Apparatus and communication method for ai / ML operation

Abstract

A communication method for artificial intelligence (AI) / machine learning (ML) operation includes transmitting, by a network open function (NEF), a charging data request message to a charging function (CHF), the charging data request message including at least one information element associated with a charging report of the AI / ML operation; and allowing and / or triggering the CHF to identify, based on the charging data request message, at least one information element associated with a charging report of the AI / ML operation.

Description

Technical Field

[0001] This disclosure relates to the field of communication systems, and more specifically, to apparatus and communication methods for artificial intelligence (AI) / machine learning (ML) operations, such as billing reports for AI / ML operations initiated by the network exposure function (NEF). Background Technology

[0002] Currently, there are standardization activities researching artificial intelligence / machine learning (AI / ML) capabilities within the 3rd generation partnership project (3GPP). However, in current technology, billing aspects for AI / ML have not yet been determined in the 5G core network (5GC).

[0003] Therefore, there is a need for devices and communication methods for artificial intelligence (AI) / machine learning (ML) operations, such as billing reports for AI / ML operations initiated by Network Open Functions (NEF), which can solve these and other problems. Summary of the Invention

[0004] One object of this disclosure is to provide apparatus and communication methods for artificial intelligence (AI) / machine learning (ML) operations, such as billing reports for AI / ML operations initiated by Network Open Functions (NEF), which can address these and other problems in the prior art.

[0005] In a first aspect of this disclosure, a communication method for artificial intelligence (AI) / machine learning (ML) operations includes: sending a billing data request message from a network open function (NEF) to a charging function (CHF), the billing data request message including at least one information element associated with a billing report of the AI / ML operation; and allowing and / or triggering the CHF to identify at least one information element associated with the billing report of the AI / ML operation based on the billing data request message.

[0006] In a second aspect of this disclosure, the communication device includes a transmitter and a triggering unit. The transmitter is configured to send a billing data request message to a billing function (CHF), the billing data request message including at least one information element associated with a billing report of an artificial intelligence (AI) / machine learning (ML) operation. The triggering unit is configured to allow and / or trigger the CHF to identify at least one information element associated with the billing report of the AI / ML operation based on the billing data request message.

[0007] In a third aspect of this disclosure, the network device includes a memory, a transceiver, and a processor coupled to the memory and the transceiver. The network device is configured to perform the methods described above.

[0008] In a fourth aspect of this disclosure, a plurality of instructions are stored on a non-transitory machine-readable storage medium, which, when executed by a computer, cause the computer to perform the method described above.

[0009] In a fifth aspect of this disclosure, a chip includes a processor configured to invoke and run a computer program stored in a memory, such that a device on which the chip is mounted performs the methods described above.

[0010] In a sixth aspect of this disclosure, a computer-readable storage medium storing a computer program causes a computer to perform the above-described method.

[0011] In a seventh aspect of this disclosure, a computer program product includes a computer program that causes a computer to perform the methods described above.

[0012] In an eighth aspect of this disclosure, a computer program causes a computer to perform the above-described method. Attached Figure Description

[0013] To more clearly illustrate the embodiments of this disclosure or related technologies, the following drawings, which will be briefly described in various embodiments, are provided. It is obvious that the drawings are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any effort.

[0014] Figure 1 This is a block diagram of a non-roaming 5G system architecture configured to implement some of the embodiments presented herein.

[0015] Figure 2 This is a block diagram representing a non-roaming reference point of a northbound API converged billing architecture configured to implement some of the embodiments presented herein.

[0016] Figure 3 This is a block diagram of a network device according to an embodiment of the present disclosure.

[0017] Figure 4This is a flowchart illustrating a communication method for artificial intelligence (AI) / machine learning (ML) operations according to embodiments of the present disclosure.

[0018] Figure 5 This is a block diagram of a communication device according to an embodiment of the present disclosure.

[0019] Figure 6 This is a flowchart illustrating an API call request to NEF using Immediate Event Billing (IEC), which is configured to implement some of the embodiments presented herein.

[0020] Figure 7 This is a flowchart illustrating an API call request to NEF using ECR, which is configured to implement some of the embodiments presented herein.

[0021] Figure 8 This is a flowchart illustrating an API notification from NEF using IEC, configured to implement some of the embodiments presented herein.

[0022] Figure 9 This is a flowchart illustrating an API notification from NEF using ECR, which is configured to implement an ECU according to some of the embodiments presented herein.

[0023] Figure 10 This is a flowchart illustrating an API call request to NEF using PEC, which is configured to implement some of the embodiments presented herein.

[0024] Figure 11 This is a flowchart illustrating an API notification from NEF using PEC, which is configured to implement some of the embodiments presented herein.

[0025] Figure 12 This is a block diagram of an example computing device according to embodiments of the present disclosure.

[0026] Figure 13 This is a block diagram of a communication system according to an embodiment of the present disclosure. Detailed Implementation

[0027] The technical content, structural features, objectives, and effects of the embodiments of this disclosure are described in detail below with reference to the accompanying drawings. Specifically, the terminology used in the embodiments of this disclosure is for the purpose of describing specific embodiments only and is not intended to limit this disclosure.

[0028] Figure 1A non-roaming 5G system architecture configured to implement some of the embodiments presented herein is illustrated. In this 5G non-roaming system architecture, network functions communicate with each other in the core network (CN) via service-based interfaces. User equipment (UE) can communicate with the core network to establish control signaling and enable the UE to use services from the CN. Examples of control signaling functions are registration, connection and mobility management, authentication and authorization, session management, etc. After control signaling has been established, the UE can then utilize user plane functions to send data to and receive data from the data network (DN) (e.g., the Internet).

