DETAILED ACTION
This Office Action is sent in response to Applicant's Response filed 06/17/2026 for 18592461. Claims 1-10 and 12-20 are pending.
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 .
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 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.
Election/Restrictions
Claims 14-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 01/16/2026.
Response to Arguments
In view of Applicant's amendments, the objection to the drawings has been withdrawn.
In view of Applicant's amendments in the 01/16/2026 claims, the objection to claim 6 has been withdrawn.
In view of Applicant's amendments, the 112 rejection of claim 11 has been withdrawn.
In view of Applicant's amendments, the 101 rejection of claims 1-13 has been withdrawn.
Applicant’s arguments with respect to the 103 rejection of claim 1 have been fully considered but are not persuasive in view of the new and/or updated citations used in the current rejection of record under Wang in view of Hudetz in response to the newly amended limitations.
In response to Applicant’s arguments against the references individually (that Wang does not disclose "identify[ing] rules and the data scheme from the same data file" [pgs. 14:5-15:4]), one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). In this case, the Office Action cites the combination of identifying a subset of rules and identifying the data schema from data as disclosed in Wang with a single data file storing a plurality of rules and a plurality of data schema as disclosed in Hudetz to teach the amended limitations of claim 1.
Claim 1 remains rejected.
Dependent claims 2-10 and 12-13 remain rejected at least based on their dependence from independent claim 1.
Claim Rejections - 35 USC § 103
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-10 and 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 9760953 B1) in view of Hudetz et al. (US 20240370479 A1).
As to claim 1, Wang discloses a method comprising:
receiving a user query of a user, wherein the user query relates to a computing problem during a completion of an electronic form stored as a data object [col. 21:48-65, user requests entry of data into electronic tax return (read: form) prepared as electronic file (read: data object) by computing device executing tax preparation application (read: computing problem)];
generating a context topic that describes a category of data relevant to the user query [cols. 15:42-62, 23:22-51, identify tax topic of tax-related category applicable to entered data];
extracting a plurality of data sets that correspond to the context topic … [cols. 15:42-61, 22:16-29, receive tax knowledge base data (read: data sets) including hierarchical tax categories];
applying a [] model to the user query and the plurality of data sets to generate a prediction text representing a predicted intent of the user [cols. 22:16-23:30, rule module (read: model) compares entered data to rule of knowledge base to determine matching data (read: prediction text) to prepare return addressing applicable topics (read: predicted user intent)];
identifying, using the prediction text and the plurality of data sets, a subset of rules in … a plurality of rules and a plurality of data schema applicable to the plurality of rules [Fig. 3, cols. 14:24-15:62, 19:35-20:12, determine at least one rule of rules from configuration files usable with rules in application based on entered data and knowledge base];
identifying, using the prediction text and the plurality of data sets, the data schema in the data file, wherein the data schema defines an output format of an output data object generated when the subset of rules is executed [Fig. 3, cols. 14:24-15:62, 19:35-20:12, determine configuration file (read: data schema) from configuration files usable with rules in application based on entered data and knowledge base, where configuration file specifies how to process presenting (read: output format) non-binding suggestion (read: output data object) generated from executing at least one rule];
reducing an amount of data being used by determining, based on the subset of rules, a subset of the plurality of data sets, wherein plurality of data sets are specific to the computing problem [cols. 15:42-61, 16:59-17:32, execute at least one rule identifying topic (read: subset) of knowledge base data applicable to situation particular (read: specific) to user utilizing tax preparation application, where identified rule eliminates (read: reduce) application presenting questions invalid to a user tax situation];
applying the subset of rules to the subset of the plurality of data sets to generate the output data object [cols. 16:59-17:54, 22:30-41, execute at least one rule for applicable tax topic to generate suggestion], wherein:
the output data object is formatted according to the data schema [col. 18:32-44, process presenting suggestion based on configuration file], and
the output data object describes information that is both related to completion of the electronic form and relevant to the predicted intent [cols. 22:30-23:30, suggestion indicates details involving completeness of data entered into form and ensures addressing applicable topics]; and
returning the output data object [cols. 19:49-20:12, 23:22-51, present generated suggestion].
