CTNF 17/203,101 CTNF 86506 DETAILED ACTION 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. This action is responsive to communications: RCE filed on 1/8/2026. Claims 1,3, 6-10, 12,15-20 are pending. Claims 1, 10 and 19 are independent. Claims 4 and 13 are newly canceled. The previous rejection of claims 1,3, 6-10, 12,15-20 under 35 USC § 103 have been withdrawn in view of the amendment. 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 [1,3,6,-10,12, 15-20] are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. With regards to claim 1, Step 2A, Prong 1 Claim 1 recites: at least one data processor; and at least one memory storing instructions which, when executed by the at least one data processor, result in operations comprising: receiving a plurality of objects from one or more of a plurality of client devices via a network; preprocessing the plurality of objects by at least normalizing one or more terms of the plurality of objects, wherein the preprocessing further comprises identifying a first term from the one or more terms as a maximization term that provides an optimum result when a value for the maximization term is maximized and further comprises negating the first term ; calculating total weightage values for each of the plurality of objects based on the normalized one or more terms ; generating , using an unsupervised learning clustering algorithm configured to perform gap analysis on the total weightage values, individual gap values associated with each one of the total weightage values and an average gap value across all of the total weightage values , the unsupervised learning clustering algorithm being executed by a server in communication with the plurality of client devices via the network; wherein the gap analysis performed by the unsupervised learning clustering algorithm comprises 1) assigning a first total weightage value to a first cluster when a gap between the first total weightage value and a second total weightage value is less than or equal to the average gap value and 2) assigning the first total weightage value to a second cluster when the gap between the first total weightage value and the second total weightage value is more than the average gap value ; identifying, using the unsupervised learning clustering algorithm, at least one of a favorable outlier and an unfavorable outlier among the plurality of objects based on a comparison between the individual gap values and the average gap value ; in response to identifying an unfavorable outlier, removing the identified unfavorable outlier from the plurality of objects ; and in response to removing the identified unfavorable outlier, providing at least one of the remaining plurality of objects . The broadest reasonable interpretation of the bolded limitations above are directed to a mental process able to be performed in the human mind or by a human using pen and paper. A human can determine a maximization term, calculate values, calculate differences or gaps between values, comparing a value to an average value, and removing a value from a list. Step 2A, Prong 1 (Yes). Step 2A, Prong 2 The additional elements in this claim include “at least one data processor”, “at least one memory”, and “a server in communication with the plurality of client devices via the network”. This element is recited at a high level of generality and thus is a generic computer component performing computer functions. Thus these are mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f). The additional elements also include “receiving a plurality of objects from one or more of a plurality of client devices via a network”. This element is directed to receiving information which is understood to be insignificant extra-solution activity. See MPEP 2106.05(g). Even when viewed in combination the additional elements do not integrate the recited judicial exception into a practical application. Step2A, Prong 2 (No). Step 2B As explained with respect to Step 2A, the only additional elements are “at least one data processor”, “at least one memory”, “a server in communication with the plurality of client devices via the network”, and “receiving a plurality of objects from one or more of a plurality of client devices via a network” is which at best is mere instructions to apply the abstract and mere data gathering, which cannot provide an inventive concept, even when considered in combination. Step 2B (No). Claim 1 is ineligible. With respect to claims 10 and 19. These claims are similar in scope to Claim 1 and are rejected under a similar rationale. The processors and memory recited in these claim are also generic computing components. Claims 10 and 19 are ineligible . Dependent Claims: Claims 3, 6, 7, 9, 12, 16, 18, and 20: These claims only recite further abstract ideas (mental processes) and thus are ineligible . Claims 8 and 17 recites further generic computer components (“user interface”) and as explained above these do not provide a practical application or inventive concept and this are ineligible . 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) 1, 6-8, 10, 15-17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dodson et al. ( US2018/0316707 ) in view of Rogers et al. ( US2013/0085785 ) and Fife et al. ( US2020/0006946 ) and Wilkinson (“ Visualizing Big Data Outliers through Distributed Aggregation ”) and Basavarajappa et al ( US 2019/0026174 ) . In regards to claim1, Dodson et al. substantially discloses a system, comprising: at least one data processor ( Dodson et al. para[0005] ); and at least one memory storing instructions which, when executed by the at least one data processor, result in operations comprising: receiving a plurality of objects from one or more of a plurality of client devices via a network( Dodson et al. para[0092] , receives raw data and converts each instance into a feature vector, para[0025] , the system 105 can be coupled with an input source 110, that can comprise a plurality