Computer-implemented method, computer program, and system (network user data provision)

Abstract

To provide a computer-implemented method and program for protecting and curing patients along with controlling infection transmission in epidemic situations.SOLUTION: In the method, a virtual network function (VNF) layer which has assigned logical channels to respective ones of the UE devices for wireless transmission of the user data obtains medical health user data from respective ones of multiple UE devices. The VNF layer maintains user-to-logical channel association data associating user-identifying data to logical channels assigned to users identified thereby. Additionally in the method, a service orchestration layer that runs on top of the VNF layer and is restricted in access to the user-identifying data of the user-to-logical channel association data examines the medical health user data, and processes the data according to the examination.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] TECHNICAL FIELD Embodiments herein relate generally to computer networks, and more particularly to providing network user data. [Background technology]

[0002] Network services may include applications that operate at the network application layer and provide data storage, manipulation, presentation, communication, or other capabilities, often implemented using a client-server architecture based on application-layer network protocols. Each network service is typically provided by a server component running on one or more computers and accessed over the network by client components running on other devices. However, both client and server components may run on the same machine. Furthermore, a dedicated server computer may provide multiple network services simultaneously.

[0003] Location-based services (LBS) are software services that use location data to control the functionality of computer systems, and LBS information services have multiple uses, for example, in social networking, entertainment, security, and multiple additional applications. LBS services utilize location services to determine the location of mobile computer systems. Location services can incorporate a variety of different location service technologies, such as Global Positioning System (GPS), cellular network location technologies, Wi-Fi®-based location technologies, and other technologies. One example of an LBS is a location-based messaging service, where notifications and other messages to users can be responsive to their respective locations.

[0004] Data structures are utilized to improve the operation of computer systems. Data structures refer to the organization of data in a computer environment for improved computer system operation. Data structure types include containers, lists, stacks, queues, tables, and graphs. Data structures are utilized to improve the operation of computer systems in terms of, for example, algorithm efficiency, memory utilization efficiency, maintainability, and reliability.

[0005] Artificial intelligence (AI) refers to intelligence exhibited by machines. Artificial intelligence (AI) research includes search and mathematical optimization, neural networks, and probability. Artificial intelligence (AI) solutions include features derived from research in a variety of different scientific and technological domains, ranging from computer science, mathematics, psychology, linguistics, statistics, and neuroscience. Machine learning is described as the field of study that gives computers the ability to learn without being explicitly programmed. Summary of the Invention [Problem to be solved by the invention]

[0006] In epidemic situations, stronger medical and health care support plays a crucial role to protect and treat patients as well as to control the transmission of infection to other people. [Means for solving the problem]

[0007] In one aspect, the shortcomings of the prior art are overcome and additional advantages are provided through the provision of a method, which may include, for example, a step of: a virtual network function (VNF) layer that has assigned a logical channel to each of a plurality of UE devices for wireless transmission of medical and health user data acquiring the medical and health user data from each of the plurality of UE devices, the VNF layer maintaining user-to-logical channel association data that associates user identification data with logical channels assigned to a user identified by the user identification data; a step of a service orchestration layer operating on the VNF layer examining the user medical and health data, the service orchestration layer being configured such that the service orchestration layer is restricted from accessing the user identification data in the user-to-logical channel association data; and a step of performing processing in response to the examining step.

[0008] In another aspect, a computer program product may be provided. The computer program product may include a computer-readable storage medium readable by one or more processing circuits and storing instructions for execution by one or more processors to perform a method. The method may include, for example, a step of: a virtual network function (VNF) layer that has assigned a logical channel to each of a plurality of UE devices for wireless transmission of medical and health user data acquiring the medical and health user data from each of the plurality of UE devices, the VNF layer maintaining user-to-logical channel association data that associates user identification data with logical channels assigned to a user identified by the user identification data; a step of a service orchestration layer operating on the VNF layer examining the user medical and health data, the service orchestration layer being configured to be restricted from accessing the user identification data in the user-to-logical channel association data; and a step of performing processing in response to the examining step.

[0009] In a further aspect, a system may be provided. The system may include, for example, a memory. The system may further include one or more processors in communication with the memory. The system may further include program instructions executable by the one or more processors via the memory to perform a method. The method may include, for example, a step of: a virtual network function (VNF) layer assigning logical channels to each of a plurality of UE devices for wireless transmission of medical and health user data acquiring the medical and health user data from each of the plurality of UE devices, the VNF layer maintaining user-to-logical channel association data that associates user identification data with logical channels assigned to a user identified by the user identification data; a step of a service orchestration layer operating on the VNF layer examining the user medical and health data, the service orchestration layer being configured such that the service orchestration layer is restricted from accessing the user identification data in the user-to-logical channel association data; and a step of performing processing in response to the examining step.

[0010] Additional functionality is realized through the techniques described herein. Other embodiments and aspects, including but not limited to methods, computer program products, and systems, are described in detail herein and are considered a part of the claimed invention. [Brief explanation of the drawings]

[0011] One or more aspects of the present invention are particularly pointed out and distinctly claimed as examples in the appended claims at the conclusion of this specification. The foregoing and other objects, features, and advantages of the present invention will become apparent from the following detailed description taken in conjunction with the accompanying drawings.

[0012] [Figure 1] 1 illustrates a computing environment according to one embodiment.

[0013] [Figure 2] 1 illustrates a computing environment according to one embodiment.

[0014] [Figure 3] 1 illustrates a computing environment according to one embodiment.

[0015] [Figure 4] 1 illustrates a computing environment according to one embodiment.

[0016] [Figure 5] 1 is a flowchart for performance by an orchestrator in cooperation with other components according to one embodiment.

[0017] [Figure 6] 1 illustrates a predictive model trained by machine learning, according to one embodiment.

[0018] [Figure 7] 1 illustrates a heat map according to an embodiment.

[0019] [Figure 8] 1 illustrates a heat map according to an embodiment.

[0020] [Figure 9] 1 illustrates a heat map according to an embodiment;

[0021] [Figure 10] 1 illustrates a computing node according to one embodiment.

[0022] [Figure 11] 1 illustrates a cloud computing environment according to one embodiment.

[0023] [Figure 12] 1 illustrates an abstraction model layer according to one embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0024] FIG. 1 illustrates a computing environment 100 for secure provisioning collection and provisioning of user data. The computing environment 100 may include multiple UE devices 110A-110Z, multiple base stations 120A-120Z, and one or more orchestrators 130. In one embodiment, the UE devices 110A-110Z may be new wireless compliant UE devices. The UE devices 110A-110Z may be in wireless network communication with the base stations 120A-120Z, and the base stations 120A-120Z may be in wired network communication with the orchestrator 130. In one embodiment, the orchestrator 130 may be defined by a computing node of a core network, which may be part of an edge enterprise entity network.

[0025] The computing environment 100 may be configured to provide for the secure collection and provision of user data. The computing environment 100 may be configured to collect and process secure user data, such as secure user medical health data.

[0026] With reference to computing environment 100, each of UE devices 110A-110Z may include an orchestration process 120R that operates on VNF process 120V. Each of base stations 120A-120Z may include an orchestration process 120R that operates on VNF process 120V. Orchestrator 130 may include orchestration process 130R. In each instance of orchestration process 110R, orchestration process 110R may define an orchestration layer that operates on the VNF layer defined by the instance of VNF process 120V in combination with the instance of VNF process 110V. UE devices 110A-110Z, base stations 120A-120Z, and orchestrator 130 may include data repositories 210, 220, and 230, respectively.

[0027] According to one embodiment, each of the base stations 120A-120Z may be an eNodeB base station conforming to the fifth generation (5G) New Radio (NR) standard. According to one embodiment, each of the base stations 120A-120Z and each of the UE devices 110A-110Z may define a wireless network that facilitates communication according to the New Radio (NR) standard.

[0028] Figure 2 illustrates a partial Open Systems Interconnection Model (OSI Model) representation of the computing environment 100 shown in Figure 1. The computing environment 100 may include a Virtual Network Function (VNF) layer operating on a physical layer (further described in connection with Figure 3) and a service orchestration layer 2000 operating on the Virtual Network Function layer 1000. According to one aspect, the VNF layer 1000 may be responsible for allocating logical channels that enable UE devices to communicate with a base station.

[0029] According to one embodiment, the present specification describes steps of obtaining user data from each of a plurality of UE devices by a Virtual Network Function (VNF) layer that has assigned a logical channel to each of the UE devices for wireless transmission of user data, examining the data of the user data, and performing processing in response to the examining step.

[0030] According to the NR standard in 5G, the logical channel is known as the NR Dedicated Traffic Channel (DTCH). In one aspect, the VNF layer 1000 may maintain user-to-logical channel association data, for example, in a table associating UE device identifiers with their assigned logical, e.g., DTCH, channels. In one embodiment, the user-to-logical channel association data may be provided as set forth in Table A. [Table A] [Table 1]

[0031] The VNF layer 1000 may distribute relevant portions of user-to-logical channel association data to each of the base stations 120A-120Z and UE devices 110A-110Z to facilitate communication between the UE devices 110A-110Z and the base stations 120A-120Z. There may be a one-to-one correspondence between DTCH channels and UE devices identified by UE device identifiers that serve as identifiers for the users. With such a feature, the data collection process 1010 may facilitate coupling between the UE devices associated with each user and the base stations, and may assign DTCH channels to each UE device and user.

[0032] In one aspect, the computing environment 100 may be configured such that user identity data of the described user-to-logical channel association data may be restricted from access by the service orchestration layer 2000. Such functionality allows the service orchestration layer 2000 to process user data specific to an individual user without the risk of accessing any user identity data associated with that individual user. When the service orchestration layer 2000 processes data for a specific user, the service orchestration layer 2000 may use the respective assigned logical (e.g., DTCH) channel as a generic user surrogate identifier, absent any identifier that actually identifies any user or includes any user identity data. The 5G NR standard facilitates communications in which a DTCH identifier can be used as a user surrogate identifier, without access to any underlying actual user identity information.

[0033] Referring to FIG. 2, the service orchestration layer 2000 of the computing environment 100 may include a 5G device and subscription management platform process 2010, an infection and suspected infection finder process 2020, and a medical logistics management process 2030.