[0029] The 5G system architecture includes the following network functions (NFs): Authentication Server Function (AUSF), Access and Mobility Management Function (AMF), Data Network (DN) (e.g., operator services, internet access, or third-party services), Unstructured Data Storage Function (UDSF), Network Exposure Function (NEF), Network Repository Function (NRF), Network Slice Admission Control Function (NSACF), Network Slice-specific and SNPN Authentication and Authorization Function (NSSAAF), Network Slice Selection Function (NSSF), Policy Control Function (PCF), Session Management Function (SMF), Unified Data Management (UDM), Unified Data Repository (UDR), and User Plane Function. Functions include: UPF, UE radio Capability Management Function (UCMF), Application Function (AF), User Equipment (UE), Radio Access Network (R)AN, 5G Equipment Identity Register (5G-EIR), Network Data Analytics Function (NWDAF), Billing Function (CHF), and Time Sensitive Networking Function (AF).The system includes: TSN AF (Time Sensitive Communication and Time Synchronization Function), TSCTSF (Time Sensitive Communication and Time Synchronization Function), DCCF (Data Collection Coordination Function), ADRF (Analytics Data Repository Function), MFAF (Messaging Framework Adaptor Function), and NSWOF (Non-Seamless WLAN Offload Function).

[0030] The following description emphasizes Figure 1 This involves some capabilities of the network function (NF) related to control signaling.

[0031] Access and Mobility Functions (AMF): The UE sends N1 messages to the AMF through the RAN node to perform control plane signaling, such as registration, connection management, mobility management, access authentication and authorization.

[0032] Session Management Function (SMF): The SMF is responsible for session management involving the establishment of PDU sessions, allowing the UE to send data to data networks (DN) such as the Internet or to application servers, as well as other functions related to session management.

[0033] Policy and Control Function (PCF): PCF provides a policy framework for managing network behavior, accessing subscription information to make policy decisions, etc.

[0034] Authentication Server Function (AUSF): AUSF supports UE authentication for 3GPP and untrusted non-3GPP access.

[0035] Unified Data Management / Storage (UDM / UDR): UDM / UDR supports the generation of 3GPP AKA certification certificates, user identity processing, subscription management, and storage. Network Slice Selection Function (NSSF): NSSF involves aspects of network slice management, such as selecting network slice instances for UEs and managing Network Slice Selection Assistance Information (NSSAI).

[0036] Network Storage Function (NRF): NRF supports service discovery in 5G networks.

[0037] Network Open Function (NEF): NEF supports opening up capabilities and events in the core network to third parties, application functions (AF), edge computing, etc.

[0038] The RAN node provides communication access from the UE to the core network for both control plane and user plane communications. The UE establishes a PDU session with the CN to transmit data services on the user plane through the (R)AN and UPF nodes of the 5G system (5GS). Using the established PDU session, uplink services are transmitted by the UE, and downlink services are received by the UE. Data services flow between the UE and the DN through intermediate nodes (R)AN and UPF.

[0039] AI / ML functionality was introduced in Release 18 of the 3GPP work. Specifically, enhancements were introduced into the 5GC functionality to determine which UEs are suitable for AI / ML operation, particularly for negotiation between the AF and NEF, and between the NEF and PCF (including the identification of services related to AI / ML usage), and for monitoring and processing the UE list (including the integrated data rates of these UEs). However, the billing aspects of AI / ML have not yet been determined, and some embodiments of this disclosure propose functions related to billing reporting from the 5GC to the CHF (billing functions).

[0040] The 3GPP documents defining the prior art are TS 32.290 and TS 32.254. TS 32.290 defines services, operations, and charging procedures using a Service Based Interface (SBI). Within these procedures, it defines triggers based on which charging reports should be re-initiated by the core network (5GC) in the CHF direction. In some embodiments of this disclosure, the trigger list needs to be expanded to support AI / ML charging reporting operations. TS 32.254 defines the open functionality for northbound Application Program Interface (API) charging. Because some embodiments of this disclosure relate to expanding the network open functionality charging reporting for the 5GC NEF, some embodiments of this disclosure need to be expanded to include NEF charging reporting support for new proposed parameters related to AI / ML operations.

[0041] As stated in TS 23.501: "During or during AI / ML operations (e.g., federated learning), the AF may request the Service NEF to provide Quality of Service (QoS) for a list of UEs, each UE identified by its UE IP address. The AF may subscribe to QoS monitoring, which may also include integrated data rate monitoring, as described in Clauses 5.45 and 4.15.6.13 of TS 23.502 [3]. The AF provides QoS parameters derived from the performance requirements listed in Clause 7.10 of TS 22.261." Furthermore, Clause 4.15.6.13 of TS 23.502 defines the process for such QoS monitoring in a multi-member AF session with the desired QoS. TS 32.254 defines how to implement NEF-based charging reporting once the NEF is triggered by a new service request received from the AF. Some embodiments of this disclosure propose to include functionality (charging functions) related to charging reporting from 5GC to CHF.

[0042] Figure 2 This illustrates a non-roaming reference point representation of a northbound API converged charging architecture configured to implement some of the embodiments presented herein. References Figure 2 N44 can be a reference point between NEF and CHF. For northbound API calls / notifications, NEF collects the following billing information: the number of northbound API calls / notifications; the identifier of the SCS / AS or AF and the associated northbound API call / notification; the timestamp of the northbound API call / notification; and northbound API related information, such as location. It should be understood that the northbound API applies here to NEF-AF communication. The northbound APIs supported by NEF through the open service set defined in 3GPP TS 23.502 are covered for converged billing reporting. Therefore, the addition of integrated data rate monitoring of the UE list for purposes such as AI / ML federated learning operations also needs to be supported, and this is related to some embodiments of this disclosure.

