CTNF 18/661,006 CTNF 101682 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Information Disclosure Statement The information disclosure statements submitted on 10/9/24, 1/16/25, 4/9/25, & 12/1/25 have been considered by the examiner and made of record in the application file. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-15 AIA Claim s 1-4, 6-9, 11, 13-20 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Zhou, Zhou (EFCam: Configuration-Adaptive Fog-Assisted Wireless Cameras with Reinforcement Learning, publication date 06-09 July 2021, [online] URL: https://ieeexplore.ieee.org/document/9491609 (Year: 2021) (hereafter referred to as “Zhou”)) . Regarding claim 1 , Zhou teaches an artificial intelligence (AI) communication method, (Zhou; Fig 2 & 3, section I - - Zhou teaches a wireless camera, called ESP32-CAM to perform image sensing, generating smaller representations of the captured images, and transmits the representations to the fog node for advanced processing using deep models). comprising: receiving, by a first apparatus, (Zhou; section I, third paragraph & Fig 2 - - Zhou teaches the “Fog Node” (the interpreted as the first device) is what receives the data from the wireless camera). AI model information (Zhou; section V part B - - Zhou teaches this information is received by the first device (Fog Node) and is embedded in the DRL agent during training (see Fig. 10 and section V part B). Further, this information is received by the first device once the DRL agent is commissioned for online execution (see section V, part B, read as “after the completion of the offline training, the DRL agent is commissioned to adapt the configuration of EFCam for industrial visual sensing tasks”). sent by a second apparatus (Zhou; Fig. 2 & 3 - - the second device is the wireless camera (see fig. 2 & 10). The information is sent by this device during the modelling phase in Fig. 10, see section V part B, read as “we model the image processing latency and energy consumption based on real data traces collected from the deployment field”). wherein the AI model information includes M groups of AI model complexity information corresponding to M AI models, (Zhou; Fig. 2, section III part B and section V part B - - Zhou teaches Autoencoder, a deep learning-based image compression method. It has two-parts, encoder and decoder. Further, Zhou teaches the complexity information are execution time and latency, read as: “we model the image processing latency and energy consumption based on real data traces collected from the deployment field”). each of the M groups of AI model complexity information is time and/or energy consumption of executing one of the M AI models, (Zhou; section V part B - - Zhou teaches “we model the image processing latency and energy consumption based on real data traces collected from the deployment field”). in each of N reference AI execution environments (Zhou; section III part A and section V part A - - Zhou teaches the execution environments are represented by the wireless camera and its operating resolution “r”, read as “supports image capture with eight resolution levels” and “a resolution, denoted by “r”, as well as the first paragraph in section V part C which describes energy and latency as being dependent on “r”). and M and N are positive integers; (Zhou; Fig 2 - - Zhou teaches M in the number of encoders in Fig. 2, and N is the number of supported resolution levels). and sending, by the first apparatus, feedback information to the second apparatus. (Zhou; section V part B - - Runtime Actions, as shown in figure 10. The DRL agent which is configured in the fog node is shown to send data towards the EF cam as seen by the orange line.) Regarding claim 2 , Zhou teaches the method of claim 1, wherein the sending the feedback information includes sending feedback information that is usable to request the second apparatus to enable an AI communication mode; or sending the feedback information that includes an evaluation result of at least one of the M AI models; or sending the feedback information that is usable to request to obtain at least one of the M AI models. (Zhou; section V, part A - - Zhou teaches the camera performs the image scaling to resize the captured images to a resolution, denoted by r ∈ [rmin,rmax], where the rmin and rmax denote the minimum and maximum resolutions, respectively. We denote c as the local image processing mode (i.e., the JPEG with a quality index and autoencoder network with a setting for its hyperparameters). The configuration action, denoted by a, is a vector a = [f,r,c]). Regarding claim 3 , Zhou teaches the method of claim 1, wherein after sending, by the first apparatus, feedback information to the second apparatus, the method comprises: receiving, by the first apparatus, configuration information sent by the second