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 2/9/26.
Claim status:
Amended claims: 1, 6, 14, 19 and 27
Canceled claims: none
Added New claims: None
Pending claims: 1-29
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 1-29 are rejected under 35 U.S.C. § 101 because the claimed invention is not directed to statutory subject matter. Specifically, the invention of claims 1-29 is directed to an abstract idea without significantly more.
Independent claims 1, 14 and 27 are directed to a method (claims 1 and 27), a system, (claim 14). Therefore on its face, each of claims 1, 14 and 27 is directed to a statutory category of invention under Step 1 of the 2019 PEG. However each of claims 1, 14 and 27 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) maintaining acquisition scoring, (ii) retrieving, at least company data pertaining to a plurality of companies and data on historical company acquisitions; (iii) processing, the retrieved company data and the data on historical company acquisitions by running formatting checks on fields of the retrieved company data and the data on historical company acquisitions, and, for a field that fails a formatting check, formatting the field to satisfy a predetermined schema, and compiling the retrieved company data and the data on historical company acquisitions into a standardized tabular dataset, (iv) generating, a binary acquisition variable for each respective company of the plurality of companies based on whether the respective company was acquired within a predetermined time period after a date associated with a corresponding record for the respective company in the standardized tabular dataset; (v) isolating, a training set and a validation set by time from the standardized tabular dataset, and the binary acquisition variable; and (vi) generating, feature values from the standardized tabular dataset by programmatically deriving ratios and growth rates for scalar variables across a plurality of predetermined time intervals selecting, a feature subset from the generated feature values based on statistically measured relationships between the feature values and the binary acquisition variable generated from the data on historical company acquisitions; (vii) recording, for each company, a set of highest impact features contributing to the acquisition score; (viii) storing, a company profile for each company, wherein the company profile stores at least the acquisition score and the set of highest impact features for the company; (ix) periodically querying, for updated company data, updating the standardized tabular dataset based on the updated company data, and updating, in the company profile, the acquisition score for the company; (x) storing, in association with the company profile, historical acquisition scores for the company over time; (xi) displaying, upon request by a user, the acquisition score and at least a portion of the set of highest impact features accessed by the user; and in response to a user selection of a plotted historical acquisition score, displaying one or more portions of the updated company data that contributed to generation of a corresponding updated acquisition score under the broadest reasonable interpretation (BRI) covers methods of organizing human activity – fundamental economic principles or practices - mitigating risk but for the recitation of generic computers and generic computer components. (Independent claims 14 and 27 recite similar limitations and the analysis is the same).
That is, other than reciting a time-indexed company profile database, at least one processor associated with at least one server, one or more external databases, a user interface, training using a training algorithm, at least one machine learning model, using at least the data on historical company acquisitions and the binary acquisition variable as training input; applying, the at least one machine learning model to the standardized tabular dataset; validating, the at least one machine learning model based on at least one performance metric; determining, using the at least one machine learning model, a likelihood of acquisition of each of the plurality of companies based on a comparison of the data on historical company acquisitions and the standardized tabular dataset for each of the plurality of companies; assigning, using the at least one machine learning model, an acquisition score to each of the plurality of companies nothing in the claim precludes the steps from being directed to organizing human activity – fundamental economic principles or practices - mitigating risk. If a claim limitation under its BRI, covers methods of organizing human activity but for the recitation of generic computers, then the limitations fall within the “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”).
This “methods of organizing human activity” is 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, a time-indexed company profile database, at least one processor associated with at least one server, one or more external databases, a user interface, training using a training algorithm, at least one machine learning model, using at least the data on historical company acquisitions and the binary acquisition variable as training input; applying, the at least one machine learning model to the standardized tabular dataset; validating, the at least one machine learning model based on at least one performance metric; determining, using the at least one machine learning model, a likelihood of acquisition of each of the plurality of companies based on a comparison of the data on historical company acquisitions and the standardized tabular dataset for each of the plurality of companies; assigning, using the at least one machine learning model, an acquisition score to each of the plurality of companies. This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements – a time-indexed company profile database, at least one processor associated with at least one server, one or more external databases, a user interface, training using a training algorithm, at least one machine learning model, using at least the data on historical company acquisitions and the binary acquisition variable as training input; applying, the at least one machine learning model to the standardized tabular dataset; validating, the at least one machine learning model based on at least one performance metric; determining, using the at least one machine learning model, a likelihood of acquisition of each of the plurality of companies based on a comparison of the data on historical company acquisitions and the standardized tabular dataset for each of the plurality of companies; assigning, using the at least one machine learning model, an acquisition score to each of the plurality of companies