[0043] According to TS 32.254, "The NEF may interact with the CHF using the Nchf as specified in TS 32.290 and TS 32.291 to perform converged billing. To provide the data required for the administrative activities (credit control, billing, accounting, statistics, etc.) outlined in TS 32.240, the NEF should be able to perform converged billing on northbound API access. The NEF should be able to perform converged billing by interacting with the CHF to bill data related to northbound API access. Billing data requests and billing data responses are exchanged between the NEF and CHF based on PEC (the IEC scenario or ECR scenario as specified in TS 32.290). When certain conditions (billingable events) are met, the NEF issues a billing data request to the CHF. The selection of the CHF may be configured in the NEF, but may also depend on the NRF." In some embodiments of this disclosure, applicable triggers in the NEF may be referenced to Section 5.4.1.2 of TS 32.254. The NEF converged charging messages (i.e., charging data requests and charging data responses) in some embodiments of this disclosure can be referenced in Section 6.2a.1.1 of TS 32.254. The structure of the charging data request and charging data response messages in some embodiments of this disclosure can be referenced in Sections 6.2a.1.2.1 and 6.2a.1.2.2 of TS 32.254.

[0044] Some solutions disclosed herein propose functionality (billing functionality) related to billing reporting from 5GC to CHF.

[0045] Solution 1:

[0046] To support integrated data rate monitoring operations for the UE list, and corresponding reporting from NEF to CHF as requested by the AF in the event of changes in the UE list or integrated data rate (e.g., supporting AI / ML joint learning operations or any other AI / ML-related operations), some embodiments of this disclosure are extended to include at least one improvement to section 6.2a.1.2.1 of TS 32.254: a new trigger, which is part of a charging data request message.

[0047] Update the UE list.

[0048] Update the integrated data rate value.

[0049] The detailed trigger definitions in some embodiments of this disclosure are extended to include at least one improvement to clause 5.4.2 of TS 32.290.

[0050] In some embodiments of this disclosure, these new triggers can allow / trigger reporting from NEF to CHF once a requested UE list change or an integrated data rate change is received in an update request from AF to NEF.

[0051] Solution 2:

[0052] To support integrated data rate monitoring of the UE list and corresponding reporting from NEF to CHF, in some embodiments, the "NEF API Billing Information" can be extended to include a list of UE identifiers and integrated data rates. The "NEF API Billing Information" is passed in billing data requests from NEF to CHF and allows CHF to understand these parameters, enabling it to perform accounting, statistics, and other functions.

[0053] Note that, as some embodiments of this disclosure extend to include at least one improvement to TS 23.502, the integrated data rate threshold defines an upper limit on the aggregated data rate of all service flows corresponding to the UE IP address list. The integrated data rate may not be enforced in the network; however, operators may use offline CDR processing for applications such as billing and statistics in agreements with service providers such as AI / ML providers.

[0054] The following text describes some embodiments of this disclosure, which are extended to include at least one improvement to TS 32.254 that defines how newly introduced billing parameters can be embedded into the specification.

[0055] Example:

[0056] Example 1:

[0057] 6.2a.1.2.1 Billing Data Request Message

[0058] Table 6.2a.1.2.1.1 shows the basic structure of the billing data request message used for NEF converged billing.

[0059] Table 6.2a.1.2.1.1: Billing Data Request Message Content

[0060]

[0061] Example 2:

[0062] 6.3.1.4 Definition of NEF API Information

[0063] 6.3.1.4.1 Definition of NEF API Billing Information

[0064] The NEF API billing information provides specific billing information for the Network Open Functions API. The detailed structure of the NEF API billing information can be found in Table 6.3.1.4.1.

[0065] Table 6.3.1.4.1: Structure of NEF API Billing Information

[0066]

[0067] Example 3:

[0068] 6.3.4 Detailed message format for converged billing

[0069] Operation types are listed in the following order: I (Initial) / U (Update) / T (Terminate) / E (Event). Therefore, when all operation types are possible, it is marked as IUTE. If a node only allows certain operation types, only the appropriate letter is used (e.g., IUT or E), as shown in the table header. The omission of operation types for a specific field is marked with "-" (e.g., IE). Furthermore, when an entire field is not allowed in a node, the entire cell is marked with "-".

[0070] Table 6.3.4.1 shows the basic structure of the fields supported in the billing data request for Open Function API online billing.

[0071] Table 6.3.4.1: Fields Supported in Billing Data Request Messages

[0072]

[0073] Figure 3An example of a network device 300 according to an embodiment of the present disclosure is shown. The network device 300 is configured to implement some embodiments of the present disclosure. Some embodiments of the present disclosure can be implemented in the network device 300 using any suitably configured hardware and / or software. The network device 300 may include a memory 301, a transceiver 302, and a processor 303 coupled to the memory 301 and the transceiver 302. The processor 303 may be configured to implement the functions, processes, and / or methods described herein. A wireless interface protocol layer may be implemented in the processor 303. The memory 301 is operatively coupled to the processor 303 and stores various information to operate the processor 303. The transceiver 302 is operatively coupled to the processor 303 and transmits and / or receives wireless signals. The processor 303 may include an application-specific integrated circuit (ASIC), other chipsets, logic circuits, and / or data processing devices. Memory 301 may include read-only memory (ROM), random access memory (RAM), flash memory, memory cards, storage media, and / or other storage devices. Transceiver 302 may include baseband circuitry for processing radio frequency signals. When embodiments are implemented in software, the techniques described herein may be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described herein. These modules may be stored in memory 301 and executed by processor 303. Memory 301 may be implemented within processor 303 or external to processor 303. In the case where memory 301 is implemented external to processor 303, memory 301 may be communicatively coupled to processor 303 by various means known in the art.