[0034] The 5G device and subscription management process 2010 may be responsible for maintaining a list of registered users of the computing environment 100. The 5G device and subscription management process 2010 may be configured to maintain a list of registered subscriber users, which may be used to record whether various UE devices of various users have installed installation packages that are installed for the UE devices' participation in the computing environment 100. However, the subscription list data generated and maintained by the 5G device and subscription management platform process 2010 may be absent any collected user data, such as collected sensitive medical user data. Instead, the computing environment 100 may be configured such that sensitive user medical health data can be associated with an assigned logical channel, e.g., a 5G DTCH channel, without reference to any user identification data.

[0035] The suspect finder process 2020 may be responsible for maintaining a list of logical channels that contain the user's location data over time, as well as the user's sensitive medical health data associated with the logical channel, but in the absence of user identification data.

[0036] The medical logistics management services process 2030 may be responsible, for example, for aggregating geospatial mapping data from multiple base stations and generating mapping data that aggregates the mapping data from the multiple base stations. Figure 3 shows an OSI diagram of the computing environment 100 illustrating physical layer features as well as further details of the virtual network function layer 1000, including the described user-to-logical channel association data provided in the data collection process 1010 by the "eNodeB-DTCH locator table." With reference to Figure 3, the UE devices 110A-110Z are generally indicated by the symbol UE devices 110, and the base stations 120A-120Z are generally indicated by the indicator base station 120.

[0037] With respect to physical network function characteristics of computing environment 100, physical network layer functions of computing environment 100 may be performed by physical layer functions of computing nodes 10, which may be respectively disposed by each of UE device 110 and base station 120. The physical network layer functions of computing nodes 10 may include, for example, spectrum management functions, radio estimator functions, data collection, and storage functions.

[0038] 3, the service orchestration layer 2000 may iteratively communicate with the data collection process 1010 to initiate data collection requests. In response to receiving a data collection request, the data collection process 1010 may request user data using the user-to-logical channel association data described.

[0039] The physical network functions layer 01 may accordingly transmit the user medical health data, along with the location data, to the data collection process 1010 of the VNF layer 1000 over the assigned DTCH logical channel. The data collection process 1010 of the VNF layer 1000 may push up the received medical health data and location data to the service orchestration layer 2000. When the generated user medical health data is pushed up to the service orchestration layer 2000, the data collection process 1010 may be restricted from accessing any user identity data to enable the user medical health data pushed up to the service orchestration layer 2000 to include DTCH data without representing any UE device identity data or surrogate user identity data without inclusion of other user identity data. In some embodiments, the data collection process 1010 defining the VNF layer 1000 may provide geospatial mapping data identifying the geospatial map locations of various users. For such functionality, the VNF layer 1000 may be in communication with a geospatial map service, such as GOOGLE MAPS® (GOOGLE MAPS® is a registered trademark of Google Inc.). When the VNF layer 1000 provides geospatial mapping data, the VNF layer 1000 may push the geospatial mapping data along with user medical health data and location data to the service orchestration layer 2000, where users are represented generically by their associated logical channels and the data push is absent any user identification data. In other embodiments, the geospatial map functionality may be performed entirely by the service orchestration layer 2000.

[0040] The VNF layer 1000 may include a data collection process 1010. The data collection process 1010 may reference the described user-to-logical channel association data to obtain user medical health data. The user medical health data may include multiple data tags as summarized in Table B. [Table B] Table 2

[0041] In one embodiment, the UE devices 110A-110Z may transmit user medical health data sets, such as those shown in Table A, to their respective base stations 120A-120Z. Data tags, such as those shown in Table B, may include, for example, a UE device UUID, a logical channel identifier, a timestamp, location coordinates, and an infection status. Embodiments herein recognize that the location of a UE device may be determined with high accuracy using various 5G technologies. The UE devices 110A-110Z and the RAN 500N may be compliant with fifth-generation (5G) technologies, including the New Radio (NR) standard and the 3GPP TS 28.530 V15.1.0 Release 15 document from the 3rd Generation Partnership Project (3GPP®) and the 3GPP Release 16 Technical Report (3GPP Release 16 Report). Densely deployed access nodes (ANs) may increase the line-of-sight (LoS) probability between user nodes (UNs) and ANs, thereby enabling highly accurate time-of-arrival (ToA) estimation. ANs in 5G networks utilize smart antenna solutions such as antenna arrays, thereby enabling accurate direction of arrival (DoA) estimation. Generally, all of the aforementioned measurements can be efficiently estimated from uplink (UL) pilot signals, particularly in a network-centric manner, without necessarily requiring additional dedicated positioning signals. Due to their large antenna arrays and wide bandwidth, 5G networks can provide a convenient environment for positioning, thereby enabling highly accurate DoA and ToA estimation, including LoS conditions. Next-generation positioning maps can be driven by AN positioning in 5G networks using 5G technology UE devices, which can also communicate with other devices and transmit their current positioning location, including latitude and longitude coordinates and the device's height using altitude calculations. Highly efficient positioning and location identification services in the 5G orchestration plane can thus be provided. Accurate positioning based on the Global Navigation Satellite System (GNSS) is becoming increasingly relevant for commercial use cases across different areas.GNSS positioning is based on information about the signals and positions of multiple satellites and is often assisted by information from mobile devices provided by cellular network operators. Real-time kinematic (RTK) is a technology that significantly improves the accuracy of GNSS positioning, narrowing the accuracy from several meters to just a few centimeters. Embodiments herein may use GNSS-RTK assistance data signaling supported by New Radio (NR) devices as provided by Release 15 by the 3rd Generation Partnership Project (3GPP) and the Release 16 Technical Report of 3GPP (3GPP Release 16 Report).

[0042] Upon receiving the data set shown in Table B, each base station may remove the user-identifying UE device UUID and pass the data set to service orchestration layer 2000 via VNF process 120V defining VNF layer 1000 such that service orchestration layer 2000 receives the data set shown in Table B absent the UE device UUID. Service orchestration layer 2000 may store the Table B data minus the user-identifying data in a data repository, such as data repository 230. Service orchestration layer 2000, for example, via a process running on base station or orchestrator 130 or both, may identify suspected infections and provide a matched data set with a suspected infection status field as shown in Table C. [Table C] [Table 3]

[0043] The service orchestration layer 2000 may track suspected infection status for users with a current infection status of not infected. Tracking suspected infection status may include assigning an infection probability to users with a current infection status of not infected. Embodiments herein may operate in 5G service orchestration in conjunction with virtual network functions and 5G user-based positioning systems to detect people suspected of having potentially contagious diseases. As mentioned, 5G has an efficient geolocation tracking system that operates with AN-based positioning systems. Embodiments herein may use the geolocation tracking system and geofencing as a backbone interface, and our invention operates on these precise location services provided in 5G virtual network functions. Embodiments herein operating in the service orchestration plane may be connected to medical services in a plan to provide backtracking calculation triggers for specific users or sets of users. Medical services may include integration with authentication-enabled platforms to avoid misuse of user location tracking. With user consent, medical information may be transmitted over logical channels that are absent of any user identification data. Once medical service authentication is accomplished, medical records for a predefined time interval can be pushed to the service along with the 5G_UUID number. In some embodiments, the VNF function can request user information from the 5G UUID. The user information can include an International Mobile Equipment Identity (IMEI) or an International Mobile Subscriber Identity (IMSI). The collected IMSI can be forwarded to the VNF layer 1000 along with a timeline input to trace the user's location over the past few days. The service can then investigate the location of potentially contagious illnesses from the user records.

[0044] In one embodiment, to assign an infection probability to a user with a currently uninfected status, the service orchestration layer 2000 may determine the number of past encounters, for example, within a threshold period of the current time. A encounter may be determined to occur, for example, when a first and second user are within a threshold distance of each other, for example, 6 feet (1.8288 meters) or 2 meters. Based on the past encounter data, the service orchestration layer 2000 may assign an infection probability.

[0045] To assign an infection probability based on the number of past encounters, the service orchestration layer 2000 may query a particular predictive model 6002 shown in FIG. 6 that has been trained using machine learning. The predictive model 6002 may have been trained with a training data set that includes (a) the number of encounters of a particular uninfected user with an infected user and (b) the uninfected user's subsequent infection status. Once trained, the predictive model 6002 can respond to query data. The query data may include the number of past detected encounters. Output data in response to the query data may include a value specifying the probability of infection.

[0046] In one embodiment, to assign infection probabilities to users, service orchestration layer 2000 may predict subsequent encounters between uninfected users and then assign infection probabilities based on the predicted encounters by querying a particular described prediction model 6002 trained with a training dataset that includes (a) the number of encounters a particular uninfected user has with infected users and (b) the uninfected user's subsequent infection status. To predict subsequent encounters, service orchestration layer 2000 may include historical movement data of detected users from which user data is collected.

[0047] The service orchestration layer 2000 may determine each user's current direction using recent historical location data. Each user's current direction may be considered a trajectory. The service orchestration layer 2000 may use each user's determined current direction to predict each user's subsequent path (position over time). To predict the subsequent path, the service orchestration layer 2000 may apply the assumption that the user will continue traveling in their current direction for the next N time periods. The service orchestration layer 2000 may determine a prediction of future encounters by examining each predicted path for each user and identifying encounters between users as they travel along each path, where the predicted path is based on the detected current path.

[0048] Once predicted subsequent misses are identified, the service orchestration layer 2000 may query the predictive model 6002 to determine the probability of infection based on the predicted number of misses for each user. The service orchestration layer 2000 may query the predictive model 6002 using historical route data for each user to determine the predicted number of misses, as described herein.

[0049] A network schematic of the computing environment 100 is shown in FIG. 4. FIG. 4 illustrates the computing environment 100 in further detail. The computing environment 100 may include UE devices 110A-110Z communicating with a data network 2000N via multiple edge enterprise entity networks 1000N, one of which is shown. Each edge enterprise entity network may include edge infrastructure owned, operated, or controlled, or a combination thereof, by a different edge entity. An edge enterprise entity may own, operate, or control, or a combination thereof, the edge network infrastructure, including a wireless network 1100N, a fronthaul / backhaul network 1200N, and a core network 1300N. Each of the different edge enterprises may be a telecommunications network provider, sometimes referred to as a communications service provider (edge ​​enterprise entity CSP). The wireless network 1100N may include base stations 120A-120Z, which, according to one embodiment, may be provided by eNodeB base stations.

