CTNF 18/379,403 CTNF 101739 DETAILED ACTION This office action is in response to the application filed on October 12, 2023. Claims 1-20 are pending and have been examined. Claims 1-20 are rejected. 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. 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 non-statutory subject matter. According to the USPTO guidelines, a claim is directed to non-statutory subject matter if: Step 1 : The claim does not fall within one of the four statutory categories of invention (process, machine, manufacture, or composition of matter) – see MPEP 2106.03 , or, Step 2 : The claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis – see MPEP 2106.04 : Step 2A, Prong 1 : Does the claim recite an abstract idea, law of nature, or natural phenomenon? Step 2A, Prong 2 : Does the claim recite additional elements that integrate the judicial exception into a practical application? Step 2B : Does the claim recite additional elements that amount to significantly more than the judicial exception? - see MPEP 2106.05 MPEP 2106.04(a)(2)(I) states: “The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations.” MPEP 2106.04(a)(2)(III) states: “Accordingly, the “mental processes” abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgements, and opinions. Further, the MPEP states: “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g. pen and paper or a slide run) to perform the claim limitation. Using the two-step inquiry, it is clear that Claims 1-20 are each directed to non-statutory subject matter as shown below: Please note the following: The following groups of claims are expressed in different statutory categories: Claims 1-10 are directed to a method for removing deficiencies from a dataset. Claims 11-17 are directed to a system for removing deficiencies for a dataset, where the system is comprised of a processor and memory storing instructions that cause the processor to perform a set of operations when executed. Claims 18-20 are directed to a non-transitory computer-readable medium for removing deficiencies from a dataset, where the computer-readable medium stores instructions that cause the processer to perform a set of operations when executed. With respect to Claims 1, 11, and 18: Step 1 : Claim 1 is directed to a method, also known as a process, which is one of the four statutory categories of patentable subject matter. Claim 11 is directed to a system comprised of a processor and memory storing instructions, corresponding to an article of manufacture, which is one of the four statutory categories of patentable subject matter. Claim 18 is directed to a non-transitory computer readable medium on which computer program instructions are stored, corresponding to an article of manufacture, which is one of the four statutory categories of patentable subject matter. Step 2A, Prong 1 : A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas: “and removing deficiencies from the first training dataset, wherein the removing the deficiencies comprises: determining that the first training dataset includes a first set of training deficiencies;” (Determining that a first training dataset includes a first set of training deficiencies covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.) “determining that the first updated training dataset includes a second set of training deficiencies” (Determining that a first updated training dataset includes a second set of training deficiencies covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.) “rectifying the first deficiency by combining the first training dataset with the first remediating data to produce a first updated training dataset that comprises the first training dataset and the first remediating data;” ; (Rectifying a first deficiency by combining a first training dataset with a first remediating data to produce a first updated training dataset covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.) “and synthesizing second remediating data that rectifies a second deficiency from the second set of training deficiencies.” (Retrieving data from a data source that rectifies a first deficiency from a first set of training deficiencies from a data set containing real-world data covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.) Step 2A, Prong 2 : The claims do not recite additional elements that integrate the judicial exception into a practical application: “A method for removing deficiencies from a dataset, the method comprising: obtaining, via an input of the synthetic training data generation tool, a first training dataset;” (Obtaining a first training dataset through the input of data within a synthetic training data general tool is regarded as a generic computer function of inputting and outputting data. Mere data gathering is considered insignificant extra-solution activity – see MPEP 2106.05(g).) “retrieving, from at least one data source, first remediating data that rectifies a first deficiency from among the first set of training deficiencies, wherein the first remediating data comprises real-world data;” (Retrieving data from a data source that rectifies a first deficiency from a first set of training deficiencies from a data set containing real-world data is regarded as a generic computer function of gathering data. Mere data gathering is considered insignificant extra-solution activity – see MPEP 2106.05(g).) Step 2B : The claims do not recite additional elements that amount to significantly more than the judicial exception. Obtaining a