[0074] In some embodiments, memory 301 stores a plurality of executable instructions that, when executed by a processor, cause processor 303 to perform operations including: sending a billing data request message from the Network Open Function (NEF) to the Charging Function (CHF), the billing data request message including at least one information element associated with a billing report of an AI / ML operation; and allowing and / or triggering CHF to identify the at least one information element associated with the billing report of the AI / ML operation based on the billing data request message.

[0075] Figure 4 A communication method for artificial intelligence (AI) / machine learning (ML) operations according to embodiments of the present disclosure is illustrated. Figure 4This is an example of a communication method 400 for artificial intelligence (AI) / machine learning (ML) operations according to embodiments of the present disclosure. The communication method 400 for artificial intelligence (AI) / machine learning (ML) operations is configured to implement some embodiments of the present disclosure. Some embodiments of the present disclosure can be implemented as the communication method 400 for artificial intelligence (AI) / machine learning (ML) operations using any suitably configured hardware and / or software. In some embodiments, the communication method 400 for artificial intelligence (AI) / machine learning (ML) operations includes: operation 402, in which a Network Open Function (NEF) sends a billing data request message to a Billing Function (CHF), the billing data request message including at least one information element associated with a billing report of the AI / ML operation; and operation 404, allowing and / or triggering the CHF to identify the at least one information element associated with the billing report of the AI / ML operation based on the billing data request message.

[0076] In some embodiments, the communication method 400 for artificial intelligence (AI) / machine learning (ML) operations further includes the operation of updating the corresponding operation by NEF when at least one information element associated with a billing report of an AI / ML operation changes.

[0077] In some embodiments, at least one information element associated with the billing report of AI / ML operations includes at least one trigger and / or NEF application programming interface (API) billing information. In some embodiments, the at least one trigger supports updating the user equipment (UE) list and / or updating the integrated data rate value. In some embodiments, the at least one trigger allows and / or triggers the billing report of AI / ML operations from NEF to CHF when the NEF receives a change in the UE list and / or integrated data rate in an update request from the application function (AF).

[0078] In some embodiments, the at least one trigger includes at least one NEF-specific trigger, and the at least one NEF-specific trigger includes an API call trigger condition in NEF, an API call response trigger condition in NEF, an API notification trigger condition in NEF, an API notification to a Network Function (NF) trigger condition in NEF, and / or an API notification acknowledgment trigger condition in NEF. The following different scenarios focus on different messages from / to NEF and scenario-based corresponding interactions with CHF.

[0079] Figure 5 A communication device according to an embodiment of the present disclosure is shown. Figure 5In some embodiments, the communication device 500 includes a transmitter 501 and a triggering unit 502. The transmitter 501 is configured to send a billing data request message to a billing function (CHF), the billing data request message including at least one information element associated with a billing report for an artificial intelligence (AI) / machine learning (ML) operation. The triggering unit 502 is configured to allow and / or trigger the CHF to identify the at least one information element associated with the billing report for the AI / ML operation based on the billing data request message.

[0080] In some embodiments, the communication device 500 further includes an updater 503 configured to update the corresponding operation when at least one information element associated with the billing report of the AI / ML operation changes. In some embodiments, the at least one information element associated with the billing report of the AI / ML operation includes at least one trigger and / or Network Open Function (NEF) Application Programming Interface (API) billing information. In some embodiments, the at least one trigger supports updating the User Equipment (UE) list and / or updating the integrated data rate value. In some embodiments, when the transmitter 501 receives a change in the UE list and / or integrated data rate in an update request from the Application Function (AF), the at least one trigger allows and / or triggers the billing report of the AI / ML operation from the triggering unit 502 to the CHF.

[0081] In some embodiments, the at least one trigger includes at least one NEF-specific trigger, and the at least one NEF-specific trigger includes an API call trigger condition in the communication device, an API call response trigger condition in the communication device, an API notification trigger condition in the communication device, an API notification to a network function (NF) trigger condition in the communication device 500, and / or an API notification acknowledgment trigger condition in the communication device. In some embodiments, the structure of the NEF API charging information includes a UE list and / or an integrated data rate. In some embodiments, the NEF API charging information is transmitted by the communication device 500 to the CHF in a charging data request, allowing the CHF to identify the UE list and / or the integrated data rate. In some embodiments, the UE list includes a list of the reported UEs' generic public subscription identifiers (GPSI) or subscriber permanent identifiers (SUPI).

[0082] In some embodiments, the integrated data rate indicates the upper limit of the aggregated data rate of all service flows corresponding to the UE list. In some embodiments, at least one supported field in the billing data request message includes the UE list and / or the integrated data rate. In some embodiments, at least one supported operation type of the UE list and / or the integrated data rate of the communication device includes an initial operation type, a termination operation type, and / or an event operation type. In some embodiments, the communication device includes a NEF.

[0083] Figures 6 to 11 Examples of triggering conditions in NEF are provided in the following table: Triggering conditions in NEF.

[0084] Table: Triggering Conditions in NEF

[0085]

[0086] Figure 6 An API call request to NEF using IEC is illustrated, which is configured to implement some of the embodiments presented herein. Figure 6 This describes a scenario for IEC mode where an API call request exists at the NEF. In operation: 1. The NEF receives an API call request from the AF. 1ch-a. The NEF sends a Billing Data Request [event] to the CHF for the received API call. 1ch-b. The CHF creates a CDR for the API call. 1ch-c. The CHF acknowledges and authorizes the API call by sending a Billing Data Response [event] to the NEF. 2. The NEF performs the operations required to complete the API call. 3. If authorized, the NEF continues API call processing and sends an API call response.

