DETAILED ACTION
This Action is responsive to Claims filed 05/07/2026.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/20/2026 was filed after the mailing date of the Non-Final Office Action on 03/11/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of the Claims
Claims 1 and 11 have been amended. Claims 10 and 20 have been cancelled. Claims 1-3, 6-9, 11-13, and 16-19 are currently pending.
Response to Arguments
Applicant's arguments filed 05/07/2026 regarding the 35 U.S.C. 103 Rejection(s) of Claims 1-3, 6-13, and 16-20 have been fully considered but they are not persuasive.
Addressing the Applicant’s arguments point-by-point:
(Page 8): The Applicant merely suggests the cited reference Au is mischaracterized without explanation or evidence support the assertion. The Examiner submits the broad interpretation of Au continues to read on the claimed “super-feature,” given limited explanation such as the instant Specification [0016].
(Pages 8-9): The Applicant asserts a distinction between the cited reference Au and the instant claim’s, suggesting there is a distinction between a single model and Au’s generating of a model or models. The Examiner submits there is insufficient technical or structural detail precluding the broad recitation of the claims from being upon by Au. The independent claims do not explicitly dictate there is one single surrogate model for all cases; therefore, since Au generates a model for each case, Au continues to read on the claimed limitations.
(Page 9): The Examiner fails to see the relevance of this argument. Whether the LOGI method taught by Au is fast or slow is irrelevant to whether or not Au reads on the instant Claim limitations. The Examiner also submits the Applicant draws a conclusion about the cited portion of Au pertaining to manual labor not directly supported by Au. The recitation is fairly broad in Au, and the Examiner submits the passage continues to be of relevance to the claim limitations.
(Page 10): In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
The cited reference Ghai was not relied upon for the rejection of previous Claim 10, from which the newly amended limitations come. The structure of cited reference Ghai is not relied upon in this Rejection, merely the teaching the local explanations for similar systems would have been well understood and reasonably implemented by a person of ordinary skill in the art before the Applicant’s effective filing date. Ghai also broadly recites connections to related work at the time of their writing within AL and iML to unlabeled and/or unsupervised systems (Sections 2.1 and 2.2).
Claim 2: THE BRI of a generic subset does not preclude a subset from containing an identical set of features, but permuted in order. If this feature is where the Applicant believes their novelty is best expressed, further technical or structural amendments are encouraged.
Claim 8: Au recites “Many variables in the dataset can be manually group…” The Examiner contends a person of ordinary skill in the art would reasonably be accessing data from a data repository, and that access would likely come in the form of some kind of query. If this feature is where the Applicant believes their novelty is best expressed, further technical or structural amendments are encouraged.
Claim 9: The Examiner submits it would have been well-known in the art at the time of the Applicant’s filing for a database tuple containing data to have a row identifier or index number. If this feature is where the Applicant believes their novelty is best expressed, further technical or structural amendments are encouraged.
See the updated 35 U.S.C. 103 Rejection(s) below.
Applicant's arguments filed 05/07/2026 regarding the 35 U.S.C. 101 Rejection of Claims 1-3, 6-13, and 16-20 have been fully considered but they are not persuasive.
See above how the Examiner submits there is insufficient technical or structural limitation precluding the cited reference Au from reading on the surrogate model and/or subset of features as a super-feature. Without further amendments, the Examiner submits the claims in their present form do not convey specific structure or implementation beyond a conventional computer. Although it may be the Applicant’s belief that the steps are unconventional, or performed in an unconventional order/way, it does not preclude the interpretable abstract idea mental process steps, and the limited structure or implementation those steps are applied to, from being recited highly generically.
The blurb cited by the Applicant has no BRI tying it to local explanations, merely training a model on manipulated data. If this feature is where the Applicant believes their novelty and/or eligibility is best expressed, further technical or structural amendments are encouraged.