first training dataset through the input of data within a synthetic training data general tool is regarded as a generic computer function of inputting and outputting data. Mere data gathering is considered insignificant extra-solution activity – see MPEP 2106.05(g). Retrieving data from a data source that rectifies a first deficiency from a first set of training deficiencies from a data set containing real-world data is regarded as a generic computer function of gathering data. Mere data gathering is considered insignificant extra-solution activity – see MPEP 2106.05(g). With respect to Claim 2: Step 2A, Prong 1 : Inherits the limitations and abstract ideas from Claim 1. Step 2A, Prong 2 : The claim does not recite additional elements that integrate the judicial exception into a practical application: “wherein the first set of training deficiencies comprises at least one from among a set of new training data, a set of incomplete training data, a set of missing training data, a set of irregular data, a set of training biases, and a set of unavailable hypothetical scenario data” (A first set of training deficiencies comprised of at least one from among a set of new training data, a set of incomplete training data, a set of missing training data, a set of irregular data, a set of training biases, and a set of unavailable hypothetical scenario data generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h).) Step 2B : The claim does not recite additional elements that amount to significantly more than the judicial exception. A first set of training deficiencies comprised of at least one from among a set of new training data, a set of incomplete training data, a set of missing training data, a set of irregular data, a set of training biases, and a set of unavailable hypothetical scenario data generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h). With respect to Claim 3, 12, and 19: Step 2A, Prong 1 : Inherits the limitations and abstract ideas from Claims 1, 11, and 18. An additional judicial exception is recited in the claims as they recite mental processes, which are abstract ideas: “wherein the synthesizing comprises utilizing at least one from among an interpolation and an extrapolation to produce a first refined training dataset from the first updated training dataset, (Synthesizing comprised of utilizing at least one from among an interpolation and an extrapolation to produce a first refined training dataset from a first updated training dataset covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.) Step 2A, Prong 2 : The claims do not recite additional elements that integrate the judicial exception into a practical application: and wherein the first refined training dataset comprises the first training dataset, the first remediating data, and the second remediating data” ; (A first refined training dataset comprised of a first training dataset, a first remediating data, and a second remediating data generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h).) Step 2B : The claims do not recite additional elements that amount to significantly more than the judicial exception. A first refined training dataset comprised of a first training dataset, a first remediating data, and a second remediating data generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h). With respect to Claims 4, 13, and 20: Step 2A, Prong 1 : Inherits the limitations and abstract ideas from Claims 3, 12, and 19. An additional judicial exception is recited in the claims as they recite mental processes, which are abstract ideas: “wherein the removing the deficiencies further comprises: determining that the first refined training dataset includes a third set of training deficiencies;” (Determining that a first refined training dataset includes a third set of training deficiencies covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.) “generating third remediating data that rectifies a third deficiency from the third set of training deficiencies;” (Generating third remediating data that rectifies a third deficiency from a third set of training deficiencies covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.) “and rectifying the third deficiency by integrating the third remediating data into the first refined training dataset to produce a first synthesized training dataset that comprises the first refined training dataset and the third remediating data” ; (Rectifying a third deficiency by integrating a third remediating data into a first refined training dataset covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.) Step 2A, Prong 2 : The claims do not recite additional elements that integrate the judicial exception into a practical application. Step 2B : The claims do not recite additional elements that amount to significantly more than the judicial exception. With respect to Claims 5 and 14: Step 2A, Prong 1 : Inherits the limitations and abstract ideas from Claims 4 and 13. An additional judicial exception is recited in the claims as they recite mental processes, which are abstract ideas: “wherein the generating the third remediating data comprises: mimicking a set of characteristics from a set of existing data, wherein the set of characteristics comprises at least one from among: user selected characteristics; and characteristics of a similar dataset that has more attributes in common with the third remediating data than another available set of data has in common with the third remediating data.” (Mimicking a set of characteristics