidedly. Moreover, they encourage rote memorization of names and structures so reducing both students’ interest in the content and the learning process. In this context, 3D reconstruction and virtual reality (VR) allows to show 2D radiological imaging datasets into a three-dimensional modality, which improves the comprehension of 2D image sequences and lets users experience a patient-specific 3D anatomy. 3D models and VR based on patient-specific anatomy offers numerous benefits, including gaining experience free from risk to patients, learning from mistakes and rehearsal of complex cases. VR could be also used to develop training programs that have proven to be of great value in acquiring laparoscopic skills outside of the operating because they demonstrate good predictive validity. It could also be hypothesized the creation of a 3D digital library related to different anatomical variations which could be shared among different Institutions. In this scenario we develop a 3D organ browsing service that satisfy the above needs and that is based on VR and AR. Figure 6.1 shows an example of browsing an organ in a VR mode



Figure 5.1: 3D organ service to navigate an organ in a VR modality (left). Detailed view on multi organs (right) This service thanks to Ghost in the Shell catalyst will benefit from the low latency and edge computation capabilities of 5G (rendering service in this case) allowing to distribute the streaming of the 3D model in a VR environment without the need to move the 3D models to the client devices and to ask complex rendering at the destination.

5.3. Face detection and emotional recognition as a service

Early facial recognition research suggests that humans innately attend to faces and can rapidly recognize faces remarkably early in life. Within 9 min after birth, infants turn their head to a 90° angle to preferentially attend to faces or face-like images. At 42 min after birth, infants can imitate facial expressions; and by 2 days old, infants can recognize the face of their mother. One-month-old infants show greater pupillary dilation to faces than to non-social stimuli, suggesting greater interest and arousal in infants when looking at faces. At SYBICA we are very keen on discovering and imitating what humans could do through the lenses of technology. In real life, human face doesn’t only provide its identity, it also provides multiple data points. The data points which can be utilized to expand its application to predict, control and help human beings itself. Experts in certain field can provide such services by interacting with human. However, when it comes to dealing with a large number of unknown faces with large number different use cases, we just aren’t skilled enough to handle a task like that. This is where we need an ecosystem consisting of platforms and processes to provide such services in a controlled and regulated manner. SYBICA’s research for AI and Computer vision shares same passion of building an ecosystem which is highly accurate and reliable to better serve communities, governments and enterprises. With some recent diversification in the ecosystem, SYBICA is using the potential of high-density networks (where 5G and IoT meet) along with AI-supported solutions to serve various industry agnostic next-gen use cases including but not limited to healthcare, law-enforcement, education, retail supply chain, transport, banking, mining and construction. Face recognition is the process of detecting and identifying a person from a camera, video or an image. Face detection is the first step for face recognition. After finding the face from an image, the system needs to identify the detected face with features. A face recognition system determines the size, position, and unique characteristics of the face. Every face has numerous, distinguishable landmarks all the various peaks and valleys that make up facial features. In addition, every human face can showcase enormous number of emotional characteristics. In the digital marketplace, identifying and taking necessary feedback action on these characteristics could lead to many modular services in the area of health and medical. To name a few, face detection and recognition could be offered as a stand-alone service where patients’ blood pressure, heart rate, oxygen level or even mental stress level could be measured merely looking into the mobile phone camera for few seconds or less than a minute’s time. This could revolutionize the medical industry where general population can monitor their health vitals through contactless means and get necessary health advice at fingertips. These stand-alone services could be combined with other necessary services to offer holistic or bundled services such as AI counselling, psychology study, expression, and behavior or patterns recognition for marketing, etc.



Following are some of the scenarios where this technology could be used: Real-time Facial expression detection service: This service can be used to evaluate facial cues in order to interpret and understand the emotional state of patients/employees including emotions such as anger, fear, happy or sad to assist in providing proper assistance. This service can also be used to improve sales and marketing discussion, customer satisfaction analysis, patient health monitoring and much more. Health and Wellness Service: By extracting essential features and research in analyzing with AI and ML algorithm SYBICA like solutions can provide medical grade accuracy to identify various health situations. System can capture data like fitness level, stress level, temperature, blood pressure and provide wellness service provider an opportunity to provide necessary health services in near real time. One can integrate with virtual assistant to provide first set of health assessment and recommendation to help patient and medical practitioner. With the sophisticated ML algorithm and supreme processing power the possibility is limitless here. Mental Health Service and research: Facial expression and health details are continuously extracted and evaluated using 5G technology. Virtual mental health services will be provided through interactive solutions where patient can get counselling services from a virtual environment without having to cross any privacy situation. Where at the same time with emotion detection and facial expression analysis in real time will help a medical mental health practitioner to understand and assess counselling methodology and its effectiveness.

