DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-11, 13 and 16-23 are pending for examination in the instant application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/05/2024 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-10, 13, 18, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Karjee et al. (Pub. No.: US 2022/0311678 A1), hereinafter “Kar” in view of Yip et al. (Pub. No.: US 2025/0056256 A1), hereinafter “Yip”.
As to claim 1. Kar discloses, A method performed by a user equipment (Kar, Abstract), the method comprising:
transmitting towards an application function, (AF) a request for splitting a machine learning (ML) inference process (Kar [0033], [0039], the IoT/UE selecting an edge and identifying a network, and then splitting the DNN and transmitting the second part to the edge.), wherein the request comprises any one or more of:
information about the UE, information about the ML inference process, and/or a request for information about a network to which the UE is connected (Kar, [0033], [0046, [0047], the specification describes using network condition information, bandwidth, historical inference time records, and benchmarking of layer inference times as inputs to select network and compute split ration); and
after transmitting the request for splitting the ML inference process, receiving split decision information indicating how to split the ML inference process, wherein the split decision information was transmitted by the AF (Kar, [0110], [0111], the IoT device computing the optimal split ration locally (DSC/E-DSC) and /or exchanging model/pipeline configuration via gRPC.
Kar however is silent to disclose, an AF making and transmitting a split-decision back to the UE as a distinct network entity.
Yip discloses a similar concept in the same field of endeavor including, network/AF decisioning role (Yip, [0048], [0049], Negotiating the splitting of the AI inference process after the AI model required for the service is identified, and after the AI media capabilities and functions of the UE and network are discovered and During the negotiation process, control messages are exchanged between the network and UE.).
Therefore, before the effective filing date of the instant application it would have been obvious to one of the skilled in the art to incorporate the teachings of “Yip” into those of “Kar” to provide a method and apparatus for providing a media service. A method performed by a network includes identifying an AI model corresponding to a service, negotiating a split inference configuration with a UE, performing a split inference with the UE, based on the AI model and the split inference configuration, and providing the service based on a result of the split inference.-
As to claim 2. The combined system of Kar and Yip discloses the invention as in parent claim above including, wherein the information about the UE indicates a location of the UE and/or information about one or more resources available at the UE (Kar, [0033], [0034]), the information about the ML inference process indicates :
i) one or more requirements on resources needed for performing the ML inference process (Kar, [0033], [0034], resources available at UE: disclosed as device capability / low-cost computing device and use of computational latency / device inference time records.);
ii) a size of intermediate output data to be generated during the ML inference process (Kar, [0033], [0039], figs 4A and 6A-6C, output of each layer and transmitting layer outputs to edge.);
iii) a time duration needed for performing the ML inference process (Kar, [0033], [0039], fig. 7A-7F, inference time/per-layer timing/inference time records used to compute split ratio and optimal split point.); and/or
iv) an accuracy requirement of the ML inference process (Kar, [0033], [0039], various attributes significant to accuracy), and the information about the network indicates any one or more of:
a rate of uplink (UL) data transmission, a rate of downlink (DL) data transmission, a network latency, and/or a network reliability (Kar, [0033], [0034], figs.5B/7D, network metrics: bandwidth, network channel quality, available bandwidth, historical inference time records, and transmission time are explicitly used to select network and compute split.).
As to claim 3. The combined system of Kar and Yip discloses the invention as in parent claim above including, the method further comprising: based on the received split decision information, selecting a part of the ML inference process (Yip, [0022], also see [0048]-[0051], fig.11-13); and
performing the selected part of the ML inference process (Kar, [0039] and [0110]-[0111]).
As to claim 4. The combined system of Kar and Yip discloses the invention as in parent claim above including, transmitting towards one or more network end points (NEs) ML sub-process data indicating a part of the ML inference process to be performed by said one or more NEs (Kar, [0039], splitting a plurality of layers of the DNN into a firs part and a second part based on the determined split ratio, and transmitting the second part to the selected at least one edge device through the identified network. [0110], [0111], the IoT device 605 sends the result621 of partial inference to the edge device 605. The edge device 605 executes the remaining PoseNet model inference and sends the result 615 back.).
As to claim 5. Is rejected for same rationale as applied to claim 1 above.
As to claim 6. Is rejected for same rationale as applied to claim 2 above.
As to claim 7. Is rejected for same rationale as applied to claim 3 above.
As to claim 8. Is rejected for same rationale as applied to claim 4 above.