from a set of existing data, where in the set of characteristics comprises at least one from among user selected characteristics and characteristics of a similar dataset that has more attributes in common with a third remediating data than another available set of data has in common with a third remediating data covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.) Step 2A, Prong 2 : The claims do not recite additional elements that integrate the judicial exception into a practical application. Step 2B : The claims do not recite additional elements that amount to significantly more than the judicial exception. With respect to Claim 6: Step 2A, Prong 1 : Inherits the limitations and abstract ideas from Claim 5. Step 2A, Prong 2 : The claim does not recite additional elements that integrate the judicial exception into a practical application: “wherein the set of characteristics comprises at least one from among a set of values from the first training dataset, a set of statistical features of the first training dataset, and a distribution of the first training dataset” ; (A set of characteristics comprising at least one from among a set of values from a first training dataset, a set of statistical features of a first training dataset, and a distribution of a first training dataset generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h).) Step 2B : The claim does not recite additional elements that amount to significantly more than the judicial exception. A set of characteristics comprising at least one from among a set of values from a first training dataset, a set of statistical features of a first training dataset, and a distribution of a first training dataset generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h). With respect to Claims 7 and 15: Step 2A, Prong 1 : Inherits the limitations and abstract ideas from Claims 5 and 14. An additional judicial exception is recited in the claims as they recite mental processes, which are abstract ideas: “wherein the removing the deficiencies further comprises: determining that the first synthesized training dataset includes a fourth set of training deficiencies;” ; (Determining that a first synthesized training dataset includes a fourth set of training deficiencies covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.) “generating fourth remediating data that rectifies a fourth deficiency from the fourth set of training deficiencies;” (Generating fourth remediating data that rectifies a fourth deficiency from a fourth set of training deficiencies covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.) “and rectifying the fourth deficiency by integrating the fourth remediating data into the first synthesized training dataset to produce a second synthesized training dataset that comprises the first synthesized training dataset and the fourth remediating data” (Rectifying a fourth deficiency by integrating a fourth remediating data into a first refined training dataset to produce a second synthesized training dataset covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.) Step 2A, Prong 2 : The claims do not recite additional elements that integrate the judicial exception into a practical application. Step 2B : The claims do not recite additional elements that amount to significantly more than the judicial exception. With respect to Claim 8: Step 2A, Prong 1 : Inherits the limitations and abstract ideas from Claim 7. An additional judicial exception is recited in the claim as it recites mental processes, which are abstract ideas: “evaluating the first AI/ML model to identify a fifth set of training deficiencies from a first performance of the first AI/ML model;” (Evaluating a first AI/ML model to identify a fifth set of training deficiencies from a first performance of a first AI/ML model covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.) “and upon identifying the fifth set of training deficiencies, repeating the removing the deficiencies in order to remove the fifth set of training deficiencies from the at least one from among the first training dataset, the first updated training dataset, the first refined training dataset, the first synthesized training dataset, and the second synthesized training dataset.” (Repeating the removing of deficiencies, upon identifying a fifth set of training deficiencies, from at least one from among a first training dataset, a first updated training dataset, a first refined training dataset, a first synthesized training dataset, and a second synthesized training dataset covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.) Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application: “utilizing at least one from among the first training dataset, the first updated training dataset, the first refined training dataset, the first synthesized training dataset, and the second synthesized training dataset, to train a first artificial intelligence and machine learning (AI/ML) model;” (Utilizing at least one from among a first training dataset, a first updated training dataset, a first refined training dataset, a first synthesized training dataset, and a second synthesized training dataset to train a first artificial intelligence and machine learning model only amounts to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1).) Step 2B : The claim does not recite additional elements that amount to significantly more than the judicial exception. Utilizing at least one from among a first training dataset, a first updated training dataset, a