DETAILED ACTION
Notice to Applicant
1. The following is a NON-FINAL Office action upon examination of application number 18/607,388 filed on 03/15/2024, in response to Applicant’s Request for Continued Examination (RCE) filed on April 06, 2026. Claims 1-20 are pending in the application and have been examined on the merits discussed below.
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
3. In the response filed April 06, 2026, Applicants amended claims 1, 7, 9, 15, and 17, and did not cancel any claims. No new claims were presented for examination.
4. Applicants’ arguments with respect to the Claim Interpretation under 35 U.S.C. 112(f); are hereby acknowledged. The arguments are not persuasive; accordingly, the Claim Interpretation under 35 U.S.C. 112(f) has been maintained.
5. Applicants’ amendments to claims 1, 7, 9, 15, and 17, are hereby acknowledged. The amendments are not sufficient to overcome the previously issued claim rejection under 35 U.S.C. 101; accordingly, this rejection has been maintained.
Response to Arguments
6. Applicants’ arguments filed April 06, 2026, have been fully considered.
7. Applicants submit “The Examiner indicates that claims 17-20 have invoked 35 U.S.C. §112(f) due to the alleged use of generic placeholders ("a previous consumer survey analysis module to," "a multi- task learned representation module to," "a persona description module to," and "a multi-task learned representation training module to") coupled with functional language, thereby creating a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. § 112(f). In view of the absence of any “means for” language, applicants respectfully submit that claims 17-20 do not invoke 35 U.S.C. § 112(f) and respectfully request interpretation of claims 17-20 without invoking 35 U.S.C. § 112(f).” [Applicants’ Remarks, 04/06/2026, page 7]
The Examiner respectfully disagrees. In response, it is noted that a claim limitation does not need to exactly recite “means for” to invoke 35 U.S.C. § 112(f). Courts have recognized that functional language combined with a generic placeholder term such as “module to” can invoke 35 U.S.C. § 112(f) when the term is a nonce word or generic substitute for “means” and does not convey sufficiently definite structure for performing the claimed function. Here, each “module to” is described only in terms of the function it performs, without reciting corresponding structure. Accordingly, the “modules to” in claim 17 are properly interpreted under 35 U.S.C. § 112(f). Accordingly, this argument is found unpersuasive.
8. Applicants submit “Each of the claims falls within at least one statutory category: claims 1-8 are directed to a process; claims 9-16 are directed to a machine (non-transitory computer-readable medium); and claims 17-20 are directed to a machine (system). Accordingly, the claims satisfy Step 1 of the eligibility analysis.” [Applicants’ Remarks, 04/06/2026, page 7]
Examiner agrees.
9. Applicants submit “To the extent the Examiner maintains that the claims recite an abstract idea, applicants respectfully submit that the claims, when read as a whole, do not recite a judicial exception.” [Applicants’ Remarks, 04/06/2026, page 8]
The Examiner respectfully disagrees. In response to Applicants’ argument that “when read as a whole, do not recite a judicial exception,” it is noted that when the claim is considered as whole,. It still recites steps that collectively amount to generating a representation of a persona, predicting responses to survey questions, and using those predictions to guide survey deployment. The claim limitations fall within groupings of abstract ideas, including certain methods of organizing human activities (i.e., market research and survey design), and mental processes (i.e., modeling and predicting human responses). Accordingly, this argument is found unpersuasive.
In response to Applicants’ argument that “As amended, independent claim 1 recites a method comprising specific computer-implemented operations that cannot be performed mentally or manually, including: "leveraging previous consumer discrete choice surveys and/or previous consumer survey responses to provide synthetic data participants to further train the choice model to answer the survey of new questions, wherein the synthetic data participants are generated by the choice model using embeddings derived from a multi-task learning process with existing consumer research datasets;" "predicting, using a trained choice model, answers from the synthetic data participants to the survey of new questions by querying a multimodal-choice model based on consumer discrete choice surveys and a survey model based on consumer survey responses," "aggregating the predicted answers from the synthetic data participants into parameters for survey deployment;" and "rolling out, by a persona design server, the survey of new questions to real human participants depending on parameters determined from the answers of the synthetic data participants to test feedback from a synthesized population before fielding the survey to the real human participants." These features recite specific technical implementations involving multi-task learned embeddings, dual-model querying architecture (multimodal-choice model and survey model), aggregation operations, and server-based deployment-operations that are inherently computer-centric and cannot be performed in the human mind or with pen and paper. The claims do not merely recite organizing human activity or mental processes; rather, they recite a specific technical solution for generating and deploying surveys using machine learning models trained on existing datasets” [Remarks at pages 7-8], the Examiner respectfully disagrees. Even when considering the claim as whole, the recited steps (i.e., leveraging, predicting, aggregating, and rolling out) collectively amount to modeling human behavior and using that modeling to guide decision making. This falls within the abstract idea groupings of certain methods of organizing human activities and mental processes. Additionally, the recitation of elements such as “multi-task learning process,” “multimodal-choice model,” and “persona design server” does not remove the claim from abstraction, as there are described at a high level and merely represent generic tools used to carry out the abstract idea.
