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 .
Claim Status
Claims 1-20 are currently pending and are presented for examination on the merits.
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 1-20 are rejected under 35 U.S.C. § 101, because they recite non-patentable subject matter under MPEP § 2106, e.g., the 2019 PEG, October update, that is directed to a judicial exception (e.g., an abstract idea, etc.) without practical application or significantly more.
More particularly, when considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Broad categories of abstract ideas include fundamental economic practices, certain methods of organizing human activities, an idea itself, and mathematical relationships/formulas. See, generally, MPEP § 2106; Alice Corporation Pty. Ltd. v. CLS Bank International, et al., 573 U.S. __ (2014) (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc.,132 S. Ct. 1289, 1294, 1297-98 (2012)); Federal Register notice titled 2014 Interim Guidance on Patent Subject Matter Eligibility (79 FR 74618), which is found at: http:// www. gpo.gov/fdsys/pkg/FR-2014-12-16/pdf/2014-29414.pdf; 2015 Update to the Interim Guidance; the 2019 Revised Patent Subject Matter Eligibility Guidance, Fed. Reg., Vol. 84, No. 4, January 7, 2019; and associated Office memoranda.
Under MPEP § 2106, Step 1, the claimed invention, taking the broadest reasonable interpretation, recites at least a process, machine, article of manufacture or composition of matter; and as such, is patent eligible.
Under MPEP § 2106, Step 2a-prong 1, Claims 1-20 recite a judicial exception(s), including a method of organizing human activity (e.g. fundamental economic principle). More particularly, the entirety of the method steps is directed towards modeling a tax year, determining tax values and anomalies in the tax return model. These are long-standing commercial practices previously performed by humans (e.g., tax preparation services, accountants, etc.) manually and via generic computing. That is to say, humans have long performed these functions through mental steps, and further, tax analysis models (such as online tax preparation software) have been used to input information into fields, so as to determine values, and anomalies (i.e., red flags, alerts, etc.). As such, the inventions include an abstract idea(s) under § 2106, and Alice Corporation.
Under step 2a-prong 2, the claims fail to recite a practical application of the exception, because the extraneous limitations (e.g., the structure—non-transitory computer-readable media, at least one processor, natural language processing, plurality of nodes comprising models defining output values and edges, determining output values based on weighted input, etc.) merely add insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g), generally link the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) and/or generally instruct an artisan to apply it (the method) across generic computing technology. A claim does not cease to be abstract for section 101 purposes simply because the claim confines the abstract idea to a particular technological environment in order to effectuate a real-world benefit. See Alice, 573 U.S. at 222; BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281, 1287 (Fed. Cir. 2018); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1353 (Fed. Cir. 2014). That is to say, the claims are not directed to a new software or computer, but rather employs pre-existing software to do what’s been previously done, albeit less efficiently or slower. “[I]t is not enough, however, to merely improve a fundamental practice or abstract process by invoking a computer merely as a tool.” Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1364 (Fed. Cir. 2020) (citations omitted). More particularly, the claims fail to recite an improvement to the functioning of a computer or technology (under MPEP § 2106.05(a)), the use of a particular machine (under § 2106.05(b)), effect a transformation or reduction of a particular article (§ 2106.05(c)), or apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (§ 2106.05(e)).
Under part 2b, the additional elements offered by the dependent claims either further delineate the abstract idea, add further abstract idea(s), adds insignificant extra-solution activity, or further instruct the artisan to apply it (the abstract idea(s)) across generic computing technology. The claims as a whole, do not amount to significantly more than the abstract idea itself. This is because no one claim effects an improvement to another technology or technical field, an improvement to the functioning of a computer itself, or move beyond a general link of the use of the abstract idea to a particular technological environment. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. Under Alice, merely applying or executing the abstract idea on one or more generic computer system (e.g., a computer system comprising a generic database; a generic element (NIC) for providing website access, etc.; a generic element for receiving user input; and a generic display on the computer, in any of their forms) to carry out the abstract idea more efficiently fails to cure patent ineligibility. See, e.g., Content Extraction, 776 F.3d at 1347 (claims reciting a “scanner” are nevertheless directed to an abstract idea); Mortg. Grader, Inc. v. First Choice Loan Serv. Inc., 811 F.3d 1314, 1324–25 (Fed. Cir. 2016) (claims reciting an “interface,” “network,” and a “database” are nevertheless directed to an abstract idea).
Courts have recognized the following computer functions to be well‐understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations, receiving, processing, and storing data, electronically scanning or extracting data from a physical document, electronic recordkeeping, automating mental tasks, and receiving or transmitting data over a network, e.g., using the Internet to gather data, MPEP 2106.05(d), wherein the italicized tasks are particularly germane to the instant invention.
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 of this title, 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:
a. Determining the scope and contents of the prior art.
b. Ascertaining the differences between the prior art and the claims at issue.
c. Resolving the level of ordinary skill in the pertinent art.
d. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1-13, and 15-20 are rejected under 35 U.S.C. § 103 as being unpatentable over US 2022/0350790 to Kalyanam et al., in view of US 2021/0019309 to Yadav et al., and further in view of US 2023/0177024 to Anand et al.
