CTNF 18/401,805 CTNF 87075 DETAILED ACTION 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. Information Disclosure Statement The information disclosure statements (IDSs) submitted on May 28, 2026 and May 28, 2026 are being considered by the examiner. Double Patenting 08-33 AIA The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over the claims of copending Application Nos. 18/536,422, 18/401,813, 18/401,810 and 18/401,807 (reference applications). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application are obvious variants of the claims of the co-pending applications. It is noted that through prosecution of all related cases, the double patenting rejection may change based on amendments through the course of examination of the instant and reference applications. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented. 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 s 1-5, 7-9 and 16-20 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Iyer et al. (U.S. Patent No. 11,836,163 B1, hereinafter referred to as “Iyer”) . Regarding claim 1, Iyer discloses a computing system facilitating database management and parameter control, comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and a memory device storing executable code that, when executed, causes the at least one processor to: (“The draft drift detection compute device 110 includes a processor 112 operatively coupled to a memory 114 (e.g., via a system bus). The data drift detection compute device 110 can be any type of device, such as a server, desktop, laptop, mobile device, internet of things device, and/or the like.”)(e.g., figure 1 and col 4 lines 16-22 and col 5 lines 63-67) ascertain whether data processing systems maintain reliability by monitoring data drift, the monitoring comprising: (“monitoring for “data drift” can be desirable to ensure that ML models work as desired.” “Data distributions associated with a model(s), such as a machine learning model, may shift over time (e.g., production versus deployment). Because data drift can negatively affect performance and reliability of a given model(s), such instances of data drift can be desirable to detect.” “In some implementations, the first set of data can include only high dimensional data, only low dimensional data, only structured data, only unstructured data, or a combination thereof. In some implementations, the first set of data is received from a remote compute device (e.g., at substantially real time).” “In some implementations, the data drift detection compute device 110 can perform outlier detection for data/vector representations. For example, each vector representation from the set of vector representations 118A (and/or set of vector representations 118B) can be compared to the set of clusters 120/set of statistical properties 122 to determine if that vector representation is an outlier.” “At 404, a statistical property associated with each cluster from the set of clusters is determined to generate a set of statistical properties (e.g., set of statistical properties 122). Examples of statistical properties can include the center, mean, range, and/or the like. In some implementations, step 404 is performed automatically (e.g., without requiring additional human input) in response to completing step 403.”)(e.g., figures 4A, 4B, 5 and 6 and col 1 lines 21-22, col 3 lines 10-14, col 10 lines 40-47 and col 12 line 67 -col 13 line 5 and col 13 lines 29-35) extracting data features from incoming input data; (“Data drift not being identified and/or being identified too late can have a myriad of consequences, such as a model making an incorrect assumption or computer or organization wasting resources.” “A set of vector representations 118B can be determined based on the set of data 116B using the vectorization model 126. The set of vector representations 118B can include lower-dimensional vector representations of the set of data 116B. The vector representations 118B could include, for example, vector embeddings or intermediate representations. In some implementations, vector representations from the set of vector representations 118B are generated as associated data events from the set of data 116B are received (e.g., automatically, substantially in real time and without requiring additional human input).”)(e.g., col 3 lines 26-30 and col 7 lines 44-54) evaluating distribution of the extracted data features over time to determine whether the distribution changes over time relative historical data features; (“In some implementations, the data drift detection compute device 110 can determine when the set of statistical properties 122 is to be replaced and/or updated (e.g., determine cluster centers for a different set of clusters). For example, if (1) comparisons of distributions against the set of statistical properties 122/distribution 124A repeatedly determine that data drift has occurred a predetermined minimum number of times, (2) comparisons of distributions against the set of statistical properties 122/distribution 124A repeatedly determine that data drift beyond a predetermined data drift threshold has occurred a predetermined minimum number of times, (3) a predetermined period of time has elapsed since the set of statistical properties 122 were generated (e.g., one minute, one hour, one day, one week, one month, one year, etc.), and/or the like, the set of statistical properties 122 can be updated. This can look like, for example, determining a new set of statistical properties for a different set of vector representations, and using the new set of statistical properties when generating distributions.”)