[0050] In the illustrated embodiment of FIG. 4, the combination of the radio network 1100N and the fronthaul network 1200N may define an edge network 500N provided by a radio access network (RAN) 500N. The edge network 500N may define edge infrastructure. The illustrated RAN 500N provides access from the UE devices 110A-110Z to the respective core networks 1300N. In an alternative embodiment, one or more edge networks 500N may be provided by a content delivery network (CDN). The UE devices 110A-110Z and the RAN 500N may be compliant with the New Radio (NR) standard and the 3GPP TS 28.530 V15.1.0 Release 15 document from the 3rd Generation Partnership Project (3GPP) and the 3GPP Release 16 Technical Report (3GPP Release 16 Report).

[0051] Each of the different UE devices 110A-110Z may be associated with a different user. The UE devices of the UE devices 110A-110Z, in one embodiment, may be computing node devices provided by a client computer, such as a mobile device, such as a smartphone or tablet, laptop, smartwatch, or PC, running one or more programs that facilitate access to services from one or more service providers. The UE devices of the UE devices 110A-110Z may alternatively be provided by, for example, an Internet of Things (IoT) sensing device.

[0052] Embodiments herein recognize that hosting service functionality on one or more computing nodes in the edge enterprise entity network 1000N can provide various advantages, including latency advantages related to the speed of service delivery to end users at UE devices 110A-110Z. The edge enterprise entity hosted service functionality may be hosted, for example, within the edge network 500N or otherwise within the edge enterprise entity network 1000N.

[0053] The data network 2000N may include, for example, the IP Multimedia Subsystem (IMS) and / or the "Internet," which may be considered a network of networks consisting of private, public, academic, business, and government networks of local to global scope connected by electronic, wireless, and optical networking technologies. The data network 2000N may include, for example, multiple non-edge data centers. Such data centers may include private enterprise data centers as well as multi-tenancy data centers provided by an IT enterprise that provides hosting of service functions developed by multiple different enterprise entities.

[0054] For example, some edge entities that own, operate, or control, or a combination of, the edge infrastructure provided by the edge network 500N may offer multi-tenancy hosting services that allow enterprises other than the edge enterprise to host their applications on one or more edge nodes within the edge enterprise entity network 1000N.

[0055] According to one embodiment, the orchestrator 130 may be deployed on a computing node of the core network 1300N. According to another embodiment, the orchestrator 130 may be deployed on one or more computing nodes of the data network 2000N. According to one embodiment, the orchestrator 130 may be distributed between computing nodes of the core network 1300N and the data network 2000N. According to one embodiment, the orchestrator 130 may be co-located on computing nodes of the core network 1300N and the data network 2000N. In one embodiment, the Management and Orchestration (MANO) computing environment may be in accordance with 3GPP TS 28.530 V15.1.0 Release 15 document by the 3rd Generation Partnership Project (3GPP) and the 3GPP Release 16 Technical Report (3GPP Release 16 Report).

[0056] Various available tools, libraries, or services, or a combination thereof, may be utilized for implementing the predictive model 6002. For example, a machine learning service may provide access to libraries and executable code for supporting machine learning functions. The machine learning service may provide access to a set of REST APIs that can be invoked from any programming language and allow the integration of predictive analytics into any application. An available REST API may provide, for example, retrieval of metadata for a given predictive model, model deployment and management of deployed models, online deployment, scoring, batch deployment, stream deployment, monitoring, and retraining deployed models. According to one possible implementation, a machine learning service offered by IBM® WATSON® may provide access to APACHE® SPARK® and IBM® SPSS® libraries (IBM® WATSON® and SPSS® are registered trademarks of International Business Machines Corporation, and APACHE® and SPARK® are registered trademarks of the Apache Software Foundation). The machine learning services offered by IBM® WATSON® can be invoked from any programming language and can provide access to a set of REST APIs that allow the integration of predictive analytics into any application. Available REST APIs can provide, for example, retrieval of metadata for a given predictive model, model deployment and management of deployed models, online deployment, scoring, batch deployment, stream deployment, monitoring, and retraining deployed models. The construction of predictive models 6002 can include, for example, the use of support vector machines (SVMs), Bayesian networks, neural networks, or other machine learning techniques, or a combination thereof.

[0057] A method for performance by orchestrator 130 in cooperation with base stations 120A-120Z and UE devices 110A-110Z is described with reference to the flowchart of Figure 5. In block 1301, orchestrator 130 may be transmitting an installation package to a base station that is owned, operated, or controlled, or a combination thereof, by an edge enterprise entity that owns, operates, or controls orchestrator 130. Upon receiving the installation package, base station 120A-120Z may install the received installation package in block 1201.

[0058] The installation package transmitted in block 1301 may include, for example, libraries and executable code for providing the functionality of VNF process 120V and orchestration process 120R, which run on each of base stations 120A-120Z, and which define VNF layer 1000 and service orchestration layer 2000, respectively. In block 1101, UE devices 110A-110Z may have transmitted registration data to orchestrator 130. The registration data may be entered into a display web page-based user interface displayed on the display of the UE device. The registration data may be transmitted to orchestrator 130 via a network including base stations 120A-120Z or another network.

[0059] Upon receiving the registration data, orchestrator 130 may establish a user subscription for storage in data repository 230 shown in FIG. 1 , and upon receiving the registration data, orchestrator 130 may transmit an installation package to UE devices 110A-110Z. In response to receiving the installation package transmitted in block 1302, UE devices 110A-110Z may install the installation package in block 1102. The installation package installed in block 1102 may include, for example, libraries and executable code defining VNF process 110V and orchestration process 110R operating on each of UE devices 110A-110Z shown in FIG. 1 , which processes define VNF layer 1000 and service orchestration layer 2000, respectively, described in connection with FIG. 2 and FIG. 3 . The installation packages installed in blocks 1201 and 1102 may also define functionality of physical network function layer 01, respectively, described in connection with FIG. 3 .

[0060] In response to transmitting the installation package in block 1302, the orchestrator 130 may proceed to block 1303. In block 1303, the orchestrator 130 may transmit update data to the base stations 120A-120Z that updates the base stations 120A-120Z with new UE device identifiers that map to registered users of the computing environment 100. Thus, when the base stations 120A-120Z receive join requests from new UE devices that have recently registered, the base stations 120A-120Z are able to recognize such new UE devices. In block 1103, the UE devices of the UE devices 110A-110Z may have transmitted join requests for reception by the base stations 120A-120Z. As mentioned, the base stations 120A-120Z may recognize the previously registered UE devices and, therefore, may respond appropriately according to the characteristics of the computing environment 100.

[0061] In response to receiving the join request, the base station of base station 120A-120Z receiving the greatest signal strength from the UE device of UE device 110A-110Z may respond by transmitting channel data indicating an assigned DTCH channel for further communication between the particular UE device, e.g., UE device 110A, and the particular base station, e.g., base station 120A, in block 1202. In blocks 1104 and 1203, a join communication may be performed such that the particular UE device, e.g., UE device 110A, becomes connected to the particular base station 120A.

[0062] As indicated by the return arrows, the UE devices 110A-110Z may repeatedly loop through blocks 1103 and 1104, repeatedly sending join requests to new base stations and repeatedly receiving join communications to communicate on the new assigned logical DTCH channels. Just as a UE device may change logical channels over which it communicates with different base stations, the base stations 120A-120Z in the VNF layer 1000 may maintain the described user-to-logical channel association data, or relevant portions thereof, to enable the base stations 120A-120Z to remain in communication with their respective subscribing UE devices. However, because the user identification data of the described user-to-logical channel association data is not shared with the service orchestration layer 2000, applications running in the service orchestration layer 2000 are unable to recover the UE device and UE information from data transmissions by the service orchestration layer 2000.

[0063] 5 , to initiate data collection, orchestrator 130 may transmit data collection request data in block 1305. The data collection request data transmitted in block 1305 may be transmitted to base stations 120A-120Z that are owned, operated, or controlled, or a combination thereof, by the edge enterprise entity operating orchestrator 130. Base stations 120A-120Z that receive the data collection request data transmitted in block 1305 may responsively transmit the data collection request data in transmission block 1205 to UE devices 110A-110Z that are currently subscribed to and connected to base stations 120A-120Z.

[0064] In response to receiving the data collection request data transmitted in block 1205, each of the UE devices 110A-110Z may transmit user medical health data to the respective base station of the currently subscribed and connected base stations 120A-120Z via the VNF process 110V in block 1105. The user medical health data transmitted in block 1105 may include sensitive user medical health data transmitted on the described assigned unique separate logical channels associated with each of the UE devices 110A-110Z, respectively.

[0065] In response to receiving the user medical health data transmitted in block 1105, base station 120Z-120Z may generate, in block 1206, via VNF process 120V, geospatial mapping data that identifies various user geospatial map locations. The geospatial maps may identify infrastructure features such as roads and buildings. For such functionality, VNF layer 1000 may be in communication with a geospatial map service, such as GOOGLE MAPS® (GOOGLE MAPS® is a registered trademark of Google Inc.).

[0066] In response to generating the mapping data, base stations 120A-120Z may transmit the geospatial mapping data to orchestrator process 130R of orchestrator 130 via VNF process 120V in block 1207. In transmitting the geospatial mapping data in block 1207, base stations 120A-120Z may associate the user health data with the assigned logical channel, although the user health data may be absent of any specific user identification data. For transmission of the geospatial mapping data in block 1207, VNF layer 1000 may push up the received health data with location data to service orchestration layer 2000. When the generated user medical health data is pushed up to the service orchestration layer 2000, the data collection process 1010 may be restricted from accessing any user identity data so that the user medical health data pushed up to the service orchestration layer 2000 can include DTCH data without representing any UE device identity data or surrogate user identity data without inclusion of other user identity data. When the VNF layer 1000 provides geospatial mapping data, the VNF layer 1000 may push the geospatial mapping data to the service orchestration layer 2000 along with the user medical health data and location data, with users generically represented by their associated logical channels and the data push being absent any user identity data. In other embodiments, the geospatial map function may be performed entirely by the service orchestration layer 2000.