first refined training dataset, a first synthesized training dataset, and a second synthesized training dataset to train a first artificial intelligence and machine learning model only amounts to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1). With respect to Claims 9 and 16: Step 2A, Prong 1 : Inherits the limitations and abstract ideas from Claims 7 and 15. Step 2A, Prong 2 : The claim does not recite additional elements that integrate the judicial exception into a practical application: “wherein the generating the fourth remediating data comprises: utilizing at least one from among: a random number generator; user settings; and a second AI/ML model, to produce the fourth remediating data.” (Utilizing at least one from among a random number generator, user settings, and a second AI/ML model to generate a fourth remediating data generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h).) Step 2B : The claim does not recite additional elements that amount to significantly more than the judicial exception. Utilizing at least one from among a random number generator, user settings, and a second AI/ML model to generate a fourth remediating data generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h). With respect to Claims 10 and 17: Step 2A, Prong 1 : Inherits the limitations and abstract ideas from Claims 9 and 16. An additional judicial exception is recited in the claim as it recites mental processes, which are abstract ideas: “continuously monitoring the input to identify a subsequent dataset that includes a sixth set of training deficiencies;” (Continuously monitoring an input in order to identify a subsequent dataset that includes a sixth set of training deficiencies covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.) “and upon identifying the subsequent dataset, repeating the removing the deficiencies in order to remove the sixth set of training deficiencies from the subsequent dataset” ; (Repeating the removal of deficiencies from a subsequent dataset upon identifying the subsequent dataset covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.) Step 2A, Prong 2 : The claim does not recite additional elements that integrate the judicial exception into a practical application. Step 2B : The claim does not recite additional elements that amount to significantly more than the judicial exception. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-02-aia AIA 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. 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. 07-23-aia AIA 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 non-obviousness. 07-21-aia AIA Claim s 1-2, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Flores et al., (Patent Application No. US11893457B2 filed on January 15, 2020, hereinafter “Flores”), in view of Nikolenko et al., (Patent Application No. US20200320345A1 filed on April 2, 2020, hereinafter “Nikolenko”) . With respect to Claims 1, 11, and 18: Flores teaches: “A method for removing deficiencies from a dataset, the method comprising: obtaining, via an input of the synthetic training data generation tool, a first training dataset;” (Column 5, Lines 43-52 recite the collection (obtaining) of signal data (first training dataset) , which is provided (inputted) into an “Evaluation Component” (akin to a synthetic training data generation tool ). “and removing deficiencies from the first training dataset, wherein the removing the deficiencies comprises: determining that the first training dataset includes a first set of training deficiencies;” (Column 5, Lines 56-65 recite the “Evaluation Component” evaluating and analyzing the signal data (first training dataset) to identify portions of space with strong, weak, or unusable signals (a first set of training deficiencies) .) “retrieving, from at least one data source, first remediating data that rectifies a first deficiency from among the first set of training deficiencies, wherein the first remediating data comprises real-world data;” (Column 4, Lines 52-61 recite the further collection (retrieving) of additional real-world data in those areas with weak signal strength (first deficiency) within the signal data (first training dataset) in order to rectify the deficiency.) “rectifying the first deficiency by combining the first training dataset with the first remediating data to produce a first updated training dataset that comprises the first training dataset and the first remediating data;” (Column 4, Lines 30-34 recite the aggregation (combining) of additional real-word data (first remediating data) collected with the initial signal data (first training dataset) to create an aggregated data set (first updated training dataset) .). Flores does not appear to explicitly disclose: “determining that the first updated training dataset includes a second set of training deficiencies;” “and synthesizing second remediating data that rectifies a second deficiency from the second set of training deficiencies. However, Nikolenko teaches: “determining that the first updated training dataset includes a second set of training deficiencies;” (Paragraph 0018 recites the existence of an updated training dataset containing combined (hybrid) data. Paragraph 0033 further recites feedback from the system used to improve training data sets (determining a second set of training deficiencies) . This creates a feedback loop of for identifying deficiencies and providing additional training dataset, improved training datasets, and/or updated training datasets