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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4, 5, and 14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 in line 2 recites “transmitting, to the first network function”. There is insufficient antecedent basis for this limitation in the claim. For examination purposes “transmitting, to the first network function” will be read as “transmitting, to the first network entity”. Similarly, claim 14 in line 3 recites “transmit, to the first network function”. There is insufficient antecedent basis for this limitation in the claim. For examination purposes “transmit, to the first network function” will be read as “transmit, to the first network entity”.
Dependent claim 5 is rejected based on its dependency on claim 4.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (US20230100253A1) in view of Zhang et al. (US20230319585A1) and Petrovic et al. (US20220210213A1).
Regarding claim 1, Zhu teaches a method performed by a user equipment (UE) in a wireless communication system, the method comprising: receiving, from a first network entity, information regarding at least one artificial intelligence (Al) model (Paragraph [0007]: In other aspects of the present disclosure, a method of wireless communication by a user equipment (UE) includes transmitting, to a base station, a request for a machine learning configuration for a network-based neural network model. The request has a UE assistance information (UAI) message, which has an information element for a model, a neural network function (NNF), and a machine learning trigger event. The method further includes activating a UE-based neural network model in response to receiving a UE model activation message from the base station. Paragraph [0088]: In some cases, it may be advantageous for a base station of a wireless communication network to dynamically configure a UE and a network entity with at least one NNF and/or one or more corresponding neural network models. This dynamic configuration may provide flexibility within the wireless communication network. Also see paragraph [0124].)
determining an Al model based on the information regarding at least one Al model (Paragraph [0038]: To avoid the flooding of too many UE ML requests, the network may configure a blacklist, and/or whitelist of UE triggers. The network may also configure a prohibit timer to prevent sending of requests too frequently. The black list, prohibit timer, and/or whitelist may be configured by a radio resource control (RRC) reconfiguration message. For a neural network function (NNF) or model in neither the whitelist nor blacklist, the UE can autonomously request network configuration. In some aspects, the network only allows requesting of a model in the whitelist. Paragraph [0088]: Instead, the UE and/or network entity may separately download a particular neural network model when indicated to use that particular neural network model. The neural network model may include a model structure and model parameters. Additionally, dynamic configuration may provide the base station with flexibility to selectively choose, at any given time and for a particular scenario, which NNF(s) and/or corresponding model(s) to use for performing one or more machine learning-based wireless communications management procedures. Moreover, dynamic configuration may allow the base station to dynamically update neural network models for NNFs.)
Zhu does not explicitly teach determining whether to use the Al model for an Al split inference service; requesting, to the first network entity, the Al split inference service.
However, Zhang teaches determining whether to use the Al model for an Al split inference service; requesting, to the first network entity, the Al split inference service (Abstract: Methods and systems for artificial intelligence (AI)-based communications are disclosed. At a second node, a task request is transmitted to a first node, the task request requiring configuration of at least one of a wireless communication functionality or a local AI model at the second node. Paragraph [0005]: In particular, the present disclosure describes AI modules (including an AI management module, and an AI execution module) that may be implemented in a network node (which is an example of a first node at which an AI management module may be implemented) and in a system node or user equipment (which are examples of a second node at which AI execution modules may be implemented). Paragraph [0022]: In any of the above examples, the task request may be a request for collaborative training of the local AI model. Paragraph [0085]: For example, a system node 120 or UE 110 may wish to implement a local AI model that is trained on local data, but may request that the AI management module 210 (e.g., at the network node 131) perform the training (e.g., the system node 120 or UE 110 may have limited computing power and/or memory resources that are required for training an AI model). It should be noted that, in some collaborative tasks in which the AI management module 210 participates in training an AI model, it may not be necessary for the AI management module 210 to understand the content of the data used to train the AI model or to understand the inferred data and/or optimization target of the AI model. It should be understood that other such tasks, including other network tasks and/or other tasks that require cooperation among multiple nodes, which may be managed by the AI management module 210, are within the scope of the present disclosure. As will be discussed further below, one or more AI models may be used together to generate inference data for a particular task. Also see paragraphs [0105] – [0107], [0137].)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide determining whether to use the Al model for an Al split inference service; requesting, to the first network entity, the Al split inference service, as taught by Zhang in the system of Zhu, so that the UE (second node) can collaborate with the first node to implement a local AI model that is trained on local data, so that the local data collection and local AI model training can be considered to be dynamic and in real-time, and the local AI model may be trained to adapt to the varying conditions of the local, dynamic network environment (Zhang: Paragraphs [0005], [0022], [0085], [0105] – [0107]).
