DETAILED ACTION
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 .
Status of Claims
The following is Office Action on the merits in response to the communication received on 9/11/26.
Claim status:
Amended claims: 1, 8-9, 16, 18-19, 22, 24
Canceled claims: 5, 13, 15, 21
Added New claims: None
Pending claims: 1-4, 6-12, 14, 16-20 and 22-24
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 1-4, 6-12, 14, 16-20 and 22-24 are rejected under 35 U.S.C. § 101 because the claimed invention is not directed to statutory subject matter. Specifically, the invention of claims 1-4, 6-12, 14, 16-20 and 22-24 is directed to an abstract idea without significantly more.
Independent claims 1, 9 and 16 are directed to one or more non-transitory computer-readable media (claim 1), a system (claim 9) and a method (claim 16). Therefore on its face, each of claims 1, 9 and 16 is directed to a statutory category of invention under Step 1 of the 2019 PEG. However each of claims 1, 9 and 16 is also directed to an abstract idea without significantly more, under Step 2A (Prong One and Prong Two) and Step 2B of the 2019 PEG, which is a judicial exception to 35 U.S.C. 101, as detailed below. Using the language of independent claim 1 to illustrate the claim recites the limitations of, (i) determining an optimal outcome, (ii) storing, at a primary layer, a calculation module configured to generate a user profile of a user; (iii) storing, at a first sublayer of the primary layer, data points and a set of government laws, wherein the data points are indicative of the user and a user region associated with the user, and wherein the set of government laws comprise federal and state laws based on the user region, wherein the first sublayer is only accessible by the calculation module, (iv) obtaining, by the calculation module at the primary layer, the data points and the set of government laws from the first sublayer; (v) generating, by the calculation module at the primary layer, the user profile comprising the data points and the set of government laws; (vi) obtaining, by an insight module from the calculation module at the primary layer, the user profile; (vii) obtaining, by the insight module and from a second sublayer, opportunity data including opportunities to achieve potential user goals; wherein the second sublayer is only accessible by the insight module, (viii) determining, by the insight module at the primary layer, insights from the user profile and the opportunity data; (ix) performing a preliminary insights check to determine if an insights result based on the insights and preliminary action items that optimize an opportunity objective is above an opportunity threshold, wherein the opportunity objective is based on the opportunities; when the opportunity objective is below the opportunity threshold, stopping processing and obtaining additional information for future processing; and when the opportunity objective is above the opportunity threshold: obtaining, by a goal module at the primary layer, a set of goals from the user; (x) generating an outcome objective based on the set of goals from the user and the insights; (xi) causing display of the set of action items to maximize the outcome objective for the user; and (xii) determining a current state of the set of goals; (xiii) estimating an end state of the set of goals, wherein the accuracy of the end state of the set of goals is based at least in part on a likelihood of correctness of the set of government laws for a given region associated with the user; (xiv) providing further guidance to the user based on the end state of the set of goals under the broadest reasonable interpretation covers mental processes and methods of organizing human activity – commercial or legal obligations. (Independent claims 9 and 16 recite similar limitations and the analysis is the same).
That is, other than reciting at least one processor, one or more non-transitory computer-readable media, the display, eliminating variables to generate a reduced set of data points including only the data points that impact the outcome objective above an outcome objective threshold using a machine learning feature engineering model, wherein the reduced set of data points saves time and processing during an inference phase of a predictive model by reducing model inputs and computational operations; and determining, using the predictive model including the reduced set of data points a set of action items that maximizes the outcome objective while constraining the outcome objective to the set of government laws nothing in the claim precludes the steps from being directed to mental processes and methods of organizing human activity – commercial or legal obligations. If a claim limitation under its BRI, covers mental processes and methods of organizing human activity but for the recitation of generic computer components, then the limitations fall within the “mental processes” and “methods of organizing human activity” grouping of abstract ideas. Therefore, claim 1 recites an abstract idea under Step 2A Prong One of the Revised Patent Subject Matter Eligibility Guidance 84 Fed.Reg 50 (“2019 PEG”).
These “mental processes” and “methods of organizing human activity” are not integrated into a practical application under Step 2A prong Two of the 2019 PEG. In particular claim 1 recites the following additional elements of, at least one processor, one or more non-transitory computer-readable media, the display, eliminating variables to generate a reduced set of data points including only the data points that impact the outcome objective above an outcome objective threshold using a machine learning feature engineering model, wherein the reduced set of data points saves time and processing during an inference phase of a predictive model by reducing model inputs and computational operations; and determining, using the predictive model including the reduced set of data points a set of action items that maximizes the outcome objective while constraining the outcome objective to the set of government laws.
The at least one processor, one or more non-transitory computer-readable media, the display, eliminating variables to generate a reduced set of data points including only the data points that impact the outcome objective above an outcome objective threshold using a machine learning feature engineering model, wherein the reduced set of data points saves time and processing during an inference phase of a predictive model by reducing model inputs and computational operations; and determining, using the predictive model including the reduced set of data points a set of action items that maximizes the outcome objective while constraining the outcome objective to the set of government laws are recited at a high-level or generality (i.e. as a generic computer performing generic computer functions) such that, they amount to no more than instructions to apply the abstract idea with a computer (see MPEP 2106.05(h). 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 claims are directed to an abstract idea.