[0087] Figure 7 An API call request to NEF using ECR is illustrated, which is configured to implement some of the embodiments presented herein. Figure 7This describes a scenario for ECR mode where an API call request exists at the NEF. In the operation: 1. The NEF receives an API call request from the AF. 1ch-a. The NEF sends a Billing Data Request [Initial] to the CHF for the received API call. 1ch-b. The CHF opens a CDR for the API call. 1ch-c. The CHF acknowledges this by sending a Billing Data Response [Initial] to the NEF. 2. The NEF performs the operations required to complete the API call. 3. If authorized, the NEF continues API call processing and sends an API call response. 3ch-a. The NEF sends a Billing Data Request [Terminate] to the CHF to terminate billing associated with the API call. 3ch-b. The CHF closes the CDR for the API call. 3ch-c. The CHF acknowledges this by sending a Billing Data Response [Terminate] to the NEF.

[0088] Figure 8 An API notification from NEF using IEC is shown, which is configured to implement some of the embodiments presented herein. Figure 8 This describes a scenario for transmitting API notifications from the NEF in IEC mode. In operation: 1. The NEF receives a notification from the NF. 1ch-a. The NEF sends a Billing Data Request [event] to the CHF to notify. 1ch-b. The CHF creates a CDR for the notification. 1ch-c. The CHF acknowledges and authorizes the notification by sending a Billing Data Response [event] to the NEF. 2. The NEF sends a notification to the AF. 3. The NEF receives an acknowledgment of the notification.

[0089] Figure 9 An API notification from NEF using ECR is shown, which is configured to implement some of the embodiments presented herein. Figure 9 This describes a scenario for delivering API event notifications from the NEF in ECR mode. In operation: 1. The NEF receives a notification from the NF. 1ch-a. The NEF sends a Billing Data Request [Initial] to the CHF to initiate the notification. 1ch-b. The CHF opens a Callable Request (CDR) for this API notification. 1ch-c. The CHF acknowledges the notification by sending a Billing Data Response [Initial] to the NEF. 2. The NEF sends a notification to the AF. 3. The NEF receives acknowledgment of the notification. 3ch-a. The NEF sends a Billing Data Request [Terminate] to the CHF to terminate the billing associated with the API event notification. 3ch-b. The CHF closes the CDR for this API notification. 3ch-c. The CHF acknowledges the notification by sending a Billing Data Response [Terminate] to the NEF.

[0090] Figure 10The example illustrates the use of PEC to make API call requests to NEF, which are configured to implement some of the embodiments presented herein. Figure 10 This describes a scenario for PEC mode where an API call request exists at the NEF. In operation: 1. The NEF receives an API call request from the AF. 2. The NEF performs the operations required to complete the API call. 3. If authorized, the NEF continues API call processing and sends an API call response. 3ch-a. The NEF sends a billing data request [event] to the CHF for the received API call. 3ch-b. The CHF creates a CDR for the API call. 3ch-c. The CHF acknowledges the request by sending a billing data response [event] to the NEF.

[0091] Figure 11 An API notification from NEF using PEC is shown, which is configured to implement some of the embodiments presented herein. Figure 11 This describes a scenario for delivering API notifications from the NEF in PEC mode. 1. The NEF receives a notification from the NF. 2. The NEF sends a notification to the AF. 2ch-a. The NEF sends a Billing Data Request [event] to the CHF to notify. 2ch-b. The CHF creates a CDR for this notification. 2ch-c. The CHF acknowledges the notification by sending a Billing Data Response [event] to the NEF. 3. The NEF receives the acknowledgment of the notification.

[0092] In some embodiments, the structure of NEF API charging information includes a UE list and / or an aggregated data rate. In some embodiments, NEF API charging information is transmitted by NEF to CHF in a charging data request, allowing CHF to identify the UE list and / or aggregated data rate. In some embodiments, the UE list contains a list of the reported UEs' Common Public Subscription Identifiers (GPSI) or User Permanent Identifiers (SUPI). In some embodiments, the aggregated data rate indicates the upper limit of the aggregated data rate for all traffic flows corresponding to the UE list. In some embodiments, at least one supported field in the charging data request message includes the UE list and / or aggregated data rate. In some embodiments, at least one supported operation type for the NEF's UE list and / or aggregated data rate includes an initial operation type, a termination operation type, and / or an event operation type.

[0093] When features are introduced into 5GC, service providers are seeking a way to monetize them. Service monetization is typically based on reports received from the core network via the Billing Function (CHF). This approach proposes a standardized mechanism that will implement monetization strategies for AI / ML services (e.g., between the service provider and the AI / ML service provider, or between the service provider and the end-user device (UE) owner). Note: The monetization strategy is not necessarily about corresponding direct accounting; it could also be about collecting network statistics and then, for example, proposing a new accounting mechanism between the service provider and the AI / ML service provider, or between the service provider and the end user.