The combination of Zhu and Zhang does not explicitly teach establishing an Al model deliver pipeline for the Al model; and establishing a media deliver pipeline for delivering media data used in the Al model.
However, Petrovic teaches establishing an Al model deliver pipeline for the Al model; and establishing a media deliver pipeline for delivering media data used in the Al model (Paragraph [0001]: The present disclosure relates generally to video, audio and related media delivery pipelines. Paragraph [0019]: The content distribution network 500 includes a content delivery system 510 including one or more content servers 512 configured to deliver downsampled media content to one or more client devices 540. Paragraph [0020]: The content delivery system 510 further includes media content storage 514 for storing video and other media content for distribution by the content distribution network 500, and neural network scaling components for downscaling media for delivery. The content server 512 is communicably coupled to the client device 540 through a network 520, which may include one or more wired and/or wireless communication networks, edge servers, the Internet, cloud services, and/or other network components. The content delivery system 510 is configured to store video content, including audio data, video data and other media data, in content storage 514, which may include one or more databases, storage devices and/or storage networks. In some embodiments, the media content is received as media stream (e.g., a livestream) and is processed through the content delivery system 510 in real time. Paragraph [0024]: The client device 540 includes or is connected to a video display and/or audio output resources depicted as a media play 550. A user may access an application on the client device 550 to select and stream media content 514 available for streaming. The client device 550 retrieves the neural network model associated with the media content to process received media content. The client device 540 is configured to decode streamed media content using decoder 544 to generate the YUV optimized media, which is in a memory format optimized for neural network processing. The YUV optimized media is upscaled by upscale neural network 546 and provided to media player 550 for display/playback as decoded/upscaled media content 552. In various embodiments, the client device 550 may include a personal computer, laptop computer, tablet computer, mobile device, a video display system, or other device configured to receive and play media content as described herein. The systems and methods described herein reduce bandwidth requirements for delivering the media content and increase streaming efficiency.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide establishing an Al model deliver pipeline for the Al model; and establishing a media deliver pipeline for delivering media data used in the Al model, as taught by Petrovic in the combined system of Zhu and Zhang, so that media content can be delivered with increased streaming efficiency (Petrovic: Paragraphs [0001], [0019] – [0025]).
Regarding claim 2, the combination of Zhu and Zhang teaches the method of claim 1 (see rejection for claim 1);
Zhu further teaches wherein the information regarding at least one Al model includes a uniform resource locator (URL) to obtain a list the at least one Al model (Paragraph [0036]: The RAN node determines a location of the parameter set and the model by transmitting a model querying request, to a location database, such as a model and data access coordinator (MDAC). The model querying request may include the model ID and the parameter set ID. The MDAC responds with a model querying response indicating locations of the model and parameter set. The MDAC may indicate the locations with a model URL (uniform resource locator) and a parameter set URL. Paragraph [0102]: Thereafter, the base station 110 (e.g., via the CU-CP 712) transmits, to the UE 120, machine learning configuration information based on the UE capability information received at time L. In some cases, the base station 110 may transmit the machine learning configuration information in an RRC reconfiguration message. The machine learning configuration information may include an indication of the at least one NNF (e.g., the accepted NNF list) and the at least one machine learning model corresponding to the at least one NNF. In some cases, the at least one NNF is indicated by an NNF ID and the at least one machine learning model is indicated by a machine learning model ID. As noted, the at least one machine learning model may be associated with a model structure and one or more sets of parameters (e.g., weights, biases, and/or activation functions). In some cases, the machine learning model ID may indicate the model structure associated with the at least one machine learning model, while the one or more sets of parameters may be indicated in the machine learning configuration information by a parameter set ID.)
Regarding claim 3, the combination of Zhu, Zhang, and Petrovic teaches the method of claim 1, further comprising (see rejection for claim 1);
The combination of Zhu and Zhang does not explicitly teach receiving, from a second network entity, intermediate data on the media deliver pipeline.
