Prosecution Insights
Last updated: April 19, 2026
Application No. 18/907,320

SYSTEMS AND METHODS FOR DATABASE MANAGEMENT INTEGRATING AI WORKFLOWS

Final Rejection §103
Filed
Oct 04, 2024
Examiner
DAYE, CHELCIE L
Art Unit
2161
Tech Center
2100 — Computer Architecture & Software
Assignee
Tdaa Technologies Corp.
OA Round
2 (Final)
76%
Grant Probability
Favorable
3-4
OA Rounds
3y 9m
To Grant
92%
With Interview

Examiner Intelligence

Grants 76% — above average
76%
Career Allow Rate
445 granted / 584 resolved
+21.2% vs TC avg
Strong +16% interview lift
Without
With
+16.0%
Interview Lift
resolved cases with interview
Typical timeline
3y 9m
Avg Prosecution
7 currently pending
Career history
591
Total Applications
across all art units

Statute-Specific Performance

§101
15.5%
-24.5% vs TC avg
§103
54.6%
+14.6% vs TC avg
§102
7.9%
-32.1% vs TC avg
§112
12.1%
-27.9% vs TC avg
Black line = Tech Center average estimate • Based on career data from 584 resolved cases

Office Action

§103
Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . DETAILED ACTION This action is issued in response to the Supplemental Response filed January 29, 2026. Claims 1-18, 20-23, and 25-26 are pending. Claims 25 and 26 are added and claims 19 and 24 are cancelled. Information Disclosure Statement The information disclosure statements (IDSs) submitted on 12/10/25 and 2/2/26 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner. Claim Rejections - 35 USC § 103 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claim(s) 1, 6-13, 18, and 20-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goslin (U.S. Patent Application No. 2021/0081498) in view of Hatfield (U.S. Patent Application No. 2023/0297914), further in view of Williams (U.S. Patent Application No. 2024/0281410). Regarding Claim 1, Goslin discloses a system comprising at least one processor operatively connected to a memory (Fig.3; par [0034], Goslin – AI system includes a processor, a memory, storage, and network interface), the at least one processor when executing configured to: generate and display a guided session for data management interaction (Fig.2; par [0028-0030], Goslin – GUI used to interact with an AI system configured to use machine learning to respond to users, wherein the user interacts with a scenario creator to select different aspects of the scenario. The options available for a given selection can depend on one or more selections); accept a user input specifying at least some of a request to be processed by a first AI mode (Fig.1; par [0023], Goslin – A user provides input to the AI system. The input includes natural language and may include text or audio speech data… par [0018] – the AI system may search for keywords in user input that have predefined associations); generate a workflow for executing a data management operation to be performed by the at least one processor using the first AI model (par [0023-0024], Goslin – a workflow for interactivity with an artificial intelligence (AI) system configured to use machine learning to interact with users… par [0033]); and process feedback on workflow (par [0022], Goslin). While Goslin teaches workflow for interactivity with an AI system; wherein it is known that workflow encompasses steps or actions to be performed. However, Goslin is not as detailed with respect to the candidate workflow constrained to comprise a plurality of steps; process feedback on respective ones of the plurality of steps; and optimize generation of a final output for the data management operation based, at least in part, on execution of the candidate workflow by the first AI model, and any feedback on the respective ones of the plurality of steps. On the other hand, Hatfield discloses wherein generating candidate workflow comprises generating the candidate workflow constrained to comprise a plurality of steps (par [0003], [0096-0097], Hatfield – using AI to assess a user’s workflow on a task includes receiving data regarding a workflow of a user completing a task; and assessing the data to identify attributes of the workflow that is expressed in a series of steps. The method may further include analyzing the steps of the workflow to identify areas of improvement; and generating augmentations from a plurality of technology fitments matched to the areas for improvement in the steps of the workflow… Each activity in a workflow may include a sequence of steps… Fig.5; par [0034-0035], [0067-0069], Hatfield - intelligent workflow comprises a plurality of steps to show a “candidate” to constrain a plurality of steps based on a requirement); process feedback on respective ones of the plurality of steps (par [0077], Hatfield – feedback surveys are used in providing an optimized workflow; par [0127-0128], Hatfield – user feedback is on the intelligent workflow is analyzed as to whether the experience is suitable or needs additional refinement; wherein the feedback may include identifying breakouts in the intelligent workflow); and optimize generation of a final output for the data management operation based, at least in part, on execution of the candidate workflow by the first AI model, and any feedback on the respective ones of the plurality of steps (par [0122-0123], Hatfield - the method can include the input at block back into the artificial intelligence models the ranked list of intelligent workflows that were established for analyzing the inputs from block for the current assessment of the workflow. Additionally, the input can be optimized workflows from the same industry and use cases at block. More specifically, at block the input can be the ranked list of intelligent workflows with the identified attributes, augmented steps and activity lists; the third round of input can be take the outputs from blocks 302 and 305, and derive, optimize and associate the intelligent workflow steps… par [0127-0129] - user feedback on the intelligent workflow is analyzed as to whether the experience is suitable or needs additional refinement; if workflow is not acceptable then adjustments are made until user confirms suitability of the intelligent workflow). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Hatfield’s teachings into the Goslin system. A skilled artisan would have been motivated to combine in order to better establish methods that capture, frame, and manage the user design experience around intelligent workflows; thus leading to optimized workflows. However, Goslin and Hatfield are not as detailed with respect to retrieving data from at least one source data target using the first AI model. On the other hand, Williams discloses retrieving data from at least one source data target using the first AI model (par [0590-0591], Williams - The workflow system may include a custom workflow actions system that may communicate with various systems, devices, and data sources. The custom workflow actions system may allow users to define custom actions. For example, a custom action definition may include all information needed for workflows (e.g., needed for workflow system) to display the custom action in a workflows application. This same definition may also specify the request format for requests coming from other services, systems, data sources, etc. in the platform as well as the handling of responses from these other services, systems, data sources, and the like.… par [0487-0489], [0494-0495], Williams – user may request a report to view sales related to vehicles by color or model; wherein a primary data source is selected (and either zero or more secondary data sources) to request and produce a report that may visualize properties and events that relate to vehicles… there are operations associated with the request such as pre-query data operations, query database operations, and post query database operations that are used to retrieve the associated results of the request … Further details about the request indicating a selected data source(s) and operations associated therewith can be found at par [0502-0504] and [0514-0515]). It would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to incorporate Williams’ teachings into the Goslin and Hatfield system. A skilled artisan would have been motivated to combine in order to provide the ability to manage and analyze the users’ needs to more effectively benefit the user. Thus, improving the functioning of computer systems, information networks, data stores, and search engines. Regarding Claim 6, the combination of Goslin in view of Hatfield, further in view of Williams, disclose the system of claim 1, wherein the at least one processor is configured to process groupings of the plurality of steps based on associated tasks (par [0132], Hatfield – processor receives data regarding a workflow of an associated task and assessing the data to identify attributes of the workflow that is expressed in a series of steps). Regarding Claim 7, the combination of Goslin in view of Hatfield, further in view of Williams, disclose the system of claim 1, wherein the at least one processor is configured to process groupings of the plurality of steps that include sub-steps associated with at least a respective one of the plurality of steps (par [0132], Hatfield). Regarding Claim 8, the combination of Goslin in view of Hatfield, further in view of Williams, disclose the system of claim 1, wherein the at least one processor is configured to: enable selection of respective ones of the plurality of steps, and display a request for feedback on a selected one of the plurality of steps (par [0142], Hatfield - receive data regarding a workflow of a user completing a task; assess the data to identify attributes of the workflow that is expressed in a series of steps; and analyze the steps of the workflow to identify areas of improvement. The program instructions can also generate, using the processor, augmentations from a plurality of technology fitments matched to the areas for improvement in the steps of the workflow; and generate, using the processor, a user experience template). Regarding Claim 9, the combination of Goslin in view of Hatfield, further in view of Williams, disclose the system of claim 8, wherein the at least one processor is configured to regenerate the selected one of the plurality of steps based at least in part on the feedback using the first AI model or regenerate the candidate workflow based at least in part on the feedback using the first AI model (par [0127-0129], Hatfield - user feedback on the intelligent workflow is analyzed as to whether the experience is suitable or needs additional refinement; if workflow is not acceptable then adjustments are made until user confirms suitability of the intelligent workflow… par [0142], Hatfield – generating augmentations form improvement in the steps of the workflow to generate an optimized workflow… par [0075-0077], Hatfield – inputting user data for a user experience, wherein the input data includes documentation of the type of tasks and sequence for the steps of the task; the data input step can include feedback data). Regarding Claim 10, the combination of Goslin in view of Hatfield, further in view of Williams, disclose the system of claim 1, further comprising a second AI model configured to generate executable code from any one or more of the plurality of steps (par [0023], Goslin – AI system includes a set of machine learning models… par [0045] – a first ML model is used based on come context and a second ML model is used based on different context). Regarding Claim 11, the combination of Goslin in view of Hatfield, further in view of Williams, disclose the system of claim 10, wherein the second AI model is a large language model (“LLM”) trained to produce executable code in response to a text or natural language input (par [0019], Goslin – the AI system selects a corresponding ML model to process the input which comprises natural language that includes text and/or audio input). Regarding Claim 12, the combination of Goslin in view of Hatfield, further in view of Williams, disclose the system of claim 11, wherein the first and second AI model are a same AI model (par [0023], Goslin – AI system includes a set of machine learning models… par [0046]). Claim 13 contains similar subject matter as claim 1 above; and is rejected under the same rationale. Claims 18 and 20-23 contain similar subject matter as claims 6-12 above; and are rejected under the same rationale. Claim(s) 2-5 and 14-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goslin in view of Hatfield, further in view of Williams, further in view of Buckland (U.S. Patent Application No. 2021/0193297). Regarding Claim 2, the combination of Goslin in view of Hatfield, further in view of Williams, disclose the system of claim 1, wherein the at least one processor is configured to trigger the first AI model to produce the candidate workflow to include at least one or more steps (par [0003], [0096-0097], Hatfield – using AI to assess a user’s workflow on a task includes receiving data regarding a workflow of a user completing a task; and assessing the data to identify attributes of the workflow that is expressed in a series of steps). While Goslin and Hatfield teach the above features; however, Gosling and Hatfield are not as detailed with respect to produce anonymized data from at least one source data target. On the other hand, Buckland discloses produce anonymized data from the at least one source data target (par [0063], Buckland - the workflow management system can have access to more than one image bank, Source A 150, Source B 151 and Source C 152. In some embodiments, the image bank may be curated, categorized, anonymized, and validated for sharing and re-use). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Buckland’s teachings into the Goslin, Hatfield, and Williams system. A skilled artisan would have been motivated to combine in order to de-identify sensitive data thus allowing for a more secure and private system. Regarding Claim 3, the combination of Goslin in view of Hatfield, further in view of Williams, and further in view of Buckland, disclose the system of claim 2, wherein the at least one processor is configured to generate a unique identifier for linking anonymized information to an original record (par [0073], Buckland – using an anonymization engine/module, the anonymized data may split the raw data into images and metadata, the two data sets are connected by a confidential key… par [0103-0104] – key allows the de-identified data to maintain traceability to the imaged (i.e., original) data). Regarding Claim 4, the combination of Goslin in view of Hatfield, further in view of Williams, and further in view of Buckland, disclose the system of claim 2, wherein the at least one processor is configured to: optimize the one or more steps configured to produce anonymized data from the at least one source data target based on providing the feedback to the first AI model (par [0122-0123], Hatfield - the method can include the input at block back into the artificial intelligence models the ranked list of intelligent workflows that were established for analyzing the inputs from block for the current assessment of the workflow. Additionally, the input can be optimized workflows from the same industry and use cases at block. More specifically, at block the input can be the ranked list of intelligent workflows with the identified attributes, augmented steps and activity lists; the third round of input can be take the outputs from blocks 302 and 305, and derive, optimize and associate the intelligent workflow steps… par [0063], Buckland - the workflow management system can have access to more than one image bank, Source A 150, Source B 151 and Source C 152. In some embodiments, the image bank may be curated, categorized, anonymized, and validated for sharing and re-use). Regarding Claim 5, the combination of Goslin in view of Hatfield, further in view of Williams, and further in view of Buckland, disclose the system of claim 4, wherein the optimization includes functions to regenerate the one or more steps configured to produce anonymized or obscured data from the at least one source data target (par [0127-0129], Hatfield - user feedback on the intelligent workflow is analyzed as to whether the experience is suitable or needs additional refinement; if workflow is not acceptable then adjustments are made until user confirms suitability of the intelligent workflow… par [0142], Hatfield – generating augmentations form improvement in the steps of the workflow to generate an optimized workflow… par [0063], Buckland - the workflow management system can have access to more than one image bank, Source A 150, Source B 151 and Source C 152. In some embodiments, the image bank may be curated, categorized, anonymized, and validated for sharing and re-use). Claims 14-17 contain similar subject matter as claims 2-5 above; and are rejected under the same rationale. Allowable Subject Matter Claims 25 and 26 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. The following is a statement of reasons for the indication of allowable subject matter: the at least one source data target comprises a plurality of source data targets; the user input specifies the plurality of source data targets; and the candidate workflow for executing the data management operation is configured to, based on the constraining to the plurality of steps, generate a mapping from the plurality of source data targets to a canonical data format. Response to Amendment Applicant’s amendments filed 1/29/2026 with respect to the 112 2nd paragraph, rejection has been considered and has been withdrawn accordingly. Applicant’s amendments and arguments filed 1/29/2026 with respect to the 101 rejection has been considered and has been withdrawn accordingly. Response to Arguments Applicant’s arguments with respect to the newly amended claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Conclusion Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Points of Contact Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHELCIE L DAYE whose telephone number is (571) 272-3891. The examiner can normally be reached on Monday-Friday 7:30-4:00pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached on 571-272-4080. 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 the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Chelcie Daye Patent Examiner Technology Center 2100 February 20, 2026 /CHELCIE L DAYE/Primary Examiner, Art Unit 2161
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Prosecution Timeline

Oct 04, 2024
Application Filed
Jun 09, 2025
Non-Final Rejection — §103
Dec 10, 2025
Response Filed
Jan 13, 2026
Applicant Interview (Telephonic)
Jan 16, 2026
Examiner Interview Summary
Feb 20, 2026
Final Rejection — §103 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

3-4
Expected OA Rounds
76%
Grant Probability
92%
With Interview (+16.0%)
3y 9m
Median Time to Grant
Moderate
PTA Risk
Based on 584 resolved cases by this examiner. Grant probability derived from career allow rate.

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