[0094] Some embodiments offer the following commercial benefits: 1. Addressing problems in the prior art. 2. Providing NEF billing reporting support for new recommended parameters related to AI / ML operations. 3. A key aspect of the innovation is the introduction of new reporting parameters (a list of affected / requested UEs and the integrated data rate), which are provided from the NEF to the CHF in the billing request and update operations in case of changes in the UE list or when the integrated data rate is exceeded. This allows the billing function to understand AI / ML operations and supports billing, credit control, statistics, etc., in the billing domain for appropriate agreements between the service provider and the AI / ML service provider and / or end user. Some embodiments of this disclosure can be used in many applications. Some embodiments of this disclosure are used by chipset vendors, video system development vendors, automotive manufacturers (including cars, trains, trucks, buses, bicycles, motorcycles, helmets, etc.), drones (unmanned aerial vehicles), smartphone manufacturers, communication equipment for public safety purposes, and AR / VR / MR device manufacturers (e.g., for gaming, conferences / seminars, educational purposes). Some embodiments of this disclosure are combinations of "technologies / processes" that can be adopted in video standards to create the final product. Some embodiments of this disclosure present technical mechanisms. At least one proposed solution, method, system, and apparatus from some embodiments of this disclosure can be used with respect to current and / or new / future standards for communication systems such as UEs, base stations, network devices, and / or communication systems. Compatible products conform to at least one proposed solution, method, system, and apparatus from some embodiments of this disclosure. The proposed solutions, methods, systems, and apparatus are widely used in UEs, base stations, network devices, and / or communication systems. With the implementation of at least one proposed solution, method, system, and apparatus from some embodiments of this disclosure, at least one modification / improvement to the method and apparatus for billing reporting for AI / ML operations is considered for standardization.

[0095] Figure 12This is an example of a computing device 1100 according to embodiments of the present disclosure. Any suitable computing device can be used to perform the operations described herein. For example, Figure 12 It demonstrates that this can be implemented using any suitable hardware and / or software configuration. Figures 1 to 11 Examples of computing devices 1100 illustrating the apparatus and / or methods are shown below. In some embodiments, computing device 1100 may include processor 1112 communicatively coupled to memory 1114 and executing computer-executable program code and / or accessing information stored in memory 1114. Processor 1112 may include a microprocessor, an application-specific integrated circuit (ASIC), a state machine, or other processing device. Processor 1112 may include any of a plurality of processing devices, including one processing device. Such a processor may include, or be able to communicate with, a computer-readable medium storing instructions that, when executed by processor 1112, cause the processor to perform the operations described herein.

[0096] Memory 1114 may include any suitable non-transitory computer-readable medium. Computer-readable media may include any electronic, optical, magnetic, or other storage device capable of providing computer-readable instructions or other program code to a processor. Non-limiting examples of computer-readable media include magnetic disks, memory chips, read-only memory (ROM), random access memory (RAM), application-specific integrated circuits (ASICs), configuration processors, optical storage devices, magnetic tape or other magnetic storage devices, or any other medium from which a computer processor can read instructions. Instructions may include processor-specific instructions generated by a compiler and / or interpreter from code written in any suitable computer programming language, including, for example, C, C++, C#, Visual Basic, Java, Python, Perl, JavaScript, and ActionScript.

[0097] The computing device 1100 may also include a bus 1116. The bus 1116 may be communicatively coupled to one or more components of the computing device 1100. The computing device 1100 may also include multiple external or internal devices, such as input or output devices. For example, the computing device 1100 is shown having an input / output (I / O) interface 1118 that can receive output from one or more input devices 1120 or provide output to one or more output devices 1122. One or more input devices 1120 and one or more output devices 1122 may be communicatively coupled to the I / O interface 1118. The communication coupling can be implemented in any suitable manner (e.g., via a printed circuit board connection, via a cable connection, via wireless communication, etc.). Non-limiting examples of input devices 1120 include touchscreens (e.g., one or more cameras for imaging a touch area or a pressure sensor for detecting pressure changes caused by a touch), mice, keyboards, or any other device that can be used to generate input events in response to physical actions of a user of the computing device. Non-limiting examples of output device 1122 include a liquid crystal display (LCD) screen, an external monitor, a speaker, or any other device that can be used to display or otherwise present the output generated by the computing device.

[0098] Computing device 1100 can execute program code that configures processor 1112 to perform the above-mentioned... Figures 1 to 11 The embodiments shown herein describe one or more of a plurality of operations. The program code may be located in memory 1114 or any suitable computer-readable medium and may be executed by processor 1112 or any other suitable processor.

[0099] The computing device 1100 may also include at least one network interface device 1124. The network interface device 1124 may include any device or group of devices adapted to establish a wired or wireless data connection to one or more data networks 1128. Non-limiting examples of the network interface device 1124 include Ethernet adapters and / or modems, etc. The computing device 1100 may transmit messages as electronic or optical signals via the network interface device 1124.

[0100] Figure 13 This is a block diagram of an example communication system 1200 according to embodiments of the present disclosure. The embodiments described herein can be implemented in the communication system 1200 using any suitably configured hardware and / or software. Figure 13A communication system 1200 is shown, which includes radio frequency (RF) circuitry 1210, baseband circuitry 1220, application circuitry 1230, memory / storage device 1240, display 1250, camera 1260, sensor 1270, and input / output (I / O) interface 1280, which are coupled to each other at least as shown.

[0101] Application circuitry 1230 may include circuitry such as, but not limited to, one or more single-core or multi-core processors. The processor may include any combination of general-purpose processors and special-purpose processors (e.g., graphics processors, application processors). The processor may be coupled to a memory / storage device and configured to execute instructions stored in the memory / storage device to enable various applications and / or operating systems to run on the system. Communication system 1200 may execute program code that configures application circuitry 1230 to perform the above-described... Figures 1 to 11 One or more of the described operations. The program code may be located in application circuit 1230 or any suitable computer-readable medium, and may be executed by application circuit 1230 or any other suitable processor.

[0102] The baseband circuit 1220 may include circuitry such as, but not limited to, one or more single-core or multi-core processors. The processor may include a baseband processor. The baseband circuitry can handle various radio control functions that enable communication with one or more wireless networks via RF circuitry. These radio control functions may include, but are not limited to, signal modulation, encoding, decoding, radio frequency offset, etc. In some embodiments, the baseband circuitry can provide communication compatible with one or more wireless technologies. For example, in some embodiments, the baseband circuitry can support communication with the evolved universal terrestrial radio access network (EUTRAN) and / or other wireless metropolitan area networks (WMAN), wireless local area networks (WLAN), and wireless personal area networks (WPAN). Embodiments where the baseband circuitry is configured to support radio communication using more than one wireless protocol may be referred to as a multi-mode baseband circuitry.