However, Petrovic teaches receiving, from a second network entity, intermediate data on the media deliver pipeline (Paragraph [0020]: The content delivery system 510 further includes media content storage 514 for storing video and other media content for distribution by the content distribution network 500, and neural network scaling components for downscaling media for delivery. The content server 512 is communicably coupled to the client device 540 through a network 520, which may include one or more wired and/or wireless communication networks, edge servers, the Internet, cloud services, and/or other network components. The content delivery system 510 is configured to store video content, including audio data, video data and other media data, in content storage 514, which may include one or more databases, storage devices and/or storage networks. In some embodiments, the media content is received as media stream (e.g., a livestream) and is processed through the content delivery system 510 in real time. Paragraph [0021]: In some embodiments, the network 520 includes optional edge servers configured to receive media content and neural network scaling models from the content server 512 and stream the media content and deliver the neural network scaling models to the client device 540. The edge servers may be geographically distributed to provide media services to regional client devices across regional networks. The client devices 540 may access content on any number edge servers connected through the network 520.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide receiving, from a second network entity, intermediate data on the media deliver pipeline, as taught by Petrovic in the combined system of Zhu and Zhang, so that media content can be delivered with increased streaming efficiency, where the content is processed in real time (Petrovic: Paragraphs [0001], [0019] – [0025]).
Regarding claim 4, the combination of Zhu, Zhang and Petrovic teaches the method of claim 1, further comprising (see rejection for claim 1);
The combination of Zhu and Petrovic does not explicitly teach transmitting, to the first network function, a status report regarding information on the Al split inference service.
However, Zhang teaches transmitting, to the first network function, a status report regarding information on the Al split inference service (Paragraph [0144]: In the example illustrated in FIG. 5A, the AI management module 210 performs continuous data collection, training of selected global AI model(s) 216 and execution of the trained global AI model(s) 216 to generate updated data (including updated globally inferred control parameter(s) and/or global model parameter(s)), to enable continuous satisfaction of the task request (e.g., satisfaction of one or more KPIs included as task requirements in the task request). The AI execution module 220 may similarly perform continuous updates of configuration parameter(s), continuous collection of local network data and optionally continuous training of the selected local AI model(s) 226, to enable continuous satisfaction of the task request (e.g., satisfaction of one or more KPIs included as task requirements in the task request). As illustrated in FIG. 5A, collection of local network data, training of global (or local) AI model(s) and generation of updated inference data (whether global or local) may be performed repeatedly as a loop, at least for the time duration indicated in the task request (or until the task request is updated or replaced), for example. Paragraph [0174]: At 566, the global data (e.g., stored in the global AI database maintained by the AI management module 210) is updated with the received local data. The method 550 may return to step 558 to retrain the selected global AI model(s) using the updated global data. For example, if the received local data include locally trained weights extracted from local AI model(s), retraining the selected global AI model(s) may include updating the weights of the global AI model(s) based on the locally trained weights. Paragraph [0175]: Steps 558 to 566 may be repeated one or more times, to continue satisfying a task request (e.g., continue providing a requested network service, or continue collaborative training of an AI model). Paragraph [0177]: For example, collaborative training may be performed by the network node 131 training an AI model on behalf of one or more system nodes 120 and/or UEs 110. Collaborative training may also be performed by the network node 131 using locally trained model parameters to update a global AI model (e.g., a form of federated learning).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide transmitting, to the first network function, a status report regarding information on the Al split inference service, as taught by Zhang in the combined system of Zhu and Petrovic, so that the UE (second node) can collaborate with the first node to implement a local AI model that is trained on local data, so that the local data collection and local AI model training can be considered to be dynamic and in real-time, and the local AI model may be trained to adapt to the varying conditions of the local, dynamic network environment (Zhang: Paragraphs [0085], [0105] – [0107], [0144], [0174], [0177]).
Regarding claim 5, the combination of Zhu, Zhang, and Petrovic teaches the method of claim 4, further comprising (see rejection for claim 4);
Zhu does not explicitly teach updating the Al model and the Al model deliver pipeline based on the information on the Al split inference service and information on a network status.