As to claim 9. The combined system of Kar and Yip discloses the invention as in parent claim above including, wherein the NE information indicates: an amount of computational resources available at said one or more NEs, and/or end-to-end network performance between one or more pairs of NEs in case said one or more NEs includes more than one NE (Kar, [0033] selects at least one edge device from a plurality of devices within a communication range of the IoT devices based on network conditions and computational latency associated with the plurality of edge devices. [0034], the preferred network 105is identified based on available bandwidth and historical inference time records of a plurality of networks associated with the IoT device.).
As to claim 10. The combined system of Kar and Yip discloses the invention as in parent claim above including, transmitting towards a network data analytics function (NWDAF) data indicating the NE information (Yip,fig.9, 11-13; [0050], [0051], once the control messages are fully exchanged and the split inference configuration is agreed upon, the information from the control messages is sued to establish the corresponding pipelines for the different data types according to the control messages. The different split inference configurations possible for an AI media split inferencing service.).
As to claim 18 is rejected for same rationale as applied to claim 1 above.
As to claim 19 is rejected for same rationale as applied to claim 9 above.
As to claim 20 is rejected for same rationale as applied to claim 4 above.
Claim(s) 11, 13, 16, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Kar” and “Yip” in view of Lee et al. (Pub. No.: US 20200322821 A1), hereinafter “Lee”.
As to claim 11. The combined system of Kar and Yip discloses the invention as in parent claim above. Kar and Yip however are silent to disclose explicitly, wherein the data indicating the NE information is transmitted as a result of the NWDAF subscribing to the AF for the data or transmitting to the AF a request for the data.
Lee discloses a similar concept in the same field of endeavor including, wherein the data indicating the NE information is transmitted as a result of the NWDAF subscribing to the AF for the data or transmitting to the AF a request for the data (Lee, [0015], According to an aspect, there is provided a network data collecting method performed by a network data analytic function (NWDAF) device. The method comprises transmitting a Naf_EventExposure_Subscribe message for a subscription of an event into an application function (AF) device; and receiving a Naf_EventExposure_Notify message from the AF device when the NWDAF subscribes the event.).
Therefore, before the effective filing date of the instant application it would have been obvious to one of the skilled in the art to incorporate the teachings of “Yip” into those of “Kar” to provide a network data collection method from application function device for network data analytic function is disclosed. The network data collection method includes transmitting a Naf_EventExposure_Subscribe message for a subscription of an event into an application function (AF) device; and receiving a Naf_EventExposure_Notify message from the AF device when the NWDAF subscribes the event.
As to claim 13. The combined system of Kar, Yip and Lee discloses the invention substantially including, further comprising receiving analytic data of said one or more types identified by said one or more analytic type identifiers, wherein the analytic data is generated based on the NE information (Lee, [0062], [0063], The NWDAF (Network Data Analytics Function) provides analytics to 5GC NFs, and OAM. Analytics information are either statistical information of the past events, or predictive information.).
As to claim 16. The combined system of Kar, Yip and Lee discloses the invention substantially including, wherein the analytic data indicates:
historical statistics and/or predictions regarding UL data transmission from the UE to each of said one or more NEs (Kar, [0034]);
historical statistics and/or predictions regarding packet delay on UL data transmission from the UE to each of said one or more NEs (Kar, [0033], [0034] and [0039]).;
historical statistics and/or predictions regarding packet loss rate on UL data transmission from the UE to each of said one or more NEs (Yip, fig.10-13 [0020], [0021] and [0050]), ; and/or
a quality of service (QoS) indicator indicating a predicted quality of service in case the ML inference process is split (Yip, fig.10-13 [0020], [0021] and [0050]).
As to claim 17. The combined system of Kar, Yip and Lee discloses the invention substantially including, further comprising determining how to split the ML inference process based on the received analytic data, wherein determining how to split the ML inference process comprises determining:
a number of ML layers for performing a part of the ML inference process at the UE (Kar, fig. 6a-6c, 7a-7f, [0039], determines a split ratio and splits a plurality of DNN layers int a first part (device) and second part (edge)).;
one or more NE identifiers identifying said one or more NEs to perform a part of the ML inference process (Yip, fig. 9, 11-13; [0048]-[0051], shows negotiation and establishment of pipelines to network endpoints);
an ML layer identifier identifying an ML layer of which an operation corresponds to the last operation performed by the UE for the ML inference process (Kar, fig. 6a-6c, [0039], transmits layer outputs and partial inference);
an ML layer identifier identifying an ML layer of which an operation corresponds to the last operation performed by one of said one or more NEs (Kar, fig. 6a-6c, [0039], transmits layer outputs and partial inference); and/or
a time period for performing a part of the ML inference process at the UE (Kar, fig.5a,b, 7d-f, [0033], [0039], use inference time, transmission time, latency etc.).