5.4. AI Counselor service

5G networks have significantly progressed towards ultra-reliable low latency high speed as well as innovative automatic network operations. Meanwhile, cognitive computing paves the way for machines to have artificial intelligence (AI) which is a reasoning ability analogous to human. For the scope of this white paper we, Professor Setsuo Tsuruta and Yoshitaka Sakurai of Meiji University have developed and are in collaboration with GITS Catalyst project team (in particular with a Canadian company: SYBICA Inc.) working on an AI Counselor called VICA “Visual Counseling Agent” [3]. VICA is an AI medical service for remote mental healthcare to teach/help emotionally distressed people through awareness by voice conversations. With emergencies like Corvid-19 massive amounts of people consult human counselors. The traffic that hospitals and existing telephone counseling currently receive is at maximum capacity and are becoming easily overwhelmed. AI counselors (VICAs) are necessary to cope with the surge in demand. They help mentally distressed or anxious people. In counseling, patients are very sensitive due to emotional distress. Unlike surgery, VICA tries to avoid intrusive operations which can further hurt these sensitive people. Instead, it uses “active/sensitive listening” to promote confession-like conversations for self-awareness by the



patients themselves. This counseling methodology was advocated by Rogers in the US and prevails (among therapists or counsellors) in the world. In real-time critical intrusive operations such as surgery, counseling requires a high-performance sensing capability to know clients’ subtle mental condition by accurately recognizing the client’s feelings through their speech along with their emotional change. However, our counseling AI machines have problems with speech recognition on conventional networks such as 4G. They use the world highest level of cloud-native voice recognition services such as Google speech-to-text cloud. However, even these servers have at least one error per two sentences on average. In some cases it can take ten seconds or more in response time, with our worst-case scenario being a complete disconnection from the network. Accurate speech recognition using majority decision by multiple recognizers requires multiplex voice transmission to cloud-native recognition servers. It also requires repetitive text transmission among counseling clients (terminals) and servers cooperating for the error recovery in case the accuracy check and its improvement are done by the counseling servers. VICA will need ultra-reliable low latency high speed networks and cloud services, as well as ultra-low latency in (multiple) inter-carrier transportation if servers are remote to ensure adequate accuracy. Data rate, bandwidth or transmission speed is a problem as in the case of emergency such as a pandemic, if a vast number of people use this system in parallel. For VICA advanced aiming at further practical usage, “active/sensitive listening” requires high performance namely quick and accurate fusion of sensor data. Besides voice data, we are looking forward to including image data for recognition of body language such as facial expression, vocal data for tone recognition, body temperature, heart rate, etc., to detect a client’s emotion or its change. If we combine all this together an extremely large amount of data is transmitted to the cloud. We are making an attempt (through this catalyst) to integrate the first version of facial expression detection for recognizing the change of emotion. A feeling of sadness can indicate anxiety whereas a feeling of happiness can indicate the client is doing fine. Defining the former as a negative feeling and the latter as a positive feeling, the change of emotion is detected during the consultation. The change of emotion is very important for VICA. It eventually will include more degrees such as very sad, neutral (neither sad nor happy) or a bit happy, very happy. Anxiety degree for VICA advanced is defined in terms of face expression measurement. It is integrated with emotion detection by voice/speech recognition. It is the indispensable collaboration to enhance the total accuracy of emotion change recognition required by VICA approaching “active/sensitive listening” type AI counseling. For complex scenarios such as a patient smiling but still in stress can be part of the next release (Ex: Movement of lips, movement of eyebrows, movement of chin, movement of chicks, eyes movement, dialogue delivery, head movement). In addition to body temperature, heart rate, etc., we need to analyze sound output, dialog delivery etc. as part of the audio analysis. Eventually, combining all the recognition analysis of various types of data will provide a further accurate sensor fusion necessary for AI counseling. We are working actively on this to integrate facial expression and human gesture-based feedback. Thus, 5G/6G networks are considered instrumental for AI counselor service, end customers can sign up for this service on the marketplace as defined in this white paper. To prove the effectiveness of 5G/6G. We have made an attempt through VICA advanced architecture using 5G/slice to conduct experiments and analyzing the results to locate any problems on conventional networks and difference with respect to 5G/6G network when it comes to an cloud based AI counselling agent.



To sum up, at present VICA has a voice interface for counseling to help distressed people by means of C.R. Roger’s “active listening” methodology [2]. This methodology requires high performance listening ability eventually using multiple sensor fusion to identify with great reliability and accuracy client’s subtle emotion and/or its changes to continue conversation towards client’s self-awareness. An advanced version is under development for this. Currently, even just voice recognition has problems on 4G networks. VICA uses Google speech-to-text cloud which is the world’s highest level of cloud-native voice recognition service. However, it sometimes takes several seconds in response time (in the worst case) as well as an error every one or two sentences. Further, among inter-carrier, even text transmission can have problems in network latency as well as speed (data rate or throughput) which become problems to VICA in cases of emergency such as a pandemic when large amounts of people use this system in parallel. Thus, 5G networks are considered useful for VICA.

Fig.5.2 Counselor Avatar of VICA in Google

Chrome Fig. 5.3 Use case picture of VICA

Figure 5.2 and 5.3 show the agent and use case picture of AI counselor (VICA). Further details on VICA have been added in Appendix – “Deep dive in VICA”.