However, Zhang teaches updating the Al model and the Al model deliver based on the information on the Al split inference service and information on a network status (Paragraph [0093]: The local AI model may be trained on locally collected network data. For example, a local AI model may be obtained by adapting a global model to local network data (e.g., by performing further training to update globally-trained parameters, using measurements of the current network performance). Paragraph [0143]: For example, if the local data from the AI execution module(s) 220 include the locally-trained weights of the local AI model(s) (if the local AI model(s) have been updated by near-RT training), the AI management module 210 may aggregate the locally-trained weights and use the aggregated result to update the weights of the selected global AI model(s) 216. After the selected global AI model(s) 216 have been updated, the selected global AI model(s) 216 may be executed to generate updated global inference data. The updated global inference data may be communicated (e.g., using output functions provided by the AICF 214) to the AI execution module 220, for example as another configuration message or as an update message. In some examples, the update message communicated to the AI execution module 220 may include only control parameters or model parameters that have changed from the previous configuration message. Paragraph [0144]: In the example illustrated in FIG. 5A, the AI management module 210 performs continuous data collection, training of selected global AI model(s) 216 and execution of the trained global AI model(s) 216 to generate updated data (including updated globally inferred control parameter(s) and/or global model parameter(s)), to enable continuous satisfaction of the task request (e.g., satisfaction of one or more KPIs included as task requirements in the task request). The AI execution module 220 may similarly perform continuous updates of configuration parameter(s), continuous collection of local network data and optionally continuous training of the selected local AI model(s) 226, to enable continuous satisfaction of the task request (e.g., satisfaction of one or more KPIs included as task requirements in the task request). As illustrated in FIG. 5A, collection of local network data, training of global (or local) AI model(s) and generation of updated inference data (whether global or local) may be performed repeatedly as a loop, at least for the time duration indicated in the task request (or until the task request is updated or replaced), for example. Paragraph [0174]: At 566, the global data (e.g., stored in the global AI database maintained by the AI management module 210) is updated with the received local data. The method 550 may return to step 558 to retrain the selected global AI model(s) using the updated global data. For example, if the received local data include locally trained weights extracted from local AI model(s), retraining the selected global AI model(s) may include updating the weights of the global AI model(s) based on the locally trained weights. Paragraph [0175]: Steps 558 to 566 may be repeated one or more times, to continue satisfying a task request (e.g., continue providing a requested network service, or continue collaborative training of an AI model). Paragraph [0177]: For example, collaborative training may be performed by the network node 131 training an AI model on behalf of one or more system nodes 120 and/or UEs 110. Collaborative training may also be performed by the network node 131 using locally trained model parameters to update a global AI model (e.g., a form of federated learning. Paragraph [0181]: At 622 a, if the task request was sent from the customer at 602 a, the network node 131 delivers the requested task to the customer (e.g., result or report of the requested service, or model parameters for a collaboratively trained AI model). At 622 b, if the task request was sent from the core network 130, the network node 131 delivers the requested task to the core network 130 (e.g., result or report of the requested service, or model parameters for a collaboratively trained AI model).)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide updating the Al model and the Al model deliver based on the information on the Al split inference service and information on a network status, as taught by Zhang in the system of Zhu, so that the UE (second node) can collaborate with the first node to implement a local AI model that is trained on local data, so that the local data collection and local AI model training can be considered to be dynamic and in real-time, and the local AI model may be trained to adapt to the varying conditions of the local, dynamic network environment (Zhang: Paragraphs [0093], [0143], [0144], [0174], [0177]).
The combination of Zhu and Zhang does not explicitly teach Al model deliver pipeline.
However, Petrovic teaches Al model deliver pipeline (Paragraph [0001]: The present disclosure relates generally to video, audio and related media delivery pipelines. Paragraph [0019]: The content distribution network 500 includes a content delivery system 510 including one or more content servers 512 configured to deliver downsampled media content to one or more client devices 540. Paragraph [0020]: The content delivery system 510 further includes media content storage 514 for storing video and other media content for distribution by the content distribution network 500, and neural network scaling components for downscaling media for delivery. The content server 512 is communicably coupled to the client device 540 through a network 520, which may include one or more wired and/or wireless communication networks, edge servers, the Internet, cloud services, and/or other network components. The content delivery system 510 is configured to store video content, including audio data, video data and other media data, in content storage 514, which may include one or more databases, storage devices and/or storage networks. In some embodiments, the media content is received as media stream (e.g., a livestream) and is processed through the content delivery system 510 in real time. Paragraph [0024]: The client device 540 includes or is connected to a video display and/or audio output resources depicted as a media play 550. A user may access an application on the client device 550 to select and stream media content 514 available for streaming. The client device 550 retrieves the neural network model associated with the media content to process received media content. The client device 540 is configured to decode streamed media content using decoder 544 to generate the YUV optimized media, which is in a memory format optimized for neural network processing. The YUV optimized media is upscaled by upscale neural network 546 and provided to media player 550 for display/playback as decoded/upscaled media content 552. In various embodiments, the client device 550 may include a personal computer, laptop computer, tablet computer, mobile device, a video display system, or other device configured to receive and play media content as described herein. The systems and methods described herein reduce bandwidth requirements for delivering the media content and increase streaming efficiency.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide Al model deliver pipeline, as taught by Petrovic in the combined system of Zhu and Zhang, so that media content can be delivered with increased streaming efficiency (Petrovic: Paragraphs [0001], [0019] – [0025]).
