CTNF 18/577,739 CTNF 93630 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 This action is responsive to Applicant’s claims field 01/09/2024. Claims 1-16 and 18-21 are currently pending and have been examined here. 12-151-10 AIA 12-51-10 Claim 17 has been canceled. Claims 19-21 are newly added. Claims 1-16 and 18 have been amended. 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 1-16 and 18-21 are rejected under 35 U.S.C. § 101. The claims are drawn to ineligible patent subject matter, because the claims are directed to a recited judicial exception to patentability (an abstract idea), without claiming something significantly more than the judicial exception itself. Claims are ineligible for patent protection if they are drawn to subject matter which is not within one of the four statutory categories, or, if the subject matter claimed does fall into one of the four statutory categories, the claims are ineligible if they recite a judicial exception, are directed to that judicial exception, and do not recite additional elements which amount to significantly more than the judicial exception itself. Alice Corp. v. CLS Bank Int'l, 375 U.S. ___ (2014). Accordingly, claims are first analyzed to determine whether they fall into one of the four statutory categories of patent eligible subject matter. Then, if the claims fall within one of the four statutory categories, it must be determined whether the claims are directed to a judicial exception to patentability (i.e., a law of nature, a natural phenomenon, or an abstract idea). In determining whether a claim is directed to a judicial exception, the claim is first analyzed to determine whether the claim recites a judicial exception. If the claim does not recite one of these exceptions, the claim is directed to patent eligible subject matter under 35 U.S.C. 101. If the claim recites one of these exceptions, the claim is then analyzed to determine whether the claim recites additional elements that integrate the exception into a practical application of that exception. Claims which integrate the exception into a practical application of that exception are directed to patent eligible subject matter under 35 U.S.C. 101. If the claim fails to integrate the exception into a practical application of that exception, the claim is directed to an abstract idea. Finally, if the claims are directed to a judicial exception to patentability, the claims are then analyzed determine whether the claims are directed to patent eligible subject matter by reciting meaningful limitations which transform the judicial exception into something significantly more than the judicial exception itself. If they do not, the claims are not directed towards eligible subject matter under 35 U.S.C. § 101. Regarding independent claims 1, 14, and 18 the claims are directed to one of the four statutory categories (a machine, a process, and an article of manufacture, respectively.) The claimed invention of independent claims 1, 14, and 18 is directed to a judicial exception to patentability, an abstract idea. The claims include limitations which recite elements which can be properly characterized under at least one of the following groupings of subject matter recognized as abstract ideas by MPEP 2106.04(a): Mathematical Concepts: mathematical relationships, mathematical formulas or equations, and mathematical calculations; Certain methods of organizing human activity: fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and Mental processes: concepts performed in the human mind (including an observation, evaluation, judgment, opinion) Claims 1, 14, and 18, as a whole, recite the following limitations: acquiring a target input sample of a target data analysis model, the target input sample comprising a plurality of features; (claims 1, 14, and 18; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could acquire a target input sample of a model) acquiring an embedding vector by transforming the target input sample from an original feature space to an embedding space, the original feature space being a feature space where the target input sample is disposed; (claims 1, 14, and 18; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could acquire an embedding vector by transforming a sample from one feature space to another) generating a neighborhood perturbation data set based on the embedding vector, the neighborhood perturbation data set comprising a plurality of first neighborhood vectors in a neighborhood of the embedding vector; (claims 1, 14, and 18; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could generate a neighborhood perturbation data set comprising a plurality of neighborhood vectors) determining a weight of each of the first neighborhood vectors based on a distance between the first neighborhood vector and the embedding vector, the distance being negatively correlated with the weight of the first neighborhood vector; (claims 1, 14, and 18; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could determine weights of neighborhood vectors based on distances) acquiring an original perturbation data set by transforming the neighborhood perturbation data set into the original feature space, the original perturbation data set comprising a plurality of second neighborhood vectors; (claims 1, 14, and 18; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could acquire an original perturbation data set by transforming a dataset into an original feature space) acquiring a plurality of output vectors by inputting the plurality of second neighborhood vectors into the target data analysis model; (claims 1, 14, and 18; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could acquire a plurality of output vectors by inputting a neighborhood vector into a data analysis model) acquiring an explainable model by training a to-be-trained explainable model, by taking the plurality of second neighborhood vectors, the plurality of output vectors, and the weight of each of the first neighborhood vectors as training samples; and (claims 1, 14, and 18; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could acquire an explainable model by training a model based on this data; alternatively, the broadest reasonable interpretation of this limitation recites mathematical concepts since the broadest reasonable interpretation of training a model sets forth mathematical operations and formulas for performing this step) acquiring, based