DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This Office action is in response to Applicant’s communication (RCE) filed on August 10, 2026. Drawings of Figures 4A-4F (filed on August 10, 2026) have been entered. The objections to the drawings are withdrawn in view of the drawings filed on August 10, 2026. Amendments to claims 1, 10, and 15 have been entered. Claims 1-20 are pending and have been examined. The statement of reasons for the indication of allowable subject matter over prior art was already discussed in the Office action mailed on September 4, 2025 and hence not repeated here, The objections to the drawings, rejections and response to arguments are stated below.
Claim Rejections - 35 USC § 101
2. 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.
3. Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a dynamic computerized feedback system for decision making recommendations and risk mitigation, which is considered a judicial exception because it falls under the category of certain of methods of organizing human activity such as fundamental economic practice (including mitigating risk) as well as commercial interactions (including fulfilling agreements) as discussed below. This judicial exception is not integrated into a practical application as discussed below. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception as discussed below.
Analysis
Step 1: In the instant case, exemplary claim 1 is directed to a system.
Step 2A – Prong one: The limitations of “A dynamic computerized feedback system for decision making recommendations and risk mitigation comprising:
a computerized system having a processor, a memory and a computer readable instructions;
wherein the computer readable instructions, when executed by the processor, are adapted to provide having an input engine wherein the input engine is adapted to receive digital input taken from a group consisting of a mortgage document, a bank statement, a debt statement, a loan document, a tax return, a retirement statement, an insurance policy, a will, a trust, a financial plan and a debt-to-income ratio analysis and create a digital input representation representing a binary conversion of the input;
wherein the computer readable instructions, when executed by the processor, are adapted to provide a similarity engine included in the computerized system adapted to receive the digital input representation, determine a document type according to a vector analysis and a database of document types, wherein the vector analysis comprises determining a cosine of an angle between a vector representing the digital input representation and a vector of the database of document types, wherein a value in each dimension of the vector representing the digital input representation corresponds to a number of times a term appears in the digital input representation and identify digital information from fields from the digital input representation for export from the digital input representation, wherein the document type is determined without receiving an identification of the document type from a user;
a database in communications with the processor and include a recommendation information representing decisions of past participants provided by prior advisors for each digital input representations identified by the similarity engine;
wherein the computer readable instructions, when executed by the processor, are adapted to provide a recommendation engine having a decision ruleset and adapted to receive an exported fields from the digital input representation, create a recommendation based upon the exported fields, a digital ruleset, and the database of recommendation information, receive a decision to implement a recommendation, and update the database of recommendation information according to a decision information; and,
wherein the computer readable instructions, when executed by the processor, are adapted to display the recommendations on a computer display in electronic communications with the computerized system” as drafted, when considered collectively as an ordered combination without the italicized portions, is a process that, under the broadest reasonable interpretation, covers methods of organizing human activity such as fundamental economic practice (including mitigating risk) as well as commercial interactions (including fulfilling agreements).
A dynamic computerized feedback system for decision making recommendations and risk mitigation, is a fundamental economic practice such as mitigating risk. The steps of “Creating a recommendation based upon the exported fields, a digital ruleset, and the database of recommendation information, receiving a decision to implement a recommendation, and updating the database of recommendation information according to a decision information; and
displaying the recommendations on a display in communications with the computerized system” considered collectively as an ordered combination, is a form of fulfilling agreements between the parties concerned. Hence, the steps of the claim, considered collectively as an ordered combination, covers the abstract category of methods of organizing human activity.
That is, other than, a computerized system having a processor, a memory and a computer readable instructions, a computer display, the engines (including the input engine, the similarity engine, and the recommendation engine), the digital input representation, the database of recommendation information and the digital ruleset, and electronic communications, nothing in the claim precludes the steps from being performed as a method of organizing human activity. The computerized system having a processor, a memory and a computer readable instructions, a computer display, the engines (including the input engine, the similarity engine, and the recommendation engine), the digital input representation, the database of recommendation information and the digital ruleset, and electronic communications are broadly interpreted to correspond to generic computer components suitably programmed to perform their respective functions. If the claim limitations, under the broadest reasonable interpretation, covers methods of organizing human activity but for the recitation of generic computer components, then it falls within the “Certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A – Prong two: The judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of a computerized system having a processor, a memory and a computer readable instructions, a computer display, the engines (including the input engine, the similarity engine, and the recommendation engine), the digital input representation, the database of recommendation information and the digital ruleset, and electronic communications to perform all the steps. A plain reading of Figures 1-2B and associated descriptions in the Specification reveals that general purpose computers suitably programmed (engines and machine learning) are used to execute the claimed steps. The processor, a memory, a computer readable instructions, and the computer display are all generic computer components suitably programmed to perform the respective functions. The electronic communication is broadly interpreted to include generic communication using the conventional networks known in the art. The database of recommendation information may be a generic database suitably programmed to hold the associate information. The digital ruleset and the digital input representation are broadly interpreted to include generic software components (including a learning model) suitably programmed to perform the respective functions. Hence, the additional elements in the claims are all generic components suitably programmed to perform their respective functions. The computerized system in all the steps is implicitly recited at a high-level of generality (i.e., as a generic computerized system performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Hence, claim 1 is directed to an abstract idea.