Regarding claim 6, Zhu teaches a method performed by a first network entity in a wireless communication system, the method comprising: transmitting, to a user equipment (UE), information regarding at least one artificial intelligence (Al) model; identifying an Al model determined based on the information regarding at least one Al model (see rejection for claim 1);
Zhu does not explicitly teach receiving a request for an Al split inference service using the Al model.
However, Zhang teaches receiving a request for an Al split inference service using the Al model (see rejection for claim 1);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide receiving a request for an Al split inference service using the Al model, as taught by Zhang in the system of Zhu, so that the UE (second node) can collaborate with the first node to implement a local AI model that is trained on local data, so that the local data collection and local AI model training can be considered to be dynamic and in real-time, and the local AI model may be trained to adapt to the varying conditions of the local, dynamic network environment (Zhang: Paragraphs [0005], [0022], [0085], [0105] – [0107]).
The combination of Zhu and Zhang does not explicitly teach establishing an Al model deliver pipeline for the Al model; and establishing a media deliver pipeline for delivering media data used in the Al model.
However, Petrovic teaches establishing an Al model deliver pipeline for the Al model; and establishing a media deliver pipeline for delivering media data used in the Al model ((Paragraph [0001]: The present disclosure relates generally to video, audio and related media delivery pipelines. Paragraph [0019]: The content distribution network 500 includes a content delivery system 510 including one or more content servers 512 configured to deliver downsampled media content to one or more client devices 540. Paragraph [0020]: The content delivery system 510 further includes media content storage 514 for storing video and other media content for distribution by the content distribution network 500, and neural network scaling components for downscaling media for delivery. The content server 512 is communicably coupled to the client device 540 through a network 520, which may include one or more wired and/or wireless communication networks, edge servers, the Internet, cloud services, and/or other network components. The content delivery system 510 is configured to store video content, including audio data, video data and other media data, in content storage 514, which may include one or more databases, storage devices and/or storage networks. In some embodiments, the media content is received as media stream (e.g., a livestream) and is processed through the content delivery system 510 in real time. Paragraph [0021]: In some embodiments, the network 520 includes optional edge servers configured to receive media content and neural network scaling models from the content server 512 and stream the media content and deliver the neural network scaling models to the client device 540. The edge servers may be geographically distributed to provide media services to regional client devices across regional networks. The client devices 540 may access content on any number edge servers connected through the network 520. Paragraph [0024]: The client device 540 includes or is connected to a video display and/or audio output resources depicted as a media play 550. A user may access an application on the client device 550 to select and stream media content 514 available for streaming. The client device 550 retrieves the neural network model associated with the media content to process received media content. The client device 540 is configured to decode streamed media content using decoder 544 to generate the YUV optimized media, which is in a memory format optimized for neural network processing. The YUV optimized media is upscaled by upscale neural network 546 and provided to media player 550 for display/playback as decoded/upscaled media content 552. In various embodiments, the client device 550 may include a personal computer, laptop computer, tablet computer, mobile device, a video display system, or other device configured to receive and play media content as described herein. The systems and methods described herein reduce bandwidth requirements for delivering the media content and increase streaming efficiency.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide establishing an Al model deliver pipeline for the Al model; and establishing a media deliver pipeline for delivering media data used in the Al model, as taught by Petrovic in the combined system of Zhu and Zhang, so that media content can be delivered with increased streaming efficiency (Petrovic: Paragraphs [0001], [0019] – [0025]).
Regarding claim 7, the combination of Zhu, Zhang, and Petrovic teaches the method of claim 6 (see rejection for claim 6);
Zhu further teaches wherein the information regarding at least one AI model includes a uniform resource locator (URL) to obtain a list the at least one AI model (see rejection for claim 2).