apparatus, wherein the configuration information is usable to indicate the first apparatus to enable the AI communication mode, or the configuration information is usable to indicate at least one of the M AI models, or the configuration information is usable to indicate a configuration parameter of at least one of the M AI models, or the configuration information is usable to indicate a method for obtaining at least one of the M AI models. (Zhou; Figure 10: Runtime State, and section V part A - - The system state, denoted by x, is a vector x = [ξ,η], where ξ represents the image processing result [. . .] the ξ ∈ {0,1} indicates whether an object is detected (here, ξ is the “configuration parameter”)). Regarding claim 4 , Zhou teaches the method of claim 2, wherein the sending the feedback information that includes an evaluation result of at least one of the M AI models includes an evaluation result of a first AI model that is usable to indicate that the first AI model matches the first apparatus, or to indicate expected time and/or energy consumption of executing of the first AI model by the first apparatus. (Zhou; Figure 10 and section V part A - - Zhou teaches the feedback is the runtime action of Figure 10 which comprises a first model. Section V part A, read as “We denote c as the local image processing mode (i.e., the JPEG with a quality index and autoencoder network with a setting for its hyperparameters). The configuration action, denoted by a, is a vector a = [f,r,c]”. Further, Zhou teaches the expected time (frames per adaptation period) of executing the AI model, read as “Let f ∈ [fmin, fmax] denote the number of frames per adaptation period τ, where fmin and fmax are the minimum and maximum number of image frames required by the application”. Regarding claim 6 , Zhou teaches an artificial intelligence (AI) communication method, (Zhou; Fig 2 & 3, section I - - Zhou teaches a wireless camera, called ESP32-CAM to perform image sensing, generating smaller representations of the captured images, and transmits the representations to the fog node for advanced processing using deep models). comprising: obtaining, by a second apparatus, (Zhou; section III, paragraph 1 and section A - - Zhou teaches the camera performs low-power image sensing, local processing for image feature extraction and compression, and data transmission. Further, Zhou teaches the strategy is to use the deep model-based techniques to locally process the raw images at the camera, thus obtaining information). AI model information, (Zhou; section V part B - - Zhou teaches this information is received by the first device (Fog Node) and is embedded in the DRL agent during training (see Fig. 10 and section V part B). Further, this information is received by the first device once the DRL agent is commissioned for online execution (see section V, part B, read as “after the completion of the offline training, the DRL agent is commissioned to adapt the configuration of EFCam for industrial visual sensing tasks”). wherein the AI model information includes M groups of AI model complexity information corresponding to M AI models, (Zhou; Fig. 2, section III part B and section V part B - - Zhou teaches Autoencoder, a deep learning-based image compression method. It has two-parts, encoder and decoder. Further, Zhou teaches the complexity information are execution time and latency, read as: “we model the image processing latency and energy consumption based on real data traces collected from the deployment field”). each of the M groups of AI model complexity information is time and/or energy consumption of executing one of the M AI models, (Zhou; section V part B - - Zhou teaches “we model the image processing latency and energy consumption based on real data traces collected from the deployment field”). in each of N reference AI execution environments (Zhou; section III part A and section V part A - - Zhou teaches the execution environments are represented by the wireless camera and its operating resolution “r”, read as “supports image capture with eight resolution levels” and “a resolution, denoted by “r”, as well as the first paragraph in section V part C which describes energy and latency as being dependent on “r”). and M and N are positive integers; (Zhou; Fig 2 - - Zhou teaches M in the number of encoders in Fig. 2, and N is the number of supported resolution levels). and sending, by the second apparatus, the AI model information to a first apparatus (Zhou; Fig. 2 & 3 - - the second device is the wireless camera (see fig. 2 & 10). The information is sent by this device during the modelling phase in Fig. 10, see section V part B, read as “we model the image processing latency and energy consumption based on real data traces collected from the deployment field”). Regarding claim 7 , Zhou teaches the method