5.5. The Composition scenario

In the following, we underline how the above services can be composed in order to offer a more advanced experience opening to more business opportunities. We will initially present an example of additive composition where two independent services are used at the same moment providing a better training experience. We then present a more complex composition where orchestration is needed. Finally we present an example of a complex choreography. Additive independent composition Let us consider a third party services used by the iLUMENS ‘Médecine Paris Nord’ University mannequin service (mannequins are currently provided by LAERDAL company, they are very robust, last many years and are modular, meaning that 3D printed organs can be added to enrich learners experience) that provides both physical mannequins to exercise with and a service accessible remotely where the mannequin parameters can be controlled in order to simulate specific pathologies or clinical situation. This service can be composed with the three-legged remote training service in Section 5.1 as a tool for the expert in the control room to deliver the simulation exercise controlling the mannequin from remote. In the training room students can be either physically present to play with the physical mannequin (even just one student if we need to preserve the social distance to comply with sanitary restrictions) or virtually present thanks to VR/AR and virtual room. The debriefing room is itself enriched by the remote mannequin service being able to see the mannequin parameters as they are modified by the expert in the control room. This composition is a mixture of virtual and physical resources that allows to simulate medical scenarios, to take advantage from the expertise of a remote expert, and being disseminated to other learners that can see the simulation evolution with a privileged view on both the control and the training room. Figure 5.4 shows this mannequin enriched training scenario.



Fig.5.4 mannequin enriched training scenario Considerations: Network resources of the two independent services can be optimize for the use in the additive composition. Orchestrated composition Let’s now consider a more complex composition scenario where we add to the above orchestration, the AR/VR assisted 3D Organ service of Section 5.2. This will allow the expert in the control room to select specific organs affected by a specific pathology (e.g., Covid-19 affected lung) to be used to enrich the mannequin simulation. The learners in the training room, using the same 3D Organ service and wearing a VR/AR helmet can be remotely trained on a more sophisticated simulation. In contemporaneous the auditors that are learning in the debrief room can themselves see the organs as navigated by the learners in the training room. In this composition 3D organs and the mannequin services need to be coordinated by an orchestrator in order to set up the simulation scenario Compositions can also put together application-level services that are natively focused on different application scenarios but that can be efficiently reused in others. Let us consider the AI counsellor (VICA) of section 5.4. It can be orchestrated/coordinated with the above medical composition since medical staffs, medical students, or related persons such as other emergent clients and their family can benefit from counselling to decrease their distress or anxiety. Considerations: Orchestration can be provided as a service with the full support of the networking level having strong requirements on service synchronization due to the necessity to have virtual organs synchronized with the mannequin simulations. Choreographic composition



Let us consider the VICA service in Section 5.4. A feeling of sadness can indicate anxiety whereas a feeling of happiness can indicate the client is doing fine. Defining the former as a negative feeling and the latter as a positive feeling, the change in feeling is detected during the consultation. The change of emotion is very important for VICA. It eventually will include more degrees such as very sad, neutral (neither sad nor happy) or a bit happy, very happy. Anxiety degree for advanced VICA is defined in terms of face expression measurement. It is integrated with emotion detection by voice/speech recognition. It is the indispensable collaboration to enhance the total accuracy of emotion change recognition required by VICA approaching “active/sensitive listening” type AI counseling. For complex scenarios such as a patient smiling but still in stress can be part of the next release (Ex: Movement of lips, movement of eyebrows, movement of chin, movement of chicks, eyes movement, dialogue delivery, head movement), in addition to body temperature, heart rate, etc., we need to analyze sound output, dialog delivery etc. as part of the audio analysis. Eventually, combining all the recognition analysis of various types of data will provide an accurate sensor fusion necessary for AI counseling. VICA can be therefore greatly enhanced by the possibility to have more reliable/accurate information about the emotional status of the distressed person. In the framework of composition of advanced services, VICA can perform in a choreographic composition with the SYBICA Inc. face detection and emotion recognition service in order to provide the dependable emotion information towards a more effective AI counselor. Considerations: This is much advanced since the two services cooperates for a specific goal in a stricter way generating a less predictable network requests. This will be part of our future work. Composition with prescriptive assurance(Assurance++) In many cases, medical services are mission-critical and unexpected interruption of undelaying communication service could lead to serious result. To maintain the complex aforementioned composed medical service which spans over multiple countries/CSPs, close collaboration between CSP's assurance system through open APIs are required. In addition, prescriptive assurance (Assurance++) makes it possible to resolve or bypass a communication service problem before it happen by predicting imminent service problems on the basis of network stations and external social information. For example, let's consider a real remote surgery instead of training, where the patient is in Paris while the doctor is in Tokyo, the communication service is provided together with Assurance++ service. During the surgery, a typhoon is forecasted to strike Tokyo and predicted to cause 5G service interruption in several regions. Assurance++ will analyze the impact on the medical service, and notify the user(the hospital) and the counterpart CSP(Orange) about the imminent service interruption, and resolve the problem by switching the access service for the medical service from 5G to hard-wired service to keep the service availability.