Regarding claim 8, the combination of Zhu, Zhang, and Petrovic teaches the method of claim 6 further comprising: (see rejection for claim 6);
The combination of Zhu and Petrovic does not explicitly teach receiving, from the UE, a status report regarding information on the AI split inference service.
However, Zhang teaches receiving, from the UE, a status report regarding information on the AI split inference service (see rejection for claim 4);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide receiving, from the UE, a status report regarding information on the AI split inference service, as taught by Zhang in the combined system of Zhu and Petrovic, so that the UE (second node) can collaborate with the first node to implement a local AI model that is trained on local data, so that the local data collection and local AI model training can be considered to be dynamic and in real-time, and the local AI model may be trained to adapt to the varying conditions of the local, dynamic network environment (Zhang: Paragraphs [0085], [0105] – [0107], [0144], [0174], [0177]).
Regarding claim 9, the combination of Zhu, Zhang, and Petrovic teaches the method of claim 8, further comprising (see rejection for claim 8);
Zhu does not explicitly teach updating the Al model and the Al model deliver pipeline based on the information on the Al split inference service and information on a network status.
However, Zhang teaches updating the Al model and the Al model deliver based on the information on the Al split inference service and information on a network status (see rejection for claim 5);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide updating the Al model and the Al model deliver based on the information on the Al split inference service and information on a network status, as taught by Zhang in the system of Zhu, so that the UE (second node) can collaborate with the first node to implement a local AI model that is trained on local data, so that the local data collection and local AI model training can be considered to be dynamic and in real-time, and the local AI model may be trained to adapt to the varying conditions of the local, dynamic network environment (Zhang: Paragraphs [0093], [0143], [0144], [0174], [0177]).
The combination of Zhu and Zhang does not explicitly teach Al model deliver pipeline.
However, Petrovic teaches Al model deliver pipeline (see rejection for claim 5);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide Al model deliver pipeline, as taught by Petrovic in the combined system of Zhu and Zhang, so that media content can be delivered with increased streaming efficiency (Petrovic: Paragraphs [0001], [0019] – [0025]).
Regarding claim 10, the combination of Zhu, Zhang, and Petrovic teaches the method of claim 6, further comprising (see rejection for claim 6);
The combination of Zhu and Petrovic does not explicitly teach triggering an inference process in a second network entity.
However, Zhang teaches triggering an inference process in a second network entity (Paragraph [0162]: At 516, local data is collected. Collected local data may include network data collected at the system node 120 itself and/or network data collected from one or more UEs 110 associated with the system node 120. Paragraph [0177]: For example, collaborative training may be performed by the network node 131 training an AI model on behalf of one or more system nodes 120 and/or UEs 110. Collaborative training may also be performed by the network node 131 using locally trained model parameters to update a global AI model (e.g., a form of federated learning. Paragraph [0178]: The network node 131 generates inferred control parameter(s) and/or model parameter(s) using one or more global AI model(s), as discussed previously, and transmits configuration information, at 604 and 606, to the system node 120 via the core network 130. The configuration information may include identification of one or more local AI models to be used at the system node 120, and one or more model parameters (e.g., weights) to configure the local AI model(s). For example, if the task is a collaborative task, the configuration information may include model parameters that were trained at the network node 131, and that are used by the system node 120 to update the local AI model(s). The configuration information may also configure the system node 120 to collect local network data for the task (e.g., to monitor KPI(s) and task requirements associated with a requested network task). Paragraph [0181]: At 622 a, if the task request was sent from the customer at 602 a, the network node 131 delivers the requested task to the customer (e.g., result or report of the requested service, or model parameters for a collaboratively trained AI model). At 622 b, if the task request was sent from the core network 130, the network node 131 delivers the requested task to the core network 130 (e.g., result or report of the requested service, or model parameters for a collaboratively trained AI model). Also see paragraphs [0179], [0180].)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide triggering an inference process in a second network entity, as taught by Zhang in the system of Zhu, so that the second network entity (system node) can collaborate with the first node to implement a local AI model that is trained on local data, so that the local data collection and local AI model training can be considered to be dynamic and in real-time, and the local AI model may be trained to adapt to the varying conditions of the local, dynamic network environment (Zhang: Paragraphs [0162], [0177] – [0181]).