However, Wang does not specifically disclose extracting a plurality of data sets that correspond to the context topic by executing a plurality of application programming interface (API) calls to a plurality of disparate data sources, wherein executing the plurality of API calls returns the plurality of data sets from the plurality of disparate data sets; wherein "a [] model" is "a machine learning model"; and a data file storing a plurality of rules and a plurality of data schema applicable to the plurality of rules.
Hudetz discloses:
extracting a plurality of data sets that correspond to the context topic by executing a plurality of application programming interface (API) calls to a plurality of disparate data sources, wherein executing the plurality of API calls returns the plurality of data sets from the plurality of disparate data sets [para 0061-0062, 0083, implement search across documents collection (read: data sets) distributed across multiple data centers (read: disparate data sources) through integrated application program interfaces using received search query for information (read: context topic)];
a machine learning model [para 0089, machine learning model]; and
a data file storing a plurality of rules and a plurality of data schema applicable to the plurality of rules [para 0069-0070, 0100, document includes rules defining organized data organized in different schema formats].
Wang and Hudetz are analogous art to the claimed invention being from a similar field of endeavor of document management systems. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify extracting data sets, the model, data comprising rules and data schema as disclosed by Wang with executing API calls, a machine learning model, a data file comprising rules and data schema as disclosed by Hudetz with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Wang as described above to easily integrate search functionality and generate insights or predictions from existing data [Hudetz, para 0062, 0090].
As to claim 2, Wang discloses the method of claim 1, wherein generating the context topic comprises:
identifying a section of the electronic form related to the user query [cols. 22:42-23:13, determine form section of return has been completed with entered data], and
wherein the category of data is relevant to the section of the electronic form [cols. 22:42-23:51, 29:50-30:12, determine tax topic applicable to completed form section of return].
As to claim 3, Wang discloses the method of claim 1, wherein extracting comprises:
identifying, based on the context topic, a plurality of disparate data sources that contain the plurality of data sets [cols. 14:24-53, 15:42-61, 16:31-17:10, specify multiple tax authorities and additional services (read: data sources)with rules including tax category];
performing, via a data integration service, a plurality of separate … calls to the plurality of disparate data sources [cols. 14:24-53, 16:31-57, 22:16-29, rule module (read: data integration service) communicates with tax authority and additional services];
receiving the plurality of data sets [cols. 15:42-61, 16:31-57, 22:16-29, receive tax knowledge base data and rules]; and
aggregating the plurality of data sets into a [] data structure configured for use as input to the [] model [cols. 15:42-61, 16:31-57, 22:16-29, receive tax knowledge base data and rules used by rule module].
However, Wang does not specifically disclose a plurality of separate application programming interface calls to the plurality of disparate data sources; and a vector data structure configured for use as input to the machine learning model.
Hudetz discloses:
a plurality of separate application programming interface calls to the plurality of disparate data sources [para 0062, 0132, access distributed services (read: disparate data sources) through application program interfaces]; and
a vector data structure configured for use as input to the machine learning model [para 0083-0084, 0089, implement model to receive search vectors as input].
Wang and Hudetz are analogous art to the claimed invention being from a similar field of endeavor of document management systems. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the data integration service and data structure as disclosed by Wang with API calls to disparate data sources and vector data structure as disclosed by Hudetz with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Wang as described above to easily integrate search functionality and improve search results [Hudetz, para 0062, 0082].
As to claim 4, Wang discloses the method of claim 3.
However, Wang does not specifically disclose wherein applying the machine learning model to the user query and the plurality of data sets comprises: converting the user query into a vector format; and adding the user query to the vector data structure.
Hudetz discloses wherein applying the machine learning model to the user query and the plurality of data sets [para 0083-0085, search manager implements model with search query and stored documents] comprises:
converting the user query into a vector format [para 0083, generate vector representation of search query]; and
adding the user query to the vector data structure [para 0083, generate contextualized embedding with search vectors].
Wang and Hudetz are analogous art to the claimed invention being from a similar field of endeavor of document management systems. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify applying the model as disclosed by Wang with converting a user query into a vector format and adding a user query to a vector data structure as disclosed by Hudetz with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Wang as described above to improve search results [Hudetz, para 0082].
As to claim 5, Wang discloses the method of claim 1, wherein applying the prediction text and the plurality of data sets to the data [] further comprises: selecting, based on the prediction text and the plurality of data sets, the subset of rules from among the plurality of rules [cols. 14:24-15:62, determine at least one rule of rules based on entered data and knowledge base].
