CTNF 19/102,682 CTNF 92362 DETAILED ACTION Claims 1-5, 7-10, 14-18, 20-23 and 29-30 have been examined and are rejected. Claims 6, 11-13, 19, 24-28 and 31-32 are canceled. 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. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-5, 7-10, 14-18, 20-23 and 29-30 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. claims 1, 14, 29 and 30 satisfy Step 1 because the claims are processes or machines. In Step 2A prong 1, the claim 1 recites “monitoring an inference performance of an artificial intelligence (Al) model, in response to determining that air interface communication is performed between the terminal and a network device based on the Al model”, which, under the broadest reasonable interpretation, is a step that is performed in the human mind. Claim 1 does not recite limitations that explain how the monitoring or determining steps are carried out. Paragraph 00120 of the specification as filed states “The terminal compares the inference result based on the AI model with the reference state to complete the monitoring of the inference performance of the AI model”. Therefore, the monitoring step is merely comparing information. Comparing information based on a decision (determination or judgement that air interface communication is performed) is performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Claims 14 and 29-30 recite similar limitations and are also directed to the abstract idea. In Step 2A prong 2, the judicial exception is not integrated into a practical application because terminal, network device, processor and memory are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. The claims, as a whole, are directing to monitoring or comparing data based on a determination. Merely monitoring information does not improve the function of the AI model, terminal or network device. In Step 2B, the claim do not include additional elements that are sufficient to amount to significantly more than the judicial exception because memory and processor are general purpose computer components, which are well-understood, routine and conventional (see Decasper et al. (U.S. PGPub 2007/0192474) paragraph 0004 where include conventional components such as a processor, a memory (e.g., RAM)… a network interface, such as a conventional modem), performing the steps recited in the claims and are not sufficient to transform a judicial exception into a patentable invention. Regarding claims 2-5, 7-10, 15-18 and 20-23, claims 2-5, 7-10, 15-18 and 20-23 recite the features of “monitoring…”, “determining…”, “intermittently monitoring…”, “updating the time interval…” and “sending”/”receiving…”. However, as stated above, “monitoring” and “determining” are mental processes. Updating the time interval merely change timing in which the mental process is performed. The sending or receiving steps are merely transmitting or receiving information, which are well-understood, routine and conventional and are insignificant extra-solution activities (see MPEP 2106.05(d)). Therefore, claims 2-5, 7-10, 15-18 and 20-23 do not add meaningful limitation to the abstract idea. The elements recited in claims 1-5, 7-10, 14-18, 20-23 and 29-30, when considered individually or in an ordered combination, fail to amount to significantly more than the abstract idea. Accordingly, claims 1-5, 7-10, 14-18, 20-23 and 29-30 are not eligible. 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 (i.e., changing from AIA to pre-AIA) 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 – 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim s 1-3, 8, 10, 14-16, 21, 23 and 29-30 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Jin et al. (U.S. PGPub 2023/0412513) . Regarding claims 1, 14 and 29-30, Jin teaches A communication method, performed by a terminal, comprising: monitoring an inference performance of an artificial intelligence (Al) model, in response to determining that air interface communication is performed between the terminal and a network device based on the Al model. (Jin, see figs. 6A-6C; see paragraphs 103-0111 where UE attaches and triggers an AI application/model, and the UE transmits a request for a distributed AI model through the AMF to the SMF…deployment can thus be conducted using the PDU session specially established for the distributed AI service...Operation 608: The UE generates a local AI inference result based on the local model portions of the AI model...AN node (e.g., gNB) performs an LAI translated-MDM QoS KPI measurement procedure based on the local AI inference result of operation 608 and based on a KPI trigger threshold comparison and/or in-advance trigger time...) Regarding claims 2 and 15, Jin teaches wherein monitoring the inference performance of the Al model comprises: monitoring the inference performance of the Al model based on an inference result of the Al model. (Jin, see figs. 6A-6C; see paragraphs 109-0114 where … The UE generates a local AI inference result based on the local model portions of the AI model (i.e., [Local Model portion, Local model weight]) and transmits the local AI inference result to the AN node (e.g., to the gNB)...The AN node (e.g., gNB) performs an LAI translated-MDM QoS KPI measurement procedure based on the local AI inference result of operation 608 and based on a KPI trigger threshold comparison and/or in-advance trigger time. ...UPF transmits a cloud AI inference result to the application server based on the EAI translated-MDM QoS KPI measurement procedure...) Regarding claims 3 and 16, Jin teaches wherein monitoring the inference performance of the Al model based on the inference result of the Al model comprises: determining an inference object corresponding to the inference result of the Al model; and (Jin, see figs. 6A-6C; see paragraphs 109-0114 where … The