DETAILED ACTION
Case Status
This Office action is in response to remarks and amendments of 2 September 2025. Claims 1, 2, 6-10, 13-19 have been examined.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 8060423
Fig. 2
Automatic categorization of financial transactions with a first classification in steps 205, 207 and a second classification in steps 207, 209 with user selection of a first classification class attribute in step 213.
US 20210211443
Par. 31
Classifying risk level of event data, the event data having been previously categorized
US 20220129923
Par. 37
Transaction record categorization and subsequent grouping for risk analysis
US 20250061291
Abstract
LLM and prompt-based summarization of categorized/classified content facets
US 20240086815
Pars. 30, 78-85
Category and sub-category-based document summarization and risk factor predictive modeling
US 20160314146
Abstract
Analysis of documents and GUI to interact with results thereof
Claim Rejections - 35 USC § 101
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, 2, 6-10, 13-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 2, 6-10, 13-19 are directed to one of the eligible categories of subject matter.
With respect to independent claim 1, 17 and 18, the classifying and generating cover performance of the limitations manually and/or in the mind (mental processes abstract idea) and as a method of organizing human activity. The receiving, providing, displaying limitations are recited at a high level of generality and do not add meaningful limitations to the abstract idea; these limitations are directed to insignificant extra solution activities (i.e. device, model). The claims as a whole merely describe how to generally “apply” the exception in a computer environment using generic computer functions or components. Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
With respect to dependent claims 2, 7, 14 the generating, orthogonality, classification cover performance of the limitations manually and/or in the mind (mental processes abstract idea) and as a method of organizing human activity. No additional elements are recited and so the claims do not provide a practical application and are not considered to be significantly more. The claims are not eligible.
With respect to dependent claims 6, 9, 10, 13 the displaying, highlighting, LLM/model are recited at a high level of generality and do not add meaningful limitations to the abstract idea. The claims as a whole merely describe how to generally “apply” the exception in a computer environment using generic computer functions or components. Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
With respect to dependent claim 8, 15, 16, 19 the generating, preponderance, classifying, verifying cover performance of the limitations manually and/or in the mind (mental processes abstract idea) and as a method of organizing human activity. The display, highlighted, applying, obtaining, receiving, rendering are recited at a high level of generality and do not add meaningful limitations to the abstract idea; these limitations are directed to insignificant extra solution activities. The claims as a whole merely describe how to generally “apply” the exception in a computer environment using generic computer functions or components. Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claim Rejections - 35 USC § 102
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 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 –
(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.
Claims 17-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zimmermann et al., Pub. No.: US 20180027006 A1, hereinafter Zimmermann.
As per claim 17, Zimmermann discloses A computer program product embodied in a non-transitory computer- readable medium comprising computer program instructions, which when executed by a computer processor, cause the computer processor to perform steps, including:
receiving a plurality of transactional data records, each data record of (pars. 111-114, 125, 543, 550);
classifying, using the data attributes, at least one of the plurality of transactional data records into(see at least par. 125, 424, 444-446, 448, 455, 552, 561-565, 582-589 for multiple examples of initial classifications/ categorizations);
classifying, using the data attributes and the first classification, each of the at least one of the plurality of transactional records (see above for multiple examples of secondary classifications such as emerging event, risk, threat, time period, trust ratings, compromised accounts, lockouts, region activity, etc. of previously/initially classified/categorized data and with respect to attributes of the data);
receiving, via a graphical user interface, a user selection of a class of the first classification (see above including at least pars. 583-587);
dynamically generating, in response to the user selection, a text summary for the at least one of the plurality of transactional data records having data attributes associated with the user selection, wherein the text summary includes the one or more classes of the second classification (see rejection of above limitations; also, fig. 34, 40-43, 50 illustrate user selected category of social networking (i.e. a class of the first classification / the selected class) with textual explanation of risk; also, at least fig.’s 66-69 and corresponding support in the detailed description illustrate/describe multiple further examples of user interaction with GUI elements corresponding to types/classes/categories of data to view other class data and corresponding text based summaries);
providing the generated text summary via the graphical user interface (see fig.’s identified above – note the text-based descriptions of the class/classes of all classifications / categorizations provided in the interactive reports and displays); and
generating, via the graphical user interface, a graphic representation for the selected class (see rejection of previous limitations including at least fig. 41, 43, 50, 66-69).
Analogous claim 18 is likewise rejected.