[0067] With the features described herein, individual users may be represented by assigned logical data channels, but there is no actual user identification data, e.g., device UUID information, associated with the assigned logical channels. In response to receiving the geospatial mapping data transmitted in block 1207, orchestrator 130 may aggregate mapping data associated with multiple different base stations via service orchestration layer 200 in aggregation block 1306. Orchestrator 130 may also identify suspected infected users via service orchestration layer 2000 in aggregation block 1306. In identifying suspected infected users, orchestrator 130 may assign an infection probability to users with a current status of not infected via orchestrator process 130R defining service orchestration layer 2000.

[0068] In one embodiment, to assign an infection probability to a user with a currently uninfected status, the service orchestration layer 2000 may determine the number of past encounters, for example, within a threshold period of the current time. A encounter may be determined to occur, for example, when a first and second user are within a threshold distance of each other, for example, 6 feet (1.8288 meters) or 2 meters. Based on the past encounter data, the service orchestration layer 2000 may assign an infection probability.

[0069] To assign infection probability based on the number of past encounters, the service orchestration layer 2000 may consult a particular predictive model 6002, such as that shown in Figure 6, that is trained by machine learning. The predictive model 6002 may have been trained with a training data set that includes (a) the number of encounters of a particular uninfected user with an infected user and (b) the uninfected user's subsequent infection status. Once trained, the predictive model 6002 can respond to query data, including the number of past detected encounters, and output data in response to the query data can include a value specifying a probability of infection.

[0070] In one embodiment, to assign infection probabilities to users, service orchestration layer 2000 may predict subsequent encounters between uninfected users and then assign infection probabilities based on the predicted encounters by querying a particular described predictive model trained with a training dataset that includes (a) the number of encounters a particular uninfected user has with infected users and (b) the uninfected user's subsequent infection status. To predict subsequent encounters, service orchestration layer 2000 may include historical movement data of detected users from which user data is collected.

[0071] The service orchestration layer 2000 may determine each user's current direction using recent historical location data. Each user's current direction may be considered a trajectory. The service orchestration layer 2000 may use each user's determined current direction to predict each user's subsequent path (position over time). To predict the subsequent path, the service orchestration layer 2000 may apply the assumption that the user will continue traveling in their current direction for the next N time periods. The service orchestration layer 2000 may determine a prediction of future encounters by examining each predicted path for each user and identifying encounters between users as they travel along each path, where the predicted path is based on the detected current path.

[0072] Once predicted subsequent misses are identified, the service orchestration layer 2000 may query the predictive model 6002 to determine the probability of infection based on the predicted number of misses for each user. The service orchestration layer 2000 may query the predictive model 6002 using historical route data for each user to determine the predicted number of misses, as described herein.

[0073] Using the predicted infection level data (assigned probability of infection data), the orchestrator 130, via the service orchestration layer 2000, may generate the heat maps shown in FIGS. 7-9. In the heat maps, the darkest areas identify areas of highest infection intensity, and lighter areas identify areas of less infection intensity. The determined infection intensity may be a function of the recorded infection status of users as well as a value for the assigned probability of infection. An area may have a small number of infected users, but may be recorded in the heat map as a high infection intensity area based on the identified suspected infected individuals having a significant value for the assigned probability of infection.

[0074] In provision block 1307, the orchestrator 130 may provide resources to combat and address the infection across the region by allocating vaccine doses or other infection treatment resources based on and in response to the infection intensity level determined by the service orchestration layer 2000, for example, as indicated by heat map mapping data. In one embodiment, the service orchestration layer 2000 may provide resources in proportion to the detected level of infection intensity, with regions with higher determined infection intensity levels being provided with proportionally more medical treatment resources. In FIG. 9 , the circled region is the region determined by the service orchestration layer 2000 to have the highest infection intensity and, therefore, may be provided with the most medical treatment resources by the service orchestration layer 2000. In output block 1308, the orchestrator 130 may initiate the automatic provision of medical treatment resources by the autonomous vehicle. In response to completion of block 1308, the orchestrator 130 may proceed to block 1309. In block 1309, the orchestrator 130 may return to block 1305 and may perform the loop of blocks 1305 through 1309 repeatedly during the deployment period of the orchestrator 130. The orchestrator 130 may also simultaneously perform the loop of blocks 1302 and 1303 repeatedly during the deployment period of the orchestrator 130.

[0075] Providing the medical treatment resources to the medical facility may include, using appropriate configuration of output block 1308, automating allocation of the medical treatment resources to the medical facility, for example, by automated placement of a delivery order to an owned enterprise or an external vehicle courier service in output block 1308, or by initiating automated, e.g., autonomous vehicle, robotic, procurement of the medical resources in output block 1308, and initiating route travel by the autonomous vehicle to a destination, e.g., the medical facility, in output block 1308.

[0076] According to one embodiment, the present disclosure describes a method for wirelessly transmitting medical and health user data from a virtual network function (VNF) layer that has assigned logical channels to each of a plurality of UE devices for wireless transmission of the medical and health user data, the VNF layer maintaining user-to-logical channel association data that associates user identification data with logical channels assigned to a user identified by the user identification data; a service orchestration layer operating on the VNF layer examining the user medical and health data, the service orchestration layer configured such that the service orchestration layer is restricted from accessing the user identification data in the user-to-logical channel association data; and performing processing in response to the examining step. The performing processing in response to the examining step may include, for example, the described processing performed in block 1306, block 1307, or block 1308, or a combination thereof.

[0077] Medical treatment resources herein may include medications, e.g., vaccines; medical equipment, e.g., syringes, bandages, tables, user health monitoring devices, etc.; and participants, e.g., doctors, nurses, and technicians. The service orchestration layer 2000 may provide resources depending on a disease, e.g., infection intensity level, in an area. The disease intensity level described herein may be a function of the total number of users with an infected status and the assigned probability of infection relative to other users. Automated autonomous vehicle procurement may be accomplished using autonomous robotic picking and packing technology, e.g., robotic technology included in the DEMANTIC® product line available from Kion Group AG. Autonomous vehicle routing may be provided using the TRIMBLE AUTOMOTIVE POSITIONING SOLUTION® available from Trimble Inc. and the NVIDIA® autonomous vehicle software, hardware, and infrastructure suite available from Nvidia Corporation.

[0078] According to one embodiment, the service orchestration layer 2000 may provide medical treatment resources as described in Tables D and E. [Table D] [Table 4] [Table E] [Table 5]

[0079] Referring to Tables D and E, it can be seen that, according to one scenario, the service orchestration layer 2000 may provide the second geographic region to include increased resources compared to the first region despite having fewer determined infected users because the service orchestration layer 2000 may identify a greater number of suspected infected users in the second region, as determined by assigning infection probability values ​​to uninfected users. Upon identifying the presence of suspected infected users, the service orchestration layer 2000 may assign infection probabilities to users whose current status is uninfected.

[0080] Embodiments herein provide methods, systems, and apparatuses operating at the 5G service orchestration layer that communicate with existing services of infected and suspected users over a DTCH logical channel established using 5G physical network capabilities and eNodeB login with user consent. Embodiments herein can provide selected infected and suspected UE devices with eNodeB radio access maps, locate the device's operating area, and proactively trigger medical logistics operations for potentially contagious diseases.

[0081] Embodiments herein can calculate medication requirements for the disease based on exposure probabilities derived using the multi-level hierarchical DTCH_LIST and predict medical logistics requirements based on the number of exposed people and the level of disease exposure in the region. This medical logistics data is pushed to each subscriber for logistics optimization and to maintain real-time advance stock at desired locations based on suspected people in the region. This will help exposed people obtain medication locally if they detect a positive infection, and also help isolate regions so that people do not need to travel across regions for medical treatment resources, such as medicines like vaccines.

[0082] Embodiments herein recognize that artificial intelligence is simplifying the lives of patients, doctors, and hospital administrators by performing tasks typically done by humans, but in less time and at a fraction of the cost. With the development of AI in medical science, deep learning models are designed to obtain information from patients and predict the cause of illness and the drug treatment to address the illness while analyzing user history. There are AI-based symptom-collecting chatbots available, as well as treatment checkers that use algorithms to diagnose and treat illnesses. These virtual agents collect user information and, accordingly, suggest medical therapies to overcome illnesses.

[0083] New deep learning-based medical tools are being deployed to streamline various diagnostic procedures. Several deep learning platforms analyze unstructured medical data (radiography images, blood tests, EKGs, patient history) to provide doctors with better insight into the real-time needs of their patients. With the enablement of next-generation AI technologies, the cognitive healthcare domain is also being expanded to AI-based medical logistics and the healthcare supply chain.

[0084] Embodiments herein recognize that 5G technology can act as a rich enabler that pushes dependent technologies to much higher levels, such as through the convergence of 1 GBPS mobility bandwidth and IoT device access. One of the key features of 5G is the network itself being intelligent and cognitive.

[0085] Embodiments herein recognize that 5G New Radio (NR) may improve performance by offering new frequency bands in wideband and millimeter wave, massive MIMO for precise angle-of-arrival estimation, and new architectural options that specifically support positioning at precise times. Enhancements in 5G device positioning may provide accurate user location, providing advantages for location-specific application development and location-driven analytics. One emerging component of location-driven analytics may be healthcare logistics, which is addressed by embodiments herein.

[0086] Embodiments herein recognize that potentially contagious or infectious diseases are caused by microorganisms, such as bacteria, viruses, parasites, and fungi, that can be spread directly or indirectly from one person to another. They can be transmitted by insect bites or through contact with infected people. These viruses often jump from person to person based on the nature of transmission. Because these potentially contagious diseases, such as COVID-19, are spread by contact, the best approach is to isolate suspected individuals and practice social distancing. For certain potentially contagious diseases (such as COVID-19), infections can spread faster than expected, resulting in crises such as the novel coronavirus disease pandemic. In such epidemic situations, stronger medical and health care support plays a critical role in controlling the transmission of infection to other people and protecting and treating patients.