UE generates a local AI inference result based on the local model portions of the AI model (i.e., [Local Model portion, Local model weight]) and transmits the local AI inference result to the AN node (e.g., to the gNB)...The AN node (e.g., gNB) performs an LAI translated-MDM QoS KPI measurement procedure based on the local AI inference result of operation 608 and based on a KPI trigger threshold comparison and/or in-advance trigger time. ...UPF transmits a cloud AI inference result to the application server based on the EAI translated-MDM QoS KPI measurement procedure...) monitoring the inference performance of the Al model based on an actual state of the inference object and the inference result of the Al model. (Jin, see figs. 6A-6C; see paragraphs 109-0114 where … The UE generates a local AI inference result based on the local model portions of the AI model (i.e., [Local Model portion, Local model weight]) and transmits the local AI inference result to the AN node (e.g., to the gNB)...The AN node (e.g., gNB) performs an LAI translated-MDM QoS KPI measurement procedure based on the local AI inference result of operation 608 and based on a KPI trigger threshold comparison and/or in-advance trigger time. ...UPF transmits a cloud AI inference result to the application server based on the EAI translated-MDM QoS KPI measurement procedure...; see also figs. 2A-2F and paragraph 0072 contain the model performance metrics when it is deployed in a distributed system in its dynamic part. Referring to FIGS. 2A, 2B, 2C, 2D, 2E, and 2F, this information includes local accuracy and inference time, edge accuracy and inference time and individual accuracy and inference time...) Regarding claims 8 and 21, Jin teaches further comprising: determining a monitoring result obtained by monitoring the inference performance of the Al model; and sending the monitoring result to the network device. (Jin, see figs. 6A-6C; see paragraphs 109-0114 where … The UE generates a local AI inference result based on the local model portions of the AI model (i.e., [Local Model portion, Local model weight]) and transmits the local AI inference result to the AN node (e.g., to the gNB)...The AN node (e.g., gNB) performs an LAI translated-MDM QoS KPI measurement procedure based on the local AI inference result of operation 608 and based on a KPI trigger threshold comparison and/or in-advance trigger time. ...UPF transmits a cloud AI inference result to the application server based on the EAI translated-MDM QoS KPI measurement procedure...) Regarding claims 10 and 23, Jin teaches wherein an inference node of the Al model is the terminal or the network device, and (Jin, see figs. 6A-6C; see paragraphs 103-0111 where UE attaches and triggers an AI application/model, and the UE transmits a request for a distributed AI model through the AMF to the SMF…deployment can thus be conducted using the PDU session specially established for the distributed AI service...Operation 608: The UE generates a local AI inference result based on the local model portions of the AI model...AN node (e.g., gNB) performs an LAI translated-MDM QoS KPI measurement procedure based on the local AI inference result of operation 608 and based on a KPI trigger threshold comparison and/or in-advance trigger time...) a performance monitoring node of the Al model is the terminal or the network device; (Jin, see figs. 6A-6C; see paragraphs 103-0111 where UE attaches and triggers an AI application/model, and the UE transmits a request for a distributed AI model through the AMF to the SMF…deployment can thus be conducted using the PDU session specially established for the distributed AI service...Operation 608: The UE generates a local AI inference result based on the local model portions of the AI model...AN node (e.g., gNB) performs an LAI translated-MDM QoS KPI measurement procedure based on the local AI inference result of operation 608 and based on a KPI trigger threshold comparison and/or in-advance trigger time...) in a case that the inference node of the Al model is the network device, and the performance monitoring node of the Al model is the terminal, the method further comprises receiving an inference result of the Al model sent by the network device; and (Jin, see figs. 6A-6C; see paragraphs 109-0114 where … The UE generates a local AI inference result based on the local model portions of the AI model (i.e., [Local Model portion, Local model weight]) and transmits the local AI inference result to the AN node (e.g., to the gNB)...The AN node (e.g., gNB) performs an LAI translated-MDM QoS KPI measurement procedure based on the local AI inference result of operation 608 and based on a KPI trigger threshold comparison and/or in-advance trigger time. ...UPF transmits a cloud AI inference result to the application server based on the EAI translated-MDM QoS KPI measurement procedure...) in a case that the inference node of the Al model is the terminal, and the performance monitoring node of the Al model is the network device, the method further comprises sending the inference result of the Al model to the network device. (Jin, see figs. 6A-6C; see paragraphs 109-0114 where … The UE generates a local AI inference result based on the local model portions of the AI model (i.e., [Local Model portion, Local model weight]) and transmits the local AI inference result to the AN node (e.g., to the gNB)...The AN node (e.g., gNB) performs an LAI translated-MDM QoS KPI measurement procedure based on the local AI inference result of operation 608 and based on a KPI trigger threshold comparison and/or in-advance trigger time. ...UPF transmits a cloud AI inference result to the application server based on the EAI translated-MDM QoS KPI measurement procedure...) Claim Rejections - 35 USC § 103 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. 