CTNF 18/358,288 CTNF 89990 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 § 112 07-30-02 AIA 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. 07-34-01 Claim 20 is 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. Re 20 : The claim says it depends from independent claim 1, yet it recites the preamble (“The NTRCM of”) independent claim 12. The claim is read as depending from claim 12. 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims are either directed to a system or a method, which is one of the statutory categories of invention. ( Step 1: YES ) The examiner has identified system Claim 1 as the claim that represents the claimed invention for analysis and is similar to Claim 12 . Claim 1 recites the limitations of ( additional elements emphasized in bold and are considered to be parsed from the remaining abstract idea): “ An apparatus to be employed as a machine learning training (MLT) management services (MnS) producer, the apparatus comprising: memory circuitry to store a machine learning (ML) model; and processor circuitry connected to the memory circuitry, wherein the processor circuitry is to operate an MLT function to: perform the ML model training using a training dataset; perform ML model validation using a validation dataset; generate an ML training report to include results of the ML model training and results the ML model validation; and send the ML training report to an MLT MnS consumer . ” Which is a process that, under its broadest which is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) as a Mental process (concept performed in the human mind) and Mathematical Concept (mathematical relationships, formulas or equations, or calculations) managing the training of a machine learning model by a management service (i.e., hierarchical arrangement of data) (see, e.g., Fig. 1 and ¶22-28). If a claim limitation, under its broadest reasonable interpretation (BRI), covers performance of the limitation as a certain method of a fundamental economic practice , then it falls within the “ Certain Methods of Organizing Human Activity ” grouping of abstract ideas. Similarly if a claim limitation under its BRI, covers performance of the limitation in the human mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. (Claims can recite a mental process even if they are claimed as being performed on a computer Gottschalk v. Benson , 409 U.S. 63; "Courts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015).) Accordingly, the claim recites an abstract idea . (Step 2A-Prong 1: YES. The claims are abstract) This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05.f), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h). In claim 1, the MnS, apparatus, processor circuitry, and memory circuitry are using generic computer components. And claim 12 additionally includes a non-transitory CRM (NTCRM). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to implement an abstract idea by adding the words “apply it” (or an equivalent) with the judicial exception. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claim 1 is directed to an abstract idea without a practical application. ( Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application ). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using computer hardware amounts to no more than mere instructions to implement an abstract idea by adding the words “apply it” (or an equivalent) with the judicial exception. Mere instructions to implement an abstract idea on or with the use of generic computer components, cannot provide an inventive concept - rendering the claim patent ineligible. Thus claim 8 is not patent eligible. (St ep 2B: NO. The claims do not provide significantly more ) The dependent claims further define the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above. The dependent claims do not include any additional elements ( dependent claims discussing different functions or types of applications/servers which, broadly read are all generic computer components that further implement the abstract idea) that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination: The dependent claims recite further steps that can be performed in the human mind. Therefore, the dependent claims are directed to an abstract idea. Thus, the aforementioned claims are not patent-eligible. 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lella et al. US 12353379 (“Lella”) in view of 3rd Generation Partnership Project; Technical Specification Group Services and System Aspects; Management and orchestration; Study on Artificial Intelligence / Machine Learning (AI/ML) management (Release 18) (available July 8, 2022) (“3GPP,” cited by applicant) . Re 1 : Lella teaches an apparatus to be employed as a machine learning training (MLT) management services (MnS) producer, the apparatus (claims 1-2, 14) comprising: memory circuitry 1020 to store a machine learning (ML) model (Fig. 8; col. 14 ll 22-42); and processor circuitry 1010 connected to the memory circuitry (Fig. 8; col. 14 ll 22-42), wherein the processor circuitry 1010 is to operate an MLT function to (claims 1, 14-15, 17, and 19): perform the ML model training using a training dataset (claims 1, 14-15, 