CTNF 18/560,756 CTNF 81645 DETAILED ACTION This non-final rejection is responsive to communication filed November 14, 2023. Claims 3-7, 9-11, and 14-20 are amended by preliminary amendment. Claims 1-20 are pending in this application. 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. Priority The present application is a 371 of PCT/US2022/054397 filed 12/30/2022. Information Disclosure Statement The information disclosure statement (IDS) submitted on June 6, 2024, April 22, 2025, June 27, 2025, and August 28, 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner. 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 12, and 20 recite: selecting, from the plurality of reference data objects, N neighbor data objects based on similarity scores of the neighbor data objects with respect to the new data object, wherein the similarity score for each neighbor data object is determined based on the one or more features of the new data object and the one or more features of the neighbor data object, where N is a natural number equal to or greater than one; generating a neighborhood feature vector for the new data object using, for each neighbor data object in the N neighbor data objects, (i) the one or more labels of the neighbor data object and (ii) the similarity score of the neighbor data to the new data object; processing the neighborhood feature vector to predict the one or more labels that are missing for the new data object. The broadest reasonable interpretation of these steps is that the steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. For example, a user can mentally select reference data objects based on similarity scores, generate neighborhood feature vectors based on labels of the neighbor data object and similarity score, and process the neighborhood feature vector to predict a missing label. This judicial exception is not integrated into a practical application. The additional elements of maintaining a dataset comprising a plurality of reference data objects that each have one or more labels, one or more features, or both, wherein each label for a reference data object defines a respective category of the reference data object and each feature for the reference data object describes a characteristic of the reference data object; receiving a request to add, to the dataset, a new data object that (i) has one or more features but (ii) is missing one or more labels; and updating the dataset to include the new data object and to associate the one or more predicted labels with the new data object are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. The additional elements of “using a machine learning model to predict the one or more labels”, and one or more computers and one or more storage devices to perform claimed operations are recited at a high level of generality such that they provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the recitations of maintain, receiving, and updating are recited at a high level of generality. These elements amount to storing and retrieving information in memory and receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Further, the recitation of a machine learning model to perform predicting and a computer to perform claimed limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. Dependent claims 2 and 13 recites the additional element wherein maintaining the dataset comprising a plurality of reference data objects comprises maintaining data describing a heterogeneous graph comprising a plurality of nodes connected by edges, each node corresponding to a different one of the reference data objects in the plurality of reference data objects, each edge representing a relationship between two nodes connected by the edge. This judicial exception is not integrated into a practical application because the limitation further describes the maintained data, and thus also amounts to adding insignificant extra-solution activity. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the recitation of maintaining is recited at a high level of generality. This element amounts to storing and retrieving information in memory and is well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Even when considered in combination, this additional element represents insignificant extra-solution activity, which does not provide an inventive concept. Dependent claims 3 and 14 recite wherein maintaining the dataset comprising a plurality of reference data objects comprises maintaining the plurality of reference data objects and their associated labels and features in a relational dataset. This judicial exception is not integrated into a practical application because the limitation further describes the maintained data, and thus also amounts to adding insignificant extra-solution activity. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the recitation of maintaining is recited at a high level of generality. This element amounts to storing and retrieving information in memory and is well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Even when considered in combination, this additional element represents insignificant extra-solution activity, which does not provide an inventive concept. Dependent claims 4 and 15 recite determining the similarity score for each neighbor data object with respect to the new data object based on one of: Euclidean distance or cosine similarity in an embedding space. The broadest reasonable interpretation of this step is that the step falls within the mental process groupings of abstract ideas because it covers concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. For example, a user can mentally or manually determine a similarity score based on Euclidean distance or cosine similarity. There are no additional elements in claims 4 and 15. Therefore, the judicial exception is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Dependent claims 5 and 16 recite determining the similarity score for each neighbor data object with respect to the new data object based on one of: a pointwise mutual information (PMI) score or a bipartite score. The broadest reasonable interpretation of this step is that the step falls within the mental process groupings of abstract ideas because it covers concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. For example, a user can mentally or manually determine a similarity score based on a pointwise mutual information (PMI) score or a bipartite score. There are no additional elements in claims 5 and 16. Therefore, the judicial exception is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Dependent claims 6 and 17 recite the additional element wherein the machine learning model comprises one of: a neural network, a logistic regression model, a support vector machine (SVM), or a decision tree or random forest model. This judicial exception is not integrated into a practical application because the limitation is recited at a high level of generality such that it provides nothing more than mere instructions to implement an abstract idea on a generic computer. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitation is recited at a high level of generality such that it provides nothing more than mere instructions to implement an abstract idea on a generic computer. Even when considered in combination, this additional element represents mere instructions to implement an abstract idea or other exception on a computer. Dependent claims 7-9 and 18 recite wherein generating the neighborhood feature vector for the new data object comprises determining a concatenation of respective similarity scores of a subset of the N neighbor data objects having a particular category among the respective categories defined by their one or more labels, wherein the subset of the N neighbor data objects include neighbor data objects that each have a positive label that defines a trusted category, and wherein the subset of the N neighbor data objects include neighbor data objects that each have a negative label that defines a non-trusted category. The broadest reasonable interpretation of these limitations is that they fall within the mental process groupings of abstract ideas because the limitations cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. For example, a user can mentally or manually determine a concatenation of respective similarity scores of a subset of the N neighbor data objects. There are no additional elements in claims 7-9 and 18. Therefore, the judicial exception is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Dependent claims 10 and 19 recite: wherein each data object represents an image, video, an audio, text, or a web page. This limitation further defines data objects selected and thus is part of the mental process. There are no additional elements in claims 10 and 19. Therefore, the judicial exception is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Dependent claim 11 recites: wherein a value of N is dependent on a total number of the labels that each reference data object has. This limitation further describes the selected data objects and thus is part of the mental process. There are no additional elements in claim 11. Therefore, the judicial exception is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15 AIA Claim 1, 3, 6, 7, 10, 12, 14, 17-20 are rejected under 35 U.S.C. 102( a)(1) and (a)(2 ) as being anticipated by Garcia et al. (US 2018/0097763 A1) (‘Garcia’) . With respect to claims 1, 12, and 20, Garcia teaches a method, a system comprising one or more computers and one or more storage devices storing instructions (Fig. 26; paragraphs 282-283); and a non-transitory computer storage medium (paragraph 284) encoded with a computer program, the computer program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: maintaining a dataset comprising a plurality of reference data objects that each have one or more labels, one or more features, or both, wherein each label for a reference data object defines a respective category of the reference data object and each feature for the reference data object describes a characteristic of the reference data object (paragraphs 69-70 and 72-75); receiving a request to add, to the dataset, a new data object that (i) has one or more features but (ii) is missing one or more labels (paragraphs 87-88); selecting, from the plurality of reference data objects, N neighbor data objects based on similarity scores of the neighbor data objects with respect to the new data object, wherein the similarity score for each neighbor data object is determined