CTNF 18/107,799 CTNF 80122 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 06-52 AIA The information disclosure statement (IDS) submitted on 2/9/2023 was filed after the mailing date of the application on 2/9/2023 . The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Rejections - 35 USC § 101 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, 6-8, 13-15, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding claims 1, 8, and 15 , Step 1 Analysis: Claims 1, 8, and 15 are directed to a method, system and computer program, which falls within one of the four statutory categories. Step 2A Prong 1 Analysis: Claims 1, 8, and 15 recites, The limitations of: “performing, a pre-analysis of entries in the history capture data, the history capture data including a plurality of database transactions corresponding to user tables, and providing formatted history capture data” is a mental process which can be performed by the human mind. A human can pre-analyze history data. “clustering, the formatted history capture data into clusters by data characteristics of interval groups” is a mental process which can be performed by the human mind. A human can group data. “performing, a post-analysis on the clusters” is a mental process which can be performed by the human mind. A human can do a second analysis after the process. “dynamically updating, capture policies corresponding to capture processes for the user tables, the updating based at least on the unit data profile provided by the post-analysis” is a mental process which can be performed by the human mind. A human can update/refresh what is thinking or change results. These limitations, as drafted, are processes that, under broadest reasonable interpretation, covers the performance of the limitation in the mind which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Step 2A Prong 2 Analysis: Claims 1, 8, and 15 recites the additional elements: “collecting, runtime history capture data by a data agent operable on a computing device; and providing formatted history capture data”, “the processor set”, “providing a unit data profile for capture data of the user tables”, “A computer program product comprising one or more computer readable storage media”, “computing device”, and “A system”. The limitation of “collecting” and “providing” are an additional element and is insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application. A computer program product comprising one or more computer readable storage media, computing device, and a system, note that these recited additional elements are a high-level recitation of generic computer components to perform the mental process and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application. Step 2B Analysis: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more. With respect to the "collecting and providing” limitation is identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more. Furthermore, the limitations “the LLM and fine-tuning the LLM based on defined tuple mappings” are generally linking the use of the judicial exception to a particular technological environment or field of use, MPEP 2106.05(h). Lastly the recitation of “A computer program product comprising one or more computer readable storage media”, “computing device”, “processor set” and “A system” and computer readable storage medium are recitation of generic computer components to perform the mental process and applied on a computer as in MPEP 2106.05(f). Therefore, the claims as a whole does not change this conclusion and the claims are ineligible. Claims 6, 13, and 19 , recites: wherein dynamically updating capture policies further comprises: selecting a default group as an expected workload for capture; selecting a peak workload from the history capture data for an interval; calculating existing capacity of the capture processes; and generating an updated capture policy for at least one user table based at least on the existing capacity of the capture processes and the peak workload, further describes the abstract idea previously identified in the independent claims. The claims recite a abstract idea and are not patent eligible. Recite the addition element of automatically deploying the updated capture policy to the capture processes. Deploying is extra solution activity and does not integrate into a practical application and the courts have recognized as well-understood routine and conventional see MPEP 2106(d) - Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. Claims 7, 14, and 20 , recite “sending an alert when an expected workload for at least one capture process exceeds a pre-determined capacity of the at least one capture process”, this additional elements of using a computer as a tool to perform the recited step amount to no more than mere instructions to apply the abstract idea using generic computer component. Claim Rejections - 35 USC § 102 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. 07-07-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. 