DETAILED ACTION This action is in response to claims filed 25 July 2023 for application 1838113 filed 25 July 2023. Currently claims 1-18 are pending. 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. Claim Rejections - 35 USC § 103 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis ( i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 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 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Chan et al. (Learning Arbiter and Combiner Trees from Partitioned Data for Scaling Machine Learning) in view of Li et al. ( Distributed Spatio-Temporal k Nearest Neighbors Join ) . Regarding claims 1, 7 and 13 , Chan discloses: A computer-implemented method for accelerated learning, the method comprising: partitioning an independent variable of input data into partitions (P3 Fig 2, Training sets T1-T4 are partitions of the input variable) creating data samples in each of the partitions, each of the data samples representing the input data and having less granularity (P3 Fig 2, Training sets T1-T4 are partitions of the input variable) ; for each of the partitions, training a machine learning model independently on each of the data samples (p3 Fig 2, Classifiers C1-C4 are independently trained on their respective training data subset) ; for each of the partitions, comparing results of training on the data samples (Fig 2 and p3 §Combiner Tree, classifiers are compared to each other by an arbiter or combiner which are trained on data from a validation set) ; for each of one or more partitions in which results of training on a predetermined number of the data samples are statistically identical at a predetermined confidence level, outputting a result of training on one of the data samples (P3 §Arbiter Tree ¶4, the best of the compared classifiers and the arbiter is the output result taken from the winning classifier, “In our experiments, we varied the number of equisized subsets of training data from 2 to 64 ensuring each was disjoint but with proportional distribution of examples of each class (i.e., the ratio of examples in each class in the whole data set is preserved).” p4 §Experiments and results ¶3 subset selection with proportional distributions is interpreted as statistically identical at a predetermined confidence level) ; for each of one or more rest partitions in which no result of training on the data samples has been outputted, merging each pair of the data samples (P3 §Arbiter Tree ¶4, after comparison data is merged with the winning classifier) ; and for each of the one or more rest partitions, using merged data samples to train the machine learning model (Fig 2 the tree can be an arbitrary number of levels, p3 §Arbiter Tree ¶4, levels closer to the top use merged data. An Arbiter is also a classifier trained on the data) . Chan does not explicitly disclose, however, Li teaches: with respect to a space/time measurement (“Data Partition for 𝑆 . In this step, as shown in Fig. 2(a), we divide 𝑆 into several spatio-temporal partitions (ST-partitions), where the numbers of objects in different partitions are almost the same to achieve a good load balance.” P4 §3) . Chan and Li are in the same field of endeavor of data partitioning and machine learning and are analogous. Chan discloses a system that partitions data and then merges it based on the outputs of classifiers trained on each subset. Li discloses partitioning of spatio-temporal data. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the known partitioning and training system disclosed by Chan to utilize the partitioned spatio-temporal data as taught by Li to yield predictable results of more efficient partitioning ad learning with the very large dimensionality of spatio-temporal data (Li p1 §1 ¶2). Regarding claims 2 , 8 and 1 4 , Chan discloses: The computer-implemented method of claim 1, further comprising: in response to determining that the results of training on the predetermined number of the data samples are statistically identical at the predetermined confidence level for all the partitions, for each of the partitions, outputting a result of training on one of the data samples (P3 §Arbiter Tree ¶4, the best of the compared classifiers and the arbiter is the output result taken from the winning classifier) . Regarding claims 3 , 9 and 1 5 , Chan discloses: The computer-implemented method of claim 1, further comprising: in response to determining that a partition of the one or more rest partitions has only single merged data sample, outputting a result of training on the single merged data sample for the partition (P3 §Arbiter Tree ¶4, after comparison data is merged with the winning classifier) . Regarding claims 4 , 10 and 1 6 , Chan discloses: The computer-implemented method of claim 1, further comprising: for each of the one or more rest partitions, comparing results of training on the merged data samples (P3 §Arbiter Tree ¶4, after comparison data is merged and the next level classifiers are compared) ; and for each of the one or more rest partitions in which results of training on a predetermined number of the merged data samples are statistically identical at the predetermined confidence level, outputting a result of training on one of the merged data samples (Fig 2 T12 and T34 and output of A12 or A34) . Regarding claims 5 , 11 and 1 7 , Chan discloses: The computer-implemented method of claim 4, further comprising: for each of the one or more rest partitions in which the results of training on the predetermined number of the merged data samples are not statistically identical at the predetermined confidence level, merging each pair of the merged data samples (Fig 2 T12 and T34) . Regarding claims 6 , 12 and 1 8 , Chan discloses: The computer-implemented method of claim 1, further comprising: for each of the one or more rest partitions, comparing results of training on the merged data samples (Fig 2 T12 and T34 and comparison at A12 or A34) ; and in response to determining that results of training on a predetermined number of the merged data samples are statistically identical at the predetermined confidence level for all the one or more rest partitions, for each of the one or more rest partitions, outputting a result of training on one of the merged data samples (Fig 2 T12 and T34 and output of A12 or A34) . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT ERIC NILSSON whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-5246 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-F: 7-3 . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FILLIN "SPE Name?" \* MERGEFORMAT James Trujillo can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (571)-272-3677 . 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. /ERIC NILSSON/ Primary Examiner, Art Unit 2151