[0087] Embodiments herein recognize that according to existing approaches, there is no way for healthcare medical logistics to use information about the number of infected and suspected people to plan and provide medical treatment resources and equipment to each area accordingly. During a pandemic situation, mechanisms exist to keep records of infected people by their area, but this is primitive evidence and can only be achieved during a pandemic situation.

[0088] Embodiments herein recognize that, today, there is no method by which medical treatment resources, such as medicines, equipment, and other resources, can be provided based on dynamically identified suspected people in an area. Embodiments herein recognize that, with existing approaches, there is no method by which information from users indicating their status (infected or suspected) for a contagious disease can be collected and logistics can be provided accordingly. While a user's medical records can be used based on the user's consent, there is no method today by which that data can be used to infer the provision of medical treatment resources, such as medicines and other medical equipment. For example, if there are five infected people in one part of a city and the disease is contagious, there are 1,000+ other people who have come into contact with these infected people in the past few days, and therefore they are suspected people who have been exposed to the infection. However, another part of the area also has five positive cases and 200 suspected people who have come into contact with the infected people. The 5G medical service invention disclosure generates information about suspected people after user consent. Embodiments herein recognize that there is an uneven distribution of infected and suspected users, and that today there is no way for medical logistics services to obtain this information and proactively provide medical treatment resources to each affected area accordingly.

[0089] Embodiments herein recognize that during large-scale infection and emergency situations, there is a greater need for an intelligent supply mechanism based on predicting per-unit requirements in the region, as there are shortages of pharmaceutical capabilities, including drugs and equipment.

[0090] Embodiments herein provide methods, systems, and apparatuses that collect information from various sources to obtain a DTCH list of infected and suspected people, and communicate with other services in a multi-domain programmable framework at the service orchestration layer of a 5G telecommunications network to trigger a medical facility logistics management system to provide medical treatment resources to each area accordingly.

[0091] An invention instance operating in the service orchestration layer of a 5G network initiates and collects information from a medical service in the 5G multi-domain layer and communicates with that service to obtain a DTCH_LIST of infected and suspected individuals. The medical service for infection and trajectory management has information about the UUIDs of infected user devices and other devices that have come into contact with exposed and infected people.

[0092] Service instances in the 5G network initiate handshakes with these services with a multi-level hierarchical suspicion finder in the plane and perform service-to-service authentication to obtain the desired information. Once the service is authenticated and user consent is validated, the service then receives the DTCH_IDs of infected and exposed people. This DTCH_LIST may be provided by a list of 5G logical channels created between the UE device and the eNodeB. The DTCH_LIST may be provided as a virtual network function in the 5G network. These logical channels may be created by the UE device and / or the base station to send and receive information over radio bearers (NR), and the VNF may track these DTCHs assigned to various devices.

[0093] Once the DTCH_LIST is received, a map-based classifier is invoked to obtain the geolocation latitude, longitude coordinates, and altitude information of the DTCH. A 5G virtual network function is invoked, which has the built-in ability to determine the location of the DTCH based on GPS and other 5G-based precise location algorithms. The DTCH location can be tracked to obtain an area of ​​operation. These areas of operation for all DTCHs in the DTCH_LIST can be collected and stored in a 5G metadata mapper object, which can be used to calculate medical treatment resource requirements. Once the locations and areas of operation are collected using the map-based classifier, the information aggregator can provide the total number of people determined to need medical treatment resources, for example, medical relief, medicine, and other medical treatment resources to address spreading illnesses.

[0094] The strength of the infected and suspected list can be calculated based on the suspected DTCH_IDs in the received DTCH_LIST. Depending on the strength of the disease (expressed using the suspected people DTCH set) and the probability of its spread, medical treatment resources, such as the amount of medical equipment and other facilities, will be determined by tracing the disease's medical database. For example, if a COVID-19 infected person requires 14 Oseltamivir® (antiviral drug - Tamiflu®) tablets, then if an area contains 1,000 suspected people, 1,000 x 14 tablets can be provided to the area. This information can be generated by sorting through a medical disease database that includes a map of the disease and the medical treatment resources provided to address the disease.

[0095] Once this information is known, a notification can be generated to the medical facility services in the area to provide the respective medical treatment resource requirements to address the illness. This notification will be consumed by subscribed medical services, which may further escalate the notification to the service provider in the 5G plane to fulfill the medical need. The notification can be escalated to the subscribed service provider.

[0096] Because medical facilities in the region are proactively provisioned with appropriate medical treatment resources by the service orchestration layer 2000, infected users will not be required to leave the region to obtain needed medication and / or other medical treatment resources, thereby helping to establish quarantines because infected users will not need to travel beyond their zone. Medicines and other resources will be proactively available even before people are actually infected. Resource provisioning will be based on infected users and suspected users who have assigned infection probabilities, so they can easily obtain medicine or other medical treatment resources when needed.

[0097] Embodiments herein use 5G technologies, including providing logical channels in wireless networks. A virtual network function (VNF) may abstract the username and / or device ID using user-to-logical channel association data, e.g., a logical channel ID created using the user in a table. By restricting the service orchestration layer 2000 from accessing the user information in the user-to-logical channel association data, services in the orchestration plane cannot backtrack to obtain the user's private information, such as IMSI and TMSI numbers, because they are abstracted in the VNF function.

[0098] Thus, embodiments herein use 5G to prevent unauthorized information access. Furthermore, transparent handling of user identity can be achieved through DTCH to UE translation in the VNF, provided by the described user-to-logical channel association data. Furthermore, the VNFs and PNFs of 5G telecommunications networks include location-based classification capabilities and user connectivity establishment mechanisms that can be leveraged by the computing environment 100. One of the primary approaches of the computing environment 100 uses DTCH, which is a logical slicing of physical bearers for transmitting specialized traffic between entities, and tracing of DTCH for eNodeB login patterns and exposing areas not possible in 4G or corresponding platforms.

[0099] Embodiments herein may include a service operating in a 5G service orchestration plane that communicates with other location-based services in a multi-domain cognitive orchestration layer and may include collecting information about dedicated logical channels in the 5G network. Medical health user data may be transmitted using the assigned logical channels and may include various medical health data, including medical health data indicating whether the user has been infected or data indicating whether the user has been exposed to an infection (e.g., route data).

[0100] Embodiments herein may further include a user consent-driven location tracing system in a 5G Virtual Network Function (VNF), where the VNF tracks DTCH locations based on 5G location technology or Global Positioning System (GPS) and latitude and longitude coordinate information for each DTCH. Embodiments herein may further include utilizing existing map-based services in the 5G-VNF using inbound or out-of-bound protocol implementations. Embodiments herein may further include invoking a 5G-based infection tracking system to obtain a list of assigned DTCHs, which may include medical health data, such as medical health data indicating whether the user is infected or exposed to a disease. Embodiments herein may provide a user access policy-driven authentication mechanism that enables access to user location tracking and metadata map records performed at the 5G VNF layer. Embodiments herein may provide, upon authentication, sending a multi-level hierarchical DTCH list of infected and suspect UE devices to a medical mapper. Embodiments herein may include the aggregation of a list of DTCHs, extracting a DTCH_LIST, and bifurcating the list based on primary, secondary, and subsequent levels of infection exposure. Embodiments herein may include metadata mapper updates for the collected DTCH_LIST and using these mapper objects to articulate the next level of insight. Embodiments herein may provide triggering of DTCH location monitoring for selected DTCHs in the DTCH_LIST. Embodiments herein may include VNF-level monitoring of the DTCH_ID and identification of access location eNodeB regions. Embodiments herein may include aggregating eNodeB radio resource maps of all DTCHs in the list to obtain the region of the user DTCH (access assurance at the eNodeB). Embodiments herein may include applying map-based classification of the eNodeB access pattern map to generate user operating region information. Embodiments herein may calculate all elements in a predefined region map and count multi-level infected and exposed people within the region.Embodiments herein may invoke a medical classifier to provide infection information and drug treatment details, collect drug policy requirements, and treat patients. Embodiments herein may further notify medical facilities of provided medical treatment resources, including associated drug requirements, and of resources provided to other medical facilities based on the region. Providing medical treatment resources to medical facilities may include automating allocation of medical treatment resources to medical facilities, for example, by automated placement of a provision order to an owned enterprise or external vehicle courier service, or by automated, e.g., autonomous vehicle, initiating robotic procurement of medical resources and initiating route movement to the destination. Embodiments herein may include sending notification of requirements to subscribed medical service providers. Embodiments herein may include generating user notifications regarding medical facility services to manage logistics. Embodiments herein may clearly delineate suspect populations within a region, calculate their drug and other medical treatment resource requirements, and accordingly perform advance notification so that emergency situations can be handled efficiently. Embodiments herein may proactively identify medical treatment resource needs to address potentially contagious illnesses during pandemic situations and therefore update relevant personnel to fill in the gaps. Embodiments herein may provide important functionality during pandemic situations where medications and other medical treatment resources to address illnesses may have limited availability. Embodiments herein may improve the responsiveness of medical facilities when an even distribution of medical treatment resources to different medical facilities does not address the needs of the different medical facilities during a medical emergency such as COVID-19. Embodiments herein provide a mechanism by which predictions of medication and other medical treatment resource usage and other interrelated facility utilization can be performed, which aids in proactive medical treatment resource allocation. Embodiments herein provide a suspected user list and, accordingly, clearly indicate the medical needs of infected and suspected users, which are assigned a probability of infection, and which aid in obtaining appropriate treatment for the infected and suspected users.The embodiments herein provide a service that does not identify infected people lists or any other user information, as it pushes information directly to medical channels using VNFs. Therefore, there is no risk of personal information being leaked. The embodiments herein provide parameters of the time that the infected user and other users are spending together, and select the allocation of provided medical treatment resources accordingly. The embodiments herein may provide logistics status reports to relevant subscribed users for timely supply of medical treatment resources to address the illness.

[0101] Embodiments herein may use 5G infrastructure for autonomous vehicle domains. The embodiments herein operate to request VNFs for dynamic changes in user-based 5G-DTCH based on medical history and associated expectations in mobility. The embodiments herein may enable IaaS or PaaS service providers to provide more accurate and optimal real-time data placement using 5G service orchestration, providing the ability to push data based on realistic conditions by automatically adjusting monitoring levels based on utilizing real-time information, etc.