07-15-aia AIA Claim s 1, 6-8, 13-15, 19-20 are rejected under 35 U.S.C. 102 as being anticipated by Ma et al. (Query based workload forecasting for self-driving database (Eff filing date of pub. 05/27/2018) (Hereinafter Ma ) . As to claim 1, 8, and 15, MA teaches a computer implemented method, comprising: collecting, by a processor set, runtime history capture data by a data agent operable on a computing device (see p. 631, col. 1, 2 paragraph, “We present a robust forecasting framework called QueryBot 5000 that allows a DBMS to predict the expected arrival rate of queries in the future based on historical data”; p. 631, col 2, second paragraph “collect enough data about its behavior and then use it to train machine learning (ML) models” and p. 642, col. 2, 3 paragraph, “Various collected/aggregated run-time statistics are also used to analyze the structure and complexity of SQL statements and the run-time behaviors of the workload”); performing, by the processor set, a pre-analysis of entries in the history capture data, the history capture data including a plurality of database transactions corresponding to user tables, and providing formatted history capture data (see page 634, col. 1, “the Pre-Processor that identifies distinct templates in the workload and records their arrival rate history” and “The Pre-Processor then performs additional formatting to normalize spacing, case, and bracket/parenthesis placement. We uset he abstract syntax tree from the DBMS’s SQL parser to identify the tokens. The outcome of this step is a generic query template. QB5000 tracks the number of queries that arrive per templates over a given time interval and then stores the final count into an internal catalog table at the end of each interval. The”); clustering, by the processor set, the formatted history capture data into clusters by data characteristics of interval groups (see page 634, col 1, paragraph 3, “then stores the final count into an internal catalog table at the end of each interval.”; col 2 paragraph 2, “The Clusterer component combines the arrival rate histories of templates with similar patterns into groups.”); performing, by the processor set, a post-analysis on the clusters and providing a unit data profile for capture data of the user tables (see page 635, col 2, paragraph 1, “The original DBSCAN algorithm evaluates whether an object belongs to a cluster by checking the minimum distance between the object and any core object of the cluster”); and dynamically updating, by the processor set, capture policies corresponding to capture processes for the user tables, the updating based at least on the unit data profile provided by the post-analysis (see page 634, col. 2 paragraph 2, “We then describe how it determines whether templates belong to the same cluster. Lastly, we present QB5000’s clustering algorithm that supports incremental updates as the application’s workload evolves, and how the framework quickly determines whether to rebuild its clusters.”). As to claims 6, 13, and 19, Ma teaches wherein dynamically updating capture policies further comprises: selecting a default group as an expected workload for capture (see page 631, col 2para phar 2, “a self-driving DBMS should choose its optimizations proactively according to the expected workload patterns in the future.”); selecting a peak workload from the history capture data for an interval (see page 631, col 2 paragraph 3, “to apply optimizations after the workload has shifted (e.g., it is entering a peak load period”); calculating existing capacity of the capture processes see page 638, col. 1paragraph 1, “We first calculate the average ratio between the volume of the largest clusters and the total workload volume for each day throughout the entire workload execution. It is calculated by dividing the volume of a given cluster by the total volume of all the clusters for that day. The results in Figure 5 show that the highest-volume clusters cover the majority of the queries in the workload.”); generating an updated capture policy for at least one user table based at least on the existing capacity of the capture processes and the peak workload (see page 631, paragraph 1, “Previous forecasting techniques model the resource utilization of the queries. Such metrics, however, change whenever the physical design of the database and the hardware resources change, thereby rendering previous forecasting models useless.”); and automatically deploying the updated capture policy to the capture processes (see page 633, col 2, paragraph 3-4, “QB5000 also automatically adjust these clusters as the workload changes over time. Every time the cluster assignment changes for templates, QB5000 re-trains its models.The Pre-Processor always ingests new queries and updates the arrival rate history for each template in the background in real time when the DBMS is running. The Clusterer and Forecaster periodically update the cluster assignments and the forecasting models. When QB5000 predicts the expected workload in the future, it uses the most recent data as the input to the models.”). As to claims 7, 14, and 20, MA teaches the computer implemented method further comprising: sending an alert when an expected workload for at least one capture process exceeds a pre-determined capacity of the at least one capture process (See page 635, at the bottom, where it explain that evaluation is made and the issues with large number of cluster can happen) . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim s 2-5, 9-12, and 16-18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see form PTO-892 for prior art numbers . Any inquiry concerning this communication or earlier communications from the examiner should be directed to BELIX M ORTIZ DITREN whose telephone number is (571)272-4081. The examiner can normally be reached M-F 9am -5pm. 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, Amy Ng can be reached at 571-270-1698. 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. BELIX M. ORTIZ DITREN Primary Examiner Art Unit 2164 /Belix M Ortiz Ditren/Primary Examiner, Art Unit 2164 Application/Control Number: 18/107,799 Page 2 Art Unit: 2164 Application/Control Number: 18/107,799 Page 3 Art Unit: 2164 Application/Control Number: 18/107,799 Page 4 Art Unit: 2164 Application/Control Number: 18/107,799 Page 5 Art Unit: 2164 Application/Control Number: 18/107,799 Page 6 Art Unit: 2164 Application/Control Number: 18/107,799 Page 7 Art Unit: 2164 Application/Control Number: 18/107,799 Page 8 Art Unit: 2164 Application/Control Number: 18/107,799 Page 9 Art Unit: 2164 Application/Control Number: 18/107,799 Page 10 Art Unit: 2164