CTFR 18/008,305 CTFR 95038 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. 12-151 AIA 26-51 12-51 Status of Claims The following is a final office action. Claims [1-6] are currently pending and have been examined on their merits. Claims 1-3 and 5 are currently amended see REMARKS February 09, 2026. 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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception that is an abstract idea without a practical application or significantly more. Step 1: Claims 1-2 recite an information processing apparatus, claims 3-4 recite a method (i.e. a process such as an act or series of steps), and claims 5-6 recite a non-transitory computer-readable medium, and therefore each claim falls within one of the four statutory categories. Step 2A prong 1 (Is a judicial exception recited?): The representative claims 1, 3, and 5 recite: determining an encryption target from a plurality of weights, initialize a selected weight set with an empty set; perform a weight selection process comprising: selecting from among the plurality of weights, a weight of an encryption target and adding the selected weight in the selected weight set, changing the selected weight, evaluating an inference accuracy with the changed weight, and repeating the weight selection process until the inference accuracy reaches a target accuracy or less; and encrypt only the weights stored in the selected weight set. Alternatively, the claims recite a mental process . The examiner finds the claims to be similar to a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis." The claims merely recite a method for selecting and changing values of information such as weights to be used in processing information such as determining an encryption target when an inference accuracy of an algorithm reaches a target accuracy. Therefore, the examiner finds the claims to be similar to examples the courts have identified as reciting a mental process including observations, evaluations, judgements, and opinions. As the method of merely selecting values for weights of an input and evaluating the output of an algorithm are steps that a person is capable of performing mentally or by using simple tools such as pen and paper. Therefore, the examiner finds the claims to be directed to an abstract idea. Step 2A Prong 2 (Is the exception integrated into a practical application?): The claims additionally recite; Claim 1: An information processing apparatus, a model trained by deep learning, the information processing apparatus comprising: a memory storing one or more instructions; and a processor configured to executed to one or more instructions, changing the selected weight in the trained model, and evaluating an inference accuracy of the trained model. Claim 3: a model trained by deep learning, changing the selected weight in the trained model, and evaluating an inference accuracy of the trained model. Claim 5: a non-transitory computer readable medium that includes a program thereon, the program causing a computer to determine and a model trained by deep learning, changing the selected weight in the trained model, and evaluating an inference accuracy of the trained model. The additional element of using generic computer elements to perform the abstract idea are directed to merely applying the known use of a computer to store and execute the method in the recited claim limitations. Therefore, the limitations merely amount to adding the words “apply it” (or an equivalent) to 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, as discussed in MPEP 2106.05(f). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. As the claims are merely directed to utilizing a generic computer to perform the abstract idea of selecting and changing a plurality of weights and evaluating an inference accuracy of an output until the inference accuracy of an algorithm reaches a target accuracy. Step 2B (Does the claim recite additional elements that amount to significantly more that the judicial exception?): As discussed above, the additional imitations amount to 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, as discussed in MPEP 2106.05(f). The claims merely recite using generic computer elements to perform the abstract idea. Therefore, the additional elements do not amount to significantly more as they do not recite any improvements to a technology or technical field. Dependent claims 2, 4, and 6 further narrow the abstract idea of evaluating the inference accuracy of the outputs of an algorithm based on an input. The dependent claim do not recite any further additional elements than those disclosed in the above analysis. Therefore, claims 1-6 are rejected under 35 U.S.C. 101. 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. 07-103 AIA The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action. 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. 07-21-aia AIA Claim (s) 1-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Powell (US 2020/0252301) in view of Iyer (US 2021/0097894) . Claims 1, 3, and 5: Powell discloses (Claim 1) An information processing apparatus for determining an encryption target from a plurality of weights of a model trained by deep learning, the information processing apparatus comprising: (Claim 3) An information processing method for determining an encryption target from a plurality of weights of a model trained by deep learning, the method comprising: (Claim 5) A non-transitory computer-readable recording medium that includes a program recorded thereon, the program causing a computer to determine an encryption target from a plurality of weights of a model trained by deep learning, and including instructions that cause the computer to carry out: a memory storing one or more instructions; and a processor configured to executed to one or more instructions to: initialize a selected weight set with an empty set; perform a weight selection process comprising: selecting from among the plurality of weights, a weight of an encryption target and adding the selected weight in the selected