(e.g., col 10 line 66 – col 11 line 17) determining whether changes to the distribution of input data influence accuracy of predictions made by a machine learning model by comparing the changes to the distribution to a deviation threshold indicative of an acceptable degree of deviation; and (“Data distributions of models (e.g., machine learning models) may change over time, such as between training and deployment. Such changes can negatively affect performance and reliability of the models (and systems incorporating the models). For example, changes in data distributions (also referred to herein as “data drift”) can cause accuracy of a given model to decrease. Considering such issues, monitoring for “data drift” can be desirable to ensure that ML models work as desired.” “the data drift detection compute device 110 can determine additional sets of vector representations for additional sets of data. In some implementations, distributions for those additional sets of vector representations can be determined based on the set of clusters 120 and set of statistical properties 122. In some implementations, distributions for those additional sets of vector representations can be determined based on a different set of clusters and statistical properties, such as a set of clusters and associated statistical properties determined based on the set of vector representations 118B and/or a set of vector representations not shown in FIG. 1. As such, the set of clusters and set of statistical properties used for generating the distribution that is to act as the baseline and be compared against another distribution(s) (e.g., from production) for detecting data drift can change over time.” “In some implementations, a vector representation is an outlier if a distance between the vector representation and the cluster nearest/most similar to the vector representation in the vector space is greater than a predetermined threshold. In some implementations, a vector representation is an outlier if a distance between the vector representation and the cluster nearest/most similar to the vector representation in the vector space is less than a predetermined threshold.” “At 409, data drift is detected between the first set of vector representations and the second set of vector representations based on a comparison of the distribution associated with the first set of vector representations with the distribution associated with the second set of vector representations. In some implementations, the data drift is detected using at least one of Jensen-Shannon divergence, Earth Mover's Distance, Wasserstein metric, or Kullback-Leibler divergence. In some implementations, step 409 is performed automatically (e.g., without requiring additional human input) in response to completing step 408.”)(e.g., col 1 lines 14-22, col 9 lines 39-54, col 10 lines 49-57 and col 14 lines 20-30) identifying a breach of the deviation threshold; and (“At 410, transmission of a signal is caused, in response to the data drift exceeding a data drift threshold, to cause a remedial action.”)(e.g., col 14 lines 31-33) trigger, based on the breach having potential to negatively influence the reliability of the data processing systems, a warning signal to be distributed to one or more user devices to facilitate corrective parameter control of one or more machine learning model parameters. (“For example, the remedial action could include causing an alert indicating that the data drift exceeds the data drift threshold to be triggered. As another example, the remedial action could include causing a compute device (e.g., data drift detection compute device 110 and/or user compute device 150) and/or machine learning model (e.g., machine learning model 156) to change and/or modify a mode of operation, such as refraining from producing additional output data, refraining from relying on recently produced output data, logging events, shutting down, operating in a restricted access mode, and/or the like.”)(e.g., col 14 lines 36-46). Regarding claim 2, Iyer discloses the computing system of claim 1. Iyer further discloses wherein the data processing systems incorporate the machine learning model, wherein the machine learning model is based on the one or more machine learning model parameters. (“Data distributions of models (e.g., machine learning models) may change over time, such as between training and deployment. Such changes can negatively affect performance and reliability of the models (and systems incorporating the models). For example, changes in data distributions (also referred to herein as “data drift”) can cause accuracy of a given model to decrease. Considering such issues, monitoring for “data drift” can be desirable to ensure that ML models work as desired.” “the data drift detection compute device 110 can determine additional sets of vector representations for additional sets of data. In some implementations, distributions for those additional sets of vector representations can be determined based on the set of clusters 120 and set of statistical properties 122. In some implementations, distributions for those additional sets of vector representations can be determined based on a different set of clusters and statistical properties, such as a set of clusters and associated statistical properties determined based on the set of vector representations 118B and/or a set of vector representations not shown in FIG. 1. As such, the set of clusters and set of statistical properties used for generating the distribution that is to act as the baseline and be compared against another distribution(s) (e.g., from production) for detecting data drift can change over time.” “In some implementations, a vector representation is an outlier if a distance between the vector representation and the cluster nearest/most similar to the vector representation in the vector space is greater than a predetermined threshold. In some implementations, a vector representation is an outlier if a distance between the vector representation and the cluster nearest/most similar to the vector representation in the vector space is less than a predetermined threshold.” “At 409, data drift is detected between the first set of vector representations and the second set of vector representations based on a comparison of the distribution associated with the first set of vector representations with the distribution associated with the second set of vector representations. In some implementations, the data drift is detected using at least one of Jensen-Shannon divergence, Earth Mover's Distance, Wasserstein metric, or Kullback-Leibler divergence. In some implementations, step 409 is performed automatically (e.g., without requiring additional human input) in response to completing step 408.”)