Lastly, in response to Applicants’ argument that “these features recite specific technical implementations involving multi-task learned embeddings, dual-model querying architecture (multimodal-choice model and survey model), aggregation operations, and server-based deployment-operations that are inherently computer-centric and cannot be performed in the human mind or with pen and paper,” it is noted that the steps related to forming a persona representation, estimating how that persona would answer questions, and using those estimates to guide survey deployment mirror human reasoning and analytical judgments, even if the claim recites performing them with machine learning models and large datasets. For the reasons above, this argument is found unpersuasive.
10. Applicants submit “Even if the Examiner were to find that the claims recite a judicial exception, the additional elements integrate any such exception into a practical application.” [Applicants’ Remarks, 04/06/2026, page 9]
In response to Applicant’s argument that “the additional elements integrate any such exception into a practical application,” it is noted that the additional elements in exemplary claim 1 are: training a choice model based on the personalization vector and the individual task models for each survey question of the query vector, further train the choice model, the choice model using embeddings derived from a multi-task learning process, using a trained choice model, a multimodal-choice model, and a persona design server, which merely serve to tie the abstract idea to a particular technological environment (computer-based operating environment) via generic computing hardware, software/instructions, which is not sufficient to amount to a practical application, as noted in MPEP 2106.05. Applicant has provided no facts/evidence, cited any portion of the Specification, nor provided a persuasive line of reasoning showing how the additional elements are integrated with the abstract idea to integrate the abstract idea into a practical application.
It is also noted that the claims are devoid of any discernible change, transformation, or improvement to a computer (software or hardware) or any existing technology. Applicant has not shown that any specific technological improvement is achieved within the scope of the claims. It bears emphasis that no server, or technological elements are modified or improved upon in any discernible manner. Instead, the result produced by the claims is simply information relating to a consumer response, which is not a technical result or improvement thereof.
Furthermore, the additional elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. For the reasons above, this argument is found unpersuasive.
In response to Applicant’s argument that “The claims impose meaningful limits on the judicial exception through the following technical features: (1) Specific Machine Learning Architecture: The claims recite a specific technical architecture comprising embeddings from multi-task learning, a multimodal-choice model, and a survey model working in combination. This is not a generic invocation of "apply it with a computer," but rather a specific arrangement of machine learning components that transforms how surveys are designed and deployed. See specification [0049] (describing the choice model querying both the multimodal-choice model based on consumer discrete choice surveys and the survey model based on consumer survey responses)” [Remarks, page 9], the Examiner respectfully disagrees. It is noted that the claims do not actually recite a “specific machine learning architecture.” Rather, the claim broadly recites components such as embedding derived from a multi-task learning process, a multi-modal choice model, and a survey model used in combination. These limitations are stated at a high-level of abstraction, without defining any particular structure, training technique, or technical mechanism that would amount to a concrete improvement in computer or model operation. Accordingly, this argument is found unpersuasive.
In response to Applicant’s argument that “Aspects of the present disclosure improve the functioning of survey design systems by enabling the testing of survey questions against a synthesized population before deployment to real participants. This reduces computational waste, improves survey quality, and enables more efficient use of computing resources. The system pilots surveys using synthetic participants to determine important parameters before fielding to real human participants. See specification [0033], [0044]” [Remarks, page 9], the Examiner respectfully disagrees. The alleged “improvement to computer functionality” is not supported because the claim does not recite a specific technical improvement to the operation of a computer or any underlying computing technology. Instead, it describes using generic machine learning models to simulate survey responses and to inform survey roll out decisions. Performing survey testing against a synthesized population before deployment does not reflect an improvement in how the computer itself operates. The alleged benefits (i.e., reduces computational waste, improves survey quality, and enables more efficient use of computing resource) are results or advantages of applying the abstract idea using generic computer tools, rather than a claimed technical solution to a computer central problem. Accordingly, this argument is found unpersuasive.
