CTNF 18/235,350 CTNF 77129 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. This Office Action is in response to the submission filed August 18, 2023. Claims 1-9 are pending. Information Disclosure Statement The information disclosure statement (IDS) submitted on August 18, 2023 is being considered by the examiner. Drawings 06-36-01 AIA Figure s 6-11 should be designated by a legend such as --Prior Art-- because only that which is old is illustrated. See MPEP § 608.02(g). Corrected drawings in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. The replacement sheet(s) should be labeled “Replacement Sheet” in the page header (as per 37 CFR 1.84(c)) so as not to obstruct any portion of the drawing figures. If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance. 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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 4 and 7 are directed to computer readable medium, method and apparatus. Claims 1, 4, and 7 recite limitations for obtaining a first projection matrix that is obtained in an n-th iteration of the machine learning processing and that indicates a correspondence between input data inputted from the natural language processing model to the classification model and output data outputted from the classification model, is a step directed to performing mathematical calculations to generate the matrix based on applying natural language processing rules and techniques to convert input into contextualized tokens and further processed to determine the classification output to be used to determine the correspondence; updating a parameter of the natural language processing model is a data organization/manipulation step that can be achieved by the person adjusting the parameters applied using the natural language processing rules and techniques; updating a parameter of the classification model by using the first projection matrix can be achieved reviewing the matrix results and adjusting the parameters of the classification model; and obtaining, in an n+1-th iteration of the machine learning processing, a second projection matrix that indicates a correspondence between input data inputted from the updated natural language processing model to the updated classification model and output data outputted from the updated classification model is a step directed to performing mathematical calculations to generate the updated matrix based on applying natural language processing rules and techniques to convert input into contextualized tokens using the updated natural language model and further processed to determine the updated classification output to be used to determine the updated correspondence, wherein the n is a natural number is a data organization step that is achieved by the person performing the steps more than once. The recited limitations are directed a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of the generic computer, apparatus, computer program product, and generic computer components. 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 claims recite an abstract idea. This judicial exception is not integrated into a practical application because the recited generic apparatus, medium, memory, processor and instructions amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, the elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The claims are not patent eligible. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as indicated with respect to integration of the abstract idea into a practical application, the additional elements of the generic apparatus, medium, memory, processor and instructions to perform the various steps amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claims are not patent eligible. Dependent claims 2-3, 5-6, and 8-9 do not integrate the judicial exception into a practical application and do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations of the dependent claims are directed to steps of organizing or manipulating data and performing mathematical calculations. 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 (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. 07-20-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 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over Applicant’s Admitted Prior Art (AAPA) in view of Gururangan et al (“Don’t Stop Pre-training: Adapt Language Models to Domains and Tasks), herein after Gururangan . AAPA [Fig 11 and para 0029-0034 of the filed specification], as disclosed in the specification, FIG. 11 is a diagram for explaining the machine learning processing of the machine learning model of related art, where in FIG. 11, a machine learning model 25 includes a natural language model 20 and a classification model 21. An output result of the natural language model 20 is input to the classification model 21. For comparison, fig 11 and fig 1 an apparatus for applicant’s embodiment of the invention are provided. PNG media_image1.png 858 748 media_image1.png Greyscale PNG media_image2.png 732 594 media_image2.png Greyscale Regarding claim 1, AAPA provides for a non-transitory computer-readable recording medium storing a machine learning program for causing a computer to execute a process for machine learning processing of a machine learning model in which a natural language processing model and a classification model are combined [fig 11], the process comprising: obtaining a first projection matrix [projection matrix A] that is obtained in an n-th iteration of the machine learning processing and that indicates a correspondence between input data inputted from the natural language processing model [element 20] to the classification model [element 21] and output data outputted from the classification model [para 0033 -- The projection matrix A is trained from the relationship between X s input to the classification model 21 and “Y′ s output from the classification model ]; updating a parameter of the classification model by using the first projection matrix [para 0032 – fine tuning the classification model]; and obtaining, in an n+1-th iteration of the machine learning processing, a second projection matrix that indicates a correspondence between input data inputted from the natural language processing model to the updated classification model and output data outputted from the updated classification model [para 0033 -- The projection matrix A is trained from the relationship between X s input to the classification model 21 and “Y′ s output from the classification model ], wherein the n is a natural number [where training the model and matrix provide for multiple iterations of a natural number value]. AAPA fails to teach updating a parameter of the natural language processing model. In a similar field of endeavor, Gururangan teaches language model adaptation, where parameters of the pre-trained language model are fine tuned for different classification tasks [sec. 3,2 Experiments] and specifically teaches the process provides improvements in system performance [abstract]. Therefore, one having ordinary skill in the art at the time of he invention would have recognized the advantages of fine tuning the parameters of the language model, as suggested by Gururangan, in the system of AAPA, for the purpose of improving system performance, as suggested by Gururangan. Regarding claim 2, the combination of AAPA and Gururangan teaches, wherein the output data outputted from the classification model is obtained based on the first projection matrix and the input data inputted to the classification model [AAPA – specification Fig 11 and para 0029-0034]. Regarding claim 3, the combination of AAPA and Gururangan teaches in the updating of the parameter of the classification model, the parameter of the classification model is updated based on an error between the obtained output data and the output data outputted from the classification model when the input data is inputted to the classification model [AAPA – specification fig 11/ para 0032]. Claims 4-6 and 7-9 are rejected under similar rationale as claims 1-3. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANGELA A ARMSTRONG whose telephone number is (571)272-7598. The examiner can normally be reached M,T,TH,F 11:30-8:00. 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, Pierre Desir can be reached at 571-272-7799. 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. ANGELA A. ARMSTRONG Primary Examiner Art Unit 2659 /ANGELA A ARMSTRONG/ Primary Examiner, Art Unit 2659 Application/Control Number: 18/235,350 Page 2 Art Unit: 2659 Application/Control Number: 18/235,350 Page 3 Art Unit: 2659 Application/Control Number: 18/235,350 Page 4 Art Unit: 2659 Application/Control Number: 18/235,350 Page 5 Art Unit: 2659 Application/Control Number: 18/235,350 Page 7 Art Unit: 2659 Application/Control Number: 18/235,350 Page 8 Art Unit: 2659 Application/Control Number: 18/235,350 Page 9 Art Unit: 2659
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