(e.g., col 1 lines 14-22, col 9 lines 39-54, col 10 lines 49-57 and col 14 lines 20-30). Regarding claim 3, Iyer discloses the computing system of claim 1. Iyer further discloses wherein the breach of the deviation threshold comprises an incremental change that is detected prior to a complete change that negatively influences the reliability of the data processing systems. (“Data distributions associated with a model(s), such as a machine learning model, may shift over time (e.g., production versus deployment). Because data drift can negatively affect performance and reliability of a given model(s), such instances of data drift can be desirable to detect. As such, some techniques described herein enable data drift to be detected. For example, a distribution for a first set of data can be determined, where attributes associated with the distribution for the first set of data can serve to indicate baseline information. Thereafter, a distribution for a second set of data can be determined based on the attributes associated with the distribution for the first set of data. The distribution for the first set of data can be compared with the distribution for the second set of data to determine if and/or how much data drift has occurred.” “In some implementations, the first set of vector representations have dimensionality less than a predetermined threshold value. In some instances, using vector representations can enable data drift to be detected with respect to data represented by the vector representations that otherwise may not have been detected if vector representations were not generated/used.”)(e.g., col 3 lines 10-24 and col 18 lines 36-42). Regarding claim 4, Iyer discloses the computing system of claim 1. Iyer further discloses wherein the deviation threshold comprises a population stability index threshold. (“the set of divergence metrics are detected using at least one of Jensen-Shannon divergence, Earth Mover's Distance, Wasserstein metric, or Kullback-Leibler divergence.” – Kullback-Leibler is considered to incorporate a population stability index score)(e.g., col 19 lines 29-32) Regarding claim 5, Iyer discloses the computing system of claim 1. Iyer further discloses wherein the extracted data features comprise at least one selected from the group consisting of numerical features, categorical features, and textual features. (“Additionally, in some instances, vector representations can account for semantic similarity, thereby allowing similarity and/or dissimilarity in meaning between text to be identified.” - text)(e.g., col 18 lines 47-50). Regarding claim 7, Iyer discloses the computing system of claim 1. Iyer further discloses wherein the evaluating comprises assigning numerical values to represent text lens and (“In some implementations, the number of clusters to be included in the set of clusters 120 can be a function of a size (e.g., count) of the set of vector representations 118A and/or set of data 116A. For example, as the number of vector representations included in the set of vector representations 118A increases, the number of clusters included in the set of clusters 120 can also increase.” “A first histogram is shown at chart 206, where N can represent, for example, a range of count values, a range of normalized count values, a range of probability values, and/or the like.”)(e.g., col 10 lines 30-36 and col 11 lines 35-38) text sentiment of textual features. (“For example, the machine learning model 156 may be used to perform functions related to natural language processing (e.g., analyzing speech, analyzing entities, analyzing sentiment, etc.)…”)(e.g., col 5 lines 28-32). Regarding claim 8, Iyer discloses the computing system of claim 1. Iyer further discloses wherein the warning signal comprises an indication of the one or more machine learning model parameters that need corrective parameter control. (“At 410, transmission of a signal is caused, in response to the data drift exceeding a data drift threshold, to cause a remedial action. With reference to FIG. 1, the signal could be transmitted to the data drift detection device 110, user compute device 150, and/or a compute device not shown in FIG. 1. For example, the remedial action could include causing an alert indicating that the data drift exceeds the data drift threshold to be triggered. As another example, the remedial action could include causing a compute device (e.g., data drift detection compute device 110 and/or user compute device 150) and/or machine learning model (e.g., machine learning model 156) to change and/or modify a mode of operation, such as refraining from producing additional output data, refraining from relying on recently produced output data, logging events, shutting down, operating in a restricted access mode, and/or the like. As another example, the remedial action could include causing a machine learning model using the second set of data to be retrained.”)(e.g., col 14 lines 31-49) Regarding claim 9, Iyer discloses the computing system of claim 1. Iyer further discloses wherein the warning signal comprises an indication of a method used to evaluate the distribution. (“For example, the remedial action could include causing an alert indicating that the data drift exceeds the data drift threshold to be triggered. As another example, the remedial action could include causing a compute device (e.g., data drift detection compute device 110 and/or user compute device 150) and/or machine learning model (e.g., machine learning model 156) to change and/or modify a mode of operation, such as refraining from producing additional output data, refraining from relying on recently produced output data, logging events, shutting down, operating in a restricted access mode, and/or the like. As another example, the remedial action could include causing a machine learning model using the second set of data to be retrained.)(e.g., col 14 lines 36-49). Regarding claim 16, Iyer discloses a computer-implemented method, comprising: (“FIGS. 4A-4B show a flowchart of a method 400 for causing a remedial action in response to detecting a data drift exceeding a data drift threshold, according to an embodiment. In some implementations, method 400 can be performed by a processor (e.g., processor 112).”)(e.g., col 12 lines 59-63) ascertaining whether data processing systems maintain reliability by monitoring data drift, the monitoring comprising: (“monitoring for “data drift” can be desirable to ensure that ML models work as desired.” “Data distributions associated with a model(s), such as a machine learning model, may shift over time (e.g., production versus deployment). Because data drift can negatively affect performance and reliability of a given model(s), such instances of data drift can be desirable to detect.” “In some implementations, the first set of data can include only high dimensional data, only low dimensional data, only structured data, only unstructured data, or a combination thereof. In some implementations, the first set of data is received from a remote compute device (e.g., at substantially real time).” “In some implementations, the data drift detection compute device 110 can perform outlier detection for data/vector representations. For example, each vector representation from the set of vector representations 118A (and/or set of vector representations 118B) can be compared to the set of clusters 120/set of statistical properties 122 to determine if that vector representation is an outlier.” “At 404, a statistical property associated with each cluster from the set of clusters is determined to generate a set of statistical properties (e.g., set of statistical properties 122). Examples of statistical properties can include the center, mean, range, and/or the like. In some implementations, step 404 is performed automatically (e.g., without requiring additional human input) in response to completing step 403.”)