5.6. The prospective vision

The vision for training and teaching healthcare professionals is that in the future, medical training simulation as iLUMENS will become key and will be extended at scale, to secure ‘a priori’ tangible medical actions. Nonetheless Mannequin service, as used at iLUMENS, Paris, can be either a physical one or based on AR/VR, Augmented/Virtual Reality, and also integrating haptics. In some specialties as ophthalmology, simulators are already extensively used for training medicine students For emergency crisis management, serious games can also be used for some cases. The role of AI/ML and of data Analytics will increase dramatically:



• by providing predictive and prescriptive analytics to support diagnosis and prognosis by physicians,

• to complement lacking human resources for emergency crisis management as highlighted by VICA counselling scenario above mentioned,

• to determine next best action, • to support administrative work related to medicine, • to manage remote monitoring of patients.

Data corpus from patients will be aggregated on local level as well as international level, in order for the outcome of AI/ML and data Analytics processing to become more relevant, but that should be done in compliance with various local, regional and international privacy rules. A whole ecosystem of brokers, aggregators, and market places will be defined, implemented and operated. The role of IoT for remote monitoring of patients will increase dramatically. The role of Drones for emergency crisis management will be explored in next GITS phase for emergency crisis management. The role of robots is important, particularly advanced in Japan for handicapped people, for elderly and for young children care, as well as for patient care In all these scenarios, 5G can play a great role by allowing huge bandwidth, very low latency and millions of connected objects, by differentiating networks SLA. And Orchestration and beyond Choreography of microservices which cooperate at application level without centralized control, with open architecture platforms exchanging via APIs, are the basis to reach this vision. The interdisciplinary cooperative work with physicians and other healthcare professionals, engineers, computer science departments at universities, public and private sectors, Telco’s, solution providers, startup companies, is mandatory to succeed. World-wide international cooperation is also necessary to deal with similar medical scenarios but also local and regional teams are necessary to handle local and regional issues such as health care organizations, language, field environment, legal and regulatory framework CSPs are well positioned to take this opportunity provided by eHealth, provided there is not too much fragmentation, and provided there are standards for heterogeneous solutions, and finally to support healthcare professionals in improving public health. What is more human friendly as this mission for the Telecom Industry?

6. GhostintheShellContributions The Catalyst is contributing to TMForum assets in several activities. The below table lists the areas where the catalyst activity identified issues and contributed to the various TMF projects in response to them.

ODA / ODF Assets Best Practices OPEN APIs Use Cases

ODA-310 - Proposal and Change request on GB999 REGISTERED

ODA-158 - TOSCA for hybrid connectivity modelling REGISTERED

ODA-436 - Multi-layer topology API REGISTERED

ODA-310 - Proposal and Change request on GB999 REGISTERED



AP-2361 - Alignment between MEF and TMF for real time intercarrier FAB communications OPEN

ODA-443 - Define Topology API Requirements to support AI based 5G network assurance CLOSE

AP-2361 -

Alignment between MEF and TMF for real time intercarrier FAB communications OPEN

7. GhostintheShellCatalystParticipantsHere is the snapshot of participants

Participants Champions

Amartus Chunghwa Telecom Ericsson Orange EXFO Meiji University IOTA NTT TIM Università degli Studi di Milano PCCW SYBICA Inc. Sparkle Université de Paris

7.1. Champions

Chunghwa Telecom

Chunghwa Telecom (CHT) is the largest digital service provider in Taiwan. CHT provides a variety of ICT services, including fixed-line, mobile, broadband, internet, cloud, and IoT services, to the enterprises and consumers. In 2018, CHT co-founded the “Taiwan 5G Alliance – CHT Pilot Team” to accelerate the development of the 5G industry and related services in Taiwan. Linking together more than 40 corporations and organizations from the government,



academia, and research sectors, the Taiwan 5G Alliance - CHT Pilot Team aims to launch pre-commercial 5G networks in 2020 with the vision of building a world-class 5G industry chain in Taiwan that is at the forefront of global development and commercial deployment. By working with the leading telecom equipment suppliers, CHT has built a 5G trial network at the Chunghwa Telecom Laboratories to verify 5G key technologies and applications services as well as interoperability between 4G and 5G networks. Also, CHT works with the Taipei city government to build up a 5G trial field at the Taipei Pop Music Center and successfully demonstrate CHT’s MEC (Multi-access Edge Computing) solution to support hologram and multi-angle online streaming services in April 2019. CHT has a strong R&D capability of in-house BSS/OSS for years. One of the main R&D focuses is to leverage TM Forum ODA, cloud, and SDN/NFV technologies to develop the end-to-end service and resource orchestrators for the fulfillment and assurance of a variety of 5G network slices. This will help CHT speed up the 5G commercialization and creation of 5G innovative business models.

