Detailed Action
Response to Amendment
This action is responsive to Applicant’s communication filed on 04/13/2026.
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.
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. If this application names joint inventors, 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 1-3, 5-9 and 13 and 15-19 are rejected are rejected under 35 U.S.C. 103 as being unpatentable over Symbolab.com (as evidenced by Symbolab.pdf (“SL”), Symbolab2.pdf (“SL2”) and Youtube.com) in view of MathChat (as evidenced by MathChat.pdf).
Regarding claim 1, Symbolab discloses a computing system comprising one or more processors and one or more non-transitory computer-readable media that collectively store instructions (SL p. 3: browser host computer inherently includes these features) that, when executed by the one or more processors, cause the computing system to perform operations the operations comprising receiving a query from a user, wherein the query comprises image data (SL2 p. 1: Scan a problem), analyzing the image data to determine determining that the query includes comprises query data describing a pedagogical exercise to be solved (SL2 p. 2), responsive to determining that the query comprises query data describing the pedagogical exercise, providing the query data as input to an explanatory model (SL p. 2: machine learning algorithms), receiving, as output from the explanatory model, a pedagogical response (SL2 p. 3), the pedagogical response including a multi-step explanation of a solution to the pedagogical exercise and providing the pedagogical response for display to a user in a user interface (SL p. 4: immediately you’re given a step-by-step solution). MathChat suggests—where Symbolab does not disclose—wherein the explanatory model is a large language model (p. 1). It would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the invention to combine the disclosures of Symbolab and MathChat in order to improve performance and accuracy.
Regarding claim 2, Symbolab discloses wherein the query data comprises one or more of text data and audio data (SL p. 2: enter the problem to be solved in the input box and click GO).
Regarding claim 3, Symbolab discloses determining that a query type associated with the query is an explanation query type, and extracting data describing the pedagogical exercise to be solved from the image data (p. 4: if ‘Hide steps’ is selected, only the answer is shown … otherwise all problem steps and the answer will be displayed).
Regarding claim 5, Symbolab discloses wherein the output of the explanatory large language model includes formatting data for use in displaying the pedagogical response (SL pp. 4-5, output is presented in html format).
Regarding claim 6, Symbolab discloses wherein the formatting data includes markup data (SL pp. 4-5, output is presented in html format).
Regarding claim 7, Symbolab discloses wherein the formatting data causes each step in the multi-step explanation to be displayed in a distinct section of a user interface (SL pp. 4-5).
Regarding claim 8, Symbolab discloses wherein each distinct section of the user interface is collapsible such that one or more steps in the multi-step explanation can be hidden (SL p. 5).
Regarding claim 9, MathChat suggests—where Symbolab does not disclose—generating a machine-learned model prompt, wherein the prompt includes the query data, context information for the query data, and instructions to the explanatory large language model (pp. 2-3). It would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the invention to combine the disclosures of SL and MathChat in order to allow for more nuanced responses by the model.
Regarding claim 13, Symbolab discloses a computer-implemented method (SL p, 2) comprising receiving, by a computing system comprising one or more processors (SL p. 3: browser host computer inherently includes these features), an image that includes a pedagogical exercise (p. 3: enter the problem to be solved using the symbol generator and click GO), analyzing the image to determine that the image comprises query data describing a pedagogical exercise to be solved (SL2 p. 2), responsive to determining that the image comprises query data describing the pedagogical exercise, extracting, by the computing system, data describing the pedagogical exercise, providing, by the computing system, the data describing the pedagogical exercise as input to an explanatory model (SL p. 2: machine learning algorithms), receiving, as output from the explanatory model, a pedagogical response (SL2 p. 3), the pedagogical response including a multi-step explanation of a solution to the pedagogical exercise, and providing the pedagogical response for display to a user in a user interface (SL p. 4: immediately you’re given a step-by-step solution). MathChat suggests—where Symbolab does not disclose—wherein the explanatory model is a large language model (p. 1). It would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the invention to combine the disclosures of Symbolab and MathChat in order to improve performance and accuracy.
Regarding claim 15, Symbolab discloses providing the multi-step explanation in a format such that each respective step can be displayed in a respective collapsible section of the user interface (SL p. 5). MathChat suggests—where Symbolab does not disclose—wherein the explanatory model is a large language model (p. 1). It would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the invention to combine the disclosures of Symbolab and MathChat in order to improve performance and accuracy.
Regarding claim 16, Symbolab discloses wherein a respective step in the multi-step explanation includes one or more of text, images, and rendered mathematical formulas (SL p. 5).
Regarding claim 17, Symbolab discloses wherein rendered mathematical formulas are rendered based on rendering data output by the model (SL p. 5). MathChat suggests—where Symbolab does not disclose—wherein the explanatory model is a large language model (p. 1). It would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the invention to combine the disclosures of Symbolab and MathChat in order to improve performance and accuracy.
Regarding claim 18, Symbolab discloses wherein images can be generated based on a description of the characteristics of an image output by the explanatory model (SL p. 5). MathChat suggests—where Symbolab does not disclose—wherein the explanatory model is a large language model (p. 1). It would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the invention to combine the disclosures of Symbolab and MathChat in order to improve performance and accuracy.
Regarding claim 19, Symbolab discloses wherein the input to the explanatory model can be multimodal (SL pp. 3-5). MathChat suggests—where Symbolab does not disclose—wherein the explanatory model is a large language model (p. 1). It would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the invention to combine the disclosures of Symbolab and MathChat in order to improve performance and accuracy.
Claims 10 and 11 are rejected are rejected under 35 U.S.C. 103 as being unpatentable over Symbolab) in view of MathChat and Pandey (US 2024/0330796 A1).
Regarding claim 10, Pandey suggests—where Symbolab does not disclose—wherein the contextual information includes user profile data describing a user current level of understanding (¶ [0148]: determining, for one or more groups of a user network, at least one group skill associated with each respective group of the one or more groups, generating a prompt based on one or more user skills of a first user registered in the user network, the generating the prompt comprising inserting at least one of the one or more user skills into a prompt template to generate the prompt). It would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the invention to combine the disclosures of SL and Pandey in order to allow for more nuanced responses by the model.
Regarding claim 11, Symbolab discloses wherein the output generated by the explanatory model designates, for a respective step in the multi-step explanation, whether the respective step should initially be displayed as collapsed or expanded (SL p. 5). MathChat suggests—where Symbolab does not disclose—wherein the explanatory model is a large language model (p. 1). It would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the invention to combine the disclosures of Symbolab and MathChat in order to improve performance and accuracy.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 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.
Allowable Subject Matter
Claims 12 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVE ROWLAND whose telephone number is (469) 295-9129. The examiner can normally be reached on M-Th 10-8. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Dmitry Suhol can be reached at (571)-272-4430. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/STEVE ROWLAND/Primary Examiner, Art Unit 3715
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