Under Step 2B of the 2019 PEG independent claim 1 does not include additional elements that are sufficient to amount to significantly more than the abstract idea. The claim(s) do 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 using at least one processor, one or more non-transitory computer-readable media, the display, eliminating variables to generate a reduced set of data points including only the data points that impact the outcome objective above an outcome objective threshold using a machine learning feature engineering model, wherein the reduced set of data points saves time and processing during an inference phase of a predictive model by reducing model inputs and computational operations; and determining, using the predictive model including the reduced set of data points a set of action items that maximizes the outcome objective while constraining the outcome objective to the set of government laws, determining an optimal outcome, storing, at a primary layer, a calculation module configured to generate a user profile of a user; storing, at a first sublayer of the primary layer, data points and a set of government laws, wherein the data points are indicative of the user and a user region associated with the user, and wherein the set of government laws comprise federal and state laws based on the user region, wherein the first sublayer is only accessible by the calculation module, obtaining, by the calculation module at the primary layer, the data points and the set of government laws from the first sublayer; generating, by the calculation module at the primary layer, the user profile comprising the data points and the set of government laws; obtaining, by an insight module from the calculation module at the primary layer, the user profile; obtaining, by the insight module and from a second sublayer, opportunity data including opportunities to achieve potential user goals; wherein the second sublayer is only accessible by the insight module, determining, by the insight module at the primary layer, insights from the user profile and the opportunity data; performing a preliminary insights check to determine if an insights result based on the insights and preliminary action items that optimize an opportunity objective is above an opportunity threshold, wherein the opportunity objective is based on the opportunities; when the opportunity objective is below the opportunity threshold, stopping processing and obtaining additional information for future processing; and when the opportunity objective is above the opportunity threshold: obtaining, by a goal module at the primary layer, a set of goals from the user; generating an outcome objective based on the set of goals from the user and the insights; causing display of the set of action items to maximize the outcome objective for the user; and determining a current state of the set of goals; estimating an end state of the set of goals, wherein the accuracy of the end state of the set of goals is based at least in part on a likelihood of correctness of the set of government laws for a given region associated with the user; providing further guidance to the user based on the end state of the set of goals, amount to instructions to apply the abstract idea with a computer. The claims are not patent eligible.
The dependent claims have been given the full two part analysis including analyzing the additional limitations individually. The Dependent claim(s) when analyzed individually are also held to be patent ineligible under 35 U.S.C. 101 because for the same reasoning as above and the additional recited limitation(s) fail to establish that the claim(s) are not directed to an abstract idea. The additional limitations of the dependent claim(s) when considered individually do not amount to significantly more than the abstract idea. Claims 2-4, 6-8, 10-13, 14, 17-20 and 22-24 merely further explain the abstract idea.
When viewed individually the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea. Accordingly claims 1-4, 6-12, 14, 16-20 and 22-24 are ineligible.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-4, 6-12, 14, 16-20 and 22-24 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Specifically, independent claims 1, 9 and 16 recite the limitation "a machine learning feature engineering model”. However, the specification fails to provide a description of this limitation. Various paragraphs describe either a machine learning model or feature engineering separately but fail to describe a machine learning feature engineering model. Therefore, the inventor has not demonstrated possession of the claimed invention. Claims 2-4, 6-8 and 22 depend from claim 1; claims 10-12, 14 and 23 depend from claim 9; and claims 17-20 and 24 depend from claim 16 and are rejected for this reason.
Response to Arguments
Applicant's arguments filed 9/11/26 have been fully considered but they are not persuasive.
35 USC § 101
The Applicant states that “the claims at least do not fall under the category of a mental processes and organizing human activity” (page 14), “the claims solve a technical problem arising in machine learning using a technical solution” (page 14) and “The present claims include a practical application of any alleged abstract idea” (page 15). The Examiner disagrees with these sentences because the claim amendments further define and recite a more narrow abstract idea, and can be performed with pen and paper. Thinning a data stream by removing a data set is not a technological improvement. The applicant has not shown how the claims improve a computer or other technology, invoke a particular machine, transform matter, or provide more than a general link between the abstraction and the technology, MPEP 2106.05(a)-(c) & (e). The Examiner disagrees that “the claims recite a data-processing architecture and model-input reduction technique, not a generic instruction to apply tax optimization on a computer.” (page 17). The claims are a business solution to the business problem of optimizing the tax filing process. The Claims do not provide an improvement over prior systems and only add details to the abstract idea. They do not address a problem particular to computer networks and merely apply the abstract idea on general computer components. The amended claims make the abstract idea more specific, and optimizing the tax filing process is not an unconventional activity. Applicant’s remarks about why these limitations provide a practical application fail to surface any technical improvement identified in the spec provided by the claimed machine learning system, therefore this is not an inventive concept and significantly more.
35 USC § 112
The claims are rejected under 35 USC § 112 as indicated above in the Office action.
Conclusion
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/M.H./Examiner, Art Unit 3694
/BENNETT M SIGMOND/Supervisory Patent Examiner, Art Unit 3694
