DETAILED ACTION
Notice to Applicant
1. The following is a FINAL Office action upon examination of application number 18/607,388 filed on 03/15/2024. 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 July 24, 2026, Applicants amended claims 1, 9, 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, 9, and 17 are hereby acknowledged. The amendments are sufficient to overcome the previously issued claim rejection under 35 U.S.C. 101; accordingly, this rejection has been withdrawn.
Response to Arguments
6. Applicants’ arguments filed July 24, 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, 07/24/2026, page 8]
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, 07/24/2026, page 9]
Examiner agrees.
9. Applicants submit “Nor do the claims fall within the "Certain Methods of Organizing Human Activity" grouping.” [Applicants’ Remarks, 04/06/2026, page 10]
The Examiner respectfully disagrees. In response to Applicants’ argument that “Nor do the claims fall within the "Certain Methods of Organizing Human Activity" grouping,” 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.
Furthermore, in response to Applicants’ argument, it is noted that 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.
Lastly, in response to Applicants’ argument that “These operations cannot be performed in the human mind or with pen and paper. Generating synthetic data participants using embeddings derived from a multi-task learning process and querying both a multimodal-choice model and a survey model to predict answers, entail machine-learned latent representations that exist only within a computational architecture. The Examiner characterizes these steps as ones that “mirror human reasoning and analytical judgments”; however, a person cannot mentally derive embeddings from a multi-task learning process across existing consumer research datasets, nor mentally query two distinct trained machine learning models. Because these steps cannot practically be performed in the human mind, they do not fall within the "Mental Processes" grouping” [Remarks at pages 9-10], 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 claims were found to recite a judicial exception, the additional elements integrate any such exception into a practical application because the claims recite a specific technical improvement to survey design systems. Claim 1 recites "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 by controlling the persona design server to restrict a number of the real human participants to receive the survey based on confidence scores derived from the synthetic data participants before fielding the survey to the number of real human participants." The specification describes that this arrangement enables testing of survey questions against a synthesized population before deployment to real participants, which reduces computational waste and enables more efficient use of computing resources. See specification |[0033], T[0044]. The Examiner characterizes these benefits as mere results of applying the abstract idea (Office Action, page 9); however, the claims tie the asserted improvement to a specific, ordered architecture: generating synthetic data participants from embeddings derived from a multi-task learning process, querying a multimodal-choice model and a survey model, aggregating the predicted answers into parameters for survey deployment, and rolling out the survey by controlling the persona design server to restrict a number of the real human participants to receive the survey depending on those parameters. This ordered set of operations constitutes a specific improvement to the manner in which surveys are computationally designed and deployed, rather than a generic invocation of a computer as a tool.” [Applicants’ Remarks, 07/24/2026, pages 10-11]
In response, it is noted that Applicants’ amendments are sufficient to overcome the §101
rejection. Accordingly, the §101 rejection of claims 1-20 is withdrawn in response to Applicants’ amendment.
11. 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
12. 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.
13. 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.
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).
Claim Rejections - 35 USC § 112
14. The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
15. Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
19. Claim 1 was amended to recite “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 by controlling the persona design server to restrict a number of the real human participants to receive the surveypopulation to determine important parameters before rolling the survey out to real human participants.” (paragraph 0033), the Specification does not actually describe “controlling the persona design server to restrict a number of the real human participants to receive the surveyEnzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968, 63 USPQ2d 1609, 1616 (Fed. Cir. 2002).
35 U.S.C. 112(a) requires that the “specification shall contain a written description of the invention.” This requirement is separate and distinct from the enablement requirement. See, e.g., Vas-Cath, Inc. v. Mahurkar, 935 F.2d 1555, 1560, 19 USPQ2d 1111, 1114 (Fed. Cir. 1991). See also Univ. of Rochester v. G.D. Searle & Co., 358 F.3d 916, 920-23, 69 USPQ2d 1886, 1890-93 (Fed. Cir. 2004) (discussing history and purpose of the written description requirement).