As per claim 19, Zimmermann discloses The system according to claim 18, further comprising computer program instructions which, when executed by a computer processor, cause the computer processor to perform steps, including:
receiving, by an input module, data from a plurality of sources (pars. 111-114, 125, 543, 550);
generating, by a processing engine applying machine learning model or models based on user-defined selection or selections to the received data, text and graphical summaries of the received data (pars. 195-201, 552-557, 567-576);
rendering, by a visualization module the generated interactive graphical and textual summaries (pars. 195-201, 552-557, 567-576).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 6-10, and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Zimmermann in view of UzZaman et al., Pub. No.: US 20240249081 A1, hereinafter UzZaman.
As per claim 1, Zimmermann discloses a computer-implemented method for generating information and providing an interactive graphical user interface, comprising the steps of:
receiving, by at least one computing device, a plurality of transactional data records, each of the plurality of data records having a plurality of data attributes (pars. 111-114, 125, 543, 550);
classifying, by the at least one computing device using the data attributes, at least one of the plurality of transactional data records into one or more classes of a first classification (see at least par. 125, 424, 444-446, 448, 455, 552, 561-565, 582-589 for multiple examples of initial classifications/ categorizations);
classifying, by the at least one computing device using the data attributes and the first classification, each of the at least one of the plurality of transactional records into one or more classes of a second classification (see above for multiple examples of secondary classifications such as emerging event, risk, threat, time period, trust ratings, compromised accounts, lockouts, region activity, etc. of previously/initially classified/categorized data and with respect to attributes of the data);
receiving, by the at least one computing device via the graphical user interface, a user selection of a class of the first classification (see above including at least pars. 583-587);
dynamically generating, by the at least one computing device in response to the user selection, a text summary for the at least one of the plurality of transactional data records having data attributes associated with the user selection, wherein the text summary includes the one or more classes of the second classification (see rejection of above limitations; also, fig. 34, 40-43, 50 illustrate user selected category of social networking (i.e. a class of the first classification / the selected class) with textual explanation of risk; also, at least fig.’s 66-69 and corresponding support in the detailed description illustrate/describe multiple further examples of user interaction with GUI elements corresponding to types/classes/categories of data to view other class data and corresponding text based summaries);
providing, by the at least one computing device, the generated text summary via the graphical user interface see fig.’s identified above – note the text-based descriptions of the class/classes of all classifications / categorizations provided in the interactive reports and displays);
providing, via the graphical user interface, a graphic representation for the selected class (see rejection of previous limitations including at least fig. 41, 43, 50, 66-69);
providing, by the at least one computing device via the graphical user interface, the received plurality of transactional data records as tabular data (see fig.’s identified above which illustrate tabular data and see par. 442, 521, 525); and
displaying, via the graphical user interface, the text summary, the graphic representation, and the tabular data (see rejection of providing limitations above).
Zimmermann does not expressly disclose, however UzZaman in the related field of endeavor of data analysis discloses wherein generating the text summary for the at least one of the transactional data records comprises generating a prompt for a text summary of the selected class and at least one class from the second classification, providing the generated prompt to a text generator model, and receiving, from the text generator model in response to the prompt, the generated text summary (see Zimmermann as cited above for the classes / classifications and text summary, and see UzZaman pars. 33, 37, 39-43, 53-54, 57-60, 69, 81-82 for the prompt generation, model, text summary, etc.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because UzZaman would have allowed Zimmermann to “provide techniques for generating customizable content based on extracted insights… determining a content format of customized content to be generated by processing the input data… generating one or more prompts to be processed by a content machine-learning model for generating the customized content… applying the content machine-learning model to the one or more prompts to generate the customized content… outputting the customized content” because this allows for “facilitating the content generation process and reducing the need for manual intervention to increase scalability.” (UzZaman, abstract and par. 25).
As per claim 2, Zimmermann as modified discloses The method according to claim 1, wherein generating the text summary further comprises generating a text summary of the class or classes attributed by the first classification and generating a text summary of the class or classes attributed by the second classification (see fig.’s identified in the rejection of claim 1 – note the text based descriptions of the class/classes of all classifications / categorizations provided in the interactive reports and displays).
As per claim 6, Zimmermann as modified discloses The method according to claim 1, further comprising displaying the classes obtained from the first classification (see at least pars. 125, 424, 444-446, 448, 455, 552, 561-565, 582-589 for multiple examples of displayed classes / categories of the initial classifications/ categorizations).