[0102] According to one example, embodiments herein may include: (1) a service orchestration layer of a 5G network initiates and invokes a MEDICA_SERVICE interconnect API over a 5G in-band protocol frame; (2) a data collector daemon may be invoked to receive streams from medical services and other interrelated 5G multi-domain services; (3) a DTCH_COLLECTOR sends an ASYNC to communicate with a multi-level hierarchical infection detector service to collect a DTCH_LIST of infected and suspected individuals; (4) information may be processed by peer services in domain and trajectory management that are invoked to collect a list of IDs; (5) services collect authorized UUIDs of infected user devices or other devices that have come into contact with exposed people and infected people; (6) a handshake COMM is afflicted with these services with a multi-level hierarchical suspicion finder in the plane; and (7) inter-service UUID and DTCH authentication may be implemented within the 5G programmable framework. (8) A stream receiver and metadata mapper are invoked to receive the DTCH_IDs of the UEs of infected and exposed people (DTCH_LIST is a list of 5G logical channels created between UE → eNodeB → Virtual Network Function → Radio Bearer (NR)). (9) Once the DTCH_LIST is received, a map-based classifier is invoked to obtain the geolocation latitude and longitude information of the DTCH. (10) A VNF instance of a DTCH locator is inserted to trace the device and return each eNodeB. (11) Frequency-based eNodeB filtering is performed for each eNodeB in the received DTCH_LIST to obtain the area location. (12) Once the location and operating area are collected using the map-based classifier, an information aggregator gives the total number of people who likely need medical relief and medicine for the spreading disease. (13) An intensity calculation is performed with the DTCH_LIST.(14) The intensity and spreading probability of the disease (expressed using the suspected people DTCH set), the amount of medical equipment and other facilities is determined by tracing the medical database of the disease (the information can be generated by classifying the medical disease database including the disease drug mapper); (15) An in-band message across the PLMQ is sent to the notification manager with the <area, medical logistics requirement> tuple; (16) This notification can be consumed as needed by the subscribed medical service in the 5G plane, which can further escalate the notification to the service; (17) If necessary, the notification can be issued to the subscribed user via the 5G built-in VNF-DTCH translation logic.

[0103] Certain embodiments herein may provide various technical computational advantages along with computational advantages for addressing problems arising in the field of computer networks and computer systems. Embodiments herein may provide secure provisioning of user data, such as sensitive medical and health user data. To provide the medical and health user data, a virtual network function (VNF) layer may be configured to include user-to-logical channel association data that facilitates participation of UE devices within a wireless network. In the wireless network, the VNF layer may assign logical channels, such as 5G New Radio (NR) DHTC channels, to each UE device associated with various users. When the VNF layer receives the user data, it may pass the user data to a service orchestration layer for further processing. In the service orchestration layer, the user data may be associated with a logical channel, which serves as a global reference for the user. However, the service orchestration layer may be restricted from accessing user identification data in the user-to-logical channel association data. In the service orchestration layer, there may be an assigned probability of infection for a user with a current status of not being infected. Users' historical location data can be used to assign infection probabilities to various users. Medical treatment resources provided to address infection can be allocated according to the assigned infection probabilities. Various decision data structures, such as decision data structures that cognitively map social media interactions in association with posted content to parameters used for better allocation, which may include digital rights allocation, can be used to drive artificial intelligence (AI) decision-making. Decision data structures such as those described herein can be updated by machine learning so that accuracy and reliability are iteratively improved over time without resource-consuming, rule-intensive processing. Machine learning processes can be performed for increased accuracy and reduced reliance on rule-based criteria and therefore reduced computational overhead.To enhance the accuracy of calculations, embodiments may feature computational platforms existing only in the field of computer networks, such as artificial intelligence platforms, and machine learning platforms. Embodiments herein may utilize data structuring processes, such as processes for converting unstructured data into a form optimized for computerized processing. Embodiments herein may examine data from diverse data sources, such as data sources that process wireless signals for user location determination. Embodiments herein may include artificial intelligence processing platforms featuring improved processes for converting unstructured data into a structured form that permits computer-based analysis and decision-making. Embodiments herein may include both specific configurations for collecting rich data into a data repository and additional specific configurations for both updating such data and using that data to drive artificial intelligence decision-making. Certain embodiments may be implemented through the use of cloud platforms or data centers in various types, including software as a service (SaaS), platform as a service (PaaS), database as a service (DBaaS), and combinations thereof, based on the type of subscription.

[0104] 10-12 illustrate various aspects of computing, including computer systems and cloud computing, in accordance with one or more aspects described herein.

[0105] Although this disclosure includes detailed descriptions of cloud computing, it should be understood in advance that implementation of the teachings described herein is not limited to cloud computing environments. Rather, embodiments of the present invention can be practiced in conjunction with any other type of computing environment now known or later developed.

[0106] Cloud computing is a service delivery model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with the service provider. The cloud model can include at least five characteristics, at least three service models, and at least four deployment models.

[0107] The characteristics are as follows:

[0108] On-Demand Self-Service: Cloud customers can unilaterally provide computing capacity, such as server time and network storage, automatically as needed, without the need for human interaction with the service provider.

[0109] Wide network access: Capabilities are available across the network and can be accessed through standard mechanisms, facilitating use by different types of thin or thick client platforms (e.g., cell phones, laptops, and PDAs).

[0110] Resource Pooling: A provider's computing resources are pooled to serve multiple customers using a multi-tenant model, with different physical and virtual resources dynamically allocated and reallocated according to demand. There is location independence in that consumers generally have no control or knowledge over the exact location of the provided resources, but may be able to specify the location at a higher level of abstraction (e.g., country, state, or data center).

[0111] Rapid Scalability: Capacity can be provided quickly and elastically, sometimes automatically, and can be quickly scaled out or quickly unbound and quickly scaled in. To the consumer, it often appears as if there is an unlimited amount of capacity available for provisioning, and any amount can be purchased at any time.

[0112] Measurable Services: Cloud systems automatically control and optimize resource utilization by leveraging metering capabilities appropriate to the type of service (e.g., storage, processing, bandwidth, and actual user accounts) at a certain level of abstraction. Resource usage can be monitored, controlled, and reported, providing transparency to both providers and consumers of the services used.

[0113] The service model is as follows:

[0114] Software as a Service (SaaS): The consumer is offered the ability to use a provider's applications running on a cloud infrastructure. The applications are accessible from a variety of client devices through thin-client interfaces such as web browsers (e.g., web-based email). The consumer does not manage or control the underlying cloud infrastructure, including networks, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.

[0115] Platform as a Service (PaaS): The ability offered to consumers is to deploy consumer-created or acquired applications written using programming languages ​​and tools supported by the provider onto a cloud infrastructure. Consumers do not manage or control the underlying cloud infrastructure, including the network, servers, operating systems, or storage, but do have control over the deployed applications and, in some cases, the configuration of the environment that hosts the applications.

[0116] Infrastructure as a Service (IaaS): The capability provided to customers is the provision of processing, storage, network, and other basic computing resources on which they can deploy and run any software, which may include operating systems and applications. Customers do not manage or control the underlying cloud infrastructure, but they do have control over the operating systems, storage, deployed applications, and possibly limited control over selected network components (e.g., host firewalls).

[0117] The deployment model is as follows:

[0118] Private Cloud: Cloud infrastructure is operated solely for one organization. A private cloud may be managed by that organization or a third party and may exist on-premise or off-premise.

[0119] Community Cloud: Cloud infrastructure is shared among several organizations to support a specific community with common interests (e.g., mission, security requirements, policies, and regulatory compliance considerations). Community clouds may be managed by those organizations or by a third party and may exist on-premises or off-premises.

[0120] Public Cloud: Cloud infrastructure is made available to the general public or large industry groups and is owned by organizations that sell cloud services.

[0121] Hybrid Cloud: Cloud infrastructure is a combination of two or more clouds (private, community, or public), where each cloud remains a unique entity but is tied together by standardized or proprietary technologies that allow for data and application portability (e.g., cloud bursting to load balance between clouds).

[0122] Cloud computing environments are service-oriented, emphasizing statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes.

[0123] Referring now to Figure 10, a schematic diagram of an example computing node is shown. Computing node 10 is merely one example of a computing node suitable for use as a cloud computing node and is not intended to suggest any limitation as to the scope of use or functionality of the embodiments of the present invention described herein. Regardless, computing node 10 may be implemented with and / or capable of performing any of the functions described above. Computing node 10 may be implemented as a cloud computing node within a cloud computing environment, or may be implemented as a computing node within a computing environment other than a cloud computing environment.

[0124] Within computing node 10 is computer system 12, which is operable in numerous other general-purpose or special-purpose computing system environments or configurations. Examples of well-known computing systems, environments, or configurations, or combinations thereof, that may be suitable for use with computer system 12 may include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or devices, and the like.

[0125] Computer system 12 may be described in the general context of computer system-executable instructions, such as program processes, being executed by a computer system. Generally, program processes may include routines, programs, objects, components, logic, data structures, etc. that perform particular tasks or implement particular abstract data types. Computer system 12 may be practiced in a distributed cloud computing environment where tasks are performed by remote processing devices connected through a communications network. In a distributed cloud computing environment, program processes may be located in local and remote computer system storage media, including memory storage devices.

[0126] 10, computer system 12 within computing node 10 is shown in the form of a computing device. Components of computer system 12 may include, but are not limited to, one or more processors 16, system memory 28, and a bus 18 coupling various system components, including system memory 28, to processor 16. In one embodiment, computing node 10 is a computing node of a non-cloud computing environment. In one embodiment, computing node 10 is a computing node of a cloud computing environment, as described herein in connection with FIGS. 11-12.

[0127] Bus 18 represents any one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures, including, by way of example and not limitation, an Industry Standard Architecture (ISA) bus, a MicroChannel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0128] Computer system 12 typically includes a variety of computer system-readable media. Such media can be any available media that can be accessed by computer system 12 and can include both volatile and nonvolatile media, removable and non-removable media.

