DETAILED ACTION Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Specification The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification. Drawings The applicant’s submitted drawings appear to be acceptable for examination purposes. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the drawings. Information Disclosure Statement As required by M.P.E.P. 609(c) , the applicant's submission of the Information Disclosure Statement, dated 28 August 2023, is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609 C(2) , a copy of the PTOL-1449 initialed and dated by the examiner is attached to the instant office action. The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered. Claim Rejections - 35 USC § 101 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. Claim(s) 1-7 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mathematical concepts and/or mental processes. This judicial exception is not integrated into a practical application and does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as described below. Step 1 for all claims: Under the first part of the analysis, claims 1-5 recite a device, claims 6 recites a device, and claim 7 recites a manufacture. Accordingly, these claims fall within the four statutory categories of invention and the analysis proceeds to Step 2A, prongs 1 and 2, and Step 2B, as described below. As per claim 1: Under step 2A, prong 1, the claim recites an abstract idea including the following mathematical concept and/or mental process elements: estimate a distribution followed by a training set – estimating a distribution from a dataset is a mathematical calculation. Alternatively/additionally – a data scientist plots the data points and estimates a distribution. estimate a distance of the input data from the training set based on the distribution – estimating a distance from a distribution to a data point is a mathematical calculation. Alternatively/additionally – the data scientist can estimate a distance between a point and a distribution. estimate an unknown degree of the input data based on the distance – the data scientist estimates the likelihood that the input is out-of-distribution (unknown degree) based on the distance to the distribution. Alternatively/additionally – calculating the unknown degree based on the distance is a mathematical calculation. correct the unknown degree based on the distribution – the data scientist corrects the likelihood that the input is out-of-distribution (unknown degree) based on the distribution. Alternatively/additionally – correcting the unknown degree based on the distribution is a mathematical calculation. and estimate a cause of an estimation error using the corrected unknown degree – the data scientist estimates a cause of an estimation error based on the corrected unknown degree. Alternatively/additionally – this is mathematical calculation based on flatness of a softmax function (see, e.g., paras. 0004, 0024, etc., of the specification as filed). If a claim, under the broadest reasonable interpretation covers a mathematical relationship between variables or numbers, a numerical formula or equation, or a mathematical calculation, it will be considered as falling within the “mathematical concepts” grouping of abstract ideas. If a claim, under the broadest reasonable interpretation covers concepts that can be performed in the human mind, or by a human using a pen and paper, including observation, evaluation, judgment, or opinion, it will be considered as falling within the “mental processes” grouping of abstract ideas. Additionally, performing mathematical calculations using a formula that could be practically performed in the human mind may be considered to fall within both the mathematical concepts grouping and the mental process grouping. See MPEP § 2106.04(a)(2). Accordingly, at step 2A, prong one, the claim is directed to an abstract idea. Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of: A class label estimation device that estimates a class label of input data and estimates a cause of an estimation error the class label estimation device comprising – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power , 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). a processor – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f). a memory storing program instructions that cause the processor to – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f). Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d). Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of: A class label estimation device that estimates a class label of input data and estimates a cause of an estimation error the class label estimation device comprising – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power , 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). a processor – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f). a memory storing program instructions that cause the processor to – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f). Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101. As per claim 2: The claim recites the following additional mathematical concept and/or mental process elements: estimate, based on the distribution, a threshold value to be used for correction of the unknown degree – calculating the threshold value is a mathematical calculation. Alternatively/additionally – the data scientist estimates a threshold to be used for correction of the unknown degree. Accordingly, at step 2A, prong one, the claim is directed to an abstract idea. Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of: wherein the program instructions cause the processor to – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f). Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d). Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of: wherein the program instructions cause the processor to – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f). Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101. As per claim 2: The claim recites the following additional mathematical concept and/or mental process elements: estimate, based on the distribution, a threshold value to be used for correction of the unknown degree – calculating the threshold value is a mathematical calculation. Alternatively/additionally – the data scientist estimates a threshold to be used for correction of the unknown degree. Accordingly, at step 2A, prong one, the claim is directed to an abstract idea. Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of: wherein the program instructions cause the processor to – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f). Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d). Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of: wherein the program instructions cause the processor to – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f). Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101. As per claim 3 : The claim recites the following additional mathematical concept and/or mental process elements: estimates the threshold value to realize a predetermined coverage in the distribution of the training set – calculating the threshold value to reach a desired coverage of the distribution is a mathematical calculation. Alternatively/additionally – the data scientist estimates a threshold to achieve the desired coverage of the distribution. Accordingly, at step 2A, prong one, the claim is directed to an abstract idea. Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of: wherein the processor – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f). Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d). Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of: wherein the processor – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f). Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101. As per claim 4: The claim recites the following additional mathematical concept and/or mental process elements: corrects the unknown degree to be divided into within-distribution and out-of-distribution with reference to the threshold value – correcting the unknown degree to divide it between within- and out-of-distribution is mathematical calculation. Alternatively/additionally – the data scientist corrects the unknown degree to include within- and out-of-distribution. Accordingly, at step 2A, prong one, the claim is directed to an abstract idea. Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of: wherein the processor – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f). Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d). Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of: wherein the processor – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f). Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101. As per claim 5: The claim recites the following additional mathematical concept and/or mental process elements: estimate a label noise degree based on a class likelihood of the input data – calculating the noise degree from the class likelihood is a mathematical calculation. Alternatively/additionally – the data scientist estimates a label noise degree from the class likelihood. estimates a cause of an estimation error using the corrected unknown degree and the label noise degree – the data scientist estimates a cause of an estimation error from the corrected unknown degree and the label noise degree. Alternatively/additionally – calculating the cause (a value) from the degrees of the corrected unknown degree and label noise degree is a mathematical calculation. Accordingly, at step 2A, prong one, the claim is directed to an abstract idea. Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of: wherein the program instructions cause the processor to – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f). wherein the processor – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f). Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d). Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of: wherein the program instructions cause the processor to – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f). wherein the processor – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f). Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101. As per claim 6 , see the rejection of claim 1 , above. As per claim 7: See the rejection of claim 6 , above, wherein under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of: A non-transitory computer-readable recording medium storing a program for causing a computer to perform the error cause estimation method according to claim 6 – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f). Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d). Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of: A non-transitory computer-readable recording medium storing a program for causing a computer to perform the error cause estimation method according to claim 6 – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f). Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101. Claim Rejections - 35 USC § 103 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. 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. 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. 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. Claim(s) 1-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Applicant’s admitted prior art (hereinafter, “AAPA”), as described in the specification , in view of Ramachandran (US 2021/0150365) . As per claim 1, AAPA teaches a class label estimation device that estimates a class label of input data and estimates a cause of an estimation error [in the class label estimation device, at the time of inference, it is possible to estimate whether the input is affected by label noise (label error in the training set) (para. 0004 in the specification as filed); which is estimating the cause of an estimation error (noise)] , the class label estimation device comprising: a processor; and a memory storing program instructions that cause the processor to: [a class label estimation device using ACGAN (para. 0003); where implementing an ACGAN requires a processor and memory storing instructions to be executed by the processor] estimate a distribution followed by a training set [ a first mechanism that identifies whether input data is data generated from a noise signal and a label signal or data of a training set that is actual data (para. 0003), which includes the distribution followed by the training set (in identifying whether the data is data of the training set) ] ; estimate an unknown degree of the input data based on the distance [ in the class label estimation device, at the time of inference, it is possible to estimate an unknown degree (out-of-distribution likelihood) indicating whether the input data is unknown by the first mechanism (para. 0004); based on the distance, below ] ; and estimate a cause of an estimation error using the corrected unknown degree [ in the class label estimation device, at the time of inference, it is possible to estimate whether the input is affected by label noise (label error in the training set) (para. 0004); using the corrected unknown degree, below ] . While AAPA teaches estimating an unknown degree of the input data and estimating a cause of the estimation error (see above), it has not been relied upon for teaching [estimating] a distance of the input data from the training set based on the distribution; and [correcting] the unknown degree based on the distribution. Ramachandran teaches a class label estimation device that estimates a class label of input data [a classification neural network with “out-of-distribution” identification, implemented on a computing device (fig. 3 ; para s . 