07-21-aia AIA Claim s 4-5 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Jin in view of Soldati et al. (U.S. PGPub 2024/0243984) . Regarding claims 4 and 17, Jin teaches all of the features of claims 1 and 14. However, Jin does not explicitly teach wherein monitoring the inference performance of the Al model comprises: intermittently monitoring the inference performance of the Al model at a time interval. Soldati teaches wherein monitoring the inference performance of the Al model comprises: intermittently monitoring the inference performance of the Al model at a time interval. (Soldati , see figs. 1 and 3-4; see paragraphs 0025-0026 a model inference function associated with the AI/ML model or algorithm to be monitored, one or more indications of a time or period of the collection of data...the indication of the type of data requested comprises one or more of historical inference input data associated with the AI/ML model to be monitored...requested historical data comprises one or more of periods of collection, data selected in a random fashion... a periodic sending with a reporting (monitoring) periodicity...) It would have been obvious to one of ordinary skill in the art, at the time the invention was filed, to combine Jin and Soldati to provide the technique of intermittently monitoring the inference performance of the Al model at a time interval of Soldati in the system of Jin in order to optimize the performance of an AI/ML model or algorithm deployed in a communication network (Soldati, see paragraph 0105). Regarding claims 5 and 18, Jin-Soldati teaches wherein intermittently monitoring the inference performance of the Al model at the time interval comprises at least one of: intermittently monitoring the inference performance of the Al model based on a preconfigured monitoring cycle; intermittently monitoring the inference performance of the Al model at a set time interval based on a preconfigured number of times of inference; or receiving a monitoring instruction sent by the network device at a set time interval, and intermittently monitoring the inference performance of the Al model based on the monitoring instruction. (Soldati , see figs. 1 and 3-4; see paragraphs 0025-0026 a model inference function associated with the AI/ML model or algorithm to be monitored, one or more indications of a time or period of the collection of data...the indication of the type of data requested comprises one or more of historical inference input data associated with the AI/ML model to be monitored...requested historical data comprises one or more of periods of collection, data selected in a random fashion... a periodic sending with a reporting (monitoring) periodicity...; see paragraph 0030 a criterion indicating that the AI/ML model should be retrained if at least one model performance metric is less than or exceeds a first threshold...performance metric is less than a first threshold for at least a pre-defined period of time) The motivation regarding to claims 4 and 17 is also applied to claims 5 and 18 . 07-21-aia AIA Claim 7 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jin-Soldati in view of Chen et al. (U.S. PGPub 2022/0114014) . Regarding claims 7 and 20, Jin-Soldati teaches all of the features of claims 4 and 17. However, Jin-Soldati does not explicitly teach further comprising: obtaining a communication state of the terminal; and updating the time interval based on the communication state of the terminal. Chen teaches further comprising: obtaining a communication state of the terminal; and (Chen, see figs. 8A-8B; see paragraph 0090-91 request AI inferences. For example, the processor can log the number of times an AI inference is requested over a period of time, such as one week...requested three times a week, the processor can, at the appropriate time, determine that three sets of obfuscated model parameters are to be created for use with the image detection AI model...provide that the precomputations are only performed once a week to replenish a secure storage exhausted of obfuscated model parameters. In embodiments where precomputations are performed more frequently, such as if the settings provide that sets can be created every time the device is idle or charging, two more sets can be precomputed if the processor determines that three sets are to be maintained in the secure storage but only one set remains.; see paragraph 0092 request more AI inferences than normal during a period of time) updating the time interval based on the communication state of the terminal. (Chen, see figs. 8A-8B; see paragraph 0090-91 request AI inferences. For example, the processor can log the number of times an AI inference is requested over a period of time, such as one week...requested three times a week, the processor can, at the appropriate time, determine that three sets of obfuscated model parameters are to be created for use with the image detection AI model...provide that the precomputations are only performed once a week to replenish a secure storage exhausted of obfuscated model parameters. In embodiments where precomputations are performed more frequently, such as if the settings provide that sets can be created every time the device is idle or charging, two more sets can be precomputed if the processor determines that three sets are to be maintained in the secure storage but only one set remains.; see paragraph 0092 request more AI inferences than normal during a period of time) It would have been obvious to one of ordinary skill in the art, at the time the invention was filed, to combine Jin-Soldati and Chen to provide the technique of obtaining a communication state of the terminal, and updating the time interval based on the communication state of the terminal of Chen in the system of Jin-Soldati in order to protect certain aspect of the AI model and reduce risk of copying and unauthorized use or reduce privacy risks (Chen, see paragraphs 0002 and 0034) . 