The Applicant highlights, on Page 15 specifically, the exact reasoning for a 101 Rejection. If the alleged improvement to the accuracy of the unsupervised and/or surrogate model is dependent on the permutation of data tuples and calculation of an importance, then per MPEP 2106.05(a), the specific improvement cannot come from interpretable abstract idea mental process(es).
The arguments for the use of a local explanation versus a global explanation are less relevant to whether the alleged improvement in the claims as written is derived from abstract idea mental process steps or from additional elements. The Examiner submits the Applicant’s arguments actually highlight why one may choose to use local explanations (as would have been known in the art such as Ghai) before the Applicant’s filing date.
The Applicant misapplies McRO and several examples from MPEP 2106.05(a)(II) in these arguments. The improvement from McRO came from non-abstract idea mental process steps, the accuracy cited in Trading Techs is a byproduct of an improved interface (non-abstract idea), the citation of Thales pertains to the mathematical concepts grouping, which is not at issue in this Rejection.
As stated above, the alleged improvements come as a direct result of data manipulation and/or observation and calculation abstract idea mental process steps. Without the aforementioned steps, there is no specific improvement realized by the training step(s). Per MPEP 2106.05(a), the specific improvement cannot come from the abstract idea.
See the updated 35 U.S.C. 101 Rejection below.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-3, 6-9, 11-13, and 16-19 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.)
Step 1:
Claims 1-3 and 6-9 recite a method for defining a plurality of super-features, which falls under the statutory category of a process. Claims 11-13 and 16-19 recite one or more non-transitory computer-readable storage media storing instructions, which falls under the statutory category of a manufacture.
Step 2A – Prong 1:
Claim 1 recites an abstract idea, law of nature, or natural phenomenon. The limitations of “defining a plurality of super-features that each contain a respective disjoint subset of features of a plurality of features;”, “performing for each super-feature of the plurality of super-features: randomly selecting a plurality of permuted values from original values of the super-feature in a plurality of unlabeled original tuples that are based on the plurality of features,”, “generating a plurality of permuted tuples, wherein each permuted tuple of the plurality of permuted tuples is based on said particular tuple and a respective permuted value of the plurality of permuted values,”, “calculating, for each super-feature of the plurality of super-features, an importance of the super-feature based on the surrogate model;”, and “generating, based on said calculating…a local explanation of the ML model that is at least one explanation selected from a group consisting of: a local explanation that indicates a most important super-feature and a local explanation that excludes a super-feature that has an importance that is below a threshold;” under the broadest reasonable interpretation, cover a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper.
Defining tuples of features, randomly selecting and generating permutations of those tuples, and generating or selecting the “best” and one that is below a threshold is practically performed within the human mind or with the aid of pencil and paper.
Step 2A – Prong 2:
The additional elements of claim 1 do not integrate the abstract idea into a judicial exception. The claim recites the additional elements “a method”, “tuple”, and “permuted tuples” are recognized as generic computer components recited at a high level of generality. Although they have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application).
The additional elements of “an unsupervised machine learning model”, “features”, “super-feature”, “inference”, “a surrogate model” and “an importance” are recognized as non-generic computer components, but are recited at a high level of generality and are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
The additional elements recited in the limitation “an unsupervised machine learning (ML) model inferring a particular inference for a particular tuple that is based on the plurality of features;”, “the unsupervised ML model inferring a respective permuted inference for each permuted tuple of the plurality of permuted tuples;”, “training, based on the permuted inferences, a surrogate model;” and “wherein the method is performed by one or more computers.” are found to be mere instructions to apply the abstract idea of defining feature tuples and generating permutations of those feature tuples (see MPEP 2106.05(f) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application).
The additional elements recited in the limitation “displaying a local explanation of the unsupervised ML model that is at least one explanation selected from a group consisting of: a local explanation that indicates a most important super-feature and a local explanation that excludes a super-feature that has an importance that is below a threshold;” merely amounts to insignificant post-solution activity (See MPEP 2106.05(g)).