17, and 19); perform ML model validation using a validation dataset (claims 1, 14-15, 17, and 19: recites the standard ML practice of using test, training, and validation data sets during training and adjustment via loss functions and backpropagation ); generate an ML training report to include results of the ML model training and results the ML model validation (claims 1, 14-15, 17, and 19: this is the result of training and measuring loss versus a previous epoch ); and Lella does not explicitly teach, yet 3GPP teaches (§§4.1-5.8): Claim 1 : send the ML training report to an MLT MnS consumer (§ 5.3.2.1: report validation to consumer for awareness; §4.2: supports training-to-inference transition). Claim 2 : wherein the MLT report IOC includes a model performance training attribute, wherein the model performance training attribute includes the results the ML model training (§ 5.1.2.1: defines indicators as structured attributes like training/validation). Claim 3 : wherein the MLT report IOC includes a model performance training attribute, wherein the model performance training attribute includes the results of the ML model training (§5.1.1-5.3.2.1: collect/analyze indicators for “healthy” training). Claim 4 : wherein the results of the ML model training includes, for each inference output by the ML model when performing on the training dataset, a training performance metric used to evaluate a performance of the ML model during the ML model training and a corresponding training performance score for the training performance metric (§5.1.2.1, 5.3.1: model-related indicators like precision for evaluation and assess performance during training to ensure optimal training). Claim 5 : wherein the model performance training attribute also includes the results of the ML model validation (§4.2: validation evaluates “performance variance” alongside training; §5.1.1: alert management to ensure issues are corrected in time). Claim 6 : wherein the results of the ML model validation includes, for each inference output by the ML model when performing on the validation dataset, a validation performance metric used to evaluate a performance of the ML model during the ML model validation and a corresponding validation performance score for the validation performance metric (§4.2: evaluate validation data; §5.3.2.1: validation indicators for performance reporting). Claim 7 : wherein the MLT report IOC includes a model performance validation attribute separate from the model performance training attribute, wherein the model performance validation attribute includes the results of the ML model validation (§5.1.2.1, 5.3: dedicated validation section/reporting, distinct from general training as well as reporting validation indicators). Claim 8 : wherein the results of the ML model validation includes, for each inference output by the ML model when performing on the validation dataset, a validation performance metric used to evaluate a performance of the ML model during the ML model validation and a corresponding validation performance score for the validation performance metric (§4.2, 5.3.1: variance on validation data reported as validation as part of performance management). Claim 9 : wherein the MLT MnS producer is a network function (NF), a management function (MF), an application function (AF), a radio access network (RAN) function, an edge compute node, an application server, or a cloud computing service (§4.1: AI/ML in RAN and AF in order to “maximise efficiency and bring intelligence.”). Claim 10 : wherein the MLT MnS consumer is an NF, an MF, an AF, a RAN function, an edge compute node, an application server, or a cloud computing service (§5.8: capability discovery/mapping for consumers like NFs/MFs). Claim 11 : wherein the MLT MnS producer is a Network Data Analytics Function (NWDAF) containing model training logical function (MTLF) and the MLT MnS consumer is an NWDAF containing analytics logical function (AnLF) (§4.1-4.2: NWDAF for analytics/inference and MTLF-like training with consumer reporting for inference phases). 3GPP teaches that reporting in the various methods detailed above allow for better performance by ensuring up-to-date information about the training cycles of a ML are quickly and efficiently reported via a MnS. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to combine Lella with 3GPP's teachings in order to ensure the training lifecycle of a ML is reported so that high-level executive decisions can timely be made to optimize the training of said ML. Re 12 : Lella teaches: A non-transitory computer-readable medium (NTCRM) 1020 comprising instructions for operating a machine learning training (MLT) function, wherein execution of the instructions by one or more processors is to cause an MLT management services (MnS) producer to (claims 1, 14-15, 17, and 19): train a machine learning (ML) model using a training dataset (claims 1, 14-15, 17, and 19); validate the ML model using a validation dataset (claims 1, 14-15, 17, and 19: recites the standard ML practice of using test, training, and validation data sets during training and adjustment via loss functions and backpropagation ); generate an ML training report to include