Meiji University

Meiji University is one of Tokyo six representative universities, ranking solidly among the best universities in Japan, currently consisting of 10 schools and 16 Graduate Schools. They include School of Interdisciplinary Mathematical Sciences which further includes Dept. of Network Design. The department is researching network systems as well as machine learning. Here, a remote medical AI system (Counseling Agent and robot) are researched and developed, collaborating with University of Milan etc. The Counseling Agent named VICA is AI medical service for remote mental healthcare to emotionally distressed persons. Such persons are increasing but human counselors and hospitals are limited. Besides, even telephone capacity is very few especially on emergency like Corvid-19, though extremely massive persons consult using voice. Thus, AI counselors including consultancy are necessary. Currently VICA communicates by voice towards client’s problem solutions. However, even the highest level voice recognition cloud used in VICA has problems of reliability, speed, etc. in current 4G networks. Through solving such problems by proposing the new (advanced version) VICA idea inclusive of 5G slice, edge computing, and image recognition in AI counselor, Meiji University is engaged in proving the usefulness of the next generation of 5G services based on slicing etc.

NTT

NTT led the Business Concept for Skynet. In Japan, there is a growing need for remote health care services to provide more efficient medical care for people on remote islands and disaster areas. While the number of elders is increasing, there are few available medical-care locally in rural area. In addition, the recent experiences in natural disaster encourages the public opinion to prepare for the incident. For this reason, NTT has been engaged in this field for several years. In order to realize the remote medical care services, communication service providers should offer flexible, guaranteed, dynamical and secured network services such as 5G network service in parallel with the platform capabilities to interact with other verticals. To expand the 5G network service quickly and at low cost, it is necessary to not only use a newly built network but also a



legacy network and to provide the service on the hybrid network. We brought in a hybrid network in Skynet and developed a domain orchestrator to manage such a network.

Orange

In France, in Europe and in MEA (Middle East and Africa) affiliates where Orange is present, the need for health care networking services is growing. Orange is highly involved in those subjects through its corporate entities as well as its enterprise business unit Orange Business Services. In addition, its wholesales activity is a key asset. Orange supports the ODA and TMF modelling for its converged IT and Network architecture. Remote health care services across multiple operators through 5G and automation across hybrid networks will enable Orange to tackle the next generation of 5G services Service assurance across multiple operators is key to ensure SLAs and manual or automatic network healing. In Ghost In The Shell, , Orange assured together with NTT the leadership of the project and the coordination with medical and computer science universities.

SYBICA Inc.

The Ghost in the Shell catalyst is aiming to make large strides within the educational and healthcare industries by delivering varying services using 5G as well as a digital marketplace. The opportunities are endless with this innovative proof of concept with its use cases related to medical simulation, artificial intelligence, and virtual reality. These new innovative and unique applications require not only a higher degree of assurance, but also far more advanced object detection and facial recognition edge enabled systems with closed loop and real time feedback for optimal user experience. The skillset and opportunities that SYBICA brings to the table makes us an obvious fit for this collaboration. Naturally we are a champion for the "Ghost in the Shell" Catalyst as its aim is to bring various supportive services such as facial expression recognition, emotional intelligence and audio/video analytics to the educational and healthcare industries using 5G. Thanks to low latency 5G connectivity, MEC and Edge enabled systems, the varieties of AI powered services (AR/VR, Drone, AI-Counselling or Medical simulation) on the digital marketplace could be limitless. For the scope of GITS catalyst, the primary role of SYBICA will be to collaborate with voice enabled counselling service and help conceptualise a fully AI powered counselling service that uses voice and video based health and wellness monitoring along with facial expression recognition & real-time feedback/actions over 5G network for patient counselling. In addition to work that is been done for GITS catalyst, for future use case SYBICA will be collaborating on epidemic/pandemic evaluation/control through AI enabled drones or robots.

Sparkle

Sparkle is a leading global service provider offering full range of IP, Data, Cloud, Data Center, Mobile Data and Voice solutions designed to meet the fast changing needs of Internet Service Providers, OTTs, Media & Content Players, Application Service Providers, Fixed and Mobile Operators as well as Multinational Corporations. Fully owned by the TIM Group, thanks to a state-of-the-art global backbone of over 540,000 kms of fiber and through an extensive



worldwide commercial presence distributed over 34 countries, Sparkle ranks #10 for voice traffic and #7 for IP globally. Through a rich portfolio of services, a cutting edge network based on the latest technologies, a globally distributed sales force and advanced customer care capabilities, Sparkle is able to fulfil its mission of providing customers with top performing and tailored solutions worldwide. Enriched by its cultural variety and diversity, Sparkle is always committed to excellent relationships with all its stakeholders and operates with constant attention to maintain a safe environment where Partners, Customers, Suppliers and Employees can live and work better. Sparkle. The world’s communication platform. Find out more about Sparkle at tisparkle.com

7.1.1. TIM

This is the era of digital life and technology is a fundamental asset to allow it. TIM enables access to digital life because it creates innovation and offers the possibility to be always connected with innovative telecommunication services, cloud, multimedia contents everywhere and on any device with simplicity and safety. Digital also means smart services for enterprises, citizen, and public administration that TIM provides. Not just technology, but culture also changes, mutually modifying and we believe that ICT technologies are relevant for a smart, sustainable and inclusive growth. http://www.telecomitalia.com/tit/en.html http://www.telecomitalia.com/tit/en/about-us/branding/brands/our-brands.html