(e.g., figures 4A, 4B, 5 and 6 and col 1 lines 21-22, col 3 lines 10-14, col 10 lines 40-47 and col 12 line 67 -col 13 line 5 and col 13 lines 29-35) extracting data features from incoming input data; (“Data drift not being identified and/or being identified too late can have a myriad of consequences, such as a model making an incorrect assumption or computer or organization wasting resources.” “A set of vector representations 118B can be determined based on the set of data 116B using the vectorization model 126. The set of vector representations 118B can include lower-dimensional vector representations of the set of data 116B. The vector representations 118B could include, for example, vector embeddings or intermediate representations. In some implementations, vector representations from the set of vector representations 118B are generated as associated data events from the set of data 116B are received (e.g., automatically, substantially in real time and without requiring additional human input).”)(e.g., col 3 lines 26-30 and col 7 lines 44-54) evaluating distribution of the extracted data features over time to determine whether the distribution changes over time relative historical data features; (“In some implementations, the data drift detection compute device 110 can determine when the set of statistical properties 122 is to be replaced and/or updated (e.g., determine cluster centers for a different set of clusters). For example, if (1) comparisons of distributions against the set of statistical properties 122/distribution 124A repeatedly determine that data drift has occurred a predetermined minimum number of times, (2) comparisons of distributions against the set of statistical properties 122/distribution 124A repeatedly determine that data drift beyond a predetermined data drift threshold has occurred a predetermined minimum number of times, (3) a predetermined period of time has elapsed since the set of statistical properties 122 were generated (e.g., one minute, one hour, one day, one week, one month, one year, etc.), and/or the like, the set of statistical properties 122 can be updated. This can look like, for example, determining a new set of statistical properties for a different set of vector representations, and using the new set of statistical properties when generating distributions.”)(e.g., col 10 line 66 – col 11 line 17) determining whether changes to the distribution of input data influence accuracy of predictions made by a machine learning model by comparing the changes to the distribution to a deviation threshold indicative of an acceptable degree of deviation; and (“Data distributions of models (e.g., machine learning models) may change over time, such as between training and deployment. Such changes can negatively affect performance and reliability of the models (and systems incorporating the models). For example, changes in data distributions (also referred to herein as “data drift”) can cause accuracy of a given model to decrease. Considering such issues, monitoring for “data drift” can be desirable to ensure that ML models work as desired.” “the data drift detection compute device 110 can determine additional sets of vector representations for additional sets of data. In some implementations, distributions for those additional sets of vector representations can be determined based on the set of clusters 120 and set of statistical properties 122. In some implementations, distributions for those additional sets of vector representations can be determined based on a different set of clusters and statistical properties, such as a set of clusters and associated statistical properties determined based on the set of vector representations 118B and/or a set of vector representations not shown in FIG. 1. As such, the set of clusters and set of statistical properties used for generating the distribution that is to act as the baseline and be compared against another distribution(s) (e.g., from production) for detecting data drift can change over time.” “In some implementations, a vector representation is an outlier if a distance between the vector representation and the cluster nearest/most similar to the vector representation in the vector space is greater than a predetermined threshold. In some implementations, a vector representation is an outlier if a distance between the vector representation and the cluster nearest/most similar to the vector representation in the vector space is less than a predetermined threshold.” “At 409, data drift is detected between the first set of vector representations and the second set of vector representations based on a comparison of the distribution associated with the first set of vector representations with the distribution associated with the second set of vector representations. In some implementations, the data drift is detected using at least one of Jensen-Shannon divergence, Earth Mover's Distance, Wasserstein metric, or Kullback-Leibler divergence. In some implementations, step 409 is performed automatically (e.g., without requiring additional human input) in response to completing step 408.”)(e.g., col 1 lines 14-22, col 9 lines 39-54, col 10 lines 49-57 and col 14 lines 20-30) identifying a breach of the deviation threshold; and (“At 410, transmission of a signal is caused, in response to the data drift exceeding a data drift threshold, to cause a remedial action.”)