Step 2B: 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, using the additional elements (identified above) to perform the steps recited in the claim, amount to no more than mere instructions to apply the exception using a generic computer components. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Hence, independent claim 1 is not patent eligible. Independent claims 10 and 15 are also not patent eligible based on similar reasoning and rationale.
Dependent claims 2-9, 11-14 and 15-20, when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations only refine the abstract idea further.
For instance, in claims 2-5, the claimed limitations
“wherein the computerized computer readable instructions are adapted to create an initial an action plan according to a comparison of a recommendations” ,
“wherein the computerized computer readable instructions are adapted to create an initial action plan according to a personal financial goal of a target participant associated with the digital input representation”,
“wherein the digital ruleset is a learning model created using multiple individual experts in the fields of consisting of tax, insurance, accounting, personal finance, wealth management, estate planning and any combination thereof”, and
“wherein the computer readable instructions are configured to digitally transmit the recommendation to a remote advisor computer system inaccessible to a target participant” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because they describe the intermediate steps of the underlying process. The additional element of the remote advisor computer system is broadly interpreted to include a generic computer system a suitably programmed to perform the respective functions. This additional element performs a traditional function recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components.
In claims 6-9, the limitations “wherein the computer readable instructions are configured to receive a recommendation compliance representing that a recommendation was accepted and modify the database of recommendation information according to the recommendation compliance”,
“wherein the database of recommendation information is a first database of recommendation information, and the computerized system is configured to digitally modify a second database of recommendation information stored on a remote computer system according to the recommendation compliance”,
“wherein the database of recommendation information is created using a natural language engine having natural language computer readable instructions, receiving natural language, generating a translation model using context, reading natural language derived data from the natural language according to a translation model, and modifying the database of recommendation information” and
“wherein the computer readable instructions are adapted to retrieve additional information from a third-party electronic source wherein the third-party electronic source is taken from the group consisting of a financial computer system, a credit computer system, an investment computer system, a mortgage computer system, a student loan computer system, an insurance computer system, and any combination thereof” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because they describe the intermediate steps of the underlying process. The additional elements of the second database of recommendation information, the natural language engine and the third-party electronic source are broadly interpreted to include a generic computer components suitably programmed to perform their respective functions. These additional elements perform their respective traditional functions recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components.
In claims 11-14, the limitations
“wherein the similarity engine is adapted to identify digital information from a document using vector analysis” ,
“wherein the similarity engine is adapted to determine a difference between the digital input representation and a preexisting digital file wherein if the difference is under a predetermined threshold the digital input representation and a preexisting digital file are determined to be similar”,
“wherein displaying the recommendation on a display in communications with the computerized system include providing a dashboard’ and
“wherein the recommendation engine is adapted to update the ruleset according to the decision information thereby providing a feedback loop according to decision information to provide a learning system through modification of the ruleset with decision information” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because they describe the intermediate steps of the underlying process.
In claims 16-20, the limitations
“wherein the computerized system is a first computerized system and is adapted to transmit the updated database to a second computerized system in communications with the first computerized system to provide the second computerized system a benefit of the decision information”,
“wherein the computerized system is a first computerized system in communications with the first computerized system and is adapted to transmit the updated ruleset to a second computerized system”,
“wherein the recommendation engine is adapted to gather input from a disparate data source, a natural language processing system, and a rolling feedback aspect of the system to provide recommendations”,
“wherein the similarity engine is adapted to use term frequency in the digital input representation to determine an input type and fields to extract from the digital input representation” and
“the similarity engine is adapted to use natural language processing to determine an input type and fields to extract from the digital input representation” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because they describe the intermediate steps of the underlying process.