weight set (Paragraph [0005-0006]; [0032-0034]; [0072-0073] a method for evaluating a relative contribution of a first group of data sets in a collection of data sets. Applying the collection of data sets to a model and generate one or more observations on the collection of data sets. Generating a curve comprising computing using the model an observation on the collection of data sets; and generating a curve for the first group of data sets by removing the first group of data sets from the collection of data sets, and generating, using the model, an observation with the first group of data sets removed. The method includes generating a measure of contributions of the group of data sets based on the curves. The training data with target output is employed to fit the matrices so as to minimize the square of the sum of the errors. Each cycle of data is passed through the model and the error is used to back-propagate through the system of equations to update the weight matrices. This process is repeated by cycling through all of the training data until convergence is reached. Once the weight matrices are calculated the model can predict the output quantities for inputs), changing the selected weight in the trained model, evaluating an inference accuracy of the trained model with the changed weight, and repeating the weight selection process until the inference accuracy reaches a target accuracy or less (Paragraph [0005-0006]; [0032-0034]; [0072-0073] a method for evaluating a relative contribution of a first group of data sets in a collection of data sets. Applying the collection of data sets to a model and generate one or more observations on the collection of data sets. Generating a curve comprising computing using the model an observation on the collection of data sets; and generating a curve for the first group of data sets by removing the first group of data sets from the collection of data sets, and generating, using the model, an observation with the first group of data sets removed. The method includes generating a measure of contributions of the group of data sets based on the curves. The training data with target output is employed to fit the matrices so as to minimize the square of the sum of the errors. Each cycle of data is passed through the model and the error is used to back-propagate through the system of equations to update the weight matrices. This process is repeated by cycling through all of the training data until convergence is reached. Once the weight matrices are calculated the model can predict the output quantities for inputs), Powell discloses a system of iteratively identifying the contribution of a data set to the accuracy of a machine learning model. However, Powell does not disclose the following claim limitations: and encrypt only the weights stored in the selected weight set. In the same field of endeavor of identifying significant data sets in a model Iyer teaches and encrypt only the weights stored in the selected weight set (Paragraph [0005-0006]; [0021-0023]; [0033]; Fig. 2, in one embodiment a method of selective encryption of a test dataset is disclosed. The method may include determining a relevancy grade associated with each of a plurality of data points within a test dataset by comparing the test dataset with a common heat map. The common heat map is generated using a plurality of training datasets. The method may further including calculating, based on the relevancy grade, an encryption level associated with each of the plurality of datapoints. The method may further include selectively encrypting at least one datapoint from the plurality of datapoints based on the encryption level associated with each of the datapoints. It may be understood that selection of the encryption keys may be based on the relevance of the data for classification. By way of an example, the greater the relevance of the data to the classification, the higher would be the strength of the encryption key assigned to the data). Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of evaluating the contributions of a dataset to a model by determining a change in the root mean square error or the absolute percentage error of a model after cycling through the dataset with the system of encrypt only the weights stored in the selected weight set as taught by Iyer (Iyer [0021]). With the motivation of encrypting and protecting a deep learning model to protect it from attacks and leaking private information (Iyer [0003]). Claims 2, 4, and 6: Modified Powell discloses the information processing apparatus as per claim 1, the information processing method as per claim 3, and the non-transitory computer-readable recording medium as per claim 5. Powell further discloses wherein the inference accuracy is evaluated using the trained model and an input evaluation data set (Paragraph [0005-0006]; [0032-0034]; [0072-0073] a method for evaluating a relative contribution of a first group of data sets in a collection of data sets. Applying the collection of data sets to a model and generate one or more observations on the collection of data sets. Generating a curve comprising computing using the model an observation on the collection of data sets; and generating a curve for the first group of data sets by removing the first group of data sets from the collection of data sets, and generating, using the model, an observation with the first group of data sets removed. The method includes generating a measure of contributions of the group of data sets based on the curves. The training data with target output is employed to fit the matrices so as to minimize the square of the sum of the errors. Each cycle of data is passed through the model and the error is used to back-propagate through the system of equations to update the weight matrices. This process is repeated by cycling through all of the training data until convergence is reached. Once the weight matrices are calculated the model can predict the output quantities for inputs). Therefore, claims 1-6 are rejected under 35 U.S.C. 103 . Response