Step 2B:
The only limitation on the performance of the described method is a limitation reciting “a method”, “tuple”, and “permuted tuples” These elements are insufficient to transform a judicial exception to a patentable invention because the recited elements are considered insignificant extra-solution activity (generic computer system, processing resources, links the judicial exception to a particular, respective, technological environment). The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components; mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (see MPEP 2106.05(f)).
The additional elements of “a machine learning model”, “features”, “super-feature”, “inference”, “a surrogate model” and “an importance” are recognized as non-generic computer components, but are recited at a high level of generality and are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
The additional elements recited in the limitation “an unsupervised machine learning (ML) model inferring a particular inference for a particular tuple that is based on the plurality of features;”, “the unsupervised ML model inferring a respective permuted inference for each permuted tuple of the plurality of permuted tuples;”, “training, based on the permuted inferences, a surrogate model;” and “wherein the method is performed by one or more computers.” are found to be mere instructions to apply the abstract idea (See MPEP 2106.05(f) indicating mere instructions to apply an abstract idea does not recite significantly more).
The additional elements recited in the limitation “displaying a local explanation of the unsupervised ML model that is at least one explanation selected from a group consisting of: a local explanation that indicates a most important super-feature and a local explanation that excludes a super-feature that has an importance that is below a threshold;” merely amounts to well-understood, routine, or conventional activity (See MPEP 2106.05(d)(II)(iv), third list).
Taken alone or in ordered combination, these additional elements do not amount to significantly more than the above-identified abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to independent claim 11.
Claim 11 recites similar limitations to claim 1, with the exception of “One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause:” (generic computer components).
Dependent Claims:
Claim 2 (claim 12) recites additional elements found to be generic computer components and a data-gathering or transmittal step (“accessing the value of a super-feature of an unlabeled original tuple of the plurality of unlabeled original tuples based on…”). This limitation is found to be mere data-gathering or data transmittal extra-solution activity steps (See MPEP 2106.05(g)) and are acknowledged to be well-understood, routine, conventional activity (see, e.g., court recognized WURC examples in MPEP 2106.05(d)(II)).
Claim 3 (claim 13) recites refinements to the additional elements of claim 1.
Claim 6 (claim 16) recites an abstract idea mental process step (“…ranking of at least two super-features of the plurality of super-features based on the importances of the at least two super-features.”)
Claim 7 (claim 17) recites refinements to the additional elements of claim 1.
Claim 8 (claim 18) recites additional elements found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
Claim 9 (claim 19) recites an abstract idea mental process step (“populating…a feature vector…”).
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
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.
Claim(s) 1-3, 6-9, 11-13, and 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Au et al. (Grouped Feature Importance and Combined Features Effect Plot, 2021), hereinafter Au and Ghai et al. (Explainable Active Learning (XAL): Toward AI Explanations as Interfaces for Machine Teachers, 2020), hereinafter Ghai.
In regards to claim 1: The present invention claims: “A method comprising: defining a plurality of super-features that each contain a respective disjoint subset of features of a plurality of features;” Au teaches “…we extend this existing definition of permutation importance to groups of features and introduce the GPFI (Grouped Permutation Feature Importance)” (Page 8, Section 2.2). Section 2.2.1 first paragraph also teaches groups of features.
“an unsupervised machine learning (ML) model inferring a particular inference for a particular tuple that is based on the plurality of features;” Au teaches their method with both supervised and unsupervised dimension reduction. (Section 4.1-4.2). The Examiner interprets this claim limitation broadly in the absence of more technical or structural detail. Au teaches “assume that there is an unknown functional relation f : X -> Y. ML algorithms try to learn this functional relationship using n ϵ N i.i.d. observations drawn from the joint space X x Y with unknown probability distribution P.” (Page 6, Section 2.1).
“performing for each super-feature of the plurality of super-features: randomly selecting a plurality of permuted values from original values of the super-feature in a plurality of unlabeled original tuples that are based on the plurality of features,” Au teaches “Consider a p-dimensional feature space X = (X1 x … x Xp) and a one dimensional target space Y. The corresponding random variables, which are generated from these spaces are denoted by X = (X1, …, Xp) and Y.” (Page 6, Section 2.1). Au makes no reference to labeling the feature groups in their algorithm(s).