In response to Applicant’s argument that “The claims affect a transformation of survey data through multiple stages: (a) translating persona descriptions into personalization vectors; (b) generating synthetic data participants using multi-task learned embeddings; (c) predicting answers by querying dual models; (d) aggregating predictions into deployment parameters; and (e) using those parameters to control survey rollout. This multi-stage transformation goes beyond mere data gathering and represents a technical solution to the technical problem of survey design optimization” [Remarks, pages 9-10], the Examiner respectfully disagrees. It is noted that the steps related to translating persona descriptions into vectors, generating synthetic participants via embeddings, querying model for predictions, and aggregating the predicted answers reflect manipulation of information within a computational workflow, not a transformation of an article to a different state or function in a manner recognized as indicative of eligibility. Accordingly, this argument is found unpersuasive.
In response to Applicant’s argument that “The claims apply the choice model with a particular machine-a persona design server-that coordinates the generation of synthetic participants, prediction of answers, aggregation of results, and deployment of surveys. The server is not merely a generic computer performing generic functions, but rather a specialized system configured to implement the specific multi-model architecture recited in the claims” [Remarks, page 10], the Examiner respectfully disagrees. Reciting a “persona design server” does not amount to a particular machine implementation where the claim lacks any specific structural or technical details that distinguish it from a generic computing system. Labeling the server as “specialized” is not supported by the claim language, as no specific technological configuration or improvement to computer operation is recited. Accordingly, this argument is found unpersuasive.
11. Applicants submit “Even if the claims were found to be directed to an abstract idea under Step 2A, the claims recite significantly more than the alleged abstract idea. The additional elements, viewed individually and as an ordered combination, amount to significantly more because they are not well-understood, routine, or conventional (WURC) in the field. The Office Action provides no evidentiary support that the following limitations are WURC: Generating synthetic data participants using embeddings derived from a multi-task learning process with existing consumer research datasets; Querying both a multimodal-choice model and a survey model to predict answers; Aggregating predicted answers into parameters for survey deployment; and Testing feedback from a synthesized population before fielding surveys to real human participants. Under the 2019 PEG, examiners must provide substantial evidence when asserting that claim elements are WURC. The Examiner has not met this burden.” [Applicants’ Remarks, 04/06/2026, pages 10-11]
Specifically, regarding the rejection under 35 U.S.C. § 101, Applicant submits that “Even if the claims were found to be directed to an abstract idea under Step 2A, the claims recite significantly more than the alleged abstract idea. The additional elements, viewed individually and as an ordered combination, amount to significantly more because they are not well-understood, routine, or conventional (WURC) in the field.” As best understood by the Examiner, Applicant’s reliance on the Berkheimer Memo is based on Applicant’s misunderstanding of the Berkheimer decision, which is germane only to Step 2B eligibility inquiry into whether certain additional claim limitations are well-understood, routine, and conventional and the evidentiary requirements to support factual findings related thereto. Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018).
Accordingly, the Examiner emphasizes that a §101 rejection, including one based on a judicial exception, does not hinge on whether or not any particular limitation or the entire claimed subject matter is directed to “well-understood, routine, and conventional activities.” Notably, a §101 rejection may be proper even none of the claim limitations are deemed well-understood, routine, and conventional. We may assume that the techniques claimed are “[g]roundbreaking, innovative, or even brilliant,” but that is not enough for eligibility. Ass’n for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); 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 search for a § 101 inventive concept is thus distinct from demonstrating §102 novelty.”); Intellectual Ventures LLC v. Symantec Corp., 838 F.3d 1307, 1315 (Fed. Cir. 2016) (same for obviousness) (Symantec).
Moreover, it is noted that the addition of non-conventional components to an abstract idea does not necessarily turn an abstraction into something concrete. Further, the Examiner points out that limitations that were found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception also include: adding the words “apply it” or equivalent with the judicial exceptions, or mere instruction to implement an abstract idea on a computer, simply appending well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, of the judicial exception. As described below, the claims of the instant application are drawn to an abstract idea. It is noted that for the role of a computer in a computer-implemented invention to be deemed meaningful, it must involve more than performance of "well-understood, routine and conventional activities previously known in the industry.” Claim 1 is directed to performing the method steps, but these limitations add nothing of substance to the underlying abstract idea.