(e.g., col 14 lines 31-33) triggering, based on the breach having potential to negatively influence the reliability of the data processing systems, a warning signal to be distributed to one or more user devices to facilitate corrective parameter control of one or more machine learning model parameters. (“For example, the remedial action could include causing an alert indicating that the data drift exceeds the data drift threshold to be triggered. As another example, the remedial action could include causing a compute device (e.g., data drift detection compute device 110 and/or user compute device 150) and/or machine learning model (e.g., machine learning model 156) to change and/or modify a mode of operation, such as refraining from producing additional output data, refraining from relying on recently produced output data, logging events, shutting down, operating in a restricted access mode, and/or the like.”)(e.g., col 14 lines 36-46). Regarding claim 17, Iyer discloses the computer-implemented method of claim 16. Iyer further discloses wherein the data processing systems incorporate the machine learning model, wherein the machine learning model is based on the one or more machine learning model parameters. (“Data distributions of models (e.g., machine learning models) may change over time, such as between training and deployment. Such changes can negatively affect performance and reliability of the models (and systems incorporating the models). For example, changes in data distributions (also referred to herein as “data drift”) can cause accuracy of a given model to decrease. Considering such issues, monitoring for “data drift” can be desirable to ensure that ML models work as desired.” “the data drift detection compute device 110 can determine additional sets of vector representations for additional sets of data. In some implementations, distributions for those additional sets of vector representations can be determined based on the set of clusters 120 and set of statistical properties 122. In some implementations, distributions for those additional sets of vector representations can be determined based on a different set of clusters and statistical properties, such as a set of clusters and associated statistical properties determined based on the set of vector representations 118B and/or a set of vector representations not shown in FIG. 1. As such, the set of clusters and set of statistical properties used for generating the distribution that is to act as the baseline and be compared against another distribution(s) (e.g., from production) for detecting data drift can change over time.” “In some implementations, a vector representation is an outlier if a distance between the vector representation and the cluster nearest/most similar to the vector representation in the vector space is greater than a predetermined threshold. In some implementations, a vector representation is an outlier if a distance between the vector representation and the cluster nearest/most similar to the vector representation in the vector space is less than a predetermined threshold.” “At 409, data drift is detected between the first set of vector representations and the second set of vector representations based on a comparison of the distribution associated with the first set of vector representations with the distribution associated with the second set of vector representations. In some implementations, the data drift is detected using at least one of Jensen-Shannon divergence, Earth Mover's Distance, Wasserstein metric, or Kullback-Leibler divergence. In some implementations, step 409 is performed automatically (e.g., without requiring additional human input) in response to completing step 408.”)(e.g., col 1 lines 14-22, col 9 lines 39-54, col 10 lines 49-57 and col 14 lines 20-30). Regarding claim 18, Iyer discloses the computer-implemented method of claim 16. Iyer further discloses wherein the breach of the deviation threshold comprises an incremental change that is detected prior to a complete change that negatively influences the reliability of the data processing systems. (“Data distributions associated with a model(s), such as a machine learning model, may shift over time (e.g., production versus deployment). Because data drift can negatively affect performance and reliability of a given model(s), such instances of data drift can be desirable to detect. As such, some techniques described herein enable data drift to be detected. For example, a distribution for a first set of data can be determined, where attributes associated with the distribution for the first set of data can serve to indicate baseline information. Thereafter, a distribution for a second set of data can be determined based on the attributes associated with the distribution for the first set of data. The distribution for the first set of data can be compared with the distribution for the second set of data to determine if and/or how much data drift has occurred.” “In some implementations, the first set of vector representations have dimensionality less than a predetermined threshold value. In some instances, using vector representations can enable data drift to be detected with respect to data represented by the vector representations that otherwise may not have been detected if vector representations were not generated/used.”)(e.g., col 3 lines 10-24 and col 18 lines 36-42). Regarding claim 19, Iyer discloses the computer-implemented method of claim 16. Iyer further discloses wherein the deviation threshold comprises a population stability index threshold. (“the set of divergence metrics are detected using at least one of Jensen-Shannon divergence, Earth Mover's Distance, Wasserstein metric, or Kullback-Leibler divergence.” – Kullback-Leibler is considered to incorporate a population stability index score)(e.g., col 19 lines 29-32). Regarding claim 20, Iyer discloses the computer-implemented method of claim 16. Iyer further discloses wherein the extracted data features comprise at least one selected from the group consisting of numerical features, categorical features, and textual features. (“Additionally, in some instances, vector representations can account for semantic similarity, thereby allowing similarity and/or dissimilarity in meaning between text to be identified.” - text)(e.g., col 18 lines 47-50) . 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. 07-23-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 nonobviousness. 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. 