Università degli Studi di Milano

Università degli Studi di Milano (UMIL) is a major higher education and research institution in Italy, offering degrees in all areas of science and humanities. The University is articulated in 33 Departments related to 8 Faculties and 2 Schools. UMIL is the only Italian University to be part of the LERU (League of European Research Universities). The Computer Science Department (DI http://www.di.unimi.it/ecm/home), founded 27 of April, 2012, performs research and teaching in the area of Computer Science and related scientific areas. The DI has 200 members overall, including its faculty, technical and administrative staff, Ph.D. students, research scholars and adjunct professor. The Secure Service–oriented Software Architectures Research (SESAR) Lab (http://sesar.di.unimi.it/), led by Prof. Ernesto Damiani and established in 2003, is part of the Department of Computer Science of the Università degli Studi di Milano. It has a staff of 10 full–time researchers and post–docs, plus a number of Ph.D. candidates with experience in several fields including, advanced mobile networking solutions, Big Data Analytics, Edge/Cloud security and performance security certification of software/services, mobile geolocation and privacy, formal verification of distributed systems, cloud testing and monitoring and microservices architecture . This concretizes in a solid background in the field of empirical software engineering and in system and network security. Long-term grants from major companies, such as Cisco Systems, Telecom Italia, Nokia Siemens Networks, British Telecom and others fund several of SESAR Lab‘s current and past researches.



PCCW - HKT

PCCW Limited is a global company headquartered in Hong Kong which holds interests in telecommunications, media, IT solutions, property development and investment, and other businesses. The Company holds a majority interest in the HKT Trust and HKT Limited*, Hong Kong’s premier telecommunications service provider and leading operator in fixed- line, broadband and mobile communication services. Beyond connectivity, HKT provides innovative smart living and business services to individuals and enterprises. PCCW also owns a fully integrated multimedia and entertainment group in Hong Kong, PCCW Media. PCCW Media operates the largest local pay-TV operation, Now TV, and is engaged in the provision of OTT (over-the-top) video service under the Viu brand in Hong Kong and other places in the region. Through HK Television Entertainment Company Limited, PCCW also operates a domestic free television service in Hong Kong. Also wholly-owned by the Group, PCCW Solutions is a leading information technology outsourcing and business process outsourcing provider in Hong Kong and mainland China. In addition, PCCW holds a majority interest in Pacific Century Premium Developments Limited, and other overseas investments. The Group had over 24,000 employees located in 50 countries and cities. About 61% of these employees work in Hong Kong and the others are based mainly in mainland China, the United States and the Philippines.

Université de Paris : iLUMENS medical training and simulation center

The interdisciplinary simulation centre iLumens ‘Paris Nord’ at the University of Paris allows healthcare professionals to train in conditions very close to reality by using manikins, without any consequences for the patient. They learn the technical skills of " savoir-faire " and the non-technical skills of " savoir-être ". This is a major challenge to better prepare them along their professional life and to improve the safety of practices around the patient. A strong research component in advanced pedagogy and tools such as 3D modeling/printing and digitalisation of teaching are used and evaluated on a daily routine.

7.2. Partners

Amartus

Amartus is a leading innovator in intra & inter-provider automation and orchestration systems and related expert services. The company was established in 2003 by leading experts from the field of network management and networking and a global customer base of leading network and cloud providers and vendors. The company is industry-recognized as a leading innovator in



network transformation, underpinned by network orchestration and automation. Amartus is an active contributor to leading industry bodies, such as MEF, TMF, and CBAN. For more information, visit www.amartus.com.

Ericsson

Ericsson is one of the leading providers of Information and Communication Technology (ICT) to service providers, with about 40% of the world’s mobile traffic carried through our networks. We enable the full value of connectivity by creating game-changing technology and services that are easy to use, adopt and scale, making our customers successful in a fully connected world. For more than 140 years, our ideas, technology and people have changed the world: real turning points that have transformed lives, industries and society as a whole. Ericsson is showcasing the roles of orchestration, assurance and charging in the multi-operator and multi-vendor ecosystem. Ericsson took also a key role in defining the way of collaborating of the future networks, in order to achieve global orchestration required to set up the inter-carrier network slices from a business and technical perspective. Ericsson Dynamic Orchestration is the key enabler for the network slice life cycle management providing orchestration through Ericsson Orchestrator and service assurance through CENX for the hybrid cross-domain and cross-operator network. Ericsson Orchestrator and CENX are integrating to the ecosystem through standard APIs. Using standard models provides a set of common policies/rules to serve the needs of closed loop automation through assurance and orchestration systems across different enablers. Ericsson Charging supports the complex inter-operator and customer business models needed for monetization of the use case by providing real-time convergent charging and policy control for the network slices and rapid creation of the relevant commercial offers.

