DETAILED ACTION
This Office Action is sent in response to Applicant's Response filed 01/16/2026 for 18592461. Claims 1-20 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
Applicant’s election without traverse of Group I, claims 1-13, in the reply filed on 01/16/2026 is acknowledged.
Claims 14-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim.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.
Drawings
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, the "selecting, prior to generating the prediction text and the plurality of data sets to the data file, the data file from among a plurality of data files" (claim 11) must be shown or the feature(s) canceled from the claim(s). No new matter should be entered.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claim 6 is objected to because of the following informalities.
Claim 6 recites "the plurality of datasets" which lacks antecedent basis and has been interpreted as "the plurality of [[datasets]] --data sets--".
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 11 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 11 recites "selecting, prior to generating the prediction text and the plurality of data sets to the data file, the data file from among a plurality of data files, wherein selecting is based on the prediction text and the plurality of data sets" which is unclear as to how the "selecting" limitation can be "based on the prediction text and the plurality of data sets" when the selecting limitation is being performed "prior to generating the prediction text and the plurality of data sets".
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claim recites receiving a user query of a user, wherein the user query relates to completion of an electronic form stored as a data object; generating a context topic that describes a category of data relevant to the user query; extracting a plurality of data sets that correspond to the context topic; applying a machine learning model to the user query and the plurality of data sets to generate a prediction text representing a predicted intent of the user; applying, to generate a subset of rules and a data schema, the prediction text and the plurality of data sets to a data file comprising a plurality of rules and a plurality of data schema applicable to the plurality of rules, wherein: the subset of rules is included in the plurality of rules and the data schema is included in the plurality of data schema, and the data schema defines an output format of an output data object generated when the subset of rules is executed; determining, based on the subset of rules, a subset of the plurality of data sets; applying the subset of rules to the subset of the plurality of data sets to generate the output data object, wherein: the output data object is formatted according to the data schema, and the output data object describes information that is both related to completion of the electronic form and relevant to the predicted intent; and returning the output data object.
The limitation of receiving a user query of a user, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, "receiving" in the context of this claim encompasses an observation of a user query.
The limitation of generating a context topic that describes a category of data relevant to the user query, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, "generating" in the context of this claim encompasses an evaluation of a context topic based on observing a data category relevant to the observed user query.
The limitation of generate a prediction text representing a predicted intent of the user, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, "generating" in the context of this claim encompasses an observation or evaluation of predicted text based on an evaluation of predicted user intent.
The limitation of generate a subset of rules and a data schema, wherein: the subset of rules is included in the plurality of rules and the data schema is included in the plurality of data schema, and the data schema defines an output format, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, "generating" in the context of this claim encompasses a user observation or evaluation of a subset of rules from an observed plurality of rules and a user observation or evaluation of a data schema from an observed plurality of data schema.
The limitation of determining, based on the subset of rules, a subset of the plurality of data sets, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, "determining" in the context of this claim encompasses an observation or evaluation of a subset of data sets from an observed plurality of data sets based on an evaluation of a subset of rules.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites an electronic form stored as a data object, extracting a plurality of data sets, applying a machine learning model, an output format of an output data object, applying the subset of rules to the subset of the plurality of data sets to generate the output data object, formatting the output data object, and returning the output data object. The extracting, applying, applying to generate, formatting the output data object, information related to completion of the electronic form and relevant to the predicted intent, and returning are recited at a high level of generality and recited so generically that they represent no more than mere instructions to apply the judicial exception on a computer [MPEP 2106.05(f)]. The electronic form stored as a data object, output format of an output data object, and information limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of electronic form management [MPEP 2106.05(h)].
The extracting represents mere data gathering that is necessary for use of the recited judicial exception, as the extracted data sets are used in the abstract mental process of generating. The extracting is recited at a high level of generality and is therefore insignificant extra-solution activity [MPEP 2106.05(g)]. Similarly, the applying, generating, formatting, and returning limitations represent extra-solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output [MPEP 2106.05(g)]. Even when viewed in combination, the additional elements in this claim do no more than automate the mental processes that user performs (e.g., the mental observation and evaluation), using the computer components as a tool.
Accordingly, 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 claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a applying a machine learning model, applying the subset of rules to the subset of the plurality of data sets to generate the output data object, formatting the output data object, and returning the output data object amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
The extracting, as discussed above, represents mere data gathering and is insignificant extra-solution activity. The applying, generating, formatting, and returning limitations, as discussed above, represent mere data output and are a nominal or tangential addition to the claim. Further, both of these elements are well-understood, routine and conventional.
With respect to the extracting, the courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional [MPEP 2106.05(d))(II), "electronic recordkeeping," and "storing and retrieving information in memory"]. With respect to the applying, generating, formatting, and returning limitations, the courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. [MPEP 2106.05(d)(II), "presenting offers and gathering statistics"].
Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible.
The dependent claims also recite limitations of identifying a section of the electronic form related to the user query (claim 2); identifying, based on the context topic, a plurality of disparate data sources that contain the plurality of data sets, aggregating the plurality of data sets into a vector data structure configured for use as input to the machine learning model (claim 3); converting the user query into a vector format, and adding the user query to the vector data structure (claim 4); selecting, based on the prediction text and the plurality of data sets, the subset of rules from among the plurality of rules (claim 5); determine the subset of the plurality of datasets, selecting the subset of the plurality of data sets based on identities of the subset of rules (claim 6); input from the subset of the plurality of data sets (claim 7); updating the data file based on at least one of an identity of the user, the context topic, and the prediction text (claim 10); selecting the data file from among a plurality of data files (claim 11); identifying an error in the electronic form (claims 12, 13) that are processes that, under its broadest reasonable interpretation, cover performance of the limitation in the mind or certain methods of organized human activity but for the recitation of generic computer components encompassing a user observation or evaluation of a form section; an observation or evaluation disparate data sources based on an observed context topic; evaluations of a vector data structure; evaluation of a vector format based on an observed user query; an observation or evaluation of a subset of rules based on observed rules, predicted text, and data sets; an observation or evaluation of a subset of datasets based on observed rule identities; an observation of input; managing personal interactions between an accountant preparing a customer tax return; an observation or evaluation of a data file from observed data files; and an observation or evaluation of a form error and thus fall within the "Mental Processes" and "Methods of Organized Human Activity" groupings of abstract ideas.
This judicial exception is not integrated into a practical application. The dependent claims recite additional limitations including performing, via a data integration service, a plurality of separate application programming interface calls to the plurality of disparate data sources (claim 3); applying the machine learning model to the user query and the plurality of data sets (claim 4); a spreadsheet, applying the prediction text and the plurality of data sets to the data file (claim 5); executing the subset of rules on the plurality of data sets (claim 6); executing the subset of rules, formatting the data object according to the data schema (claim 7); converting the output data object to a user interface, executing an algorithm on the output data object (claim 8); performing updating the data file (claim 10) that are recited at a high level of generality and recited so generically that they represent no more than mere instructions to apply the judicial exception on a computer [MPEP 2106.05(f)] and generally link the use of the judicial exception to the technological environment of electronic form management [MPEP 2106.05(h)] and do not impose any meaningful limits on practicing the abstract idea. The dependent claims also recite additional limitations of receiving the plurality of data sets (claim 3); presenting the output data object to the user, presenting the user interface, adding an output of the algorithm as an entry on the electronic form, storing the output data object, transmitting the output data object to an automated process (claim 8); generating the data file (claim 9) that represent insignificant extra-solution activity including nominal or tangential additions to the claim, amounting to mere data collection or data output [MPEP 2106.05(g)]. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of receiving, presenting, presenting, adding, storing, and transmitting are recited at a high level of generality which are well-understood, routine, or conventional activities [MPEP 2106.05(d))(II), "presenting offers and gathering statistics", "electronic recordkeeping", "storing and retrieving information in memory", "receiving or transmitting data over a network"] and remain insignificant extra-solution activity even upon reconsideration [MPEP 2106.05(f), 2106.05(g)]. Mere instructions to apply an exception using generic computer components, linking the use of an exception to a technological field of use, and insignificant extra-solution activity cannot provide an inventive concept. The claims are not patent eligible.
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 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)];
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 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)];
applying, to generate a subset of rules and a data schema, the prediction text and the plurality of data sets to [] data [] comprising 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 (read: subset) of rules and configuration file (read: data schema) from configuration files usable with rules in application based on entered data and knowledge base], wherein:
the subset of rules is included in the plurality of rules and the data schema is included in the plurality of data schema [Fig. 3, cols. 14:24-15:22, 19:35-20:12, determine at least one rule of stored rules and configuration file of stored configuration files], and
the data schema defines an output format of an output data object generated when the subset of rules is executed [Fig. 3, cols. 19:35-20:12, configuration file specifies how to process presenting (read: output format) non-binding suggestion (read: output data object) generated from executing at least one rule];
determining, based on the subset of rules, a subset of the plurality of data sets [cols. 15:42-61, 16:59-17:32, execute at least one rule to identify applicable topic (read: subset) of knowledge base];
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 wherein "a [] model" is "a machine learning model"; and a data file comprising a plurality of rules and a plurality of data schema applicable to the plurality of rules.
Hudetz discloses:
a machine learning model [para 0089, machine learning model]; and
a data file comprising a plurality of rules and a plurality of data schema applicable to the plurality of rules [para 0069-0070, 0100, document includes schema organizing data and rules defining organized 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 model and data comprising rules and data schema as disclosed by Wang with a machine learning model and 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 generate insights or predictions from existing data [Hudetz, para 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.
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