In all the dependent claims, the judicial exception is not integrated into a practical application because the limitations are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. Also, the claims do not affect an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer system itself; the claims do not affect a transformation or reduction of a particular article to a different state or thing; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment. In addition, the dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. The claims as a whole, do not amount to significantly more than the abstract idea itself. For these reasons, the dependent claims also are not patent eligible.
Response to Arguments
4. Applicant’s arguments against objections to the drawings are moot in view of the withdrawn objections.
In response to Applicants arguments on pages 10-15 of the Applicant’s remarks that the claims are patent-eligible under 35 USC 101 when considered under MPEP 2106, the Examiner respectfully disagrees.
The fact that the claims are Patent-Ineligible when considered under the MPEP 2106 has already been addressed in the rejection and hence not all the details of the rejection are repeated here.
Response to Applicants’ arguments regarding Step 2A – Prong one and Prong two:
The claim(s) recite(s) a dynamic computerized feedback system for decision making recommendations and risk mitigation, which is considered a judicial exception because it falls under the category of certain of methods of organizing human activity such as fundamental economic practice (including mitigating risk) as well as commercial interactions (including fulfilling agreements) as discussed in the rejection. A dynamic computerized feedback system for decision making recommendations and risk mitigation, is a fundamental economic practice such as mitigating risk. The steps of “Creating a recommendation based upon the exported fields, a digital ruleset, and the database of recommendation information, receiving a decision to implement a recommendation, and updating the database of recommendation information according to a decision information; and displaying the recommendations on a display in communications with the computerized system” considered collectively as an ordered combination is a form of fulfilling agreements between the parties concerned. Hence, the steps of the claim, considered collectively as an ordered combination, covers the abstract category of methods of organizing human activity. The additional elements (identified in the rejection) are used as tools in their ordinary capacity to apply the abstract idea.
According to MPEP 2106, limitations that are indicative of integration into a practical application include:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e).
In the instant case, the judicial exception is not integrated into a practical application, because none of the above criteria is met. The claims only recite the additional elements of a computerized system having a processor, a memory and a computer readable instructions, a computer display, the engines (including the input engine, the similarity engine, and the recommendation engine), the database of recommendation information and the digital ruleset, and electronic communications to perform all the claimed steps. A plain reading of Figures 1-2B and descriptions in associated paragraphs including at least paragraphs [0054] – [0062] reveals that general purpose computers suitably programmed (engines and machine learning) are used to execute the claimed steps. The processor, a memory and computer readable instructions, and the computer display are all generic computer components suitably programmed to perform the respective functions. The electronic communication is broadly interpreted to include generic communication using the conventional networks known in the art. The database of recommendation information may be a generic database suitably programmed to hold the associate information. The digital ruleset and the digital input representation are broadly interpreted to include generic software components (including a learning model) suitably programmed to perform the respective functions. Hence, the additional elements in the claims are all generic components suitably programmed to perform their respective functions. The computerized system in all the steps is implicitly recited at a high-level of generality (i.e., as a generic computerized system performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Hence, the claims are directed to an abstract idea.
The claimed features including those recited on pages 11-13 of the remarks such as “components and operations including a similarity engine that performs vector analysis "wherein the vector analysis comprises determining a cosine of an angle between a vector representing the digital input representation and a vector of the database of document types, wherein a value in each dimension of the vector representing the digital input representation corresponds to a number of times a term appears in the digital input representation……. the document type is determined without receiving an identification of the document type from a user….. These limitations define specific computational operations, cosine-similarity vector analysis over term-frequency vectors and automated document classification without user input ….. the similarity engine performs cosine-similarity vector analysis to automatically determine document types by comparing digital input representations against a database of document types …… automated identification of document types without human intervention or pre-labeling …… binary conversion transforms varied document formats into "a standardized machine-readable binary format ….. determining a cosine of an angle between a vector representing the digital input representation and a vector of the database of document types, wherein a value in each dimension of the vector representing the digital input representation corresponds to a number of times a term appears in the digital input representation," and that "the document type is determined without receiving an identification of the document type from a user” may be characterized as improvements in the abstract idea of a dynamic computerized feedback system for decision making recommendations and risk mitigation, using the additional elements as tools in their ordinary capacity. All the limitations of all the claims have been considered in arriving at the overall abstract idea of a dynamic computerized feedback system for decision making recommendations and risk mitigation. Hence, the claims recite an abstract idea and are directed to an abstract idea. An improvement in abstract idea is still abstract (SAP America v. Investpic *2-3 (“We may assume that the techniques claimed are “groundbreaking, innovative, or even brilliant,” but that is not enough for eligibility. Association for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); accord buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1352 (Fed. Cir. 2014). Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. See Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 89–90 (2012); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (“A claim for a new abstract idea is still an abstract idea). The additional elements (identified in the rejection) are generic computer components used to apply the abstract idea.