With respect to Claims 1, and 8, Kaly teaches a method for calculating a tax return model associated with a tax year (FIGS. 4, 6, 7, 8A, 9A; [0089]), and one or more non-transitory computer-readable media comprising computer-executable instructions ([0100]) that, when executed by at least one processor ([0100];[0027]), perform a method of generating a computational tax structure modeling a tax year ([0002], tax returns are done on a yearly basis), the method comprising: obtaining tax information associated with the tax year, the tax information comprising tax instructions ([0028-29]); parsing a plurality of tax fields from the tax information ([0028-29]); determining a plurality of dependencies between the plurality of tax fields ([0029]); and generating, using the plurality of dependencies and the plurality of tax fields, the computational tax structure comprising: a plurality of nodes including a plurality of executable models ([0026], “anomaly detection models” throughout), wherein each of the plurality of models configured to calculate an output values ([0034];[0042];[0089]).
Kaly fails to expressly teach, but Yadav teaches a plurality of edges (inputs), the plurality of edges including a plurality of weights including a first weight, the plurality of weights corresponding to a plurality of output values including the output value, wherein the plurality of nodes are connected to at least one node from the plurality of nodes by at least one edge from the plurality of edges ([0005];[0380];[0503]). Yadav teaches a recognition model ([0059];[0501]). Yadav further teaches identifying a source node of a plurality of nodes (which may be associated with an anomaly at any given time as taught by Kaly), including the substep of evaluating a second weight amongst a plurality of weights associated with the source node ([0005-09];[0502-05];[0526]). Yadav discusses erroneous answers provided by natural language processing and the difficulty in re-questioning. [0002] It would have been obvious to one of ordinary skill in the art to modify Kaly to include weighted edges in the data structure so as to better provide answers (e.g., outputs).
Kaly fails to teach, but Anand teaches the input of unstructured tax information ([0070); generating using a name recognition model ([0059];[0074]) trained via natural language processing ([0047], NLP unit 3710), name-value pairs corresponding to the information ([0247];[0256]); populating the structure with a plurality of values ([0071];[0171-176]). The combination of Kaly and Anand teaches in response to populating the computational tax structure with the plurality of tax field values (Anand [0071], populating data into the analysis system therein), automatically executing the plurality of executable models of the plurality of nodes (Kaly, see “executed by the processor” throughout). Anand discusses that “analytic tools are inefficient, costly to utilize, and/or require substantial configuration and training.” ([0001]) It would have been obvious to one of ordinary skill in the art to modify Kaly, to teach the added limitations, in order to better improve efficiency, cost, and simplicity as taught by Anand.
With respect to Claim 2, Kaly teaches wherein the computational tax structure is a first tax structure, and the tax year is a first tax year, wherein the method further comprises: receiving a second tax structure modeling a second tax year, the second tax year being different than the tax year; and comparing the second tax structure to the tax structure such that a set of differences between the tax year and the second tax year may be determined. ([0093];[0095]) Moreover, in so far as Claim 2 recites calculation of a new values after a period, it is noted that mere redundancy, duplicity, or repetition of existing structure or steps has been deemed obvious under § 103 analysis. St. Regis Paper Co. v. Bemis Co., 193 USPQ 8. See also, MPEP § 2144.05 which states: In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960)
With respect to Claim 3, Kaly teaches obtaining tax return information associated with a taxpayer, the tax return information including a plurality of tax field values (Abstract;[0002]); and inputting the plurality of tax field values into the computational tax structure to create a tax return model (Abstract) such that the plurality of models calculate the plurality of output values ([0089]).
With respect to Claims 4, and 9, Kaly teaches detecting, by an anomaly detector, an anomaly associated with the plurality of output values (Abstract;[0089]).
With respect to Claim 5, Kaly teaches causing display of, by an interface associated with a user, at least a portion of the tax return model; and indicating the anomaly such that the anomaly is emphasized on the computational tax structure displayed. [0040]
With respect to Claim 6, Kaly fails to expressly teach, but Yadav teaches wherein the computational tax structure is a directed acyclic graph. [0005] Under the same rationale as Claim 1, it would have been obvious to one of ordinary skill in the art to modify Kaly to include this limitation taught by Yadav.
With respect to Claim 7, Kaly teaches wherein the tax return model comprises a tax return value, the tax return value corresponding to an amount owed or entitled to by the taxpayer. ([0002];[0029], “tax preparation” and “tax returns” teaches this)
With respect to Claim 10, the combination of Kaly and Yadav teaches wherein the tax return model further comprises: the plurality of nodes outputting the plurality of output values ([0089]); the plurality of edges, the plurality of edges corresponding to the plurality of output values (claim 1 above); and Kaly further teaches a plurality of indicators attached to at least one anomalous node from the plurality of nodes and at least one anomalous edge from the plurality of edges, the plurality of indicators configured to indicate the at least one anomalous node and the at least one anomalous edge corresponding to the anomaly ([0089], flagging anomalous items).