where needed.) “and synthesizing second remediating data that rectifies a second deficiency from the second set of training deficiencies.” (Paragraph 0047 recites a model analyzer(MAU) designed to process an output of an AI model and establish what changes should be made to parameter values during the generation process by a synthetic data generator (SDG) in order to improve the quality or accuracy of the model analyzer. The MAU provides data to the SDG, where the values are changed in order to improve quality, and where the SDG generates this updated synthetic data set (synthesizing second remediating data that rectifies a second deficiency from a second set of training deficiencies) . It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the present application to implement Claims 1, 11, and 18 that utilized the teachings of Flores and the teachings of Nikolenko, which are both in the same field of invention. A PHOSITA would have been motivated to combine Nikolenko’s dataset refinement and feedback loop process with Flores’s data augmentation process in order to enable continued identification of dataset deficiencies and their respective remediation, which would in turn improve the quality of datasets used and overall accuracy and performance of the AI model. With respect to Claim 2: Flores teaches: “wherein the first set of training deficiencies comprises at least one from among a set of new training data, a set of incomplete training data, a set of missing training data, a set of irregular data, a set of training biases, and a set of unavailable hypothetical scenario data” (Column 5, Lines 56-65 recite the “Evaluation Component” evaluating and analyzing the signal data (first training dataset) to identify portions of space with strong, weak, or unusable signals (a first set of training deficiencies) . It is inherently understood that weak or unusable signal data would be considered a deficiency of at least one from among a set of incomplete or missing training data since additional real-world data would need to be collected in order to make up for unusable data.) 07-21-aia AIA Claim s 3-10, 12-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Flores et al., (Patent Application No. US11893457B2 filed on January 15, 2020, hereinafter “Flores”), in view of Nikolenko et al., (Patent Application No. US20200320345A1 filed on April 2, 2020, hereinafter “Nikolenko”), in further view of “SMOTE: Synthetic Minority Over-Sampling Technique” by Chawla et al., (Non-patent literature published on June 9, 2011, hereinafter “Chawla”) . With respect to Claim 3, 12, and 19: Flores and Nikolenko combined teach: “wherein the synthesizing comprises utilizing at least one from among an interpolation and an extrapolation to produce a first refined training dataset from the first updated training dataset ,” (Column 6, Lines 47-52 from Flores recite the aggregation of simulated data and signal (real-world) data to create a (first) updated training dataset . Column 5, Lines 27-30 from Flores further recite how as more data is collected, the training set can be continuously refined in order to create an improved training dataset (first refined training dataset).) “and wherein the first refined training dataset comprises the first training dataset, the first remediating data, and the second remediating data” (Column 6, Lines 47-52 from Flores recite the aggregation of collected signal (real-world) data (first remediating data) and simulated/synthesized data (second remediating data) , adding onto an original training dataset (first training dataset) . Column 5, Lines 27-30 from Flores further recite how as more data is collected, the training set can be continuously refined in order to create an improved training dataset (first refined training dataset) . ) Flores and Nikolenko combined do not appear to explicitly disclose: “ wherein the synthesizing comprises utilizing at least one from among an interpolation and an extrapolation to produce a first refined training dataset from the first updated training dataset,” However, Chawla teaches: “ wherein the synthesizing comprises utilizing at least one from among an interpolation and an extrapolation to produce a first refined training dataset from the first updated training dataset,” (Page 8, Section 4.2 recites the generation of synthetic samples by taking the difference between the feature vector (sample) under consideration and its nearest neighbor, akin to the process of interpolation .) It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the present application to implement the claims that utilized the combined teachings of Flores and Nikolenko with the teachings of Chawla, which are all in the same field of invention. A PHOSITA would have been motivated to incorporate an interpolation technique for synthetic data generation from Chawla into the iterative dataset refinement feedback process of Flores and Nikolenko in order cover deficient data and improve dataset completeness, resulting in more representative training data and improved AI/ML model performance. With respect to Claims 4, 13, and 20: Flores and Nikolenko combined teach: “wherein the removing the deficiencies further comprises: determining that the first refined training dataset includes a third set of training deficiencies;” (Paragraph 0018 from Nikolenko recites the existence of an updated training dataset containing combined (hybrid) data. Paragraph 0033 from Nikolenko further recites feedback from the system used to improve training