As per claim 7, Zimmermann as modified discloses The method according to claim 1, wherein the second classification is orthogonal in respect to the first classification (see rejection of claim 1 – note that the various classifications / categories are orthogonal because they are independent of one another).
As per claim 8, Zimmermann as modified discloses The method according to claim 1, further comprising the display of classes obtained from(see at least pars. 424, 444-446, 448, 455, 552, 541, 584, 561-565, 582-589 for multiple examples of displayed classes / categories of the initial classifications/ categorizations and secondary classifications such as multiple preponderant classes including emerging event, risk, threat, time period, trust ratings, compromised accounts, lockouts, region activity, etc. of previously/initially classified/categorized data).
As per claim 9, Zimmermann as modified discloses The method according to claim 1, further comprising highlighting part or parts of the generated text summary, in particular the part or parts being those corresponding to a class or classes from the second classification attributed to the received data records having been attributed the selected class (see rejection of claim 8).
As per claim 10, Zimmermann as modified discloses The method according to claim 1, further comprising highlighting part or parts of the generated graphic representation, in particular the part or parts being those corresponding to a class or classes from the second classification attributed to the received data records having been attributed the selected class, further in particular the part or parts being those corresponding to a class or classes from the second classification above a predetermined classification threshold (see rejection of claim 8 including at least pars. 148, 149, 160, 181, 221, 276, 293, 307, 554).
As per claim 13, Zimmermann as modified discloses The method according to claim 1, wherein the text generator model is an LLM,(UzZaman, 40, 51, 57-60, 69).
As per claim 14, Zimmermann as modified discloses The method according to claim 1, wherein the first classification is based in a predetermined knowledge-area; and/or the second classification is based in a predetermined risk class (see rejection of classifying limitations of claim 1).
As per claim 15, Zimmermann as modified discloses The method according to claim 1, wherein classifying comprises applying a pretrained discriminative model, in particular the pretrained discriminative model comprising feature embedding pre-obtained from training transactional data records and corresponding classification (pars. 195-201, 552-557, 567-576).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Zimmermann as modified above and further in view of Setlur et al., Pub. No.: US 20240338378 A1, hereinafter Setlur.
As per claim 16, Zimmermann as modified discloses The method according to claim 1. Zimmermann as modified does not expressly disclose however Setlur in the related field of endeavor of data analysis discloses further comprising verifying the generated text summary for generated text hallucination comprising one or more of: verifying if all numerical data present in the generated text summary is present in the received data records or present in numerical statistics computed from the received data records, in particular the numerical statistics comprising average, sum, median, deviation, minimum or maximum (Setlur, par. 121); obtaining from the text generator model a plurality of generated text summaries and verifying if data present in the plurality of generated text summaries are present in all generated text summaries; and previously including instructions in the prompt for explicitly generating precalculated numerical statistics computed from the received data records and verifying if all numerical data present in the generated text summary is present in the precalculated numerical statistics (Setlur, par. 121).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Setlur would have allowed Zimmermann to “display a dynamic text summary describing the generated visualization … employ a large language model (LLM)-based approach. Passing the chart data as-is to an LLM application to generate a description can generate wrong statistics or even hallucinations depending on the data domain context. To overcome these challenges but still provide an eloquent description, some implementations follow a combined approach using both basic statistical computations and an LLM. Specifically, the input to an LLM-based chat application is a prompt containing a statistical description that is extracted from the generated visualization using a set of heuristics defined in prior data insight recommendation tools” (Setlur, par. 121).
Response to Arguments
Applicant's arguments filed 2 September 2025 have been considered.
Arguments directed to the 35 USC 101 are not persuasive. The amendments add additional data processing and display features but the claims remain directed to the abstract idea of organizing, classifying, and summarizing information for presentation to a user, which is a mental process and/or a method of organizing human activity. The recited components (devices, GUI, models, etc.) are generic and merely implement the abstract idea using convention computer functions without a technological improvement to the functioning of the computer itself or another technology. Displaying results in text, graphical, and tabular form does not meaningfully limit the claim or integrate the abstract idea into a practical application.
With respect to the prior art rejection, page 11 of the remarks presents arguments that are conclusory and fail to specifically identify how any cited claim limitations are absent from the prior art. Arguments without supporting evidence, technical reasoning, or citation to specific portion of the reference are not persuasive. The rejection includes detailed element-by-element mappings showing where each limitation is disclosed, and Applicant has not addressed those findings.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/SYED H HASAN/Primary Examiner, Art Unit 2154