[0129] System memory 28 may include computer system-readable media in the form of volatile memory, such as random access memory (RAM) 30 or cache memory 32, or both. Computer system 12 may further include other removable / non-removable volatile / non-volatile computer system storage media. By way of example only, a storage system 34 may be provided for reading from and writing to a non-removable, non-volatile magnetic medium (not shown, but commonly referred to as a "hard drive"). Although not shown, a magnetic disk drive may be provided for reading from and writing to a removable, non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive may be provided for reading from or writing to a removable, non-volatile optical disk, such as a CD-ROM, DVD-ROM, or other optical medium. In such cases, each may be connected to bus 18 by one or more data media interfaces. As further shown and described below, memory 28 may include at least one program product having a set of (e.g., at least one) program processes configured to perform the functions of embodiments of the present invention.

[0130] One or more programs 40 having a set of (at least one) program process 42, as well as an operating system, one or more application programs, other program processes, and program data, may be stored in memory 28, by way of example and not limitation. The one or more programs 40, including the program processes 42, may generally perform the functions described herein. In one embodiment, UE devices 110A-110Z may include one or more computing nodes 10 and may include one or more programs 40 for performing the functions described with reference to UE devices 110A-110Z. In one embodiment, base stations 120A-120Z may include one or more computing nodes 10 and may include one or more programs 40 for performing the functions described with reference to base stations 120A-120Z. In one embodiment, orchestrator 130 may include one or more computing nodes 10 and may include one or more programs 40 for performing the functions described with reference to orchestrator 130. In one embodiment, VNF layer 1000 may be performed using one or more computing nodes 10 and may be defined by one or more programs 40 for performing the functions described with reference to VNF layer 1000. In one embodiment, VNF layer 2000 may be performed using one or more computing nodes 10 and may be defined by one or more programs 40 for performing the functions described with reference to VNF layer 2000.

[0131] Computer system 12 may also communicate with one or more external devices 14, such as a keyboard, pointing device, display 24, etc., that allow a user to interact with computer system 12, or any device (e.g., network card, modem, etc.) that allows computer system 12 to communicate with one or more other computing devices, or both. Such communication may occur via an input / output (I / O) interface 22. Still further, computer system 12 may communicate with one or more networks, such as a local area network (LAN), a general wide area network (WAN), or a public network (e.g., the Internet), or a combination thereof, via a network adapter 20. As shown, network adapter 20 communicates with other components of computer system 12 via bus 18. While not shown, it should be understood that other hardware or software components, or combinations thereof, may be used with computer system 12. Examples include, but are not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems. In addition to, or instead of, having external devices 14 and display 24 that may be configured to provide user interface functionality, computing node 10 in one embodiment may include a display 25 connected to bus 18. In one embodiment, display 25 may be configured as a touchscreen display and may be configured to provide user interface functionality, such as facilitating virtual keyboard functionality and entry of total data. Computer system 12 in one embodiment may also include one or more sensor devices 27 connected to bus 18. One or more sensor devices 27 may alternatively be connected through I / O interface 22.The one or more sensor devices 27 may, in one embodiment, include a global positioning sensor (GPS) device and may be configured to provide the location of the computing node 10. In one embodiment, the one or more sensor devices 27 may alternatively or additionally include, for example, one or more cameras, gyroscopes, temperature sensors, humidity sensors, pulse sensors, blood pressure (bp) sensors, or audio input devices. The computer system 12 may include one or more network adapters 20. In Figure 11, the computing node 10 is illustrated as being implemented in a cloud computing environment and is therefore referred to in the context of Figure 11 as a cloud computing node.

[0132] Referring now to FIG. 11 , an exemplary cloud computing environment 50 is shown. As shown, the cloud computing environment 50 comprises one or more cloud computing nodes 10 with which local computing devices used by cloud consumers, such as, for example, a personal digital assistant (PDA) or mobile phone 54A, a desktop computer 54B, a laptop computer 54C, or an automobile computer system 54N, or combinations thereof, may communicate. The nodes 10 may communicate with each other. They may be physically or virtually grouped in one or more networks (not shown), such as a private cloud, a community cloud, a public cloud, or a hybrid cloud, or combinations thereof, as described hereinabove. This enables the cloud computing environment 50 to provide infrastructure, platform, or software, or combinations thereof, as a service, without requiring cloud consumers to maintain resources on their local computing devices. It will be understood that the types of computing devices 54A-54N shown in FIG. 11 are intended to be examples only, and that computing node 10 and cloud computing environment 50 can interact with any type of computerized device over any type of network and / or network-addressable connection (e.g., using a web browser).

[0133] Referring now to Figure 12, there is shown a set of functional abstraction layers provided by cloud computing environment 50 (Figure 11). It should be understood in advance that the components, layers, and functions shown in Figure 12 are intended to be merely exemplary, and embodiments of the present invention are not limited thereto. As shown, the following layers and corresponding functions are provided:

[0134] Hardware and software layer 60 includes hardware and software components. Examples of hardware components include mainframe 61, RISC (reduced instruction set computer) architecture-based servers 62, servers 63, blade servers 64, storage devices 65, and networks and network components 66. In some embodiments, software components include network application server software 67 and database software 68.

[0135] The virtualization layer 70 provides an abstraction layer within which examples of virtual entities can be provided: virtual servers 71, virtual storage 72, virtual networks including virtual private networks 73, virtual applications and operating systems 74, and virtual clients 75.

[0136] In one example, management layer 80 may provide the functions described below: Resource provisioning 81 provides dynamic procurement of computing and other resources used to execute tasks within the cloud computing environment. Metering and pricing 82 tracks costs as resources are utilized within the cloud computing environment and bills or invoices for the consumption of these resources. In one example, these resources may comprise application software licenses. Security provides identity verification of cloud users and tasks, as well as protection of data and other resources. User portal 83 provides access to the cloud computing environment for users and system administrators. Service level management 84 allocates and manages cloud computing resources to ensure required service levels are met. Service level agreement (SLA) planning and fulfillment 85 provides proactive provisioning and procurement of cloud computing resources where future requirements are predicted according to SLAs.

[0137] Workload tier 90 provides examples of functions for which a cloud computing environment may be utilized. Examples of workloads and functions that may be provided from this tier include mapping and navigation 91, software development and lifecycle management 92, virtual classroom instruction delivery 93, data analytics processing 94, transaction processing 95, and processing components 96 for providing user data as described herein. Processing components 96 may be implemented using one or more programs 40 described in FIG. 10.

[0138] The present invention may be a system, method, or computer program product, or combination thereof, at any possible level of technical detail integration. The computer program product may include a computer-readable storage medium (or multiple computer-readable storage media) having computer-readable program instructions for causing a processor to perform aspects of the present invention.

[0139] A computer-readable storage medium may be a tangible device capable of holding and storing instructions for use by an instruction execution device. The computer-readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes the following: portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD), memory sticks, floppy disks, mechanically encoded devices such as punch cards or raised structures with instructions recorded in grooves, and any suitable combination of the foregoing. As used herein, computer-readable storage media should not be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., light pulses passing through a fiber optic cable), or electrical signals transmitted through wires.

[0140] The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to each computing / processing device or to an external computer or external storage device via a network (e.g., the Internet, a local area network, a wide area network, or a wireless network, or a combination thereof). The network may include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, or edge servers, or a combination thereof. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and transfers the computer-readable program instructions to a computer-readable storage medium in each computing / processing device for storage.

[0141] The computer-readable program instructions for carrying out the operations of the present invention may be either source code or object code written in any combination of one or more programming languages, including assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data for integrated circuits, or object-oriented programming languages ​​such as Smalltalk®, C++, or the like, procedural programming languages ​​such as the C programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), and the connection may be to an external computer (e.g., over the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA) may execute computer-readable program instructions to personalize the electronic circuitry by utilizing state information of the computer-readable program instructions to perform aspects of the present invention.

[0142] Aspects of the present invention are described herein with reference to flowchart illustrations or block diagrams, or combinations thereof, of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations or block diagrams, or combinations thereof, and combinations of blocks in the flowchart illustrations or block diagrams, or combinations thereof, can be implemented by computer-readable program instructions.

[0143] These computer-readable program instructions may be provided to a computer processor or other programmable data processing apparatus to create a machine, such that the instructions executing on the computer processor or other programmable data processing apparatus create means for implementing the functions / acts specified in one or more blocks of the flowcharts or block diagrams, or a combination thereof. These computer-readable program instructions may also be stored on a computer-readable storage medium and can instruct a computer, programmable data processing apparatus, or other device, or a combination thereof, to function in a particular manner, such that a computer-readable storage medium having instructions stored thereon includes an article of manufacture containing instructions that implement aspects of the functions / acts specified in one or more blocks of the flowcharts or block diagrams, or a combination thereof.

[0144] The computer-readable program instructions may also be loaded into a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be run on the computer, other programmable apparatus, or other device to generate a computer-implemented process, whereby the instructions run on the computer, other programmable apparatus, or other device implement the functions / operations specified in one or more blocks of the flowchart or block diagram, or a combination thereof.

[0145] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of instructions, including one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions shown in the blocks may occur out of the order shown in the figures. For example, two blocks shown in succession may actually be realized as a single step, or may be executed concurrently, substantially concurrently, in a partially or fully time-overlapping manner, or the blocks may possibly be executed in reverse order depending on the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart diagrams, and combinations of blocks in the block diagrams and / or flowchart diagrams, may be implemented by special-purpose hardware-based systems that perform the specified functions or operations or execute a combination of special-purpose hardware and computer instructions.

[0146] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It is further understood that the terms "comprise" (and any form of comprise, such as "comprises" and "comprising"), "have" (and any form of have, such as "has" and "having"), "include" (and any form of include, such as "includes" and "including"), and "contain" (and any form of contain, such as "contains" and "containing") are open-ended linking verbs. Consequently, a method or device that "comprises," "have," "include," or "contain" one or more steps or elements possesses those one or more steps or elements, but is not limited to possessing only those one or more steps or elements. Similarly, a method step or device element that "comprises," "has," "includes," or "contains" one or more features possesses those one or more features, but is not limited to possessing only those features. As used herein, the term "based on" encompasses relationships where the elements are partially based and relationships where the elements are fully based. Methods, products, and systems described as having a certain number of elements may be implemented with fewer or more than the specified number of elements. Furthermore, a device or structure that is configured in a certain way is configured in at least that way, but may also be configured in ways not recited.