Regarding claim 11, Zhu teaches a user equipment (UE) in a wireless communication system, the UE comprising: at least one transceiver; and at least one processor operatively coupled with the at least one transceiver, wherein the at least one processor is configured to: (Paragraph [0047]: Some UEs may be considered a customer premises equipment (CPE). UE 120 may be included inside a housing that houses components of UE 120, such as processor components, memory components, and/or the like. Paragraph [0133]: At block 1502, the user equipment transmits, to a base station, a request for a machine learning configuration for a network-based neural network model. The request may comprise a UE assistance information (UAI) message. The UAI message may comprise an information element for a model, a neural network function (NNF), and a machine learning trigger event. Paragraph [0134]: At block 1504, the user equipment activates a UE-based neural network model in response to receiving a UE model activation message from the base station. Also see paragraphs [0055], [0056]).
receive, from a first network entity, information regarding at least one artificial intelligence (AI) model, determine an AI model based on the information regarding at least one AI model (see rejection for claim 1);
Zhu does not explicitly teach to determine whether to use the AI model for an AI split inference service, request, to the first network entity, the AI split inference service.
However, Zhang teaches to determine whether to use the AI model for an AI split inference service, request, to the first network entity, the AI split inference service (see rejection for claim 1);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine whether to use the Al model for an Al split inference service, request, to the first network entity, the Al split inference service, as taught by Zhang in the system of Zhu, so that the UE (second node) can collaborate with the first node to implement a local AI model that is trained on local data, so that the local data collection and local AI model training can be considered to be dynamic and in real-time, and the local AI model may be trained to adapt to the varying conditions of the local, dynamic network environment (Zhang: Paragraphs [0005], [0022], [0085], [0105] – [0107]).
The combination of Zhu and Zhang does not explicitly teach to establish an AI model deliver pipeline for the AI model, and establish a media deliver pipeline for delivering media data used in the AI model.
However, Petrovic teaches to establish an AI model deliver pipeline for the AI model, and establish a media deliver pipeline for delivering media data used in the AI model (see rejection for claim 1);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to establish an Al model deliver pipeline for the Al model; and establish a media deliver pipeline for delivering media data used in the Al model, as taught by Petrovic in the combined system of Zhu and Zhang, so that media content can be delivered with increased streaming efficiency (Petrovic: Paragraphs [0001], [0019] – [0025]).
Regarding claim 12, the combination of Zhu, Zhang and Petrovic teaches the UE of claim 11 (see rejection for claim 11);
Zhu further teaches wherein the information regarding at least one AI model includes a uniform resource locator (URL) to obtain a list the at least one AI model (see rejection for claim 2).
Regarding claim 13, the combination of Zhu, Zhang and Petrovic teaches the UE of claim 11, wherein the at least one processor is further configured to: (see rejection for claim 11);
The combination of Zhu and Zhang does not explicitly teach to receive, from a second network entity, intermediate data on the media deliver pipeline.
However, Petrovic teaches to receive, from a second network entity, intermediate data on the media deliver pipeline (see rejection for claim 3);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to receive, from a second network entity, intermediate data on the media deliver pipeline, as taught by Petrovic in the combined system of Zhu and Zhang, so that media content can be delivered with increased streaming efficiency, where the content is processed in real time (Petrovic: Paragraphs [0001], [0019] – [0025]).
Regarding claim 14, the combination of Zhu, Zhang and Petrovic teaches the UE of claim 11, wherein the at least one processor is further configured to: (see rejection for claim 11);
The combination of Zhu and Petrovic does not explicitly teach to transmit, to the first network function, a status report regarding information on the AI split inference service.
However, Zhang teaches to transmit, to the first network function, a status report regarding information on the AI split inference service (see rejection for claim 4);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to transmit, to the first network function, a status report regarding information on the AI split inference service, as taught by Zhang in the combined system of Zhu and Petrovic, so that the UE (second node) can collaborate with the first node to implement a local AI model that is trained on local data, so that the local data collection and local AI model training can be considered to be dynamic and in real-time, and the local AI model may be trained to adapt to the varying conditions of the local, dynamic network environment (Zhang: Paragraphs [0085], [0105] – [0107], [0144], [0174], [0177]).