based on the one or more features of the new data object and the one or more features of the neighbor data object, where N is a natural number equal to or greater than one (paragraphs 89-90); generating a neighborhood feature vector for the new data object using, for each neighbor data object in the N neighbor data objects, (i) the one or more labels of the neighbor data object and (ii) the similarity score of the neighbor data to the new data object (paragraphs 89-90 and 94); processing the neighborhood feature vector using a machine learning model to predict the one or more labels that are missing for the new data object (paragraphs 91 and 94); and updating the dataset to include the new data object and to associate the one or more predicted labels with the new data object (paragraphs 96-97). With respect to claims 3 and 14, Garcia teaches wherein maintaining the dataset comprising a plurality of reference data objects comprises maintaining the plurality of reference data objects and their associated labels and features in a relational dataset (paragraphs 49, 94, and 189). With respect to claims 6 and 17, Garcia teaches wherein the machine learning model comprises one of: a neural network, a logistic regression model, a support vector machine (SVM), or a decision tree or random forest model (paragraphs 100, 113, and 172). With respect to claims 7 and 18, Garcia teaches wherein generating the neighborhood feature vector for the new data object comprises determining a concatenation of respective similarity scores of a subset of the N neighbor data objects having a particular category among the respective categories defined by their one or more labels (paragraphs 90, 93, and 94). With respect to claims 10 and 19, Garcia teaches wherein each data object represents an image, video, an audio, text, or a web page (paragraphs 47 and 68) . Claim Rejections - 35 USC § 103 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-21-aia AIA Claim s 2 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Garcia et al. (US 2018/0097763 A1) (‘Garcia’) in view of Walker et al. (US 2020/0311110 A1) (‘Walker’) . With respect to claims 2 and 13, Garcia teaches maintaining the dataset comprising a plurality of reference data objects. Garcia does not explicitly teach wherein maintaining the dataset comprising a plurality of reference data objects comprises maintaining data describing a heterogeneous graph comprising a plurality of nodes connected by edges, each node corresponding to a different one of the reference data objects in the plurality of reference data objects, each edge representing a relationship between two nodes connected by the edge. Walker teaches wherein maintaining the dataset comprising a plurality of reference data objects comprises maintaining data describing a heterogeneous graph comprising a plurality of nodes connected by edges, each node corresponding to a different one of the reference data objects in the plurality of reference data objects, each edge representing a relationship between two nodes connected by the edge (paragraphs 11, 26, and 42). It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified the dataset of Garcia to describe a graph as taught by Walker to enable similarity determination of graph data based on improved determination of features that is based on both network and entity-specific information (Walker, abstract, paragraph 13). Further, the modification would only entail swapping one type of data for another type of data to achieve classification of data . 07-21-aia AIA Claim s 4 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Garcia et al. (US 2018/0097763 A1) (‘Garcia’) in view of Remis et al. (US 2019/0138554 A1) (‘Remis’) . With respect to claims 4 and 15, Garcia teaches determining similarity score for each neighbor data object with respect to the new data object. Garcia does not explicitly teach wherein determining the similarity score for each neighbor data object with respect to the new data object based on one of: Euclidean distance or cosine similarity in an embedding space. Remis teaches determining the similarity score for each neighbor data object with respect to the new data object based on one of: Euclidean distance or cosine similarity in an embedding space (paragraphs 56, 62, and 74). It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified the similarity score of Garcia to be determined based on one of: Euclidean distance or cosine similarity in an embedding space as taught by Remis to enable nearest neighbor search to be used to find objects most similar to a queried or target data object by using vector-based representation of objects to facilitate efficient processing and analysis on the objects using the underlying values within their respective feature vectors (Remis, abstract, paragraphs 56 and 62). Further, it would have been obvious to try or swap known similarity scoring techniques to determine similarity of neighboring data objects . 