07-21-aia AIA Claim 6 is r ejected under 35 U.S.C. 103 as being unpatentable over I yer in view of Penfield et al. (U.S. Publication No. 2024/0135164 A1, hereinafter referred to as “Penfield”). R egarding claim 6, Iyer discloses the computing system of claim 1. However, Iyer does not appear to specifically disclose wherein the extracting includes calculating a unique value ratio that is used to determine whether the data features are categorical features or textual features. On the other hand, Penfield, which relates to automatic identification of lessons-learned incident records (title), does disclose wherein the extracting includes calculating a unique value ratio that is used to determine whether the data features are categorical features or textual features. (Another type of identifiable field is the “categorical” field which is a field with a small number of unique textual values. The categorical field may be for example a “consequences” field that categorizes the consequences of an accident with field entries or values “low”, “moderate”, and “high”. A third field type that may be identified by the preprocessing 103 is a “small-vocab” field which is a textual column but one that has too many entries or unique values such as words or phrases to fall within a “categorical” type field, but has a vocabulary (unique words) that is too small to be classified as a “free-text” field. An example would be a field titled “TypeOfEquipmentFailure” which may read “Mechanical failure—gas coolers”. A fourth category or type of field is a “Quantity-based” field that typically contains numeric and textual information describing or capturing area, volume, or time information, generally with the textual entry in this field being limited to describing a unit of measurement. An example of a “Quantity-based” field is a field entry in a document “OnsiteAreaAffected” with a value of “16 sq ft”. A fifth category of field is a “Date and time” field that contains date and time information, an example would include a “Date” field with a value of “2018-06-29 22:00:00”. Other field types or categories are also possible, which have different criteria or parameters, and may be added to the categories disclosed herein, or even replace these, depending on the embodiment.”)(e.g., paragraph [0034]). Iyer discloses data drift detection to improve data integrity for data within a database. E.g., title. Iyer discloses that distributions of data are compared over time to determine data drift, and an alert can be sent to the user as a threshold is exceeded. However, Iyer does not appear to specifically disclose calculating a unique value ratio to determine whether the data feature is categorical or textual. On the other hand, Penfield, which relates to automatic identification of lessons-learned incident records (title), provides that it is known that data can be processed and types of fields can be identified by calculating uniqueness of values. E.g., paragraph [0034]. This provides a manner to optimize the manner to identify the type of values that are trained and how to identify changes and optimizes performance of the model. E.g., paragraphs [0037]-[0039]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s claimed invention to incorporate the determination of textual and categorical features based on a uniqueness ratio as disclosed in Penfield to Iyer to optimize the performance of the system and to improve the manner in which items are identified for subsequent data drift detection analysis . 07-21-aia AIA Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Iyer in view of Maughan et al. (U.S. Publication No. 2017/0330109 A1, hereinafter referred to as “Maughan”) . Regarding claim 10, Iyer discloses the computing system of claim 1. Iyer discloses histograms (e.g., col 12 lines 51-58); however, Iyer does not appear to specifically disclose wherein the warning signal comprises a graphical depiction of the distribution. On the other hand, Maughan, which relates to predictive drift detection and correction (title), does disclose wherein the warning signal comprises a graphical depiction of the distribution. (“In a further embodiment, the retrain module 302 may prompt a user or other client 104 with one or more options for repairing or healing detected drift…. The retrain module 302 may display to a user or other client 104 an old/original distribution of values and a new/drifted distribution of values (e.g., side by side, overlaid, or the like), one or more histograms of old/original values and/or new/drifted values, display a problem or change in the data leaving it to the user to determine a repair, or the like.”)(e.g., paragraph [0083]). Iyer discloses data drift detection to improve data integrity for data within a database. E.g., title. Iyer discloses that distributions of data are compared over time to determine data drift, and an alert can be sent to the user as a threshold is exceeded. However, Iyer does not appear to specifically disclose including the generated histogram within the alert that is sent to the user. On the other hand, Maughan, which relates to predictive drift detection and correction (title), provides that (t)he retrain module 302 may display to a user or other client 104 an old/original distribution of values and a new/drifted distribution of values (e.g., side by side, overlaid, or the like), one or more histograms of old/original values and/or new/drifted values, display a problem or change in the data leaving it to the user to determine a repair, or the like. E.g., paragraph [0083]. This provides the user to better understand the underlying data causing the drift, and gives the user more information to determine the appropriate repair. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s claimed invention to incorporate the visualization of the histogram to the user as disclosed in Maughan to Iyer to enhance the users experience and to better understand the data causing the drift so the user can better understand how to take action in response to the detected drift . 07-21-aia AIA Claim s 11-15 are rejected under 35 U.S.C. 103 as being unpatentable over Iyer in view of Friedman et al. (U.S. Publication No. 2023/0316003 A1, hereinafter referred to as “Friedman”) . Regarding claim 11, Iyer discloses a computing system facilitating database management and data drift detection, comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and a memory device storing executable code that, when executed, causes the at least one processor to: (“The draft drift detection compute device 110 includes a processor 112 operatively coupled to a memory 114 (e.g., via a system bus). The data drift detection compute device 110 can be any type of device, such as a server, desktop, laptop, mobile device, internet of things device, and/or the like.”)