Furthermore, it is noted that only those additional elements (analyzed under 2B) that are deemed “conventional” need to comply with Berkheimer. When elements are just part of “apply it” [abstract idea] on a computer, under MPEP 2106.05(f), no evidence is needed. Citations for conventionality to MPEP 2106.05 were already provided. Arguing abstract elements for Berkheimer is not persuasive. See BSG Tech, LLC v. Buyseasons, Inc., 899 F.3d 1281,1290 (Fed. Cir. 2018) states “Our precedent has consistently employed this same approach. If a claim’s only “inventive concept” is the application of an abstract idea using conventional and well-understood techniques, the claim has not been transformed into a patent-eligible application of an abstract idea. See, e.g., Berkheimer, 881 F.3d at 1370 (holding claims lacked an inventive concept because they “amount to no more than performing the abstract idea of parsing and comparing data with conventional computer components”). For the reasons above, this argument is found unpersuasive.
12. Applicants submit “Amended claim 1 now recites: "wherein the synthetic data participants are generated by the choice model using embeddings derived from a multi-task learning process with existing consumer research datasets." This noted feature is not taught or suggested by Kumar, Khaled, nor Ambroziak. Kumar teaches generating persona vectors for actual customers based on their communication transcripts, not synthetic participants for survey design. Khaled teaches predicting responses for actual survey respondents who have provided partial responses, not creating synthetic participants. Ambroziak teaches predicting answers to optimize which questions to include in a survey but does not teach generating synthetic data participants using embeddings derived from multi-task learning.” [Applicants’ Remarks, 04/06/2026, page 12]
As best understood by Examiner, Applicants appear to argue that the feature “wherein the synthetic data participants are generated by the choice model using embeddings derived from a multi-task learning process with existing consumer research datasets" is not taught or suggested by Kumar, Khaled, nor Ambroziak. In response, the Examiner notes that Applicant’s amendments to claim 1 are deemed sufficient to overcome the §103 rejection of claim 1. Accordingly, this rejection has been withdrawn.
13. Applicants submit “Amended claim 1 now recites: "by querying a multimodal-choice model based on consumer discrete choice surveys and a survey model based on consumer survey responses." (Emphasis added.) This specific dual-model architecture, as recited by claim 1, is not taught or suggested by the applied references.” [Applicants’ Remarks, 04/06/2026, page 13]
As best understood by Examiner, Applicants appear to argue that the feature “by querying a multimodal-choice model based on consumer discrete choice surveys and a survey model based on consumer survey responses" is not taught or suggested by the applied references. In response, the Examiner notes that Applicant’s amendments to claim 1 are deemed sufficient to overcome the §103 rejection of claim 1. Accordingly, this rejection has been withdrawn.
14. Applicants submit “Amended claim 1 now recites: "aggregating the predicted answers from the synthetic data participants into parameters for survey deployment." While Ambroziak teaches compiling questions into a survey ([0056]), Ambroziak does not teach aggregating predicted answers from synthetic data participants into parameters that control how surveys are deployed.” [Applicants’ Remarks, 04/06/2026, page 13]
In response to Applicants’ argument that Ambroziak does not teach aggregating predicted answers from synthetic data participants into parameters for survey deployment, the Examiner notes that Applicant’s amendments to claim 1 are deemed sufficient to overcome the §103 rejection of claim 1. Accordingly, this rejection has been withdrawn.
15. Applicants submit “Moreover, the cited references teach away from the claimed approach. Khaled explicitly teaches predicting responses for actual survey respondents to reduce survey burden (T[0022]), not creating synthetic participants. Ambroziak teaches using predicted answers to determine which questions to include in surveys distributed to actual respondents (10056]), not to test surveys with synthetic participants first.” [Applicants’ Remarks, 04/06/2026, page 14]
The Examiner respectfully disagrees. It is first noted that Khaled does not teach away from “the claimed approach.” Although Khaled discussed predicting responses for actual respondent to reduce survey burden, Khaled’s disclosure of generating predicted responses for unanswered or unprovided survey questions using respondent data suggests constructing modeled representation of users. This is conceptually consistent with using a modeled or simulated respondent profile, and does not exclude extending such prediction techniques to synthetic or derived participants. Nothing in Khaled criticizes discourages, or otherwise discredits using predicted-response models beyond actual respondents.
Similarly, Ambroziak does not teach away from using predicted answers for pre-deployment survey evaluation. While Ambroziak applies predictions to determine which questions to include in surveys for actual distribution, this is fundamentally the same predictive framework used to optimize survey design based on modeled responses. Using predictions to inform survey composition or deployment decisions does not preclude applying these same predictions in a simulated testing environment. There is no express or implicit discouragement of evaluating surveys using modeled or synthetic respondent data prior to deployment. For the reasons above, this argument is found unpersuasive.