[0103] In various embodiments, baseband circuit 1220 may include circuitry that operates on signals not strictly considered to be at baseband frequencies. For example, in some embodiments, the baseband circuitry may include circuitry that operates on signals having an intermediate frequency (IF), which is between the baseband frequency and the radio frequency (RF). RF circuit 1210 may use modulated electromagnetic radiation to achieve communication with a wireless network via a non-solid-state medium. In various embodiments, RF circuitry may include switches, filters, amplifiers, etc., to facilitate communication with a wireless network. In various embodiments, RF circuitry 1210 may include circuitry that operates on signals not strictly considered to be radio frequencies. For example, in some embodiments, RF circuitry may include circuitry that operates on signals having an intermediate frequency (IF), which is between the baseband frequency and the radio frequency.

[0104] In various embodiments, the above regarding Figures 1 to 11 The transmitter circuitry, control circuitry, or receiver circuitry described in the apparatus and / or methods illustrated herein may be wholly or partially embodied in one or more of the RF circuitry, baseband circuitry, and / or application circuitry. As used herein, “circuit” may mean, be part of, or include: an application-specific integrated circuit (ASIC), electronic circuitry, a (shared, dedicated, or grouped) processor and / or (shared, dedicated, or grouped) memory executing one or more software or firmware programs; combinational logic circuitry; and / or other suitable hardware components providing the said functionality. In some embodiments, the electronic device circuitry may be implemented in one or more software modules or firmware modules, or the functionality associated with the circuitry may be implemented by one or more software modules or firmware modules. In some embodiments, some or all of the components of the baseband circuitry, application circuitry, and / or memory / storage device may be implemented together on a system on a chip (SOC). The memory / storage device 1240 may be used to load and store, for example, data and / or instructions for the system. The memory / storage device used in one embodiment may include any combination of suitable volatile memory (e.g., dynamic random access memory (DRAM)) and / or non-volatile memory (e.g., flash memory).

[0105] In various embodiments, I / O interface 1280 may include one or more user interfaces designed to enable user interaction with the system and / or peripheral component interfaces designed to enable interaction with peripheral components of the system. User interfaces may include, but are not limited to, physical keyboards or keypads, touchpads, speakers, microphones, etc. Peripheral component interfaces may include, but are not limited to, non-volatile memory ports, universal serial bus (USB) ports, audio jacks, and power interfaces. In various embodiments, sensor 1270 may include one or more sensing devices for determining environmental conditions and / or location information relevant to the system. In some embodiments, sensors may include, but are not limited to, gyroscope sensors, accelerometers, proximity sensors, ambient light sensors, and positioning units. Positioning units may also be part of, or interact with, baseband and / or RF circuitry to communicate with components of a positioning network (e.g., global positioning system (GPS) satellites).

[0106] In various embodiments, display 1250 may include a display, such as a liquid crystal display (LCD) and a touchscreen display. In various embodiments, communication system 1200 may be a mobile computing device, such as, but not limited to, a laptop, tablet, netbook, ultrabook, smartphone, AR / VR glasses, etc. In various embodiments, the system may have more or fewer components and / or different architectures. Where appropriate, the methods described herein may be implemented as a computer program. The computer program may be stored on a storage medium, such as a non-transitory storage medium.

[0107] Those skilled in the art will understand that each of the units, algorithms, and steps described and disclosed in the embodiments of this disclosure is implemented using electronic hardware or a combination of computer software and electronic hardware. Whether these functions operate in hardware or software depends on the application conditions and the design requirements of the technical solution. Those skilled in the art can implement the functions of each specific application in different ways, and such implementation should not exceed the scope of this disclosure. Those skilled in the art will understand that he / she can refer to the working process of the systems, devices, and units in the above embodiments, as the working processes of the above systems, devices, and units are substantially the same. For ease of description and simplification, these working processes will not be described in detail.

[0108] It should be understood that the systems, devices, and methods disclosed in the embodiments of this disclosure can be implemented in other ways. The above embodiments are merely exemplary. The division of units is based solely on logical function, while other divisions exist in the implementation. It is possible to combine or integrate multiple units or components into another system. It is also possible to omit or skip certain features. On the other hand, the mutual coupling, direct coupling, or communication coupling shown or discussed, whether implemented indirectly in an electrical, mechanical, or other form or through communication, operates through some ports, devices, or units.

[0109] The units used for explanation may or may not be physically separate. The units used for display may or may not be physical units, i.e., located in one place or distributed across multiple network units. Some or all units may be used depending on the purpose of the embodiment. Furthermore, in multiple embodiments, each functional unit among the multiple functional units in each embodiment may be integrated into a single processing unit, physically independent, or integrated into a single processing unit with two or more units.

[0110] If software functional units are implemented, used, and sold as a product, they can be stored in a readable storage medium within a computer. Based on this understanding, the technical solutions proposed in this disclosure can be implemented substantially or partially in the form of a software product. Alternatively, a portion of a technical solution beneficial to conventional technology can be implemented in the form of a software product. The software product in the computer is stored in a storage medium and includes multiple commands for a computing device (e.g., a personal computer, server, or network device) to execute all or some of the steps disclosed in the embodiments of this disclosure. The storage medium includes a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a floppy disk, or other types of media capable of storing program code.

[0111] While this disclosure has been described in conjunction with what are considered to be the most practical and preferred embodiments, it should be understood that this disclosure is not limited to the disclosed embodiments, but is intended to cover various arrangements made without departing from the broadest interpretation of the appended claims.