of computing systems arranged as a network); preprocessing the plurality of objects by at least normalizing one or more terms of the plurality of objects ( Dodson et al. fig. 6 604 para[0094] , normalizes values of feature vectors for each instance); identifying, using the unsupervised learning clustering algorithm, at least one of an unfavorable outlier among the plurality of objects ( Dodson et al. para[0053] , uses unsupervised learning clustering to identify outliers); in response to identifying an unfavorable outlier, removing the identified unfavorable outlier from the plurality of objects ( Dodson et al. para[0075] , removes outlier considered to be of negative value); and in response to removing the identified unfavorable outlier, providing at least one of the remaining plurality of objects ( Dodson et al. para[0086] , displays remaining data as clustering maps). Dodson does not explicitly disclose identifying at least one of a favorable outlier and an unfavorable outlier among the plurality of objects. However Rogers et al. substantially discloses identifying at least one of a favorable outlier and an unfavorable outlier among the plurality of objects ( Rogers et al. para[0026] , identifies favorable and unfavorable outliers). It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the anomaly detection method of Dodson et al. with the monitoring method of Rogers et al. in order to identify and encourage best practices ( Rogers et al. para[0026] ). Dodson does not explicitly disclose wherein the preprocessing further comprises identifying a first term from the one or more terms as a maximization term that provides an optimum result when a value for the maximization term is maximized and further comprises negating, the first term. However Fife et al. substantially discloses wherein the preprocessing further comprises identifying a first term from the one or more terms as a maximization term that provides an optimum result when a value for the maximization term is maximized and further comprises negating, the first term ( Fife et al. para[0103] , negates terms to find a minimum instead of a maximum, para[0154] , identifies set of parameters to be optimized to minimize cost function). It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the anomaly detection method of Dodson et al. with the optimization method of Fife et al. in order to identify actions which provide economically optimal control ( Fife et al. para[0005] ). Dodson et al. does not explicitly disclose calculating total weightage values for each of the plurality of objects based on the normalized one or more terms; generating, using an unsupervised learning clustering algorithm configured to perform gap analysis on the total weightage values, individual gap values associated with each one of the total weightage values and an average gap value across all of the total weightage values, the unsupervised learning clustering algorithm being executed by a server in communication with the plurality of client devices via the network; based on a comparison between the individual gap values and the average gap value. However Wilkson discloses calculating total weightage values for each of the plurality of objects based on the normalized one or more terms ( Wilkinson pg259 section 3.2 para2 , Normalization is commonly done in clustering and outlier detection. This prevents variables with large variances having disproportional influence on Euclidean distances.); generating, using an unsupervised learning clustering algorithm configured to perform gap analysis on the total weightage values, individual gap values associated with each one of the total weightage values and an average gap value across all of the total weightage values, the unsupervised learning clustering algorithm being executed by a server in communication with the plurality of client devices via the network ( Wilkinson pg259 section 3.2 para8 , produces thousands of balls rather than a few clusters. In rare instances the resulting exemplars and members can be dependent on the order of the data, but not enough to affect identification of outliers because of the large number of exemplars produced.); based on a comparison between the individual gap values and the average gap value ( Wilkinson pg257 section 2.1.3 para5 , Wainer and Schacht adapted Tukey’s gapping idea for a version of the test that weighted extreme values more than middle ones.). It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the anomaly detection method of Dodson et al. with the outlier identification method of Wilkinson in order to identify unusual scores in the middle of the distributions as well as in the extremes ( Wilkinson pg257 section 2.1.3 para3 ). Dodson et al. does not explicitly disclose wherein the gap analysis performed by the unsupervised learning clustering algorithm comprises 1) assigning a first total weightage value to a first cluster when a gap between the first total weightage value and a second total weightage value is less than or equal to the average gap value and 2) assigning the first total weightage value to a second cluster when the gap between the first total weightage value and the second total weightage value is more than the average gap value; However Basavarajappa et al. discloses wherein the gap analysis performed by the unsupervised learning clustering algorithm comprises 1) assigning a first total weightage value to a first cluster when a gap between the first total weightage value and a second total weightage value is less than or equal to the average gap value and 2) assigning the first total weightage value to a second cluster when the gap between the first total weightage value and the second total weightage value is more than the average gap value ( Basavarajappa et al. fig. 4 para[0048]-[0049], a first weightage value (node 414) is assigned to first cluster (410) if gap value (similarity-distance) between first weightage value (node 