16. Applicants’ remaining arguments either logically depend from the above-rejected arguments, in which case they too are unpersuasive for the reasons set forth above, or they are directed to features which have been newly added via amendment. Therefore, this is now the Examiner's first opportunity to consider these limitations and as such any arguments regarding these limitations would be inappropriate since they have not yet been examined. A full rejection of these limitations will be presented later in this Office Action.
Claim Interpretation
17. The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
18. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a multi-task learned representation module to, a persona description module to, and a multi-task learned representation training module to in claim 17.
The claim limitations “a multi-task learned representation module to,” “a persona description module to,” and “a multi-task learned representation training module to” invokes 112(f). Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. The Specification describes that the “modules” are implemented by a computer (par. [0059]). Accordingly, the structure corresponding to the “multi-task learned representation module to,” “persona description module to,” and “multi-task learned representation training module to” recited in claim 17 is interpreted as being embodied as a generic computer programmed with software to perform the corresponding functions recited in these claims.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
19. 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.
20. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
21. 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 eligibility analysis in support of these findings is provided below, in accordance with MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1-8), non-transitory computer-readable medium (claims 9-16), and system (claims 17-20) is directed to at least one potentially eligible category of subject matter (i.e., process, article of manufacture, and machine, respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 1-20 is satisfied.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls into the “Certain Methods of Organizing Human Activity” abstract idea set forth in MPEP 2106 since the claims set forth steps for sales/marketing purposes, which amounts to sales or marketing activities or behaviors (under “commercial or legal interactions” in MPEP 2106) within the “Certain Methods of Organizing Human Activity” abstract idea grouping, and steps that can be performed in the human mind (including observation, evaluation, judgment, opinion), and therefore fall under the “Mental Processes” abstract idea grouping. With respect to independent claim 1, the limitations reciting the abstract idea are indicated in bold below: translating, using a personalization model, a persona description into a personalization vector, in which the personalization vector represents a synthetic person; creating a query vector including an individual task model for each question of a survey of new questions; training a choice model based on the personalization vector and the individual task models for each survey question of the query vector to predict a response of the synthetic persona for each of the survey of new questions based on the persona description; leveraging previous consumer discrete choice surveys and/or previous consumer survey responses to provide synthetic data participants to further train the choice model to answer the survey of new questions, wherein the synthetic data participants are generated by the choice model using embeddings derived from a multi-task learning process with existing consumer research datasets; predicting, using a trained choice model, answers from the synthetic data participants to the survey of new question by querying a multimodal-choice model based on consumer discrete choice surveys and a survey model based on consumer survey responses; aggregating the predicted answers from the synthetic data participants into parameters for survey deployment; and rolling out, by a persona design server, the survey of new questions to real human participants depending on parameters determined from the answers of the synthetic data participants to test feedback from a synthesized population before fielding the survey to the real human participants. These steps describe managing steps for sales/marketing purposes, which amounts to sales or marketing activities or behaviors and are part of the abstract idea falling under “Certain Methods of Organizing Human Activity” and steps that can be performed in the human mind, and therefore fall under the “Mental Processes” abstract idea grouping.