0035, 0047, 0060; etc.)] , the class label estimation device comprising: a processor; and a memory storing program instructions that cause the processor to: [a classification neural network with “out-of-distribution” identification, implemented on a computing device that includes a processor executing a program stored in memory (fig. 3; paras. 0035, 0047, 0060; etc.) ] estimate a distribution followed by a training set [the system determines a distribution of training samples, and may generate additional samples that are structurally similar to in-domain and semantically out-of- distribution for training the classifier (abstract, etc.)] ; estimate a distance of the input data from the training data based on the distribution [ the distance of samples from the distribution may be determined (paras. 0056, 0065; fig. 7; etc.) ] ; and correct the unknown degree based on the distribution [ a penalty may be introduced based upon a distance from the distribution (paras. 0056, 0066; fig. 7; etc.) and the loss function used to correct the outputs of the network (para. 0059, etc.) ; which corrects the unknown degree ] . AAPA and Ramachandran are analogous art, as they are within the same field of endeavor, namely training a model/classifier for classifying inputs, including determining whether samples are in-distribution or out-of-distribution. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include estimating the distance of inputs from the training data distribution and correcting the likelihood that new inputs are out-of-distribution, as taught by Ramachandran, in the estimating a likelihood of whether input samples are out-of-distribution (unknown degree) and using the unknown degree to determine a cause of error in the system taught by AAPA. Ramachandran provides motivation as [when the image does not belong to any of the pre-defined classes, the output probabilistic distributions from neural network can be highly inaccurate. In this case, pixels of the input image may be referred to as “out-of-domain” or “out-of-distribution” (OOD), as opposed to “in-domain” or “in-distribution” (ID) when the input image belongs to one of the pre- defined classes (para. 0004, etc.) and, when the neural network is only trained to classify an input sample into pre-defined classes, the classification output in response to an OOD input can be highly inaccurate and even completely erroneous. Thus, to more accurately classify the input data, an OOD classification scheme is needed to identify inputs that may go beyond the pre-defined classes (para. 0005, etc.)] . As per claim 2, AAPA/Ramachandran teaches wherein the program instructions cause the processor to estimate, based on the distribution, a threshold value to be used for correction of the unknown degree [ the network may be trained to penalize outputs that are a pre-defined distance away from the origin, which may be tuned during training (Ramachandran: para. 0056, etc.); where the pre-defined distance away is the threshold ] . As per claim 3, AAPA/Ramachandran teaches wherein the processor estimates the threshold value to realize a predetermined coverage in the distribution of the training set [ the network may be trained to penalize outputs that are a pre-defined distance away from the origin, which may be tuned during training (Ramachandran: para. 0056, etc.); where the pre-defined distance away is the threshold realizing a predetermined coverage ] . As per claim 4, AAPA/Ramachandran teaches wherein the processor corrects the unknown degree to be divided into within-distribution and out-of-distribution with reference to the threshold value [ the distance value is used by the network to estimate probabilities of whether input samples are in-distribution or out-of-distribution (Ramachandran: para. 0066, etc.) ] . As per claim 5, AAPA/Ramachandran teaches wherein the program instructions cause the processor to estimate a label noise degree based on a class likelihood of the input data [ the class label estimation device estimates whether the input data is affected by label noise (label error in the training set) using flatness of a softmax vector in the second mechanism (that estimates the category label) (AAPA: para. 0004, etc.) ] , and wherein the processor estimates a cause of an estimation error using the corrected unknown degree and the label noise degree [ the class label estimation device estimates whether the input data is affected by label noise (label error in the training set) (AAPA: para. 0004, etc.); which is a cause of the estimation error ] . As per claim 6, see the rejection of claim 1, above. As per claim 7, AAPA/Ramachandran teaches a non-transitory computer-readable recording medium storing a program for causing a computer to perform the error cause estimation method according to claim 6 [ the system may be implemented as a non-transitory, tangible, machine readable media storing executable code to perform the functions of the invention (Ramachandran: para. 0040, fig. 3, etc.) ] . Conclusion The following is a summary of the treatment and status of all claims in the application as recommended by M.P.E.P. 707.07(i) : claims 1-7 are rejected. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Nandy et al. (Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples, Jan 2021, pgs. 1-24) – discloses a system including uncertainty estimation models with a loss function to maximize the gap/distance between in-domain and OOD examples . Ren et al. (Likelihood Ratios for Out-of-Distribution Detection, Dec 2019, pgs. 1-21) – discloses generative models for OOD detection via discriminative neural networks and a likelihood ratio for correcting the model(s). The examiner requests, in response to this Office action, that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application. When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections. See 37 CFR 1.111(c). Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT GEORGE GIROUX whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-9769 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-F 10am-6pm . 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, FILLIN "SPE Name?" \* MERGEFORMAT Omar Fernandez Rivas can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 571-272-2589 . 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. /GEORGE GIROUX/ Primary Examiner, Art Unit 2128