414) and second weightage value (node 412) is less than distance between first weightage value (node 414) and another weightage value (node 422)). It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the anomaly detection method of Dodson et al. with the statistical analysis method of Basavarajappa et al. in order to cluster values based on weightage matrices (Basavarajappa et al. para[0039] ). In regards to claim 6, Dodson et al. as modified by Rogers et al., Fife et al., Wilkinson, and Basavarajappa et al. discloses the system of claim 1, wherein the normalizing includes determining a z-score for the one or more terms for each of the plurality of objects ( Dodson et al. para[0055], determines z-score (anomaly score) identifying and normalizing outlier values). In regards to claim 7, Dodson et al. as modified by Rogers et al., Fife et al., Wilkinson, and Basavarajappa et al. discloses the system of claim 1, wherein the calculating of the total weightage values comprises determining a sum of the normalized one or more terms for each of the plurality of objects ( Dodson et al. para[0095] ). In regards to claim 8, Dodson et al. as modified by Rogers et al., Fife et al., Wilkinson, and Basavarajappa et al. discloses the system of claim 1, wherein the providing at least one of the remaining plurality of objects comprises: generating a user interface including an indication of the at least one of the remaining plurality of objects including the favorable outlier ( Dodson et al. para[0078] ); and causing the generated user interface to be presented at a client device among the plurality of client devices ( Dodson et al. para[0080] ). Claims 10 and 15-17 recite substantially similar limitations to claims 1, and 6-8. Thus claims 10 and 15-17 are rejected along the same rationale as claims 1 and 6-8. Claim 19 recites substantially similar limitations to claim 1. Thus claim 19 is rejected along the same rationale as claim 1. In regards to claim 20, Dodson et al. as modified by Rogers et al., Fife et al., Wilkinson, and Basavarajappa et al. discloses the system of claim 1, wherein at least one of the plurality of objects comprises a document including one or more terms, wherein the first term comprises a quality term that provides optimum result when the value for the quality term is maximized and wherein the quality term is negated before the total weightage values are calculated ( Fife et al. fig. 2para[0101] , reads configuration, external inputs, and process variables to identify control variables that improve achievement of objectives, para[0103] identify variables to be minimized or maximized, a maximization function can be converted to a minimization function by negating the function). It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the anomaly detection method of Dodson et al. with the optimization method of Fife et al. in order to identify actions which provide economically optimal control ( Fife et al. para[0005] ) . 07-21-aia AIA Claim (s) 3, 9, 12 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dodson et al. in view of Rogers et al., Fife et al., Wilkinson, Basavarajappa et al. and McEntire et al. ( US11,704,576 ) . In regards to claim 3, Dodson et al. as modified by Rogers et al., Fife et al., Wilkinson, and Basavarajappa et al. discloses the system of claim 1. Dodson et al. does not explicitly disclose wherein the unsupervised learning clustering algorithm further comprises: sorting the total weightage values calculated for the plurality of objects. However McEntire et al. substantially discloses wherein the unsupervised learning clustering comprises: sorting the total weightage values calculated for the plurality of objects ( McEntire et al. col49 ln40-55, organizes aggregate values into voxels). It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the with the co-associated cluster identification method of McEntire et al in order to identify covariates to optimize clustering ( McEntire et al. col49 ln13-25 ). In regards to claim 9, Dodson et al. as modified by Rogers et al., Fife et al., Wilkinson, and Basavarajappa et al. discloses the system of claim 1. Dodson et al. does not explicitly disclose However McEntire et al. substantially discloses wherein plurality of objects comprise a plurality of bids ( McEntire et al. col22 ln37-45 ). It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the with the bids of McEntire et al in order to identify the best price being offered ( McEntire et al. col21 ln33-48 ). Claims 12 and 18 recite substantially similar limitations to claims 3, and 9. Thus claims 12 and 18 are rejected along the same rationale as claims 3 and 9. Response to Arguments Applicant’s arguments with respect to claims 1, 3, 6-10, 12, and 15-20 have been considered but are moot because the arguments do not apply the current rejection. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS HASTY whose telephone number is (571)270-7775. The examiner can normally be reached Monday-Friday 8:30am-5:00pm. 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, Matt Ell can be reached at (571)270-3264. 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. /N.H/Examiner, Art Unit 2141 /MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141 Application/Control Number: 17/203,101 Page 2 Art Unit: 2141 Application/Control Number: 17/203,101 Page 3 Art Unit: 2141 Application/Control Number: 17/203,101 Page 4 Art Unit: 2141 Application/Control Number: 17/203,101 Page 5 Art Unit: 2141 Application/Control Number: 17/203,101 Page 6 Art Unit: 2141 Application/Control Number: 17/203,101 Page 7 Art Unit: 2141 Application/Control Number: 17/203,101 Page 8 Art Unit: 2141 Application/Control Number: 17/203,101 Page 9 Art Unit: 2141 Application/Control Number: 17/203,101 Page 10 Art Unit: 2141 Application/Control Number: 17/203,101 Page 11 Art Unit: 2141 Application/Control Number: 17/203,101 Page 12 Art Unit: 2141
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