With respect to Claim 11, Kaly teaches causing display of the plurality of indicators attached to the at least one anomalous node and the at least one anomalous edge. [0040]
With respect to Claim 12, Kaly teaches causing display of, by a user interface associated with a user, at least one weight from the plurality of weights. (FIGS. 13,14;[0380])
With respect to Claim 13, Kaly teaches indicating, to a user, a document from the tax return information associated with the anomaly. ([0485], tax data)
With respect to Claim 15, Kaly teaches a system (FIG. 1) for modeling a tax return for a tax year comprising: a word transformer operable to obtain tax information ([0002]) and parse a plurality of tax fields from the tax information ([0002]); a relationship transformer operable to obtain the plurality of tax fields and determine a plurality of dependencies between the plurality of tax fields ([0029]); and one or more non-transitory computer-readable media comprising computer- executable instructions (FIG. 1) that, when executed by at least one processor, perform a method of modeling the tax return for the tax year, the method comprising: obtaining, from a data store, the tax information associated with the tax year, the tax information comprising tax instructions; parsing, by the word transformer, the plurality of tax fields from the tax information; determining, by the relationship transformer, the plurality of dependencies between the plurality of tax fields; generating, using the plurality of dependencies and the plurality of tax fields, a tax structure comprising: a plurality of nodes including a plurality of models, the plurality of models configured to calculate a plurality of output values ([0034-38]).
Kaly teaches obtaining tax return information associated with the tax return of a taxpayer ([0002]), the tax return information including a plurality of tax field values; inputting the plurality of tax field values into the tax structure to generate a tax return model such that the plurality of models calculate the plurality of output values ([0023]); and causing display of, by a user interface associated with a user, a portion of the tax return model ([0040]).
Yadav teaches a plurality of edges, the plurality of edges including a plurality of weights, the plurality of weights corresponding to the plurality of output values, wherein the plurality of nodes is connected to at least one node from the plurality of nodes by at least one edge from the plurality of edges. ([0005];[0380];[0503]). Yadav discusses erroneous answers provided by natural language processing and the difficulty in re-questioning. [0002] It would have been obvious to one of ordinary skill in the art to modify Kaly to include weighted edges in the data structure so as to better provide answers (e.g., outputs).
With respect to Claim 16, Kaly teaches wherein the word transformer applies at least one natural language processing technique to the plurality of tax fields from the tax information. (see natural language processing throughout)
With respect to Claim 17, Kaly teaches identifying an anomaly associated with the plurality of output values, wherein the portion of the tax return model displayed includes a set of nodes from the plurality of nodes and a set of edges from the plurality of edges corresponding to the anomaly. [0038]
With respect to Claim 18, Kaly teaches wherein the plurality of nodes being connected to the at least one node by the at least one edge is indicative of at least one dependency from the plurality of dependencies between the plurality of tax fields. [0029]
With respect to Claim 19, Kaly teaches determining a presence of an anomaly in the plurality of tax field values; and causing display of, by the user interface associated with the user, an indicator associated with the anomaly, the indicator configured to emphasize an anomalous node from the plurality of nodes associated with the plurality of tax field values. (Abstract, “flagging an anomaly”)
With respect to Claim 20, Kaly teaches wherein the plurality of models comprising the plurality of nodes are configured to utilize linear regression modeling. ([0034];[0037-38], regression operations)
Response to remarks
Applicant’s remarks submitted on 3/12/2026 have been fully considered, but are not persuasive where rejections/objections are maintained. The amendments fail to recite an innovative concept, as using natural language processing to train a named-entity recognition model, determining an output value based on one input corresponding to a weight, obtaining a tax return model by populating with tax field values and then executing the models, performing an extraction, etc. As such, the § 101 claim rejections are maintained because the claims continue to recite inventions directed to the automation and modeling of an abstract idea (long-standing tax preparation services). No one limitation recites an innovative concept, and the extraneous limitations are directed to how to apply the abstract idea across generic computing technology. The totality of the extraneous limitations in Claim 1 (and similar) describe the functioning of generic computing technology, and as such are insufficient. For example, a name-recognition module merely supplants and emulates what the human brain has long performed in that context, particularly where using natural language processing to train. The innovative steps (i.e., that which has no prior manual or mental equivalence), offered by Applicant are not persuasive.
As per the prior art, Claim 14 is not rejected under the prior art, because it would require an additional (or Fourth) reference (e.g., Shear), which results in hindsight and piecemeal.
The combination of references would have taught one of ordinary skill in the art, each and every limitation of the remaining claims. Anand teaches a machine learning model and analysis system that determines name-value pairs, working with unstructured tax data, populates the structure with values, etc. Please note that US 2022/0188700 to Khavronin et al and US 2016/0034305 to Shear et al have been added to the record, and are also germane to the instant invention. Please note that the applied reference(s) need not use the same terminology, or disclose the limitation verbatim, and also that the entirety of a prior art reference is to be applied to the respective claim(s), such that the pinpoint citations above are exemplary and provided for Applicant’s benefit; other locations within the applied reference(s) may further support the rejection. MPEP 2141.02(VI).
Conclusion
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/WILLIAM J JACOB/Examiner, Art Unit 3696
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