Because the above-noted limitations recite steps falling within the “Certain methods of organizing human activity” abstract idea grouping and the “Mental Processes” abstract idea grouping, they have been determined to recite at least one abstract idea when evaluated under Step 2A Prong One of the eligibility inquiry. Independent claims 9 and 17 recite similar limitations as those recited in claim 1 and therefore are found to recite the same abstract idea(s) as claim 1.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. With respect to independent claims 1, 9, and 17, the additional elements are: training a choice model based on the personalization vector and the individual task models for each survey question of the query vector, further train the choice model, the choice model using embeddings derived from a multi-task learning process, using a trained choice model, a multimodal-choice model, and a persona design server (claim 1); program code, an interactive persona design system, a processor, program code to translate, program code to create, program code to train a choice model based on the personalization vector and the individual task models for each survey question of the query vector, program code to leverage, further train the choice model, generated by the choice model using embeddings derived from a multi-task learning process, program code to predict, using a trained choice model, a multimodal-choice model, program code to aggregate, program code to roll out, and a persona design server (claim 9); a multi-task learned representation module to, a persona description module to, a multi-task learned representation training module to train a choice model based on the personalization vector and the individual task models for each survey question of the query vector, further train the choice model, generated by the choice model using embeddings derived from a multi-task learning process, a persona design server, using a trained choice model, and a multimodal-choice model (claim 17). These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serve to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to independent claims 1, 9, and 17, the additional elements are: training a choice model based on the personalization vector and the individual task models for each survey question of the query vector, further train the choice model, the choice model using embeddings derived from a multi-task learning process, using a trained choice model, a multimodal-choice model, and a persona design server (claim 1); program code, an interactive persona design system, a processor, program code to translate, program code to create, program code to train a choice model based on the personalization vector and the individual task models for each survey question of the query vector, program code to leverage, further train the choice model, generated by the choice model using embeddings derived from a multi-task learning process, program code to predict, using a trained choice model, a multimodal-choice model, program code to aggregate, program code to roll out, and a persona design server (claim 9); a multi-task learned representation module to, a persona description module to, a multi-task learned representation training module to train a choice model based on the personalization vector and the individual task models for each survey question of the query vector, further train the choice model, generated by the choice model using embeddings derived from a multi-task learning process, a persona design server, using a trained choice model, and a multimodal-choice model (claim 17). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serve to link the use of the judicial exception to a particular technological environment and does not amount to significantly more than the abstract idea itself. Notably, Applicant’s Specification describes that generic computer devices that may be used to implement the invention, which cover virtually any computing device under the sun (Specification at paragraph [0021]). Accordingly, the generic computer involvement in performing the claim steps merely serves to generally link the use of the judicial exception to a particular technological environment, which does not add significantly more to the claim. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976.).
Even if the “training a choice model based on the personalization vector and the individual task models for each survey question of the query vector” was evaluated as elements beyond software/code for a generic computer to execute, it is noted that that the claimed “training” is recited at a high level of generality these elements amount to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Polleri et al., US 11,562,267 B2 (col. 15, lines 64-67 & col. 16, lines 1-15: “In conventional systems, training models are provided that are essentially default training models hard coded into the design system for training and retraining the digital assistant or chatbot.”).
Even if the “multimodal-choice model” was evaluated as elements beyond software/code for a generic computer to execute, it is noted that that the claimed “multimodal-choice model” is recited at a high level of generality these elements amount to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Recasens Continente et al., US 2025/0181887 A1 (paragraph 0122: “Conventional models for multimodal perception typically produce a single output for the multiple inputs.”).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent claims 2-8, 10-16, and 18-20 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One are found to merely recite details that serve to narrow the same abstract idea recited in the independent claims accompanied by the same generic computing elements or software as those addressed above in the discussion of the independent claims, which is not sufficient to amount to a practical application or add significantly more, or other additional elements that fail to amount to a practical application or add significantly more, as noted above. In particular, dependent claims 2-8 recite “further comprising leveraging previous consumer discrete choice surveys and/or the previous consumer survey responses to provide synthetic data participants to answer the survey of new questions,” “further comprising tuning the personalization vector and the individual task models of the query vector for each of the questions of the survey of new questions to add/remove data points,” “in which the persona description comprises a natural language and/or a collection of visuals,” “further displaying the response of the synthetic persona for each of the survey of new questions,” “in which contents of the personalization vector comprise an embedding, generated by a multi-task learning process using existing consumer research datasets,” “further comprising feeding consumer discrete choice surveys and customer survey responses enable generation of the query vector, in which the query vector connects each consumer survey question to the choice model,” “further comprising modifying the persona description to detect an impact on a personalized synthetic survey response to each of the questions of the survey of new questions,” however these limitations are part of the same abstract idea as addressed in the independent claims that falls within the “Certain Methods of Organizing Human Activity” and “Mental Processes” abstract idea groupings. Accordingly, these steps are part of the same abstract idea(s) set forth in the independent claims. When evaluated under Step 2A Prong Two and Step 2B, the additional elements do not amount to a practical application or significantly more since they merely require generic computing devices (or computer-implemented instructions/code) which as noted in the discussion of the independent claims above is not enough to render the claims as eligible. The additional elements recited in the dependent claims comprise “a large language model (LLM)-based query generation system” (claims 7 and 15), “program code” (claims 10-11, 13, and 15-16), “ a display” (claim 19). However, when evaluated under Step 2A Prong Two and Step 2B, these additional elements rely on generic computing elements or software for generally linking the judicial exception to a particular technological environment, which does not amount to a practical application. MPEP 2106.05(g)/(h). Under Step 2B, the use of such generic computing elements has been recognized by courts as insufficient to amount to significantly more than the abstract idea. See, Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQe2d 1681, 1701 (Fed. Cir. 2015).