Regarding claim 15, Zhu teaches a first network entity in a wireless communication system, the first network entity comprising: at least one transceiver; and at least one processor operatively coupled with the at least one transceiver, wherein the at least one processor is configured to: (Paragraph [0008]: Other aspects of the present disclosure are directed to an apparatus for wireless communication by a base station having a memory and one or more processor(s) coupled to the memory. The processor(s) is configured to receive a user equipment (UE) radio capability and a UE machine learning capability. Paragraph [0131]: At block 1408, the base station configures the network entity with the neural network model. For example, the base station (e.g., using the antenna 234, MOD/DEMOD 232, TX MIMO detector 230, transmit processor 220, controller/processor 240, and/or memory 242) may configure the network entity. The base station may configure the network entity in response to receiving a UE message from a UE triggering the configuring. Paragraph [0134]: For example, the base station (e.g., using the controller/processor 280, and/or memory 82) may activate the neural network model. Activation enables use of the neural network model. Also see paragraphs [0055], [0056]).
transmit, to a user equipment (UE), information regarding at least one artificial intelligence (AI) model, identify an AI model determined based on the information regarding at least one AI model (see rejection for claim 1);
Zhu does not explicitly teach to receive a request for an AI split inference service using the AI model.
However, Zhang teaches to receive a request for an Al split inference service using the Al model (see rejection for claim 1);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to receive a request for an Al split inference service using the Al model, as taught by Zhang in the system of Zhu, so that the UE (second node) can collaborate with the first node to implement a local AI model that is trained on local data, so that the local data collection and local AI model training can be considered to be dynamic and in real-time, and the local AI model may be trained to adapt to the varying conditions of the local, dynamic network environment (Zhang: Paragraphs [0005], [0022], [0085], [0105] – [0107]).
The combination of Zhu and Zhang does not explicitly teach to establish an AI model deliver pipeline for the AI model, and establish a media deliver pipeline for delivering media data used in the AI model.
However, Petrovic teaches to establish an AI model deliver pipeline for the AI model, and establish a media deliver pipeline for delivering media data used in the AI model (Paragraph [0001]: The present disclosure relates generally to video, audio and related media delivery pipelines. Paragraph [0019]: The content distribution network 500 includes a content delivery system 510 including one or more content servers 512 configured to deliver downsampled media content to one or more client devices 540. Paragraph [0020]: The content delivery system 510 further includes media content storage 514 for storing video and other media content for distribution by the content distribution network 500, and neural network scaling components for downscaling media for delivery. The content server 512 is communicably coupled to the client device 540 through a network 520, which may include one or more wired and/or wireless communication networks, edge servers, the Internet, cloud services, and/or other network components. The content delivery system 510 is configured to store video content, including audio data, video data and other media data, in content storage 514, which may include one or more databases, storage devices and/or storage networks. In some embodiments, the media content is received as media stream (e.g., a livestream) and is processed through the content delivery system 510 in real time. Paragraph [0021]: In some embodiments, the network 520 includes optional edge servers configured to receive media content and neural network scaling models from the content server 512 and stream the media content and deliver the neural network scaling models to the client device 540. The edge servers may be geographically distributed to provide media services to regional client devices across regional networks. The client devices 540 may access content on any number edge servers connected through the network 520. Paragraph [0024]: The client device 540 includes or is connected to a video display and/or audio output resources depicted as a media play 550. A user may access an application on the client device 550 to select and stream media content 514 available for streaming. The client device 550 retrieves the neural network model associated with the media content to process received media content. The client device 540 is configured to decode streamed media content using decoder 544 to generate the YUV optimized media, which is in a memory format optimized for neural network processing. The YUV optimized media is upscaled by upscale neural network 546 and provided to media player 550 for display/playback as decoded/upscaled media content 552. In various embodiments, the client device 550 may include a personal computer, laptop computer, tablet computer, mobile device, a video display system, or other device configured to receive and play media content as described herein. The systems and methods described herein reduce bandwidth requirements for delivering the media content and increase streaming efficiency.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to establish an AI model deliver pipeline for the AI model, and establish a media deliver pipeline for delivering media data used in the AI model, as taught by Petrovic in the combined system of Zhu and Zhang, so that media content can be delivered with increased streaming efficiency (Petrovic: Paragraphs [0001], [0019] – [0025]).
Conclusion
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/L.C./Examiner, Art Unit 2461
/HUY D VU/Supervisory Patent Examiner, Art Unit 2461
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