data sets, akin to determining that a first refined training dataset includes a third set of training deficiencies) . This creates a feedback loop of for identifying deficiencies and providing additional training dataset, improved training datasets, and/or updated training datasets where needed.) “and rectifying the third deficiency by integrating the third remediating data into the first refined training dataset to produce a first synthesized training dataset that comprises the first refined training dataset and the third remediating data” (Paragraph 0047 from Nikolenko recites a model analyzer (MAU) designed to process an output of an AI model and establish what changes should be made to parameter values during the generation process by a synthetic data generator (SDG) in order to improve the quality or accuracy of the model analyzer. The MAU provides data to the SDG, where the values are changed in order to improve quality (rectify a deficiency) , and where the SDG generates this updated synthetic data set containing a first refined training dataset and a third remediating (synthetic) data. It is inherently understood that new remediation data is constantly added (a repetitive process) to the original dataset without replacing anything.) Flores and Nikolenko combined do not appear to explicitly disclose: “generating third remediating data that rectifies a third deficiency from the third set of training deficiencies;” “ and rectifying the third deficiency by integrating the third remediating data into the first refined training dataset to produce a first synthesized training dataset that comprises the first refined training dataset and the third remediating data” However, Chawla teaches: “generating third remediating data that rectifies a third deficiency from the third set of training deficiencies;” (Page 8, Section 4.2 recites the generation of synthetic samples by taking the difference between the feature vector (sample) under consideration and its nearest neighbor, akin to the process of interpolation. This creates a third set of remediating data.) “ and rectifying the third deficiency by integrating the third remediating data into the first refined training dataset to produce a first synthesized training dataset that comprises the first refined training dataset and the third remediating data” (Page 8, Section 4.2 recites the generation of synthetic samples by taking the difference between the feature vector (sample) under consideration and its nearest neighbor, akin to the process of interpolation. This creates a third set of remediating data that can be integrated into another dataset in order to rectify a third deficiency.) With respect to Claims 5 and 14: Flores and Nikolenko combined teach: “wherein the generating the third remediating data comprises: mimicking a set of characteristics from a set of existing data, wherein the set of characteristics comprises at least one from among: user selected characteristics;” (Paragraph 0018 from Nikolenko recites synthetic data used to remediate a dataset comprising artificial data that mimics real world data (mimicking a set of characteristics from a set of existing data) . Paragraph 0025 from Nikolenko further recites synthetic data parameters can be changed (varied) during further generation of data, akin to user settings that can change parameters. It is inherently understood that Nikolenko’s feedback loop analysis to remediate deficiencies in data would mean the altered training data set is equivalent to a third remediating data set that has undergone several feedback loops.) Flores and Nikolenko combined do not appear to explicitly disclose: “and characteristics of a similar dataset that has more attributes in common with the third remediating data than another available set of data has in common with the third remediating data.” However, Chawla teaches: “and characteristics of a similar dataset that has more attributes in common with the third remediating data than another available set of data has in common with the third remediating data.” (Page 8, Section 4.2 recites the generation of synthetic samples from the most similar existing data samples (through a k nearest neighbors algorithm), akin to mimicking characteristics of a similar dataset that has more attributes in common with a remediated dataset than another set of data has in common with the same remediated dataset .) With respect to Claim 6: Flores and Nikolenko combined teach: “ wherein the set of characteristics comprises at least one from among a set of values from the first training dataset, a set of statistical features of the first training dataset, and a distribution of the first training dataset” (Paragraph 0018 from Nikolenko recites synthetic data used to remediate a dataset comprising artificial data that mimics real world data (mimicking a set of characteristics from a set of existing data akin to a set of values from a first training dataset) . Paragraph 0025 from Nikolenko further recites synthetic data parameters can be changed (varied) during further generation of data, akin to user settings that can change parameters, which can then by mimicked.) Flores and Nikolenko combined do not appear to explicitly disclose: “ wherein the set of characteristics comprises at least one from among a set of values from the first training dataset, a set of statistical features of the first training dataset, and a distribution of the first training dataset” However, Chawla teaches: “ wherein the set of characteristics