(e.g., figure 1 and col 4 lines 16-22 and col 5 lines 63-67) perform database management and data structure management using a trained artificial intelligence model by: (“The machine learning model 156 may have been trained during a training phase, and be used thereafter during a deployment/production phase. In some implementation, the machine learning model 156 can use only high-dimensional data, only low dimensional data, only unstructured data, only structured data, or a combination thereof.”)(e.g., col 5 lines 39-44) inserting training data into the artificial intelligence model, (“The machine learning model 156 may be designed and/or trained at any compute device, such as data drift detection compute device 110, user compute device 150, and/or a compute device not shown in FIG. 1. In some implementations, the machine learning model 156 was not designed and/or trained at user compute device 150.”)(e.g., col 5 lines 32-38) deploying the trained artificial intelligence model and apply the trained artificial intelligence model to input data that influences the target variable; and (“Another potential advantage of receiving data from multiple compute devices is that the first remote compute device can be used for a first task (e.g., training a model) and second remote compute device can be used for a second task (e.g., using a deployed model). In some implementations of method 400, the first set of data and the second set of data are received from the same compute device.”)(e.g., col 15 lines 35-42) predicting, from the input data, a distribution of one or more data characteristics that would cause data drift leading to performance degradation of the trained artificial intelligence model. (“Some implementations of method 500 can further include generating a second set of clusters from at least one of the second set of vector representations or the third set of vector representations in response to detecting data drift between (1) the first set of vector representations and the second set of vector representations, and (2) the first set of vector representations and the third set of vector representations. Some implementations of method 500 can further include determining a statistical property associated with each cluster from the second set of clusters to generate a second set of statistical properties. Some implementations of method 500 can further include associating, using the second set of statistical properties and to generate a distribution associated with a fourth set of vector representations, each vector representation from the fourth set of vector representations to a cluster from the second set of clusters.”)(e.g., col 18 lines 8-24) However, Iyer does not appear to specifically disclose the artificial intelligence model comprising an iterative training and testing loop; training the artificial intelligence model by predicting a target variable and iteratively adjusting weights and calculations during each subsequent iteration to improve predictability of the target variable; On the other hand, Friedman, which relates to natural language processing for identifying bias in a span of text (title), does disclose the artificial intelligence model comprising an iterative training and testing loop; training the artificial intelligence model by predicting a target variable and iteratively adjusting weights and calculations during each subsequent iteration to improve predictability of the target variable; (“Models may be run against a training dataset for several epochs (e.g., iterations), in which the training dataset is repeatedly fed into the model to refine its results. For example, in a supervised learning phase, a model is developed to predict the output for a given set of inputs, and is evaluated over several epochs to more reliably provide the output that is specified as corresponding to the given input for the greatest number of inputs for the training dataset. In another example, for an unsupervised learning phase, a model is developed to cluster the dataset into n groups, and is evaluated over several epochs as to how consistently it places a given input into a given group and how reliably it produces the n desired clusters across each epoch. Once an epoch is run, the models are evaluated and the values of their variables are adjusted to attempt to better refine the model in an iterative fashion. In various aspects, the evaluations are biased against false negatives, biased against false positives, or evenly biased with respect to the overall accuracy of the model. The values may be adjusted in several ways depending on the machine learning technique used. For example, in a genetic or evolutionary algorithm, the values for the models that are most successful in predicting the desired outputs are used to develop values for models to use during the subsequent epoch, which may include random variation/mutation to provide additional data points. One of ordinary skill in the art will be familiar with several other machine learning algorithms that may be applied with the present disclosure, including linear regression, random forests, decision tree learning, neural networks, deep neural networks, etc.”)(e.g., paragraphs [0041]-[0042]). Iyer discloses data drift detection to improve data integrity for data within a database. E.g., title. Iyer discloses that distributions of data are compared over time to determine data drift, and an alert can be sent to the user as a threshold is exceeded. However, Iyer does not appear to specifically disclose the manner in which the natural language model is trained. On the other hand, Friedman provides that it is known to train a natural language model over several iterations by adjusting variables to refine the model. This provides an effective manner to train the model and to ensure the model is accurate. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s claimed invention to incorporate the training disclosed in Friedman to Iyer as a specific manner to train the natural language model of Iyer in an effective manner to ensure model accuracy. Regarding claim 12, Iyer in view of Friedman discloses the computing system of claim 11. Iyer further discloses wherein the executable code, when executed, further causes the at least one processor to: transmit a control signal associated with a data drift alert to one or more computing devices that the distribution of the one or more data characteristics would likely cause the performance degradation; (“Because data drift can negatively affect performance and reliability of a given model(s), such instances of data drift can be desirable to detect.” “transmission of a signal is caused, in response to the data drift exceeding a data drift threshold, to cause a remedial action. With reference to FIG. 1, the signal could be transmitted to the data drift detection device 110, user compute device 150, and/or a compute device not shown in FIG. 1. For example, the remedial action could include causing an alert indicating that the data drift exceeds the data drift threshold to be triggered.”)