However, Wang does not specifically disclose wherein "the data []" is "the data file", wherein the data file comprises a spreadsheet, and wherein the spreadsheet specifies the data schema.
Hudetz discloses:
applying the prediction text and the plurality of data sets to the data file [para 0083-0084, 0088-0089, implement model trained with contextualized search query and documents on electronic document],
wherein the data file comprises a spreadsheet [para 0069-0070, 0099-0100, document includes spreadsheet], and
wherein the spreadsheet specifies the data schema [para 0100, document includes structured data organized in schema].
Wang and Hudetz are analogous art to the claimed invention being from a similar field of endeavor of document management systems. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the data and data schema as disclosed by Wang with the data file comprising a spreadsheet specifying a data schema as disclosed by Hudetz with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Wang as described above to improve search results [Hudetz, para 0082].
As to claim 6, Wang discloses the method of claim 1, wherein determining the subset of the plurality of data sets comprises at least one of:
executing the subset of rules on the plurality of data sets to determine the subset of the plurality of datasets [cols. 15:42-61, 16:59-17:32, execute at least one rule to identify applicable topic of knowledge base , note strikethrough indicates non-selected alternatives];
As to claim 7, Wang discloses the method of claim 1, wherein applying the subset of rules to the subset of the plurality of data sets comprises:
executing the subset of rules on input from the subset of the plurality of data sets [cols. 16:59-17:54, 22:30-41, execute at least one rule for applicable tax topic identified in response to input]; and
formatting the data object according to the data schema [col. 18:32-44, process presenting suggestion based on configuration file].
As to claim 8, Wang discloses the method of claim 1, wherein returning the output data object comprises at least one of:
converting the output data object to a user interface and presenting the user interface to the user [cols. 19:49-20:12, 23:22-51, process suggestion to generate screen displayed to user];
As to claim 9, Wang discloses the method of claim 1, further comprising: generating the data [] prior to receiving the user query [cols. 19:25-48, 20:63-21:10, generate rules and knowledge base before receiving user entry].
However, Wang does not specifically disclose wherein "the data []" is "the data file".
Hudetz discloses generating the data file prior to receiving the user query [para 0089, 0098-0101, gather document data before receiving search query].
Wang and Hudetz are analogous art to the claimed invention being from a similar field of endeavor of document management systems. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the generated data prior to receiving a user query as disclosed by Wang with generating a data file as disclosed by Hudetz with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Wang as described above to increase model accuracy and effectiveness [Hudetz, para 0101].
As to claim 10, Wang discloses the method of claim 1, further comprising:
updating the data [] based on at least one of an identity of the user [cols. 27:54-28, 28:24-42, update table of rules based on user dependency status],
wherein updating is performed prior to applying the prediction text and the plurality of data sets to the data [] [cols. 27:54-28:42, iteratively determine matching data to applicable topics as user dependency status is updated].
However, Wang does not specifically disclose wherein "the data []" is "the data file".
Hudetz discloses the data file [para 0069-0070, 0100, document includes structured data].
Wang and Hudetz are analogous art to the claimed invention being from a similar field of endeavor of document management systems. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the data as disclosed by Wang with the data file as disclosed by Hudetz with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Wang as described above to increase model accuracy and effectiveness [Hudetz, para 0101].
As to claim 12, Wang discloses the method of claim 1, further comprising:
identifying an error in the electronic form,
wherein at least one of the plurality of data sets relates to the error [cols. 22:42-23:21, identify error in return based on rule of knowledge base].
As to claim 13, Wang discloses the method of claim 1, further comprising:
identifying an error in the electronic form,
wherein at least one of the plurality of data sets relates to the error, and
wherein at least one of the subset of rules relates to the error [cols. 22:42-23:21, identify error in return based on rule of knowledge base].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jia et al. (US 20250238874 A1) generally discloses utilizing machine learning models to validate form input based on received user queries.
Broyles et al. (US 20210406716 A1) generally disclose processing tax preparation forms as structured data files with rules and data schema.
Chiang et al. (US 10977745 B1) generally discloses a spreadsheet data file comprising rules and data schema.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINDA HUYNH whose telephone number is (571)272-5240 and email is linda.huynh@uspto.gov. The examiner can normally be reached M-F between 9am-5pm.
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/LINDA HUYNH/Primary Examiner, Art Unit 2172
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