Claims

1. A communication method for artificial intelligence (AI) / machine learning (ML) operations, comprising: The Network Open Function (NEF) sends a billing data request message to the Charging Function (CHF), the billing data request message including at least one information element associated with a billing report for AI / ML operations; as well as Allow and / or trigger the CHF to identify at least one information element associated with the billing report of the AI / ML operation based on the billing data request message.

2. The method according to claim 1, further comprising: When at least one information element associated with the billing report of the AI / ML operation changes, the corresponding operation is updated by the NEF.

3. The method according to claim 1 or 2, wherein, The at least one information element associated with the billing report of the AI / ML operation includes at least one trigger and / or NEF application interface API billing information.

4. The method according to claim 3, wherein, The at least one trigger supports updating the User Equipment (UE) list and / or updating the integrated data rate value.

5. The method according to claim 3 or 4, wherein, When the NEF receives a change in the UE list and / or the integrated data rate in an update request from the application function AF, the at least one trigger allows and / or triggers the charging report for the AI / ML operation from the NEF to the CHF.

6. The method according to any one of claims 3 to 5, wherein, The at least one trigger includes at least one NEF-specific trigger, and the at least one NEF-specific trigger includes an API call trigger condition in the NEF, an API call response trigger condition in the NEF, an API notification trigger condition in the NEF, an API notification to a Network Function (NF) trigger condition in the NEF, and / or an API notification acknowledgment trigger condition in the NEF.

7. The method according to any one of claims 3 to 6, wherein, The structure of the NEF API charging information includes a UE list and / or integrated data rates.

8. The method according to claim 7, wherein, The NEF API charging information is transmitted by the NEF to the CHF in the charging data request, and allows the CHF to identify the UE list and / or the integrated data rate.

9. The method according to claim 7 or 8, wherein, The UE list contains a list of General Public Subscription Identifier (GPSI) or User Permanent Identifier (SUPI) for the reported UEs.

10. The method according to any one of claims 7 to 9, wherein, The integrated data rate indicates the upper limit of the aggregated data rate of all service flows corresponding to the UE list.

11. The method according to any one of claims 1 to 10, wherein, At least one of the supporting fields in the billing data request message includes a UE list and / or integrated data rate.

12. The method according to claim 11, wherein, The NEF-supported UE list and / or integrated data rate include at least one operation type including initial operation type, termination operation type and / or event operation type.

13. A communication device, comprising: A transmitter configured to send a billing data request message to a billing function (CHF), the billing data request message including at least one information element associated with a billing report of an artificial intelligence (AI) / machine learning (ML) operation; as well as A triggering unit configured to allow and / or trigger the CHF to identify at least one information element associated with the billing report of the AI / ML operation based on the billing data request message.

14. The communication device of claim 13, further comprising an updater configured to update the corresponding operation when the at least one information element associated with the billing report of the AI / ML operation changes.

15. The communication device according to claim 13 or 14, wherein, The at least one information element associated with the billing report of the AI / ML operation includes at least one trigger and / or Network Open Function (NEF) application interface API billing information.

16. The communication device according to claim 15, wherein, The at least one trigger supports updating the User Equipment (UE) list and / or updating the integrated data rate value.

17. The communication device according to claim 15 or 16, wherein, When the transmitter receives a change in the UE list and / or integrated data rate in an update request from the application function AF, the at least one trigger allows and / or triggers a charging report for the AI / ML operation from the triggering unit to the CHF.

18. The communication device according to any one of claims 15 to 17, wherein, The at least one trigger includes at least one NEF-specific trigger, and the at least one NEF-specific trigger includes an API call trigger condition in the communication device, an API call response trigger condition in the communication device, an API notification trigger condition in the communication device, an API notification to a network function (NF) trigger condition in the communication device, and / or an API notification acknowledgment trigger condition in the communication device.

19. The communication device according to any one of claims 15 to 18, wherein, The structure of the NEF API charging information includes a UE list and / or integrated data rates.

20. The communication device according to claim 19, wherein, The NEF API billing information is transmitted by the communication device to the CHF in a billing data request, and allows the CHF to identify the UE list and / or the integrated data rate.

21. The communication device according to claim 19 or 20, wherein, The UE list contains a list of the Common Public Subscription Identifier (GPSI) or User Permanent Identifier (SUPI) of the reported UEs.

22. The communication device according to any one of claims 19 to 21, wherein, The integrated data rate indicates the upper limit of the aggregated data rate of all service flows corresponding to the UE list.

23. The communication device according to any one of claims 13 to 22, wherein, At least one of the supporting fields in the billing data request message includes a UE list and / or integrated data rate.

24. The communication device according to claim 23, wherein, The communication device supports at least one operation type for the UE list and / or the integrated data rate, including an initial operation type, a termination operation type, and / or an event operation type.

25. The communication device according to any one of claims 13 to 24, wherein, The communication equipment includes NEF.

26. A network device, comprising: Memory; transceiver; as well as A processor coupled to the memory and the transceiver; The network device is configured to perform the method according to any one of claims 1 to 12.

27. A non-transitory machine-readable storage medium storing instructions that, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 12.

28. A chip, comprising: A processor configured to invoke and run a computer program stored in a memory, such that a device on which the chip is mounted performs the method according to any one of claims 1 to 12.

29. A computer-readable storage medium storing a computer program, wherein, The computer program causes the computer to perform the method according to any one of claims 1 to 12.

30. A computer program product comprising a computer program, wherein, The computer program causes the computer to perform the method according to any one of claims 1 to 12.

31. A computer program, wherein, The computer program causes the computer to perform the method according to any one of claims 1 to 12.

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