The time-indexed company profile database, at least one processor associated with at least one server, one or more external databases, user interface, training using a training algorithm, at least one machine learning model, using at least the data on historical company acquisitions and the binary acquisition variable as training input; applying, the at least one machine learning model to the standardized tabular dataset; validating, the at least one machine learning model based on at least one performance metric; determining, using the at least one machine learning model, a likelihood of acquisition of each of the plurality of companies based on a comparison of the data on historical company acquisitions and the standardized tabular dataset for each of the plurality of companies; assigning, using the at least one machine learning model, an acquisition score to each of the plurality of companies are recited at a high-level or generality (i.e. as a generic computer performing generic computer functions) such that, they amount to generally linking the abstract idea to a computer network (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 a time-indexed company profile database, at least one processor associated with at least one server, one or more external databases, a user interface, training using a training algorithm, at least one machine learning model, using at least the data on historical company acquisitions and the binary acquisition variable as training input; applying, the at least one machine learning model to the standardized tabular dataset; validating, the at least one machine learning model based on at least one performance metric; determining, using the at least one machine learning model, a likelihood of acquisition of each of the plurality of companies based on a comparison of the data on historical company acquisitions and the standardized tabular dataset for each of the plurality of companies; assigning, using the at least one machine learning model, an acquisition score to each of the plurality of companies, maintaining acquisition scoring, retrieving, at least company data pertaining to a plurality of companies and data on historical company acquisitions; processing, the retrieved company data and the data on historical company acquisitions by running formatting checks on fields of the retrieved company data and the data on historical company acquisitions, and, for a field that fails a formatting check, formatting the field to satisfy a predetermined schema, and compiling the retrieved company data and the data on historical company acquisitions into a standardized tabular dataset, generating, a binary acquisition variable for each respective company of the plurality of companies based on whether the respective company was acquired within a predetermined time period after a date associated with a corresponding record for the respective company in the standardized tabular dataset; isolating, a training set and a validation set by time from the standardized tabular dataset, and the binary acquisition variable; and generating, feature values from the standardized tabular dataset by programmatically deriving ratios and growth rates for scalar variables across a plurality of predetermined time intervals selecting, a feature subset from the generated feature values based on statistically measured relationships between the feature values and the binary acquisition variable generated from the data on historical company acquisitions; recording, for each company, a set of highest impact features contributing to the acquisition score; storing, a company profile for each company, wherein the company profile stores at least the acquisition score and the set of highest impact features for the company; periodically querying, for updated company data, updating the standardized tabular dataset based on the updated company data, and updating, in the company profile, the acquisition score for the company; storing, in association with the company profile, historical acquisition scores for the company over time; displaying, upon request by a user, the acquisition score and at least a portion of the set of highest impact features accessed by the user; and in response to a user selection of a plotted historical acquisition score, displaying one or more portions of the updated company data that contributed to generation of a corresponding updated acquisition score amount to generally linking the abstract idea to a computer network. The claims are not patent eligible.
The dependent claims have been given the full two part analysis including analyzing the additional limitations both individually and in combination. 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 2-13, 15-26 and 28-29 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 1-29 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 1-29 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 Claim 1 recites “formatting the field to satisfy a predetermined schema” at line 10, “generating, by the at least one processor, feature values from the standardized tabular dataset” at lines 20-21 and “updating the standardized tabular dataset based on the updated company data” at page 3 lines 17-18. Claim 14 recites “formatting the field to satisfy a predetermined schema” at line 10, “generate feature values from the standardized tabular dataset” at line 21 and “update the standardized tabular dataset based on the updated company data” at page 9 line 17. Claim 27 recites “formatting the field to satisfy a predetermined schema” at line 9, “generating, by the at least one processor, feature values from the standardized tabular dataset” at page 14 lines 4-5 and “updating the standardized tabular dataset based on the updated company data” at page 15 lines 2-3. There is no discussion in the specification of a schema or standardized table. Paragraph [0035] states “In certain embodiments, the processor 120 may be configured to retrieve, update, or query data from the database 150”, but doesn’t mention a standardized tabular dataset. Paragraph [0039] mentions a Dataframe once. Paragraph [0053] states “periodically querying (e.g., on a monthly basis), by the at least one processor 120, the one or more external databases 60 for updated company data; step 229, saving, by the at least one processor 120, the updated company data within the database 140 of the memory 130”, but doesn’t mention a standardized tabular dataset. Paragraph [0056] states “saving the updated data pertaining to the plurality of registered investment advisers within the memory 130 and/or database 140”, but doesn’t mention a standardized tabular dataset. Therefore, it has not been demonstrated that the inventor had possession of the claimed invention. Claims 2-13 depend from Claim 1, Claims 15-26 depend from Claim 14 and Claims 28-29 depend from Claim 27 and are rejected for these reasons.
Response to Arguments
Applicant's arguments filed 2/9/26 have been fully considered but they are not persuasive.
35 USC § 101
The Applicant states independent claim 1 is not properly characterized as a "method of organizing human activity," "fundamental economic practice," or "mitigating risk" within the meaning of the USPTO's abstract idea groupings.” (page 16). The Examiner disagrees with the sentence because the claims are an improvement of the abstract idea only. It is a business solution to a business problem of evaluating factors for acquisition of a company. 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 claim language itself is directed to a particular computer-implemented workflow as claimed for maintaining and operating a time-indexed database system” (page 17) and “claim 1 integrates any alleged exception into a practical application because it features a concrete, computer-implemented workflow” (page 18). The Claims are directed to evaluating factors for acquisition of a company – i.e. pre-sale activity. 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 evaluating factors for acquisition of a company 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(b)
The Applicant’s arguments and amendments overcome the 112(b) Rejections, therefore, the Rejection(s) are moot.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARLA HUDSON whose telephone number is (571)272-1063. The examiner can normally be reached M-F 9:30 a.m. - 5:30 p.m. ET.
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/M.H./Examiner, Art Unit 3694
/BENNETT M SIGMOND/Supervisory Patent Examiner, Art Unit 3694