comprises at least one from among a set of values from the first training dataset, a set of statistical features of the first training dataset, and a distribution of the first training dataset” (Page 8, Section 4.2 recites the generation of synthetic samples from the most similar existing data samples (through a k nearest neighbors algorithm), akin to mimicking characteristics of a similar dataset that has more attributes in common with a remediated dataset than another set of data has in common with the same remediated dataset .) With respect to Claims 7 and 15: Flore and Nikolenko combined teach: “wherein the removing the deficiencies further comprises: determining that the first synthesized training dataset includes a fourth set of training deficiencies;” (Paragraph 0018 from Nikolenko recites the existence of an updated training dataset containing combined (hybrid) data. Paragraph 0033 from Nikolenko further recites feedback from the system used to improve training data sets, akin to determining that a first synthesized training dataset includes a fourth set of training deficiencies . This creates a feedback loop of for identifying deficiencies and providing additional training dataset, improved training datasets, and/or updated training datasets where needed.) “and rectifying the fourth deficiency by integrating the fourth remediating data into the first synthesized training dataset to produce a second synthesized training dataset that comprises the first synthesized training dataset and the fourth remediating data” (Paragraph 0047 from Nikolenko recites a model analyzer (MAU) designed to process an output of an AI model and establish what changes should be made to parameter values during the generation process by a synthetic data generator (SDG) in order to improve the quality or accuracy of the model analyzer. The MAU provides data to the SDG, where the values are changed in order to improve quality (rectify a deficiency) , and where the SDG generates this updated synthetic data set (second synthesized training dataset) containing a first synthesized training dataset and a fourth remediating (synthetic) data. It is inherently understood that new remediation data is constantly added (a repetitive process) to the original dataset without replacing anything.) Flores and Nikolenko combined do not appear to explicitly disclose: “generating fourth remediating data that rectifies a fourth deficiency from the fourth set of training deficiencies;” “ and rectifying the fourth deficiency by integrating the fourth remediating data into the first synthesized training dataset to produce a second synthesized training dataset that comprises the first synthesized training dataset and the fourth remediating data” However, Chawla teaches: “generating fourth remediating data that rectifies a fourth deficiency from the fourth set of training deficiencies;” (Page 8, Section 4.2 recites the generation of synthetic samples by taking the difference between the feature vector (sample) under consideration and its nearest neighbor, akin to the process of interpolation. This creates a fourth set of remediating data.) “ and rectifying the fourth deficiency by integrating the fourth remediating data into the first synthesized training dataset to produce a second synthesized training dataset that comprises the first synthesized training dataset and the fourth remediating data” (Page 8, Section 4.2 recites the generation of synthetic samples by taking the difference between the feature vector (sample) under consideration and its nearest neighbor, akin to the process of interpolation. This creates a fourth set of remediating data that can be integrated into another dataset in order to rectify a fourth deficiency.) With respect to Claim 8: Flores and Nikolenko combined teach: “utilizing at least one from among the first training dataset, the first updated training dataset, the first refined training dataset, the first synthesized training dataset, and the second synthesized training dataset, to train a first artificial intelligence and machine learning (AI/ML) model;” (Column 6, Lines 47-52 from Flores recite the aggregation of simulated data and signal (real-world) data to create an updated training dataset fed into a “Training Component”, which means utilizing at least one of a first updated training dataset to train one or more machine learning (ML) models. ) “evaluating the first AI/ML model to identify a fifth set of training deficiencies from a first performance of the first AI/ML model;” (Paragraph 0033 from Nikolenko further recites feedback from the system used to improve training data sets (determining a fifth set of training deficiencies) . This creates a feedback loop of for identifying deficiencies and providing additional training dataset, improved training datasets, and/or updated training datasets where needed. Paragraph 0047 further recites a model analyzer (MAU) designed to process an output of an AI model and establish what changes should be made to parameter values during the generation process by a synthetic data generator (SDG) in order to improve the quality or accuracy of the model analyzer (a first performance of a first AI/ML model) .) Flores and Nikolenko combined do not appear to explicitly disclose: “and upon identifying the fifth set of training deficiencies, repeating the removing the deficiencies in order to remove the fifth set of training deficiencies from the at least one from among the first training dataset, the first updated training