training results based on training the ML model and validation results based on the validation of the ML model ; and send the ML training report to an MLT MnS consumer. Lella does not explicitly teach, yet 3GPP teaches (§§4.1-5.8): Claim 12 : send the ML training report to an MLT MnS consumer (§ 5.3.2.1: report validation to consumer for awareness; §4.2: supports training-to-inference transition). Claim 13 : wherein the MLT report IOC includes a model performance training attribute (§ 5.1.2.1: defines indicators as structured attributes like training/validation). Claim 14 : wherein the model performance training attribute includes the results the ML model training (§ 5.1.2.1: defines indicators as structured attributes like training/validation). Claim 15 : wherein the training results include respective training performance metrics for each inference output by the ML model when performing on the training dataset and corresponding training performance scores for each of the respective training performance metrics (§5.1.2.1, 5.3.1: model-related indicators like precision for evaluation and assess performance during training to ensure optimal training). Claim 16 : wherein the model performance training attribute also includes the validation results (§4.2: validation evaluates “performance variance” alongside training; §5.1.1: alert management to ensure issues are corrected in time). Claim 17 : wherein the validation results include respective validation performance metrics for each inference output by the ML model when performing on the validation dataset and corresponding validation performance scores for each of the respective validation performance metrics (§4.2: evaluate validation data; §5.3.2.1: validation indicators for performance reporting). Claim 18 : wherein the MLT report IOC includes a model performance validation attribute separate from the model performance training attribute, wherein the model performance validation attribute includes the validation results (§5.1.2.1, 5.3: dedicated validation section/reporting, distinct from general training as well as reporting validation indicators). Claim 19 : wherein the validation results include respective validation performance metrics for each inference output by the ML model when performing on the validation dataset and corresponding validation performance scores for each of the respective validation performance metrics (§4.2, 5.3.1: variance on validation data reported as validation as part of performance management). Claim 20 : wherein the MLT MnS producer is a network function (NF), a management function (MF), an application function (AF), a radio access network (RAN) function, an edge compute node, an application server, or a cloud computing service, and wherein the MLT MnS consumer is another NF, another MF, another AF, another RAN function, the edge compute node, another edge compute node, the application server, another application server, the cloud computing service, or another cloud computing service (§4.1-4.2: NWDAF for analytics/inference and MTLF-like training with consumer reporting for inference phases). 3GPP teaches that reporting in the various methods detailed above allow for better performance by ensuring up-to-date information about the training cycles of a ML are quickly and efficiently reported via a MnS. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to combine Lella with 3GPP's teachings in order to ensure the training lifecycle of a ML is reported so that high-level executive decisions can timely be made to optimize the training of said ML. Conclusion Relevant prior art considered : US 20250355779 teaching an apparatus is disclosed, said apparatus comprising means for determining, for a given use case, a behavioral requirement policy for a machine learning model, means for providing an indication of the behavioral requirement policy to an analytics producer and means for receiving, from the analytics producer, a performance evaluation metric determined based on the behavioral requirement policy. Any inquiry concerning this communication or earlier communications from the examiner should be directed to GERALD J SUFLETA II whose telephone number is (571)272-4279. The examiner can normally be reached M-F 9AM-6PM EDT/EST. 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, ABDULMAJEED AZIZ can be reached at (571) 270-5046. 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. GERALD J. SUFLETA II Primary Examiner Art Unit 2875 /GERALD J SUFLETA II/Primary Examiner, Art Unit 2875 Application/Control Number: 18/358,288 Page 2 Art Unit: 2875 Application/Control Number: 18/358,288 Page 3 Art Unit: 2875 Application/Control Number: 18/358,288 Page 4 Art Unit: 2875 Application/Control Number: 18/358,288 Page 5 Art Unit: 2875 Application/Control Number: 18/358,288 Page 6 Art Unit: 2875 Application/Control Number: 18/358,288 Page 7 Art Unit: 2875 Application/Control Number: 18/358,288 Page 8 Art Unit: 2875 Application/Control Number: 18/358,288 Page 9 Art Unit: 2875 Application/Control Number: 18/358,288 Page 10 Art Unit: 2875 Application/Control Number: 18/358,288 Page 11 Art Unit: 2875 Application/Control Number: 18/358,288 Page 12 Art Unit: 2875