“generating a plurality of permuted tuples, wherein each permuted tuple of the plurality of permuted tuples is based on said particular tuple and a respective permuted value of the plurality of permuted values,” Au teaches “Here…is a |G|-dimensional random vector of features, which is an independent replication of X-G := (Xj)…. Also this random vector is independent of both the target variable and the random vector of remaining features…” (Page 8, Section 2.2.1).
“and the unsupervised ML model inferring a respective permuted inference for each permuted tuple of the plurality of permuted tuples;” Au teaches calculation a feature importance (inference) based on the groups (tuples) of features (Page 8, Section 2.2.1, Equation 8.)
“training, based on the permuted inferences, a surrogate model;” While a different algorithm, Au teaches generating another model (surrogate model), learned on the output from a single group (Page 10, Section 2.3.2). Given that Au indicates using such a separate model can be useful when computational costs are high or resources are low (Section 2.3.2 first paragraph), it would have been obvious to one of ordinary skill in the art to incorporate various aspects of the known algorithms taught by Au.
“calculating, for each super-feature of the plurality of super-features, an importance of the super-feature based on the surrogate model;” Au teaches methods of calculating the importance of a feature group directly (Page 8, Section 2.2.1, Equation 9), as well as inferring an importance from all features other than the selected group (Page 9, Section 2.2.2, Equation 10). The algorithm of Section 2.3.2 also teaches inferring a group’s importance (Section 2.3.2, Equation 13).
“wherein the method is performed by one or more computers.” See above for Au’s method and Section 5, where the method is tested on computing devices.
While Au teaches the CFEP, Au fails to explicitly teach:
“and generating, based on said calculating, and displaying a local explanation of the unsupervised ML model that is at least one explanation selected from a group consisting of: a local explanation that indicates a most important super-feature and a local explanation that excludes a super-feature that has an importance that is below a threshold;” However, Ghai, in a similar field of endeavor of XAI, teaches “Our work leverages local explanations to accompany AL algorithms’ instance queries. Compared to other approaches including example based and rule based explanations [35], Feature importance [35, 72] is the most popular form of local explanations. It justifies the model’s decision for an instance by the instance’s important features indicative of the decision (e.g., “because the patient shows symptoms of sneezing, the model diagnosed him having a cold”). Local feature importance can be generated by different XAI algorithms depending on the underlying model and data. Some algorithms are model-agnostic [62, 72], making them highly desirable and popular techniques. Local importance can be presented to users in different formats [59], such as described in texts [27], or by visualizing the importance values [19, 69].” (Page 6).
Ghai teaches “We suspect that, besides algorithmic interest, the reason is that it is much easier for lay people to consider keywords as top features for text classifiers compared to other types of data. For example, one may come up with keywords that are likely indicators for the topic of “baseball”, but it is challenging to rank the importance of attributes in a tabular database of job candidates. One possible solution is to allow people to access the model’s own reasoning with features and then make incremental adjustments. This idea underlies recent research into visual analytical tools that support debugging or feature engineering work [40, 47, 96].” (Page 5). Additionally, per the Applicant’s instant Specification (Background), the use of local versus global explanations may improve accuracy of the explanation. It would have been obvious to one of ordinary skill in the at the time of the Applicant’s filing to combine the feature importance system of Au with local explanation knowledge and benefits expressed in Ghai in order to realize benefits to AI result explanation and visualization.