07-21-aia AIA Claim s 9 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Jin in view of Chen et al. (U.S. PGPub 2022/0114014) . Regarding claims 9 and 22, Jin teaches all of the features of claims 8 and 21. However, Jin does not explicitly teach wherein sending the monitoring result to the network device comprises at least one of: in response to determining that the monitoring result comprises more than one monitoring result, sending the more than one monitoring result to the network device respectively; in response to determining that the monitoring result comprises more than one monitoring result, sending an inference performance result to the network device, wherein the inference performance result is obtained based on the more than one monitoring result; in response to the monitoring result meeting a preset condition, sending the monitoring result meeting the preset condition to the network device; cyclically sending the monitoring result to the network device based on a preconfigured inference performance reporting cycle; or sending the monitoring result to the network device based on a reporting instruction sent by the network device. Chen teaches wherein sending the monitoring result to the network device comprises at least one of: in response to determining that the monitoring result comprises more than one monitoring result, sending the more than one monitoring result to the network device respectively; in response to determining that the monitoring result comprises more than one monitoring result, sending an inference performance result to the network device, wherein the inference performance result is obtained based on the more than one monitoring result; in response to the monitoring result meeting a preset condition, sending the monitoring result meeting the preset condition to the network device; cyclically sending the monitoring result to the network device based on a preconfigured inference performance reporting cycle; or sending the monitoring result to the network device based on a reporting instruction sent by the network device. (Chen, see figs. 5-6 and 8A-8B; see paragraph 0090-91 request AI inferences. For example, the processor can log the number of times an AI inference is requested over a period of time, such as one week...requested three times a week, the processor can, at the appropriate time, determine that three sets of obfuscated model parameters are to be created for use with the image detection AI model...provide that the precomputations are only performed once a week to replenish a secure storage exhausted of obfuscated model parameters.; see paragraphs 0075-0076 the TEE provides the inference result to the AI service in the non-TEE to output the inference result to the client.) It would have been obvious to one of ordinary skill in the art, at the time the invention was filed, to combine Jin-Soldati and Chen to provide the technique of sending the monitoring result to the network device comprises at least one of: in response to determining that the monitoring result comprises more than one monitoring result, sending the more than one monitoring result to the network device respectively; in response to determining that the monitoring result comprises more than one monitoring result, sending an inference performance result to the network device, wherein the inference performance result is obtained based on the more than one monitoring result; in response to the monitoring result meeting a preset condition, sending the monitoring result meeting the preset condition to the network device; cyclically sending the monitoring result to the network device based on a preconfigured inference performance reporting cycle; or sending the monitoring result to the network device based on a reporting instruction sent by the network device of Chen in the system of Jin-Soldati in order to protect certain aspect of the AI model and reduce risk of copying and unauthorized use or reduce privacy risks (Chen, see paragraphs 0002 and 0034) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. This includes: U.S. PGPub 2022/0083389, which describes AI inference hardware resource scheduling; U.S. PGPub 2022/0286927, which describes a method and apparatus for support of machine learning or artificial intelligence techniques for handover management in communication system; and U.S. PGPub 2019/0042955, which describes systems and methods of initiating and performing contextualized AI inferencing. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MENG VANG whose telephone number is (571)270-7023. The examiner can normally be reached M-F 8AM-2PM, 3PM-5PM. 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, NICHOLAS TAYLOR can be reached at (571) 272-3889. 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. /MENG VANG/Primary Examiner, Art Unit 2443 Application/Control Number: 19/102,682 Page 2 Art Unit: 2443 Application/Control Number: 19/102,682 Page 3 Art Unit: 2443 Application/Control Number: 19/102,682 Page 4 Art Unit: 2443 Application/Control Number: 19/102,682 Page 5 Art Unit: 2443 Application/Control Number: 19/102,682 Page 6 Art Unit: 2443 Application/Control Number: 19/102,682 Page 7 Art Unit: 2443 Application/Control Number: 19/102,682 Page 8 Art Unit: 2443 Application/Control Number: 19/102,682 Page 9 Art Unit: 2443 Application/Control Number: 19/102,682 Page 12 Art Unit: 2443 Application/Control Number: 19/102,682 Page 13 Art Unit: 2443 Application/Control Number: 19/102,682 Page 14 Art Unit: 2443 Application/Control Number: 19/102,682 Page 15 Art Unit: 2443 Application/Control Number: 19/102,682 Page 16 Art Unit: 2443
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