of claim 6, wherein after sending, by the second apparatus, the AI model information to a first apparatus, the method comprises: receiving, by the second apparatus, feedback information sent by the first apparatus, wherein the feedback information is usable to request the second apparatus to enable an AI communication mode, or the feedback information includes an evaluation result of at least one of the M AI models, or the feedback information is usable to request to obtain at least one of the M AI models. (Zhou; section V, part A - - Zhou teaches the camera performs the image scaling to resize the captured images to a resolution, denoted by r ∈ [rmin,rmax], where the rmin and rmax denote the minimum and maximum resolutions, respectively. We denote c as the local image processing mode (i.e., the JPEG with a quality index and autoencoder network with a setting for its hyperparameters). The configuration action, denoted by a, is a vector a = [f,r,c]). Regarding claim 8 , Zhou teaches the method of claim 7, wherein after receiving, by the second apparatus, feedback information sent by the first apparatus, the method comprises: sending, by the second apparatus, configuration information to the first apparatus, wherein the configuration information is usable to indicate the first apparatus to enable the AI communication mode, or the configuration information is usable to indicate at least one of the M AI models, or the configuration information is usable to indicate a configuration parameter of at least one of the M AI models , or the configuration information is usable to indicate a method for obtaining at least one of the M AI models (Zhou; Figure 10: Runtime State, and section V part A - - The system state, denoted by x, is a vector x = [ξ,η], where ξ represents the image processing result [. . .] the ξ ∈ {0,1} indicates whether an object is detected (here, ξ is the “configuration parameter”)). Regarding claim 9 , Zhou teaches the method of claim 7, wherein the receiving the feedback information that includes an evaluation result of at least one of the M AI models includes receiving an evaluation result of a first AI model comprises that is usable to indicate that the first AI model matches the first apparatus, or to indicate expected time and/or energy consumption of executing of the first AI model by the first apparatus (Zhou; Figure 10 and section V part A - - Zhou teaches the feedback is the runtime action of Figure 10 which comprises a first model. Section V part A, read as “We denote c as the local image processing mode (i.e., the JPEG with a quality index and autoencoder network with a setting for its hyperparameters). The configuration action, denoted by a, is a vector a = [f,r,c]”. Further, Zhou teaches the expected time (frames per adaptation period) of executing the AI model, read as “Let f ∈ [fmin, fmax] denote the number of frames per adaptation period τ, where fmin and fmax are the minimum and maximum number of image frames required by the application”. Regarding claim 11 , Zhou teaches the method of claim 6, wherein the sending, by the second apparatus, the AI model information to the first apparatus includes: periodically sending, by the second apparatus, the AI model information to the first apparatus; or when in response to the first apparatus accessing a network in which the second apparatus is located, sending, by the second apparatus, the AI model information to the first apparatus; or in response to the first apparatus establishing a communication connection to the second apparatus, sending, by the second apparatus, the AI model information to the first apparatus; or in response to structures or computing amounts of the M AI models changing, sending, by the second apparatus, the AI model information to the first apparatus. (Zhou; Figure 10 and section V part A - - Zhou teaches the feedback is the runtime action of Figure 10 (interpreted as a communication connection) which comprises a first model. section V part A, read as “We denote c as the local image processing mode (i.e., the JPEG with a quality index and autoencoder network with a setting for its hyperparameters). The configuration action, denoted by a, is a vector a = [f,r,c]”. Further, the runtime state shows the camera (second apparatus) sends AI model information back to the DRL agent which is located in the fog node). Regarding claim 13 , Zhou teaches the method of claim 7, wherein the sending, by the second apparatus, the AI model information to a first apparatus includes sending AI model information corresponding to M AI models that includes a first AI model, the N reference AI execution environments include a first reference AI execution environment, time of executing the first AI model in the first reference AI execution environment is a first time value, a first time level, or a first