In regards to claim 2: The present invention claims: “accessing the value of a super-feature of an unlabeled original tuple of the plurality of unlabeled original tuples based on an offset into an array that consists of values of the subset of features of the super-feature of the plurality of unlabeled original tuples.” Au teaches “Here, X[j]…is the p dimensional random variable vector of features, where ~Xj is an independent replication of Xj. The random variable ~Xj has the same distribution as Xj , but is independent of all other features and the target variable. In practice, this is done by permuting the values in the data column of the jth feature. The idea behind this method is to break the association between the jth feature and the target variable by permuting the feature values.” (Page 7, Section 2.2 The Examiner interprets this limitation broadly based on the generic use of “offset.” In the absence of more technical detail, the Examiner interprets this merely as a data structure access a person of ordinary skill in the art at the time of Au and Ghai’s writing would have been reasonably aware of, and aware of how to iterate through or access a tabular dataset such as a “jth feature”).
In regards to claim 3: The present invention claims: “wherein a first super-feature of the plurality of super-features contains more features than a second super-feature of the plurality of super-features.” Au teaches “In this illustrative example, we have two predefined groups of features where the first group contains x1, x2 and x3 and features x4 and x5 belong to the second group.” (Page 23, Section 4.3)
In regards to claim 6: The present invention claims: “wherein the local explanation comprises a ranking of at least two super-features of the plurality of super-features based on the importances of the at least two super-features.” Ghai teaches “For example, one may come up with keywords that are likely indicators for the topic of “baseball”, but it is challenging to rank the importance of attributes in a tabular database of job candidates. One possible solution is to allow people to access the model’s own reasoning with features and then make incremental adjustments” (Page 5, one of ordinary skill in the art combining Au and Ghai would reasonably rank the features based on importance).
In regards to claim 7: The present invention claims: “wherein at least one selected from the group consisting of: said plurality of unlabeled original tuples does not include said particular tuple, the values of a particular super-feature of the plurality of super-features of the plurality of unlabeled original tuples do not contain a value of the particular super-feature in the particular tuple, and the values of the plurality of features in the plurality of unlabeled original tuples do not contain the value of a particular feature of the plurality of features in the particular tuple.” Au teaches “Also this random vector is independent of both the target variable and the random vector of remaining features,” (Page 8, Section 2.2.1, mapping to the particular group not being included or being distinct from the other feature groups).
In regards to claim 8: The present invention claims: “wherein a particular super-feature of the plurality of super- features represents one selected from the group consisting of: a database connection, a database table, query criteria, a result of a database statement, and a kind of database statement.” See above where Au teaches what kinds of data may be used, see also where Au teaches testing their algorithm(s) on a dataset of mobile data (Section 5, the Examiner interprets “a result of a database statement” broadly as merely a dataset resultant from a query. A person of ordinary skill in the art at the time of Au’s writing would be aware of how to obtain such a tabular dataset from a database or other storage medium, regardless of whether Au explicitly teaches such a medium).
In regards to claim 9: The present invention claims: “wherein said training the surrogate model comprises populating at least one selected from the group consisting of: a feature vector that identifies at least one unlabeled original tuple of the plurality of unlabeled original tuples, a feature vector that contains an identifier of the particular tuple, a feature vector that does not contain a Boolean, a feature vector that contains at least one array offset, and a feature vector that contains only integers.” Au teaches “While it may be too limiting to estimate the performance of a model based on one feature only, it can be informative to see how much a group of features(e.g., all measurements from a specific medical device) can reduce the expected loss in contrast to a null model. The Leave-One-Group-In (LOGI) method could be particularly helpful in settings where information on additional groups of measures will inflict significant costs (e.g., adding functional imaging data for a diagnosis)” (Page 10, Section 2.3.2, a person of ordinary skill in the art at the time of Au’s writing would know to include, obtain, or generate a unique identifier, such as a database row or tuple ID, when working with predicting data from unique ore specific machines or with unique or specific patients).
In regards to claim 11-13 and 16-19: Claims 11-13 and 16-19 recites similar limitations to claims 1-3 and 6-9, with the exception of “One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause…” therefore, both sets of claims are similarly rejected.
Conclusion
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 GRIFFIN T BEAN whose telephone number is (703)756-1473. The examiner can normally be reached M - F 7:30 - 4:30.
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/GRIFFIN TANNER BEAN/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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