To satisfy the written description requirement, a patent specification must describe the claimed invention in sufficient detail that one skilled in the art can reasonably conclude that the inventor had possession of the claimed invention. See, e.g., Moba, B.V. v. Diamond Automation, Inc., 325 F.3d 1306, 1319, 66 USPQ2d 1429, 1438 (Fed. Cir. 2003); Vas-Cath, Inc. v. Mahurkar, 935 F.2d at 1563, 19 USPQ2d at 1116. However, a showing of possession alone does not cure the lack of a written description. Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 969-70, 63 USPQ2d 1609, 1617 (Fed. Cir. 2002).
An original claim may lack written description support when (1) the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved or (2) a broad genus claim is presented but the disclosure only describes a narrow species with no evidence that the genus is contemplated. See Ariad Pharm., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1349-50 (Fed. Cir. 2010) en banc. The written description requirement is not necessarily met when the claim language appears in ipsis verbis in the specification. "Even if a claim is supported by the specification, the language of the specification, to the extent possible, must describe the claimed invention so that one skilled in the art can recognize what is claimed. The appearance of mere indistinct words in a specification or a claim, even an original claim, does not necessarily satisfy that requirement." Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968, 63 USPQ2d 1609, 1616 (Fed. Cir. 2002).
Applying the above legal principles to the facts of the case at hand, the Examiner concludes that the Applicants' disclosure fails to sufficiently disclose possession at the time of the invention. Thus, the disclosure in the Specification and drawings fails to demonstrate that the applicant was in possession of the invention and/or had reduced the invention to practice and/or the invention was ready for patenting at the time of the filing of the instant application. Because the disclosure of the instant application fails adequately describe the structure and functionality described above, it fails to clearly convey the information that the applicant has invented the subject matter which is claimed. Claims 9 and 17 recite similar limitations as claim 1 and are therefore determined to fail to comply with the written description requirement. Accordingly, claims 1-20 are rejected under 112(a). Applicant is reminded this is a written description rejection, not an enablement rejection.
All claims dependent from above rejected claims are also rejected due to dependency.
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, comprising a threshold number of responses needed before deployment to real human participants, a confidence score for each survey question, and/or a predicted response distribution for each survey question; determining, based on the aggregated parameters, whether the survey of new questions meets deployment criteria before rolling out to the real human participants; and controlling the persona design server to restrict a number of the real human participants to receive the survey based on confidence scores derived from the synthetic data participants before fielding the survey to the number of 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 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”), 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, 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, and aggregating the predicted answers from the synthetic data participants into parameters for survey deployment, comprising a threshold number of responses needed before deployment to real human participants, a confidence score for each survey question, and/or a predicted response distribution for each survey question. It also lacks the claimed mechanism for controlling the persona design server to restrict a number of the real human participants to receive the survey based on confidence scores derived from the synthetic data participants before fielding the survey to the number of 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, comprising a threshold number of responses needed before deployment to real human participants, a confidence score for each survey question, and/or a predicted response distribution for each survey question; determining, based on the aggregated parameters, whether the survey of new questions meets deployment criteria before rolling out to the real human participants; 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 by controlling the persona design server to restrict a number of the real human participants to receive the survey based on confidence scores derived from the synthetic data participants before fielding the survey to the number of 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, comprising a threshold number of responses needed before deployment to real human participants, a confidence score for each survey question, and/or a predicted response distribution for each survey question; program code to determine, based on the aggregated parameters, whether the survey of new questions meets deployment criteria before rolling out to real human participants; 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 by controlling the persona design server to restrict a number of the real human participants to receive the survey based on confidence scores derived from the synthetic data participants before fielding the survey to the number of 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, comprising a threshold number of responses needed before deployment to real human participants, a confidence score for each survey question, and/or a predicted response distribution for each survey question, to determine, based on the aggregated parameters, whether the survey of new questions meets deployment criteria before rolling out to the real human participants, 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 by controlling the persona design server to restrict a number of the real human participants to receive the survey based on confidence scores derived from the synthetic data participants before fielding the survey to the number of real human participants,” as recited in amended claim 9, 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. 112(a).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Burningham et al., Pub. No.: US 2016/0370954 A1 – describes limiting a respondent's participation to only one survey at one time.
Holzinger, Andreas, et al. "Personas for artificial intelligence (AI) an open source toolbox." IEEE Access 10 (2022): 23732-23747 – describes guidelines and material supporting persona development for AI as well as templates and pictures for persona visualization.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Darlene Garcia-Guerra/
Primary Examiner, Art Unit 3625