The Examiner does not see the parallel between the Applicant’s claims and those in Example 42 of the USPTO's Subject Matter Eligibility Examples. Therefore, the Applicant’s arguments are not persuasive.
The alleged advantages such as “the binary conversion improving computer processing efficiency by enabling the processor to perform computational operations on the data that would not be possible with non- standardized formats, thereby enhancing the computer's ability to process and analyze documents from disparate sources …. the similarity engine automatically identifying and exporting specific fields from digital input representations based on the determined document type, eliminating the need for manual data entry or field mapping …. improving computer functionality by automating the data extraction process through computational analysis of document structure and content, reducing processing time and improving accuracy compared to manual data entry systems …. improving computer system performance over time by modifying the recommendation database based on actual implementation results, enabling the system to learn from past decisions and providing increasingly accurate recommendations …. creating a rolling feedback system that continuously improves the accuracy and relevance of recommendations” are due to improvements in the abstract idea of a dynamic computerized feedback system for decision making recommendations and risk mitigation, using the additional elements as tools in their ordinary capacity. It does not involve any improvements to another technology, technical field, or improvements to the functioning of the computer itself. Therefore, the Applicants’ arguments are not persuasive.
Response to Applicants’ arguments regarding Step 2B:
As discussed in the rejection, the claims 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, using the additional elements (identified in the rejection) to perform the claimed steps, amount to no more than mere instructions to apply the exception using a generic computer component. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, the claims are not patent eligible.
The claimed features including those recited on pages 14-15 of the remarks such as “combination of the input engine performing binary conversion, the similarity engine performing cosine-similarity vector analysis over term-frequency vectors for automatic document-type determination" without receiving an identification of the document type from a user," and the recommendation engine implementing a rolling feedback mechanism through database updates …... automated document classification through defined mathematical operations without user pre-identification, standardized binary processing of disparate document formats, and continuous system refinement through feedback” may be characterized an improvement in the abstract idea of a dynamic computerized feedback system for decision making recommendations and risk mitigation, using the additional elements as tools in their ordinary capacity.
The alleged advantages such as “automated analysis file generation through field identification …. identifying relevant fields from the digital input representation and export them into a structured analysis file format that enables efficient downstream processing by the recommendation engine ….. automated structuring of extracted data improving processing efficiency by eliminating manual data organization and creating machine-optimized data structures for subsequent computational analysis …. enabling the computer system to refine both its data and its decision-making algorithms …. allowing the system to learn from past decisions at both the data level and the algorithmic level, providing increasingly sophisticated recommendations over time …. integrating structured field data from the analysis file with historical recommendation data and algorithmic rulesets to generate recommendations through specific computational operations …. providing a standardized format for the recommendation engine to perform its computational analysis” that Applicants tout do not concern an improvement in computer capabilities but instead relate to an improvement in the abstract idea of a dynamic computerized feedback system for decision making recommendations and risk mitigation, for which a computer system is used as a tool in its ordinary capacity. The computer system is merely a platform on which the abstract idea is implemented. Therefore, the Applicants’ arguments are not persuasive.
For these reasons and those discussed in the rejection, the rejections under 35 USC § 101 are maintained.
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(a) Boraiah; Manjunath et al. (US Patent 11954733 B1) discloses a computing system that includes a network interface circuit and a processing circuit configured to receive a plurality of issuer ratings associated with issuers from a plurality of analyst devices, wherein the plurality of issuer ratings include qualitative ratings, generate numerical quality ratings for the issuers based on the qualitative ratings of the issuers, receive a plurality of investment parameters from a manager device, the plurality of investment parameters defining characteristics of a custom investment strategy, generate a dataset regarding securities issued by the issuers based on the numerical quality ratings of the issuers, the dataset including securities issued by the issuers having a numerical quality rating over a pre-determined value, generate the custom investment strategy by selecting securities from the dataset based on the numerical quality ratings and the plurality of investment parameters, and provide the custom investment strategy to the manager device.
6. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Narayanswamy Subramanian whose telephone number is (571) 272-6751. The examiner can normally be reached Monday-Friday from 9:00 AM to 5:00 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Abhishek Vyas can be reached at (571) 270-1836. The fax number for Formal or Official faxes and Draft to the Patent Office is (571) 273-8300.
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/Narayanswamy Subramanian/
Primary Examiner
Art Unit 3691
September 25, 2026