to Arguments Applicant’s arguments, see REMARKS, filed February 09, 2026, with respect to the rejections of claims 1-6 under U.S.C. 101 have been fully considered but are not persuasive. The applicant argues that the claims are directed to a technical improvement of a computer-centric technical improvement in encrypted circuit technology for deep learning models. However, the examiner respectfully disagrees as the claims recite a method for selecting weights i.e. a dataset to encrypt by performing the steps of selecting from among a plurality of weights, changing the selected weights, evaluating an inference accuracy with the changed weight, and repeating the weight selection process until the inference accuracy reaches a target accuracy or less. The claims recite a mental process as the claims recite concepts the courts have stated as being a mental process such as observation, evaluation, judgement, and opinion. As the claims merely recite a series of steps to evaluate a series of weights and determining an associated inference accuracy to select which weights to encrypt. A person is capable of mentally, or with simple tools such as pen and paper, to perform the steps of selecting and changing weights and evaluate an inference accuracy to determine which weights to encrypt. Merely selecting a set of data from a plurality of sets and changing the selection and evaluating the results is a mental process. Additionally, the courts have stated that using a computer as a tool to perform a mental process and performing a mental process in a computer environment are still considered to be mental processes. The additional elements of an an information processing apparatus and a trained model for performing the abstract idea of selecting, evaluating, and encrypting the weights stored in a selected weight set are directed to merely “apply it” or applying generic computer elements to perform the abstract idea of collecting information, analyzing it, and determining a result. Therefore, the claims are not directed to a practical application. The claims additionally do not amount to significantly more as the additional elements are directed to merely “apply it” and therefore do not recite an improvement to a technology or technical element. Merely selecting and evaluating a plurality of datasets and triggering a response such as encrypting information based on the evaluation is not an improvement to a computer or technical element but using generic computer elements to perform the abstract idea. Therefore, the examiner maintains the current 101 rejection. Applicant argues that claims 2, 4, and 6 are allowable as being dependent on claims 1, 3, and 5 and therefore are rejected under the same rejection. Applicant’s arguments, see REMARKS, filed February 09, 2026, with respect to the rejections of Claims 1-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Powell (US 2020/0252301) in view of Iyer (US 2021/0097894) are moot as the claims were amended which required further search and consideration and new art was applied. Claims 1, 3, and 5: Applicant argues that the current prior art does not disclose the amended claim limitations. However, upon further search and consideration the examiner finds that the newly applied prior art discloses the newly amended claim limitations. Powell discloses a system of cycling through a plurality of sets of data to determine the contribution of each sets of data on the error rate of a machine learning model to determine which sets are the most important values. Which can be used in combination with Iyer which teaches a system of encrypting the most relevant or important datasets of a machine learning model to help secure the machine learning model and reduce the risk of a data leak. Therefore, The combination of Powell and Iyer are found to teach the amended claim limitations. The examiner finds that the combination of prior art as capable of teaching the current claimed limitations. Therefore, claims 1, 3, and 5 are newly rejected under U.S.C. 103. Claims 2, 4, and 6 were argued as being allowable only as being dependent on claims 1, 3, and 5. Therefore, they are also rejected under the same rejection as above. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Hsyu (US 2021/0174210) Method and electronic device for selecting deep neural network hyperparameters. Sasson (US 2021/0035021) Systems and methods for monitoring of a machine learning model. Goldszmidt (US 2023/0124380) Automated input-data monitoring to dynamically adapt machine-learning techniques. Yonetani (US 2022/0358749) Interface apparatus, inference method, and computer-readable storage medium storing an interference program. Zhang (US 2020/0394559) Collecting observations for machine learning. Xue (US 2020/0167639) Automatic monitoring and adjustment of machine learning model training. Michigami (US 2019/0065974) Interference device, inference system, and inference method. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to COREY RUSS whose telephone number is (571)270-5902. The examiner can normally be reached on M-F 7:30-4:30. 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, Lynda Jasmin can be reached on 5712726782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /COREY RUSS/Examiner, Art Unit 3629 Application/Control Number: 18/008,305 Page 2 Art Unit: 3629 Application/Control Number: 18/008,305 Page 3 Art Unit: 3629 Application/Control Number: 18/008,305 Page 4 Art Unit: 3629 Application/Control Number: 18/008,305 Page 5 Art Unit: 3629 Application/Control Number: 18/008,305 Page 6 Art Unit: 3629 Application/Control Number: 18/008,305 Page 7 Art Unit: 3629 Application/Control Number: 18/008,305 Page 8 Art Unit: 3629 Application/Control Number: 18/008,305 Page 9 Art Unit: 3629 Application/Control Number: 18/008,305 Page 10 Art Unit: 3629 Application/Control Number: 18/008,305 Page 11 Art Unit: 3629 Application/Control Number: 18/008,305 Page 12 Art Unit: 3629 Application/Control Number: 18/008,305 Page 13 Art Unit: 3629
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