07-21-aia AIA Claim s 5 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Garcia et al. (US 2018/0097763 A1) (‘Garcia’) in view of Stokes, III et al. (US 2020/0120110 A1) (‘Stokes’) . With respect to claims 5 and 16, Garcia teaches determining similarity score for each neighbor data object with respect to the new data object. Garcia does not explicitly teach determining the similarity score for each neighbor data object with respect to the new data object based on one of: a pointwise mutual information (PMI) score or a bipartite score. Stokes teaches determining the similarity score for each neighbor data object with respect to the new data object based on one of: a pointwise mutual information (PMI) score or a bipartite score (paragraphs 34, 64, and 81). It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified the similarity score of Garcia to be determined based on one of: a pointwise mutual information (PMI) score or a bipartite score as taught by Stokes to enable degree of interrelatedness between the neighboring input vectors to be determined based on an association and/or similarity measures (Stokes, paragraph 34). Further, it would have been obvious to try or swap known similarity scoring techniques to determine similarity of neighboring data objects . 07-21-aia AIA Claim s 8, 9 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Garcia et al. (US 2018/0097763 A1) (‘Garcia’) in view of Yamamoto et al. (US 2017/0351819 A1) (‘Yamamoto’) . With respect to claim 8, Garcia teaches claim 7. Garcia does not explicitly teach wherein the subset of the N neighbor data objects include neighbor data objects that each have a positive label that defines a trusted category. Yamamoto teaches wherein the subset of the N neighbor data objects include neighbor data objects that each have a positive label that defines a trusted category (paragraphs 21, 75, 84, 90, and 136). It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified Garcia to have positive labels as taught by Yamamoto to enabled weights to be applied to good and bad data objects, and thus improve classification and determination of labels for related items (Yamamoto, abstract). The modification would further establish relatedness between data objects based on both known negative categories and known positive categories, thereby increasing the accuracy of predictions. With respect to claim 9, Garcia teaches claim 7. Garcia does not explicitly teach wherein the subset of the N neighbor data objects include neighbor data objects that each have a negative label that defines a non-trusted category. Yamamoto teaches wherein the subset of the N neighbor data objects include neighbor data objects that each have a negative label that defines a non-trusted category (paragraphs 21, 75, 84, 90, and 136). It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified Garcia to have negative labels as taught by Yamamoto to enabled weights to be applied to good and bad data objects, and thus improve classification and determination of labels for related items (Yamamoto, abstract). The modification would further establish relatedness between data objects based on both known negative categories and known positive categories, thereby increasing the accuracy of predictions. With respect to claim 11, Garcia teaches determining a value of N (paragraphs 66 and 92) Garcia does not explicitly teach wherein a value of N is dependent on a total number of the labels that each reference data object has. Yamamoto teaches wherein a value of N is dependent on a total number of the labels that each reference data object has (paragraph 84). It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified Garcia to have a value of N that is dependent on a total number of the labels that each reference data object has as taught by Yamamoto because Garcia teaches that the number of k nearest neighbors (i.e. N) can be one or greater than one to either assign (to the target object) the class label of the object whose feature vector is nearest to the feature vector of the target object being classified or to use a majority vote of the k nearest feature vectors to assign a class label (Garcia, paragraph 66). Therefore, it would be obvious to try any value of N, up to the size of neighbors. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALICIA M WILLOUGHBY whose telephone number is (571)272-5599. The examiner can normally be reached 9-5:30, EST, M-F. 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, Ajay Bhatia can be reached at 571-272-3906. 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. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ALICIA M WILLOUGHBY/Primary Examiner, Art Unit 2156 Application/Control Number: 18/560,756 Page 2 Art Unit: 2156 Application/Control Number: 18/560,756 Page 4 Art Unit: 2156 Application/Control Number: 18/560,756 Page 5 Art Unit: 2156 Application/Control Number: 18/560,756 Page 6 Art Unit: 2156 Application/Control Number: 18/560,756 Page 7 Art Unit: 2156 Application/Control Number: 18/560,756 Page 8 Art Unit: 2156 Application/Control Number: 18/560,756 Page 9 Art Unit: 2156 Application/Control Number: 18/560,756 Page 10 Art Unit: 2156 Application/Control Number: 18/560,756 Page 11 Art Unit: 2156 Application/Control Number: 18/560,756 Page 12 Art Unit: 2156 Application/Control Number: 18/560,756 Page 13 Art Unit: 2156 Application/Control Number: 18/560,756 Page 14 Art Unit: 2156 Application/Control Number: 18/560,756 Page 15 Art Unit: 2156 Application/Control Number: 18/560,756 Page 16 Art Unit: 2156
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