Even if the “large language model (LLM)-based query generation system” was evaluated as an element beyond software/code for a generic computer to execute, it is noted that that the claimed use of a large language model (LLM) is recited at a high level of generality, this element amounts to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Liu et al., US 2025/0181626 A1 (paragraph 0087: “well-known generative large language models”).
Similarly, with respect to dependent claims 5, 13, and 19 the displaying step, when evaluated under Step 2A Prong Two and Step 2B, amounts to insignificant extra-solution output activity, which does not amount to a practical application (MPEP 2106.05(g)), nor add significantly more because such activity has been recognized as well-understood, routine, and conventional and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
For more information, see MPEP 2106.
Allowable Subject Matter
19. Claims 1-20 are allowable over prior art. With respect to independent claims 1, 9, and 17, the closest prior art, Kumar et al. (US 2025/0053992 A1), Khaled (US 2021/0233107 A1), and Ambroziak et al. (US 2023/0252388 A1), collectively teach features for translating, using a personalization model, a persona description into a personalization vector, in which the personalization vector represents a synthetic person; creating a query vector including an individual task model for each question of a survey of new questions; training a choice model based on the personalization vector and the individual task models for each survey question of the query vector to predict a response of the synthetic persona for each of the survey of new questions based on the persona description; leveraging previous consumer discrete choice surveys and/or previous consumer survey responses to provide synthetic data participants to further train the choice model to answer the survey of new questions, predicting, using a trained choice model, answers from the synthetic data participants to the survey of new questions; and rolling out, by a persona design server, the survey of new questions to real human participants depending on parameters determined from the answers of the synthetic data participants [See Office Action mailed 01/13/2026 for prior art citations pertinent to the above-noted subject matter]
However, with respect to the amended limitations, while Khaled suggests aggregate the predicted answers from the synthetic data participants (paragraph 0041: the response prediction system can determine a similarity score between two digital survey questions and/or two responses…A similarity score can include a measure of closeness between two vectors (e.g., respondent vectors or other feature vectors) in a vector space, where the vectors include latent features that represent observable features of a respondent (or a digital survey question or a response) as well as unobservable (e.g., deep) features. Indeed, the response prediction system can generate a cluster of respondent vectors (or digital survey question vectors or response vectors) that indicates a group of respondents (or digital survey questions or responses) within a threshold distance of one another in vector space. A similarity score can indicate a likelihood that a digital survey question will elicit redundant (or similar) responses from two or more respondents and/or can indicate a likelihood that two responses are redundant.”), the prior art of record either individually or in combination does not teach wherein the synthetic data participants are generated by the choice model using embeddings derived from a multi-task learning process with existing consumer research datasets; predicting, using a trained choice model, answers from the synthetic data participants to the survey of new questions by querying a multimodal-choice model based on consumer discrete choice surveys and a survey model based on consumer survey responses; aggregating the predicted answers from the synthetic data participants into parameters for survey deployment; and rolling out, by a persona design server, the survey of new questions to real human participants depending on parameters determined from the answers of the synthetic data participants to test feedback from a synthesized population before fielding the survey to the real human participants, as recited in amended claim 1 (and as similarly encompassed by independent claims 9 and 17).
With respect to amended independent claim 1, while newly found art (Perera et al., Pub. No.: US 2024/0177180 A1) teaches wherein the synthetic data participants are generated paragraph 0011: “simulating a consumer response to each respective candidate product concept using a plurality of simulated agents”); and testing feedback from a synthesized population before fielding the survey to the real human participants (paragraph 0007: “a product concept testing directed to elicit, unknown, unmet or high in demand needs of a consumer group using, an aspect of the invention, agent-based learning and simulation in the idea discovery stage of the FMCG product innovation process.”; paragraph 0131: “ Launching a new product and testing the market acceptance of this new product is such a complex problem and can be evaluated through a simulation at low cost relative to the actual launch and real-world testing which might cost millions of dollars. The purpose of simulation is either to better understand the operation of a target system, or to make predictions about a target system's performance. It can be viewed as an artificial white room which allows one to gain insight but also to test new theories and practices without disrupting the daily routine of the focal organization.”), it does not teach generating synthetic data participants using embeddings derived from a multi-task learning process with existing consumer research datasets, nor does it disclose predicting responses by querying a multimodal-choice model based on consumer discrete choice surveys and a survey model based on consumer survey responses; aggregating the predicted answers from the synthetic data participants into parameters for survey deployment. It also lacks the claimed mechanism where the aggregated predicted answers from the synthetic data participants are used to roll out, by a persona design server, the survey of new questions to real human participants.