on the explainable model, weights of the plurality of features in the target input sample. (claims 1, 14, and 18; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could acquire weights of features in a target input sample based on an explainable model) The above elements, as a whole, recite mental processes since, but for the requirement to implement the above steps on a set of generic computer components, the entirety of the above set of steps could be performed by a human using their mind, pen and paper, and simple observation, evaluation, and judgment. Furthermore, as a whole, the claims recite mathematical concepts since they outline a mathematical process interpreting a model. Moving forward, the above recited abstract idea is not integrated into a practical application. The added limitations do not represent an integration of the abstract idea into a practical application because: the claims represent mere instructions to implement an abstract idea on a computer, and merely use a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). the claims merely add insignificant extra-solution activity to the judicial exception (activity which can be characterized as incidental to the primary purpose or product that is merely a nominal or tangential addition to the claim). See MPEP 2106.05(g) and/or the claims represent mere general linking of the use of the judicial exception to a particular technological environment or field of use. See MPEP 2016.05(h) Beyond those limitations which recite the abstract idea, the following limitations are added: An apparatus for explaining a model, comprising: (claim 14; the broadest reasonable interpretation of this limitation represents mere instructions to implement the abstract idea on a generic computer used as a tool in its ordinary capacity; alternatively, the broadest reasonable interpretation of this limitation represents mere general linking of the abstract idea to a particular computer environment or field of use) a processor; and (claim 14; the broadest reasonable interpretation of this limitation represents mere instructions to implement the abstract idea on a generic computer used as a tool in its ordinary capacity; alternatively, the broadest reasonable interpretation of this limitation represents mere general linking of the abstract idea to a particular computer environment or field of use) a memory configured to store one or more instructions executable by the processor; wherein the processor, when loading and executing the one or more instructions, is caused to perform: (claim 14; the broadest reasonable interpretation of this limitation represents mere instructions to implement the abstract idea on a generic computer used as a tool in its ordinary capacity; alternatively, the broadest reasonable interpretation of this limitation represents mere general linking of the abstract idea to a particular computer environment or field of use) A non-transient computer storage medium storing at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set, when loaded and executed by a processor, causes the processor to perform (claim 18; the broadest reasonable interpretation of this limitation represents mere instructions to implement the abstract idea on a generic computer used as a tool in its ordinary capacity; alternatively, the broadest reasonable interpretation of this limitation represents mere general linking of the abstract idea to a particular computer environment or field of use) The claims, as a whole, are directed to the abstract idea(s) which they recite. The claim limitations do not present improvements to another technological field, nor do they improve the functioning of a computer or another technology. Nor do the claim limitations apply the judicial exception with, or by use of a particular machine. The claims do not effect a transformation or reduction of a particular article to a different state or thing. See MPEP 2106.05(c). None of the hardware in the claims "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment' that is, implementation via computers” such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP 2106.05(e); Alice Corp. v. CLS Bank Int’l (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)). Therefore, because the claims recite a judicial exception (an abstract idea) and do not integrate the judicial exception into a practical application, the claims, as a whole, are directed to the judicial exception. Turning to the final prong of the test (Step 2B), independent claims 1, 14, and 18 do not include additional elements that are sufficient to amount to significantly more than the judicial exception, because there are no meaningful limitations which transform the exception into a patent eligible application. As outlined above, the claim limitations do not present improvements to another technological field, nor do they improve the functioning of a computer or another technology. Nor do the claim limitations apply the judicial exception with, or by use of a particular machine. The claims do not effect a transformation or reduction of a particular article to a different state or thing. See MPEP 2106.05(c). None of the hardware in the claims "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment' that is, implementation via computers” such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP 2106.05(e); Alice Corp. v. CLS Bank Int’l (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)). Furthermore, no specific limitations are added which represent something other than what is well-understood, routine, and conventional activity in the field. See MPEP 2106.05(d). Besides performing the abstract idea itself, the generic computer components only serve to perform the court-recognized well-understood computer functions of receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory. See MPEP 2106.05(d). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation. The specification details any combination of a generic computer system program to perform the method. Generically recited computer elements do not add a meaningful limitation to the abstract idea because they would be routine in any computer implementation and because the Alice decision noted that generic structures that merely apply the abstract ideas are not significantly more than the abstract ideas. Therefore, independent claims 1, 14, and 18 are rejected under 35 U.S.C. §101 as being directed to ineligible subject matter. Claims 2-13, 15-16, and 19-21, recite the same abstract idea as their respective independent claims. The following additional features are added in the dependent claims: Claims 2, 15 and 21: wherein acquiring the embedding vector by transforming the target input sample from the original feature space to the embedding space comprises: inputting the target input sample into an encoder in an autoencoder, and transforming the target input sample into the embedding vector by the encoder, wherein the autoencoder comprises the encoder and a decoder. The broadest reasonable interpretation of this limitation amounts to the mere requirement to “apply” the abstract idea using an autoencoder and decoder since the autoencoder and decoder are recited at a high level of generality, the result of using the autoencoder and decoder is recited without providing the details as to how the result is achieved, and this element merely acts in its ordinary capacity to perform an existing function. Claim 3 and 15: wherein acquiring the original perturbation data set by transforming the neighborhood perturbation data set into the original feature space comprises: inputting the neighborhood perturbation data set into the decoder in the autoencoder, and transforming the neighborhood perturbation data set into the original perturbation data set by the decoder. The broadest reasonable interpretation of this limitation amounts to the mere requirement to “apply” the abstract idea using an autoencoder and decoder since the autoencoder and decoder are recited at a high level of generality, the result of using the autoencoder and decoder is recited without providing the details as to how the result is achieved, and this element merely acts in its ordinary capacity to perform an existing function. Claim 4 and 16: wherein prior to inputting the target input sample into the encoder in the autoencoder, and transforming the target input sample into the embedding vector by the encoder, the method further comprises: acquiring a data set of the target data analysis model, the data set comprising a plurality of training samples and labels corresponding to the training samples; acquiring a trained neural network by training, based on the training sample of the target data analysis model, a to-be-trained neural network, the to-be-trained neural network comprising an input layer and an entity embedding layer connected to the input layer; and determining an embedding matrix between the input layer and the entity embedding layer in the trained neural network as the encoder. The broadest reasonable interpretation of this limitation amounts to the mere requirement to “apply” the abstract idea using an autoencoder and decoder since the autoencoder and decoder are recited at a high level of generality, the result of using the autoencoder and decoder is recited without providing the details as to how the result is achieved, and this element merely acts in its ordinary capacity to perform an existing function. Furthermore, the broadest reasonable interpretation of this limitation amounts to the mere requirement to “apply” the abstract idea using an a neural network since the neural network is recited at a high level of generality, the result of using the neural network is recited without providing the details as to how the result is achieved, and this element merely acts in its ordinary capacity to perform an existing function. The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could determine an embedding matrix between an input layer and an entity embedding layer. Claim 5: wherein the embedding matrix is configured to transform an input feature into a real-valued dense feature. The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could transform an input feature into a real-valued dense feature. Claim 6 and 19: wherein upon determining the embedding matrix between the input layer and the entity embedding layer in the trained neural network as the encoder, the method further comprises: training, by the encoder, a to-be-trained decoder, such that the decoder is capable of transforming a vector transformed into the embedding space by the encoder back to the original feature space. The broadest reasonable interpretation of this limitation amounts to the mere requirement to “apply” the abstract idea using an autoencoder and decoder since the autoencoder and decoder are recited at a high level of generality, the result of using the autoencoder and decoder is recited without providing the details as to how the result is achieved, and this element merely acts in its ordinary capacity to perform an existing function. Furthermore, the broadest reasonable interpretation of this limitation amounts to the mere requirement to “apply” the abstract idea using an a neural network since the neural network is recited at a high level of generality, the result of using the neural network is recited without providing the details as to how the result is achieved, and this element merely acts in its ordinary capacity to perform an existing function. The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could transform a vector back into an original feature space. Claim 7 and 20: wherein prior to acquiring the trained neural network by training, based on the training sample of the target data analysis model, the to-be-trained neural network, the method further comprises: transforming, in a case that the training sample in the data set of the target data analysis model comprises a non-numerical feature, the non-numerical feature into a one-hot code. The broadest reasonable interpretation of this limitation amounts to the mere requirement to “apply” the abstract idea using an a neural network since the neural network is recited at a high level of generality, the result of using the neural network is recited without providing the details as to how the result is achieved, and this element merely acts in its ordinary capacity to perform an existing function. The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could transform a non-numerical feature into a hot code if the dataset is non-numerical. Claim 8: wherein the explainable model is one of a linear model, a decision-making tree, or a descent rule list model. The broadest reasonable interpretation of this limitation merely alters the type of model used in the abstract idea above, and therefore further recites