EXFO

EXFO provides end-to-end (e2e), independent, equipment vendor agnostic, visibility into the virtual network through both active (real-time) e2e service and network test and monitoring, as well as passive (in depth) monitoring and correlation of call data records (CDRs), to generate the key performance indicators (KPIs) and key quality indicators (KQIs) required to drive policy-driven operations. This visibility enables both real-time alerting to network issues, such as failures and degradation, and early warning for slowly degrading performance of the e2e customer experience. EXFO Worx, along with the EXFO virtual verifier (vVerifier), provides for the orchestration of e2e active test and monitoring to: 1.) provide activation testing of both the network slice instance and the e2e service being requested, to ensure the service can be built as specified and; 2.) provide the ongoing monitoring of the e2e service to ensure service level agreement (SLA) requirements continue to be met, and generate alerts when they are not. EXFO Nova Element Manager along with the EXFO virtual passive probe (vProbe) continuously monitor CDRs within the network, correlating records from different systems to generate a complete view of every service, e2e, identifying failures associated with the SLAs in place and reporting those failures. Both EXFO Worx and Nova Element Manager use open, restful APIs to communicate events northbound.



IOTA

IOTA’s Tangle is an open, feeless, and scalable distributed ledger, designed to support frictionless data and value transfer. IOTA is the first distributed ledger built for the “Internet of Everything” - a network for exchanging value and data between humans and machines. No blocks. No Miners. When you send an IOTA transaction you validate two other transactions. This allows IOTA to overcome the cost and scalability limitations of blockchain. The IOTA network is built to for the Internet of Things, with tamper-proof data, feeless micro transactions, and low resource requirements. IOTA is posed to play a central role in the next industrial revolution, enabling economic relationships between machines and bridging the human and machine economies. The Tangle is IOTA's network. It immutably records the exchange of data and value. It ensures that the information is trustworthy and cannot be tampered with nor destroyed.

8. ConclusionsandNextSteps“Ghost in the shell” Catalyst is using Skynet blueprint to offer end to end medical services based on advanced network services offered by service providers using broadband networks and 5G technologies. The softwarization and virtualization of network components enables a new agility in network and service delivery as well as the associated assurance services. By extending the eco-system built on TM Forum assets to the Health care environment, we open new collaboration ways between people, institutions and private companies. Our Goal for DTW in October was originally to demonstrate through live demos the relevance of the GITS collaboration model and actually implement solutions when 5G becomes widely available and adopted. The Pandemic prevented us from doing, but the need still exits and we look forward to complete our task in a possible next phase.

9. References[1] Ericsson Insights and Reports: “Understanding the opportunities for operators in

healthcare” https://www.ericsson.com/en/networks/trending/insights-and-reports/5g-healthcare

[2] C. R. Rogers (1957) The necessary and sufficient conditions of therapeutic personality change. Journal of Consulting Psychology 21(2): 95-103. http://dx.doi.org/10.1037/h0045357

[3] Y. Sakurai, S. Tsuruta, et.al.: "VICA, a Visual Counseling Agent for Emotional Distress", Journal of Ambient Intelligence and Humanized Computing, Vol.10, No. 12, pp.4993-5005, Springer-Verlag GmbH, DOI 10.1007/s12652-019-01180-x, (2019.1).

[4] STL Partners: “5G’S Healthcare Imapct: 1 billionpatients with improved access in 2030” Executive Briefing www.stlpartners.com

[5] Vertemati, M., Cassin, S., Rizzetto, F., Vanzulli, A., Elli, M., Sampogna, G., & Gallieni, M. (2019). A virtual reality environment to visualize three-dimensional patient-specific models by a mobile head-mounted display. Surgical innovation, 26(3), 359-370.

[6] GLF CBAN - “Communication Business Automation Network (CBAN) Whitepaper ver.1.0”- link



10. Appendix–“DeepdiveinVICA”

10.1. VICA: Virtual Intelligent Counseling Agent

Counselor Avatar of VICA in Google Chrome

VICA Counselling scenario

CA(AI) : Please tell me your problem or emotional distress. Client: I don’t like remote surgery because my friend passed away from it. CA(AI) : You don’t like remote surgery because my friend passed away from it, is this correct? Please tell me more? Client: I tried to remove the bad parts of my friend’s organ (according to remote instruction) but cut an important part due to his organ’s moving a bit. CA(AI) :Hmm I see! You tried to remove the bad parts of your friend’s organ but cut an important part due to his organ’s moving a bit, is this correct? Can you please tell me more in detail? Client: His organ moved a bit and the remote instruction became invalid. CA(AI) : You felt his organ moved a little and the remote instruction became invalid, is this correct? And then? (Is there relation between remote surgery and instruction?) Client: Oh, I felt relieved and rather fine. The remote instruction will not become invalid due to Ultra Reliable Low Latency in 5G.

VICA Counseling Methodology People are very sensitive due to emotional distress.