The following is a statement of reasons for the indication of allowable subject matter: The claims are directed to allowable subject matter because the prior art of record either individually or in combination does not teach: “A method for generating an interactive persona design system, the method comprising: translating, using a personalization model, a persona description into a personalization vector, in which the personalization vector represents a synthetic person; creating a query vector including an individual task model for each question of a survey of new questions; training a choice model based on the personalization vector and the individual task models for each survey question of the query vector to predict a response of the synthetic persona for each of the survey of new questions based on the persona description; leveraging previous consumer discrete choice surveys and/or previous consumer survey responses to provide synthetic data participants to further train the choice model to answer the survey of new questions, wherein the synthetic data participants are generated by the choice model using embeddings derived from a multi-task learning process with existing consumer research datasets; predicting, using a trained choice model, answers from the synthetic data participants to the survey of new questions by querying a multimodal-choice model based on consumer discrete choice surveys and a survey model based on consumer survey responses; aggregating the predicted answers from the synthetic data participants into parameters for survey deployment; and rolling out, by a persona design server, the survey of new questions to real human participants depending on parameters determined from the answers of the synthetic data participants to test feedback from a synthesized population before fielding the survey to the real human participants,” as recited in amended claim 1, “A non-transitory computer-readable medium having program code recorded thereon for generating an interactive persona design system, the program code being executed by a processor and comprising: program code to translate, using a personalization model, a persona description into a personalization vector, in which the personalization vector represents a synthetic person; program code to create a query vector including an individual task model for each question of a survey of new questions; program code to train a choice model based on the personalization vector and the individual task models for each survey question of the query vector to predict a response of the synthetic persona for each of the survey of new questions based on the persona description; program code to leverage previous consumer discrete choice surveys and/or previous consumer survey responses to provide synthetic data participants to further train the choice model to answer the survey of new questions, wherein the synthetic data participants are generated by the choice model using embeddings derived from a multi-task learning process with existing consumer research datasets; program code to predict, using a trained choice model, answers from the synthetic data participants to the survey of new questions by querying a multimodal-choice model based on consumer discrete choice surveys and a survey model based on consumer survey responses; program code to aggregate the predicted answers from the synthetic data participants into parameters for survey deployment; and program code to roll out, by a persona design server, the survey of new questions to real human participants depending on parameters determined from the answers of the synthetic data participants to test feedback from a synthesized population before fielding the survey to the real human participants,” as recited in amended claim 9, and “An interactive persona design system, the system comprising: a multi-task learned representation module to translate, using a personalization model, a persona description into a personalization vector, in which the personalization vector represents a synthetic person; a persona description module to create a query vector including an individual task model for each question of the survey of new questions; a multi-task learned representation training module to train a choice model based on the personalization vector and the individual task models for each survey question of the query vector to predict a response of the synthetic persona for each of the survey of new questions based on the persona description and to leverage previous consumer discrete choice surveys and/or previous consumer survey responses to provide synthetic data participants to further train the choice model to answer the survey of new questions, wherein the synthetic data participants are generated by the choice model using embeddings derived from a multi-task learning process with existing consumer research datasets; and a persona design server to predict, using a trained choice model, answers from the synthetic data participants to the survey of new questions by querying a multimodal-choice model based on consumer discrete choice surveys and a survey model based on consumer survey responses, to aggregate the predicted answers from the synthetic data participants into parameters for survey deployment, and to roll out the survey of new questions to real human participants depending on parameters determined from the answers of the synthetic data participants to test feedback from a synthesized population before fielding the survey to the real human participants,” as recited in amended claim 17, thus rendering claims 1-20 as allowable over prior art. However, 1-20 are not allowable because they remain rejected under 35 U.S.C. 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Myers, Pub. No.: US 2024/0346547 A1 – describes a system that generates a learning model for one or more virtual personas based on received data. The artificial intelligence model uses the received data regarding customers to determine clusters of similar personalities and behaviors to create personas.
White et al., Pub. No.: US 2013/0282626 A1 – describes a computational system for performing an online choice model.
Aemmer, Zack, and Don MacKenzie. "Generative population synthesis for joint household and individual characteristics." Computers, Environment and Urban Systems 96 (2022): 101852 – describes that population synthesis provides a means to scale this microdata to represent larger regions for use in microsimulation.
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/Darlene Garcia-Guerra/
Primary Examiner, Art Unit 3625
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