[0147] Numerical values ​​and other values ​​set forth herein, whether expressly set forth or inherently derived by the description of this disclosure, are considered to be modified by the term "about." As used herein, the term "about" defines the numerical boundaries of the modified value, including, but not limited to, ranges and values ​​up to and including the modified numerical value. That is, numerical values ​​can include the actual value explicitly set forth and other values ​​that are or can be decimals, fractions, or other multiples of the actual value set forth and / or described in this disclosure.

[0148] The corresponding structure, material, and acts of any means or step-plus-function element in the following claims, and their equivalents, where available, are intended to include that structure, material, or act for performing a function in combination with other claimed elements as specifically claimed. The description set forth herein has been presented for purposes of illustration and description, but is not intended to be exhaustive or limiting to the disclosed forms. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the present disclosure. The embodiments selected and described herein are intended to best explain the principles of one or more aspects and practical applications described herein, and to enable those skilled in the art to understand one or more aspects as described herein for various embodiments with various modifications, as anticipated and adapted for the particular use.

Claims

1. a virtual network function (VNF) layer that has assigned a logical channel to each of a plurality of UE devices for wireless transmission of medical and health user data, acquiring the medical and health user data from each of the plurality of UE devices, the VNF layer maintaining user-to-logical channel association data that associates user identification data with logical channels assigned to users identified by the user identification data; a step of examining the medical health data of the medical health user data by a service orchestration layer operating on the VNF layer, the service orchestration layer being configured such that the service orchestration layer is restricted from accessing the user identification data of the user-to-logical channel association data; performing a process in response to the checking step; A computer-implemented method comprising:

2. 2. The computer-implemented method of claim 1, wherein the logical channel is a fifth generation (5G) compliant DTCH channel.

3. 3. The computer-implemented method of claim 1, further comprising: receiving authorization from a particular user associated with the medical health user data for the VNF layer to process the particular user's user data, the authorization being in the absence of authorization allowing the service orchestration layer to access user identification data of the particular user.

4. 3. The computer-implemented method of claim 1, wherein the step of investigating the medical health data of the medical health user data by the service orchestration layer operating on the VNF layer includes medical health data identifying an infection status (infected or not infected) of a set of users and a trajectory of the set of users over time, and the computer-implemented method comprises providing medical treatment resources to address an infection in a certain geographical area depending on the infection status (infected or not infected) of the set of users and the location of the set of users over time.

5. 3. The computer-implemented method of claim 1, wherein the step of investigating the medical and health user data by the service orchestration layer operating on the VNF layer includes medical and health data identifying an infection status (infected or not infected) of a set of users and a trajectory of the set of users over time, the computer-implemented method comprising providing medical treatment resources to address infection in a geographical area according to the infection status (infected or not infected) of the set of users and a location of the set of users over time, the computer-implemented method comprising determining past interactions of users belonging to the set of users according to the location of the set of users over time, and identifying users suspected of infection according to the determining step.

6. 3. The computer-implemented method of claim 1, wherein the step of investigating the medical and health user data includes medical and health data identifying an infection status (infected or not infected) of a set of users and a trajectory of the set of users over time, and the computer-implemented method comprises providing medical treatment resources to address an infection in a geographical area depending on the infection status (infected or not infected) of the set of users and the location of the set of users over time, and the computer-implemented method comprises predicting past crossings of users belonging to the set of users depending on the location of the set of users over time, and identifying users suspected of infection depending on the predicting step.

7. The service orchestration layer operating on the VNF layer may include examining the medical health data of the medical health user data, the medical health data identifying an infection status (infected or not infected) of a set of users and a trajectory of the set of users over time, the computer-implemented method comprising providing medical treatment resources to address an infection in a geographical area according to the infection status (infected or not infected) of the set of users and the location of the set of users over time, the computer-implemented method comprising: and identifying users suspected of being infected in response to the predicting step, wherein the providing step is performed such that a first region having a first total number of infected users is provided with a first medical treatment resource and a second region having a second total number of infected users is provided with a second medical treatment resource, the second total number being less than the first total number and the second medical treatment resource being more than the first medical treatment resource.

8. The processor a virtual network function (VNF) layer that has assigned a logical channel to each of a plurality of UE devices for wireless transmission of medical and health user data, acquiring the medical and health user data from each of the plurality of UE devices, the VNF layer maintaining user-to-logical channel association data that associates user identification data with a logical channel assigned to a user identified by the user identification data; a procedure for a service orchestration layer operating on the VNF layer to investigate medical health data of the medical health user data, the service orchestration layer being configured such that the service orchestration layer is restricted from accessing the user identification data of the user-to-logical channel association data; a step of carrying out a process in accordance with the step of checking; A computer program for executing

9. 9. The computer program product of claim 8, wherein the logical channel is a fifth generation (5G) compliant DTCH channel.

10. 10. The computer program product of claim 8, further comprising: causing the processor to execute a procedure for receiving, from a particular user associated with the medical health user data, permission for the VNF layer to process user data of the particular user, wherein the permission is absent from permission allowing the service orchestration layer to access user identification data of the particular user.

11. 10. The computer program of claim 8 or 9, wherein the service orchestration layer operating on the VNF layer is configured to: examine the medical health data of the medical health user data, the medical health data including medical health data identifying an infection status (infected or not infected) of a set of users and a trajectory of the set of users over time; and the computer program causes the processor to execute a procedure for providing medical treatment resources to address an infection in a certain geographical area depending on the infection status (infected or not infected) of the set of users and the location of the set of users over time.

12. 10. The computer program of claim 8 or 9, wherein the service orchestration layer operating on the VNF layer is configured to: investigate the medical and health data of the medical and health user data, the medical and health data including medical and health data identifying an infection status (infected or not infected) of a set of users and a trajectory of the set of users over time; and the computer program causes the processor to perform a procedure of providing medical treatment resources to deal with an infection in a certain geographical area depending on the infection status (infected or not infected) of the set of users and the location of the set of users over time; and the computer program causes the processor to perform a procedure of identifying past interactions of users belonging to the set of users depending on the location of the set of users over time, and a procedure of identifying users suspected of infection depending on the identifying procedure.

13. 10. The computer program of claim 8 or 9, wherein the service orchestration layer operating on the VNF layer is configured to: investigate the medical health data of the medical health user data, the medical health data including medical health data identifying an infection status (infected or not infected) of a set of users and a trajectory of the set of users over time; and the computer program causes the processor to perform a procedure of providing medical treatment resources to deal with an infection in a certain geographical area depending on the infection status (infected or not infected) of the set of users and the location of the set of users over time; and predicting past crossings of users belonging to the set of users depending on the location of the set of users over time, and identifying users suspected of infection depending on the predicting procedure.

14. The service orchestration layer, operating on the VNF layer, is configured to: cause the processor to execute a procedure for providing medical treatment resources to address an infection in a geographical area according to the infection status (infected or not infected) of the set of users and the location of the set of users over time; and the procedure for examining the medical health data of the medical health user data includes medical health data identifying an infection status (infected or not infected) of a set of users and a trajectory of the set of users over time; and the computer program causes the processor to execute a procedure for providing medical treatment resources to address an infection in a geographical area according to the infection status (infected or not infected) of the set of users and the location ...

10. The computer program product of claim 8, further comprising: a step of predicting past encounters of users belonging to the set of users according to the locations of the target set of users; and a step of identifying users suspected of being infected according to the predicting step; wherein the providing step is performed such that a first region having a first total number of infected users is provided with a first medical treatment resource, and a second region having a second total number of infected users is provided with a second medical treatment resource, the second total number being less than the first total number, and the second medical treatment resource being more than the first medical treatment resource.

15. Memory and at least one processor in communication with the memory; a virtual network function (VNF) layer that has assigned a logical channel to each of a plurality of UE devices for wireless transmission of medical and health user data, acquiring the medical and health user data from each of the plurality of UE devices, the VNF layer maintaining user-to-logical channel association data that associates user identification data with logical channels assigned to users identified by the user identification data; a step of examining the medical health data of the medical health user data by a service orchestration layer operating on the VNF layer, the service orchestration layer being configured such that the service orchestration layer is restricted from accessing the user identification data of the user-to-logical channel association data; performing a process in response to the checking step; program instructions executable by one or more processors via said memory to perform a method comprising: A system comprising:

16. 16. The system of claim 15, wherein the logical channel is a fifth generation (5G) compliant DTCH channel.

17. 17. The system of claim 15 or 16, wherein the method comprises receiving authorization from a particular user associated with the medical health user data for the VNF layer to process the particular user's user data, the authorization being in the absence of authorization allowing the service orchestration layer to access user identification data of the particular user.

18. 17. The system of claim 15 or 16, wherein the step of investigating the medical health data of the medical health user data by the service orchestration layer operating on top of the VNF layer includes medical health data identifying an infection status (infected or not infected) of a set of users and a trajectory of the set of users over time, and the method comprises providing medical treatment resources to address the infection in a certain geographical area depending on the infection status (infected or not infected) of the set of users and the location of the set of users over time.

19. 17. The system of claim 15 or 16, wherein the step of investigating the medical health data of the medical health user data by the service orchestration layer operating on the VNF layer includes medical health data identifying an infection status (infected or not infected) of a set of users and a trajectory of the set of users over time, the method comprising providing medical treatment resources to address infection in a certain geographical area depending on the infection status (infected or not infected) of the set of users and a location of the set of users over time, the method comprising determining past interactions of users belonging to the set of users depending on the location of the set of users over time, and identifying users suspected of infection depending on the determining step.

20. 17. The system of claim 15 or 16, wherein the service orchestration layer operating on the VNF layer, wherein the step of investigating the medical health data of the medical health user data includes medical health data identifying an infection status (infected or not infected) of a set of users and a trajectory of the set of users over time, the method comprising providing medical treatment resources to address infection in a geographical area depending on the infection status (infected or not infected) of the set of users and a location of the set of users over time, the method comprising predicting past crossings of users belonging to the set of users depending on the location of the set of users over time, and identifying users suspected of infection depending on the predicting step.

Images