dataset, the first refined training dataset, the first synthesized training dataset, and the second synthesized training dataset.” However, Chawla teaches: “and upon identifying the fifth set of training deficiencies, repeating the removing the deficiencies in order to remove the fifth set of training deficiencies from the at least one from among the first training dataset, the first updated training dataset, the first refined training dataset, the first synthesized training dataset, and the second synthesized training dataset.” (Page 8, Section 4.2 recites the generation of synthetic samples by taking the difference between the feature vector (sample) under consideration and its nearest neighbor, akin to the process of interpolation. This creates a fifth set of remediating data that can be integrated into another dataset in order to remove the deficiencies.) With respect to Claims 9 and 16: Flores and Nikolenko combined do not appear to explicitly disclose: “wherein the generating the fourth remediating data comprises: utilizing at least one from among: a random number generator; user settings; and a second AI/ML model, to produce the fourth remediating data.” However, Chawla teaches: “wherein the generating the fourth remediating data comprises: utilizing at least one from among: a random number generator; user settings; and a second AI/ML model, to produce the fourth remediating data.” (Page 8, Section 4.2 recites the generation of synthetic samples (fourth remediating data) by multiplying the difference between a sample and its nearest neighbor by a random number between 0 and 1 before adding the result to the original sample, akin to the use of a random number generator to generate synthetic remediating samples.) With respect to Claims 10 and 17: Flores and Nikolenko combined teach: “continuously monitoring the input to identify a subsequent dataset that includes a sixth set of training deficiencies;” (Paragraph 0018 from Nikolenko recites the existence of an updated training dataset containing combined (hybrid) data. Paragraph 0033 from Nikolenko further recites feedback from the system used to improve training data sets, akin to continuously monitoring the input to identify a subsequent dataset that includes a sixth set of training deficiencies . This creates a feedback loop of for identifying deficiencies and providing additional training dataset, improved training datasets, and/or updated training datasets where needed.) Flores and Nikolenko combined do not appear to explicitly disclose: “and upon identifying the subsequent dataset, repeating the removing the deficiencies in order to remove the sixth set of training deficiencies from the subsequent dataset” However, Chawla teaches: “and upon identifying the subsequent dataset, repeating the removing the deficiencies in order to remove the sixth set of training deficiencies from the subsequent dataset” (Page 8, Section 4.2 recites the generation of synthetic samples by taking the difference between the feature vector (sample) under consideration and its nearest neighbor, akin to the process of interpolation. This creates a sixth set of remediating data that can be integrated into another dataset in order to remove the deficiencies.) Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Vibha Bhat whose telephone number is (571)-272-7091 . The examiner can normally be reached on Monday – Thursday from 8:00 AM to 5:00 PM EST and every other Friday from 8:00 AM to 4:00 PM EST . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. See MPEP § 713.01. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at https://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mariela Reyes , can be reached at telephone number (571)-270-1006 . The fax phone number for the organization where this application or proceeding is assigned is ( 571)-273-8300. Information regarding the status of an application 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://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 (572)-272-1000. /Vibha Bhat/Examiner Art Unit 2142 /Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142 Application/Control Number: 18/379,403 Page 2 Art Unit: 2142 Application/Control Number: 18/379,403 Page 3 Art Unit: 2142 Application/Control Number: 18/379,403 Page 4 Art Unit: 2142 Application/Control Number: 18/379,403 Page 5 Art Unit: 2142 Application/Control Number: 18/379,403 Page 6 Art Unit: 2142 Application/Control Number: 18/379,403 Page 7 Art Unit: 2142 Application/Control Number: 18/379,403 Page 8 Art Unit: 2142 Application/Control Number: 18/379,403 Page 9 Art Unit: 2142 Application/Control Number: 18/379,403 Page 10 Art Unit: 2142 Application/Control Number: 18/379,403 Page 11 Art Unit: 2142 Application/Control Number: 18/379,403 Page 12 Art Unit: 2142 Application/Control Number: 18/379,403 Page 13 Art Unit: 2142 Application/Control Number: 18/379,403 Page 14 Art Unit: 2142 Application/Control Number: 18/379,403 Page 15 Art Unit: 2142 Application/Control Number: 18/379,403 Page 16 Art Unit: 2142 Application/Control Number: 18/379,403 Page 17 Art Unit: 2142 Application/Control Number: 18/379,403 Page 18 Art Unit: 2142 Application/Control Number: 18/379,403 Page 19 Art Unit: 2142 Application/Control Number: 18/379,403 Page 20 Art Unit: 2142 Application/Control Number: 18/379,403 Page 21 Art Unit: 2142 Application/Control Number: 18/379,403 Page 22 Art Unit: 2142 Application/Control Number: 18/379,403 Page 23 Art Unit: 2142
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