time range, and energy consumption of executing the first AI model in the first reference AI execution environment is a first energy consumption value, a first energy consumption level, or a first energy consumption range. (Zhou; section V part A and section V part C - - Zhou teaches in section V part A that “We denote c as the local image processing mode (i.e., the JPEG with a quality index and autoencoder network with a setting for its hyperparameters). The configuration action, denoted by a, is a vector a = [f,r,c].” Further, the execution environments are the resolutions r, and the latency and energy are resolution dependent (see section V part C, first paragraph describing energy and latency as dependent on “r”). Regarding claim 14 , Zhou teaches a first apparatus, (Fig 2 - - Zhou teaches a fog node) comprising: one or more processors and one or more memories, wherein the one or more memories are coupled to the one or more processors, the one or more memories are configured to store computer instructions, (Zhou; figure 2 - - Zhou teaches the fog node comprises memories and storage for processing the neural network algorithm and performing the adaptation functions). wherein in response to the one or more processors executing the computer instructions cause the one or more processors, to perform (Zhou; figure 2 - - wherein in response to execution of the AI neural network algorithm functions, the fog node comprising memories and storage, performs the adaptation functions). receiving (Zhou; section I, third paragraph & Fig 2 - - Zhou teaches the “Fog Node” (the interpreted as the first device) is what receives the data from the wireless camera). artificial intelligence (AI) model information (Zhou; section V part B - - Zhou teaches this information is received by the first device (Fog Node) and is embedded in the DRL agent during training (see Fig. 10 and section V part B). Further, this information is received by the first device once the DRL agent is commissioned for online execution (see section V, part B, read as “after the completion of the offline training, the DRL agent is commissioned to adapt the configuration of EFCam for industrial visual sensing tasks”). sent by a second apparatus, (Zhou; Fig. 2 & 3 - - the second device is the wireless camera (see fig. 2 & 10). The information is sent by this device during the modelling phase in Fig. 10, see section V part B, read as “we model the image processing latency and energy consumption based on real data traces collected from the deployment field”). wherein the AI model information includes M groups of AI model complexity information corresponding to M AI models, (Zhou; Fig. 2, section III part B and section V part B - - Zhou teaches Autoencoder, a deep learning-based image compression method. It has two-parts, encoder and decoder. Further, Zhou teaches the complexity information are execution time and latency, read as: “we model the image processing latency and energy consumption based on real data traces collected from the deployment field”). each of the M groups of AI model complexity information is time and/or energy consumption of executing one of the M AI models, (Zhou; section V part B - - Zhou teaches “we model the image processing latency and energy consumption based on real data traces collected from the deployment field”). in each of N reference AI execution environments (Zhou; section III part A and section V part A - - Zhou teaches the execution environments are represented by the wireless camera and its operating resolution “r”, read as “supports image capture with eight resolution levels” and “a resolution, denoted by “r”, as well as the first paragraph in section V part C which describes energy and latency as being dependent on “r”). and M and N are positive integers; (Zhou; Fig 2 - - Zhou teaches M in the number of encoders in Fig. 2, and N is the number of supported resolution levels). Regarding claim 15 , Zhou teaches the apparatus of claim 14, wherein the one or more processors (Zhou; section V, part A - - Zhou teaches an action “a” is performed (which is the configuration action/runtime action) at the current time step with a system state of x, which contains “c”, the local image processing mode.) are configured to send the feedback information that is usable to request the second apparatus to enable an AI communication mode; or send the feedback information that includes an evaluation result of at least one of the M AI models; or send the feedback information that is usable to request to obtain at least one of the M AI models. (Zhou; section V, part A - - Zhou teaches the camera performs the image scaling to resize the captured images to a resolution, denoted by r ∈ [rmin,rmax], where the rmin and rmax denote the minimum and maximum resolutions, respectively. We denote c as the local image