This differs from surgery as VICA avoids intrusive operations (even mentally) which can easily hurt people. VICA uses “active/sensitive listening” to promote confession-like conversations for self-awareness However, it requires extremely accurate sensing. This method was advocated by Rogers C.R. in US. It prevails in the world (among therapists or counsellors)

VICA Feature and Mechanism Natural Language Processing (voice/text) in basic version + sensor fusion (video/sensor data) in advanced version 2-1) paraphrase & digging: For empathy, VICA replies mirror sentence + tag-question + affirmative nods (UmUm), and digs e.g. ‘concretely?’ The number of affirmative nods can be changed. 2-2) emotional change summarization: To promote client’s self-awareness, VICA summarizes clients’ emotion change, e.g. Initial (anxious) -> now/middle (a bit clear) -> end (clear), 2-3) deep digging: eventual utterances have emotional background. VICA asks relation between the repeated word and previous/ initial eventual utterance such as a desire or goal having emotional background.

VICA Uniqueness 1) Difference from other chatbot:

a) VICA adds weight to counseling b) VICA promotes confession-like conversations through “active/ sensitive listening” toward self-awareness of emotionally distressed people. c) Anxious people are very sensitive about how they are regarded or empathized? Ultra-reliability/ultra-accuracy is required in listening or recognition of voice (/image etc. in advanced version) -> 5/6G.

2) Difference from other counseling AI a) unlike ELIZA, VICA never expands but digs the topic. b) unlike other counseling AI, VICA never asks the 5W’s or How. c) unlike unskilled human counselors, besides mirroring/repeating, VICA adds empathic phrases e.g., “Hmm I see”, “you feel” (he dislikes you).



10.2. VICA basic (voice version): System Architecture

VICA basic Experiment on 4G – Problems and why 5G VICA basic (version) is experimented to find problems on 4G and why 5G.

1) VICA basic does not have the accuracy improving functions like majority decision in voice recognition, since the heavy network load is caused by repetitive sending of voice data and control data.

2) It often fell down (especially in communicating with cloud speech-to-text servers) or made errors. This made distressed people too irritated to continue the conversation toward self-awareness.

3) Errors tend to occur in cases where users do not talk clearly or when the Internet is crowded (late evening or especially on emergency).

4) Why 5G: Such errors especially falling down in communication with voice recognition cloud are expected to decrease by 5G/slice including proactive assurance approach.

Experimental result (voice recognition/total response (ms)) date Voice (ms) total (ms) User’s saying 20:58 - - Recognition stop by cloud speech-to-text due to stumbled/clogged wording 20:58 3434 3584 I'm in trouble because I can't write a dissertation 20:58 5325 2423 I have a cold because my throat hurts

- - Recognition stop by cloud speech-to-text due to unclear pronunciation

20:59 10108 2401 Wherever you go at the hospital said better ... if you take a rest lazy …. (word errors by long sentence)

10.3. VICA advanced for ultra-reliability

Emotion of visually distressed people especially in an emergency is very sensitive or subtle which can lead to them being easily hurt. Counseling should have neither surgery-like physical nor emotional intrusion but “active/ sensitive listening” for empathy etc. is important. It requires highly accurate recognition capability through multiple decisions and so on to detect subtle emotional changes. Highly accurate detection is obtained if speech words are integrated with body language including face/body gesture, etc. Such sensor data fusion integrated with speech words is indispensable. It will be realized in VICA advanced version on 5/6G.

user

avatar Speechsynthesizer

CloudspeechAPI

WebServer(Nginx)

JavaScripton GoogleChrome

Recognitionerrorrecoverybasic

+ CRECA

WebSocket /Script

Cloudspeech-to-textserver

text

text

text

text t voice

voice voice

text

AIterminalofVICA AIserverofVICA

image

VisualCounselingAvatar(VICA)basicArchitecture



Generally speaking, body language such as shaking a fist and nodding when expressing joy of awareness or acceptance depends on nations or cultures. VICA has advanced settings that can adjust the number of nodding or Hmm I see… depending on cultures or regions. To demonstrate VICA (advanced version) use case which integrates more emotion change recognizers to understand body language such as face expression, we are working with a Canadian company (SYBICA Inc.). Further we wish to extend the collaboration not only for indoor use cases but also for the outdoor use cases where identifying distressed people is important during an epidemic or pandemic situation and real time decision making would be a key. These requires 5G/6G.

VICA advanced (new version): System Architecture

VICA advanced: Network (Slicing) Architecture



1) To prevent cloud speech recognition from frequent errors and occasionally several seconds of delay in 4G, VICA has four levels of performance catalogues according to emergency degree. 2) AI terminal (VICA client) monitor the (recognition clouds) delay to send it to VICA server. 3) VICA clients’ base requirements according to the catalogue level and monitoring results are sent via EA (or Skynet-like API) for 5G slice to automatically assure the basic requirement including NW configuration/topology change. 4) VICA servers both in 5G area and 4G area log the base requirements and monitoring results of each catalogue level of VICA terminals for 5G PoC.

Documents

TM Forum Catalyst Whitepaper Ghost in the shell