one or more abstract ideas for the reasons outlined above. Claim 9: acquiring, based on the explainable model, the weights of the plurality of features in the target input sample comprises: acquiring, in a case that the explainable model is a linear model, coefficients of a plurality of features in the explainable model; and determining the coefficients of the plurality of features as weights of the plurality of features. The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could acquire coefficients of feature and determine coefficients as weights in this manner. Claim 10: wherein acquiring the explainable model by training the to-be-trained explainable model, by taking the plurality of second neighborhood vectors, the plurality of output vectors, and the weight of each of the first neighborhood vectors as the training samples, comprises: training the to-be-trained explainable model, by taking the plurality of second neighborhood vectors, the plurality of output vectors, and the weight of each of the first neighborhood vectors as training samples; and determining, in a case that approximate values of decisions of the to-be-trained explainable model and the target data analysis model are greater than or equal to a preset decision value, the to-be-trained explainable model as the explainable model. The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could perform each of the above training and determining steps. Claim 11: wherein upon acquiring, based on the explainable model, the weights of the plurality of features in the target input sample, the method further comprises: optimizing the target data analysis model based on the weights of the plurality of features. The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could optimize target data analysis based on weights of features. Claim 12: wherein the target input sample comprises a plurality of features of an object; and the target data analysis model is a prediction model for the object, or the target data analysis model is a classification model for the object. The broadest reasonable interpretation of this limitation merely alters the target input sample and objects used in the abstract idea above, and therefore further recites one or more abstract ideas for the reasons outlined above. Claim 13: wherein the object comprises a user, an animal, or an item. The broadest reasonable interpretation of this limitation merely alters the target input sample and objects used in the abstract idea above, and therefore further recites one or more abstract ideas for the reasons outlined above. The above limitations do not represent a practical application of the recited abstract idea. The claim limitations do not present improvements to another technological field, nor do they improve the functioning of a computer or another technology. Nor do the claim limitations apply the judicial exception with, or by use of a particular machine. The claims do not effect a transformation or reduction of a particular article to a different state or thing. See MPEP 2106.05(c). None of the hardware in the claims "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment' that is, implementation via computers” such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP 2106.05(e); Alice Corp. v. CLS Bank Int’l (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)). Therefore, because the claims recite a judicial exception (an abstract idea) and do not integrate the judicial exception into a practical application, the claims are also directed to the judicial exception. Furthermore, the added limitations do not direct the claim to significantly more than the abstract idea. No specific limitations are added which represent something other than what is well-understood, routine, and conventional activity in the field. See MPEP 2106.05(d). Accordingly, none of the dependent claims 2-13, 15-16, and 19-21, individually, or as an ordered combination, are directed to patent eligible subject matter under 35 U.S.C. 101. Please see MPEP §2106.05(d)(II) for a discussion of elements that the Courts have recognized as well-understood, routine, conventional, activity in particular fields. Please see MPEP §2106 for examination guidelines regarding patent subject matter eligibility. Citation of Pertinent Prior Art 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "" Why should I trust you?" Explaining the predictions of any classifier." Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016. Ribeiro teaches a model used for explaining predictions of classifiers, but does not appear to teach acquiring an original perturbation data set by transforming a perturbation data set into an original feature space. Brenning, Alexander. "Transforming feature space to interpret machine learning models." arXiv preprint arXiv:2104.04295 (2021). Brenning teaches transforming feature spaces in order to interpret machine learning models but does not appear to explicitly teach acquiring an original perturbation data set by transforming a perturbation data set into an original feature space . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to EMMETT K WALSH whose telephone number is (571)272-2624. The examiner can normally be reached Mon.-Fri. 6 a.m. - 4:45 p.m.. 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, Jessica Lemieux can be reached at 571-270-3445. 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. /EMMETT K. WALSH/Primary Examiner, Art Unit 3626 Application/Control Number: 18/577,739 Page 2 Art Unit: 3626 Application/Control Number: 18/577,739 Page 3 Art Unit: 3626 Application/Control Number: 18/577,739 Page 4 Art Unit: 3626 Application/Control Number: 18/577,739 Page 5 Art Unit: 3626 Application/Control Number: 18/577,739 Page 6 Art Unit: 3626 Application/Control Number: 18/577,739 Page 7 Art Unit: 3626 Application/Control Number: 18/577,739 Page 8 Art Unit: 3626 Application/Control Number: 18/577,739 Page 9 Art Unit: 3626 Application/Control Number: 18/577,739 Page 10 Art Unit: 3626 Application/Control Number: 18/577,739 Page 11 Art Unit: 3626 Application/Control Number: 18/577,739 Page 12 Art Unit: 3626 Application/Control Number: 18/577,739 Page 13 Art Unit: 3626 Application/Control Number: 18/577,739 Page 14 Art Unit: 3626 Application/Control Number: 18/577,739 Page 15 Art Unit: 3626 Application/Control Number: 18/577,739 Page 16 Art Unit: 3626
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