processing mode (i.e., the JPEG with a quality index and autoencoder network with a setting for its hyperparameters)). Regarding claim 16 , Zhou teaches the apparatus of claim 14, wherein after sending feedback information to the second apparatus, one or more processors are configured to perform: receiving configuration information sent by the second apparatus, wherein the configuration information is usable to indicate the first apparatus to enable the AI communication mode, or the configuration information is usable to indicate at least one of the M AI models, or the configuration information is usable to indicate a configuration parameter of at least one of the M AI models, or the configuration information is usable to indicate a method for obtaining at least one of the M AI models. (Zhou; Figure 10: Runtime State, and section V part A - - The system state, denoted by x, is a vector x = [ξ,η], where ξ represents the image processing result [. . .] the ξ ∈ {0,1} indicates whether an object is detected (here, ξ is the “configuration parameter”)). Regarding claim 17 , Zhou teaches the apparatus of claim 15, wherein the at least one AI model includes a first AI model, and an evaluation result of the first AI model includes whether the first AI model matches the first apparatus, or an evaluation result of the first AI model includes expected time and/or energy consumption of executing of the first AI model by the first apparatus. (Zhou; Figure 10 and section V part A - - Zhou teaches the feedback is the runtime action of Figure 10 which comprises a first model. Section V part A, read as “We denote c as the local image processing mode (i.e., the JPEG with a quality index and autoencoder network with a setting for its hyperparameters). The configuration action, denoted by a, is a vector a = [f,r,c]”. Further, Zhou teaches the expected time (frames per adaptation period) of executing the AI model, read as “Let f ∈ [fmin, fmax] denote the number of frames per adaptation period τ, where fmin and fmax are the minimum and maximum number of image frames required by the application”. Regarding claim 18 , Zhou teaches a second apparatus, comprising one or more processors and one or more memories, wherein the one or more memories are coupled to the one or more processors, the one or more memories are configured to store computer program code, the computer program code comprises computer instructions, wherein in response to the one or more processors executing the computer instructions cause the one or more processors, to perform (Zhou; section II, paragraphs 1 & 2 - - Zhou teaches the implementation of a sensing front-end chip with embedded pre-processors on the focal-plane of the image sensors to extract and convert low-level features of the analog visual signal to a digital format. In addition, Zhou teaches the adaptation of a visual sensing approach that directly pipelines analog pixels straight from the camera to the wireless radio, which helps eliminate the need of the power-consuming hardware components (e.g., ADCs, codecs) as well as the memory for storing the images. obtaining artificial intelligence (AI) model information, (Zhou; section III, paragraph 1 and section A - - Zhou teaches the camera performs low-power image sensing, local processing for image feature extraction and compression, and data transmission. Further, Zhou teaches the strategy is to use the deep model-based techniques to locally process the raw images at the camera, thus obtaining information). wherein the AI model information includes M groups of AI model complexity information corresponding to M AI models, (Zhou; Fig. 2, section III part B and section V part B - - Zhou teaches Autoencoder, a deep learning-based image compression method. It has two-parts, encoder and decoder. Further, Zhou teaches the complexity information are execution time and latency, read as: “we model the image processing latency and energy consumption based on real data traces collected from the deployment field”). each of the M groups of AI model complexity information is time and/or energy consumption of executing one of the M AI models, (Zhou; section V part B - - Zhou teaches “we model the image processing latency and energy consumption based on real data traces collected from the deployment field”). in each of N reference AI execution environments (Zhou; section III part A and section V part A - - Zhou teaches the execution environments are represented by the wireless camera and its operating resolution “r”, read as “supports image capture with eight resolution levels” and “a resolution, denoted by “r”, as well as the first paragraph in section V part C which describes energy and latency as being dependent on “r”). and M and N are positive integers; (Zhou; Fig 2 - - Zhou teaches M in the number of encoders in Fig. 2, and