(e.g., col 3 lines 11-14 and col 14 lines 31-38) receive, from the one or more computing devices, one or more requests to retrain the artificial intelligence model; and (“As another example, the remedial action could include causing a compute device (e.g., data drift detection compute device 110 and/or user compute device 150) and/or machine learning model (e.g., machine learning model 156) to change and/or modify a mode of operation, such as refraining from producing additional output data, refraining from relying on recently produced output data, logging events, shutting down, operating in a restricted access mode, and/or the like. As another example, the remedial action could include causing a machine learning model using the second set of data to be retrained.”)(e.g., col 14 lines 38-49) Iyer in view of Friedman further discloses retrain the artificial intelligence model using an updated target variable that accounts for the one or more data characteristics. (“As another example, the remedial action could include causing a machine learning model using the second set of data to be retrained.”)(Iyer: e.g., col 14 lines 46-49)(“Once an epoch is run, the models are evaluated and the values of their variables are adjusted to attempt to better refine the model in an iterative fashion.”)(Friedman: e.g., paragraph [0042]). Regarding claim 13, Iyer in view of Friedman discloses the computing system of claim 12. Iyer further discloses wherein the data drift alert comprises an indication of the one or more data characteristics likely to cause the performance degradation. (“For example, the remedial action could include causing an alert indicating that the data drift exceeds the data drift threshold to be triggered. As another example, the remedial action could include causing a compute device (e.g., data drift detection compute device 110 and/or user compute device 150) and/or machine learning model (e.g., machine learning model 156) to change and/or modify a mode of operation, such as refraining from producing additional output data, refraining from relying on recently produced output data, logging events, shutting down, operating in a restricted access mode, and/or the like.”)(e.g., col 14 lines 36-46). Regarding claim 14, Iyer in view of Friedman discloses the computing system of claim 12. Iyer further discloses wherein the data drift alert comprises an indication of a method used to predict the distribution. (“For example, the remedial action could include causing an alert indicating that the data drift exceeds the data drift threshold to be triggered. As another example, the remedial action could include causing a compute device (e.g., data drift detection compute device 110 and/or user compute device 150) and/or machine learning model (e.g., machine learning model 156) to change and/or modify a mode of operation, such as refraining from producing additional output data, refraining from relying on recently produced output data, logging events, shutting down, operating in a restricted access mode, and/or the like. As another example, the remedial action could include causing a machine learning model using the second set of data to be retrained.)(e.g., col 14 lines 36-49). Regarding claim 15, Iyer in view of Friedman discloses the computing system of claim 12. Iyer in view of Freedman further discloses wherein the retraining comprises iteratively predicting the updated target variable and adjusting respective weights and respective calculations being used to predict the updated target variable. (“As another example, the remedial action could include causing a machine learning model using the second set of data to be retrained.”)(Iyer: e.g., col 14 lines 46-49)(“Some implementations relate to the usage of context (e.g., surrounding words in a sentence) to detect and characterize language bias. FIG. 8 illustrates context-aware, span-based parsing with transformer-based NLP architecture repurposed and retrained to assess clinical language bias for some implementations.”)(Friedman: e.g., paragraphs [0108] and [0129]). Conclusion 07-96 The prior art made of record, listed on form PTO-892, and not relied upon is considered pertinent to applicant's disclosure. Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICHARD L BOWEN whose telephone number is (571)270-5982. The examiner can normally be reached Monday through Friday 7:30AM - 4:00PM EST. 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, Aleksandr Kerzhner can be reached at (571)270-1760. 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. /RICHARD L BOWEN/ Primary Examiner, Art Unit 2165 Application/Control Number: 18/401,805 Page 2 Art Unit: 2165 Application/Control Number: 18/401,805 Page 3 Art Unit: 2165 Application/Control Number: 18/401,805 Page 4 Art Unit: 2165 Application/Control Number: 18/401,805 Page 5 Art Unit: 2165 Application/Control Number: 18/401,805 Page 6 Art Unit: 2165 Application/Control Number: 18/401,805 Page 7 Art Unit: 2165 Application/Control Number: 18/401,805 Page 8 Art Unit: 2165 Application/Control Number: 18/401,805 Page 9 Art Unit: 2165 Application/Control Number: 18/401,805 Page 10 Art Unit: 2165 Application/Control Number: 18/401,805 Page 11 Art Unit: 2165 Application/Control Number: 18/401,805 Page 12 Art Unit: 2165 Application/Control Number: 18/401,805 Page 13 Art Unit: 2165 Application/Control Number: 18/401,805 Page 14 Art Unit: 2165 Application/Control Number: 18/401,805 Page 15 Art Unit: 2165 Application/Control Number: 18/401,805 Page 16 Art Unit: 2165 Application/Control Number: 18/401,805 Page 17 Art Unit: 2165 Application/Control Number: 18/401,805 Page 18 Art Unit: 2165 Application/Control Number: 18/401,805 Page 19 Art Unit: 2165 Application/Control Number: 18/401,805 Page 20 Art Unit: 2165 Application/Control Number: 18/401,805 Page 21 Art Unit: 2165 Application/Control Number: 18/401,805 Page 22 Art Unit: 2165 Application/Control Number: 18/401,805 Page 23 Art Unit: 2165
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