Claim(s) 21 and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Kar” in view of Lee et al. (Pub. No.: US 20200322821 A1), hereinafter “Lee”.
As to claim 21. Lee discloses the invention substantially including, a method performed by a network data analytics function, (NWDAF), the method comprising:
receiving network endpoint (NE) information about one or more NEs, wherein the NE information was transmitted by an application function (AF) (Lee, [0015], transmitting a Naf_EventExposure_Subscribe message for a subscription of an event into an application function (AF) device; and receiving a Naf_EventExposure_Notify message from the AF device when the NWDAF subscribes the event. and [0084], Data provided by the external party may be collected by NWDAF via NEF for analytics generation purpose. NEF handles and forwards requests and notifications between NWDAF and AF.);
using at least the received NE information, generating analytic data (Lee, [0062], states NWDAF generates statistical or predictive analytics from collected data, expressly teaches NWDAF generates predictive analytics and [0074] that NWDAF provisions analytics to NFs/AFs i.e. NWDAF can produce analytics that an AF could use to decide split configuration. Further Kar discloses, the specific network/latency/bandwidth and per-layer timing metrics used to compute split ratios, see [0033], [0039]); and
transmitting towards the AF the generated analytic data (Lee, [0074] and [0084], states NWDAF supports analytics provisioning to Afs and that NEF handles/forwards notification between NWDAF and AF, enabling NWDAF . AF delivery of analytics).
Lee however does silent to disclose explicitly, generating analytics data for splitting a machine learning (ML) inference process.
Kar however discloses the invention in the same field of endeavor including, generating analytics data for splitting a machine learning (ML) inference process (Kar, [0033], [0039] discloses, the specific network/latency/bandwidth and per-layer timing metrics used to compute split ratios and [0009], determining a split ratio based on at least one of an inference time of the DNN and a transmission time required for transmitting output of each layer of the DNN from the IoT device to the selected at least one edge device.).
Therefore, before the effective filing date of the instant application it would have been obvious to one of the ordinary skilled in the art to incorporate the teachings of “Kar” into those of “Lee” to provide a method for execution of deep neural network (DNN) in an internet of things (IoT) edge network are provided. In an embodiment, at least one edge device within communication range of an IoT device are selected. Further, a network for connecting the IoT device with the at least one selected edge device is identified. A split ratio is determined based on an inference time of the DNN and a transmission time required for transmitting output of each layer of DNN from the IoT device to the selected at least one edge device.
As to claim 23. The combined system of Lee, and Kar discloses the invention substantially including, wherein the NE information indicates:
an amount of computational resources available at said one or more NEs, and/or end-to-end network performance between one or more pairs of NEs in case said one or more NEs includes more than one NE (Kar, [0033] selects at least one edge device from a plurality of devices within a communication range of the IoT devices based on network conditions and computational latency associated with the plurality of edge devices. [0034], the preferred network 105is identified based on available bandwidth and historical inference time records of a plurality of networks associated with the IoT device.).
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Lee and Kar” as applied above, in view of Yip et al. (Pub. No.: US 2025/0056256 A1), hereinafter “Yip”.
As to claim 22. The combined system of Lee and Kar discloses the invention substantially including, wherein the analytic data indicates:
historical statistics and/or predictions regarding UL data transmission from the UE to each of said one or more NEs (Kar, [0034]);
historical statistics and/or predictions regarding packet delay on UL data transmission from the UE to each of said one or more NEs (Kar, [0033], [0034] and [0039]).
Kar however is silent to disclose explicitly, historical statistics and/or predictions regarding packet loss rate on UL data transmission from the UE to each of said one or more NEs; a quality of service (QoS) indicator indicating a predicted quality of service in case the ML inference process is split.
Yip however discloses the invention in the same field of endeavor including, historical statistics and/or predictions regarding packet loss rate on UL data transmission from the UE to each of said one or more NEs (Yip, fig.10-13 [0020], [0021] and [0050]); and/or
a quality of service (QoS) indicator indicating a predicted quality of service in case the ML inference process is split (Yip, fig.10-13 [0020], [0021] and [0050]).
Therefore, before the effective filing date of the instant application it would have been obvious to one of the ordinary skilled in the art to incorporate the teachings of “Yip” into those of “Lee and Kar” to provide a method for providing a media service. A method performed by a network includes identifying an AI model corresponding to a service, negotiating a split inference configuration with a UE, performing a split inference with the UE, based on the AI model and the split inference configuration, and providing the service based on a result of the split inference.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see the attached PTO-892.
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/Tauqir Hussain/Primary Examiner, Art Unit 2446