N is the number of supported resolution levels). Regarding claim 19 , Zhou teaches the apparatus of claim 18, wherein after sending the AI model information to a first apparatus, the one or more processors are configured to perform: receiving feedback information sent by the first apparatus, wherein the feedback information is usable to request the second apparatus to enable an AI communication mode, or the feedback information includes an evaluation result of at least one of the M AI models, or the feedback information is usable to request to obtain at least one of the M AI models. (Zhou; section V, part A - - Zhou teaches the camera performs the image scaling to resize the captured images to a resolution, denoted by r ∈ [rmin,rmax], where the rmin and rmax denote the minimum and maximum resolutions, respectively. We denote c as the local image processing mode (i.e., the JPEG with a quality index and autoencoder network with a setting for its hyperparameters). The configuration action, denoted by a, is a vector a = [f,r,c]). Regarding claim 20 , Zhou teaches the apparatus of claim 18, wherein after receiving feedback information sent by the first apparatus, the one or more processors are configured is to perform: sending configuration information to the first apparatus, wherein the configuration information is usable to indicate the first apparatus to enable the AI communication mode, or the configuration information is usable to indicate at least one of the M AI models, or the configuration information is usable to indicate a configuration parameter of at least one of the M AI models, or the configuration information is usable to indicate a method for obtaining at least one of the M AI models. (Zhou; Figure 10: Runtime State, and section V part A - - The system state, denoted by x, is a vector x = [ξ,η], where ξ represents the image processing result [. . .] the ξ ∈ {0,1} indicates whether an object is detected (here, ξ is the “configuration parameter”)) . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 5 & 10 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou (EFCam: Configuration-Adaptive Fog-Assisted Wireless Cameras with Reinforcement Learning, publication date 06-09 July 2021, [online] URL: https://ieeexplore.ieee.org/document/9491609 (Year: 2021) (hereafter referred to as “Zhou”)), as applied to claims 1 & 6 above, in view of Krishnan, US 12041534 B2, hereinafter “Krishnan” . Regarding claim 5 , Zhou teaches the method of claim 1. Zhou fails to teach wherein before receiving, by the first apparatus, AI model information sent by a second apparatus, the method comprises: sending, by the first apparatus, request information to the second apparatus, wherein the request information is usable to request the second apparatus to send the AI model information to the first apparatus. However, Krishnan teaches the method of claim 1, wherein before receiving, by the first apparatus, AI model information sent by a second apparatus, the method comprises: sending, by the first apparatus, request information to the second apparatus, wherein the request information is usable to request the second apparatus to send the AI model information to the first apparatus (Krishnan; Fig 5 and col. 18, lines 20-24 - - Krishnan teaches the UE (interpreted as the first apparatus) sends an information request to the Network entity (interpreted as the second apparatus). Krishnan further teaches the Network entity (interpreted as the second apparatus) is triggered to send requested information as seen in step 525 of fig. 5).). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s claimed invention to have incorporated the teachings of Krishnan into the invention of Zhou to include the feature of sending, by the first apparatus, request information to the second apparatus, wherein the request information is usable to request the second apparatus to send the AI model information to the first apparatus in order to allow the first apparatus to initiate and control the timing of AI model information delivery on an as needed basis instead of relying just on network initiated transmission, thereby improving efficiency of AI model information exchange by reducing unnecessary transmissions of AI model information to devices that have not yet requested it (see Krishnan Fig 5 and col. 18, lines 20-24). Regarding claim 10 , Zhou teaches the method of claim 6. Zhou fails to teach wherein before the sending, by the second apparatus, the AI model information to the first apparatus, the method comprises: receiving, by the second apparatus, request information sent by the first apparatus, wherein the request information is usable to request the second apparatus to send the AI model information to the first apparatus. However, Krishnan teaches wherein before the sending, by the second apparatus, the AI model information to the first apparatus, the method comprises: receiving, by the second apparatus, request information sent by the first apparatus, wherein the request information is usable to request the second apparatus to send the AI model information to the first apparatus (Krishnan; Fig 5 and col. 18, lines 20-24 - - Krishnan teaches the UE (interpreted as the first apparatus) sends an information request to the Network entity (interpreted as the second apparatus). Krishnan further teaches the Network entity (interpreted as the second apparatus) is triggered to send requested information as seen in step 525 of fig. 5). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s claimed invention to have incorporated the teachings of Krishnan into the invention of Zhou to include the feature of sending, by the first apparatus, request information to the second apparatus, wherein the request information is usable to request the second apparatus to send the AI model information to the first apparatus in order to allow the first apparatus to initiate and control the timing of AI model information delivery on an as needed basis instead of relying just on network initiated transmission, thereby improving efficiency of AI model information exchange by reducing unnecessary transmissions of AI model information to devices that have not yet requested it (see Krishnan Fig 5 and col. 18, lines 20-24) . 07-21-aia AIA Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Zhou (EFCam: Configuration-Adaptive Fog-Assisted Wireless Cameras with Reinforcement Learning, publication date 06-09 July 2021, [online] URL: https://ieeexplore.ieee.org/document/9491609 (Year: 2021) (hereafter referred to as “Zhou”)), as applied to claim 7 above, in view of Pezeshki, US 12041692 B2, hereinafter “Pezeshki” . Regarding claim 12 , Zhou teaches the method of claim 7. Zhou fails to teach wherein the receiving the feedback information includes receiving feedback information that is response information of the AI model information. However, Pezeshki teaches wherein the receiving the feedback information includes receiving feedback information that is response information of the AI model information (Pezeshki; abstract and col. 2, lines 22-24 - - teaches the base station receives, from each of the number of UEs, a machine learning processing capability report. Further, the method reports, to the base station, a machine learning processing capability. The method also transmits, to the base station, gradient updates or weight updates to the machine learning model). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s claimed invention to have incorporated the teachings of Pezeshki into the invention of Zhou to include the feature of wherein the receiving the feedback information includes receiving feedback information that is response information of the AI model information in order to enable the second apparatus to assess whether the AI model it transmitted is compatible with and properly functioning at the first apparatus, thereby allowing the second apparatus to make informed decisions about subsequent AI model selection, configuration, or retraining based on real performance data reported by the first apparatus (see Pezeshki, abstract and col. 2, lines 22-24). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRES RAFAEL SANCHEZ whose telephone number is (571)272-8776. The examiner can normally be reached 7:30-9:00. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anthony Addy can be reached at 571-272-7795. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ANDRES RAFAEL SANCHEZ/Examiner, Art Unit 2645 /ANTHONY S ADDY/Supervisory Patent Examiner, Art Unit 2645 Application/Control Number: 18/661,006 Page 2 Art Unit: 2645 Application/Control Number: 18/661,006 Page 3 Art Unit: 2645 Application/Control Number: 18/661,006 Page 4 Art Unit: 2645 Application/Control Number: 18/661,006 Page 5 Art Unit: 2645 Application/Control Number: 18/661,006 Page 6 Art Unit: 2645 Application/Control Number: 18/661,006 Page 7 Art Unit: 2645 Application/Control Number: 18/661,006 Page 8 Art Unit: 2645 Application/Control Number: 18/661,006 Page 9 Art Unit: 2645 Application/Control Number: 18/661,006 Page 10 Art Unit: 2645 Application/Control Number: 18/661,006 Page 11 Art Unit: 2645 Application/Control Number: 18/661,006 Page 12 Art Unit: 2645 Application/Control Number: 18/661,006 Page 13 Art Unit: 2645 Application/Control Number: 18/661,006 Page 14 Art Unit: 2645 Application/Control Number: 18/661,006 Page 15 Art Unit: 2645 Application/Control Number: 18/661,006 Page 16 Art Unit: 2645 Application/Control Number: 18/661,006 Page 17 Art Unit: 2645 Application/Control Number: 18/661,006 Page 18 Art Unit: 2645 Application/Control Number: 18/661,006 Page 19 Art Unit: 2645
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