Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Status of Claims
This Non-Final Office Action is in response to Applicant’s Request for Continued Examination (RCE) filed 02/13/2026.
In accordance with Applicant’s amendment, claim 1 is amended and claims 29-31 are added as new claims.
Claims 1, 8-10, and 21-29 are currently pending, whereas claims 30-31 are restricted by original presentation (as explained below) and are therefore withdrawn from consideration as being directed to a non-elected invention.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submissions filed on 02/13/2026 have been entered.
Election/Restriction
Election by Original Presentation: Newly submitted claims 31-32 are directed to an invention that is independent or distinct from the invention originally claimed for the following reasons: Invention I (1, 8-10, and 21-28), Invention II (claim 31), and Invention III (claim 32) are related as subcombinations disclosed as usable together in a single combination (See, e.g., Spec. at par. [00109], noting “Embodiments of the system and/or method can include every combination and permutation of the various system components and the various method processes, wherein one or more instances of the method and/or processes described herein can be performed asynchronously (e.g., sequentially), concurrently (e.g., in parallel), or in any other suitable order”). The subcombinations are distinct if they do not overlap in scope and are not obvious variants, and if it is shown that at least one subcombination is separately usable.
In the instance case, Invention I has separate utility such as for populating unfilled answer fields using a continuously updated ML model. Invention II has separate utility such as for using survey question records and accumulated previously generated responses for prior survey question records to automate retrieval and downstream computational analysis by external computing systems. Invention III has separate utility such as for applying a pre-trained multi-task neural network model to generate categorical probability distribution over response options pursuant to generating simulated survey response data. See MPEP § 806.05(d).
In addition, restriction is proper because the inventions are independent and distinct (as noted above) and there would be a serious search/examination burden if restriction were not required because the inventions would require a different field of search (for example, searching different classes/subclasses or electronic resources, or employing different search queries). In the instant case, examination of each of the inventions may necessitate searching different subclasses and/or employing different search queries based on a number of distinct features recited in each invention.
For example:
Invention I includes at least the following distinct features not required by Inventions II or III: initializing…a virtual respondent by a trained ML model, and iteratively updating the contextual vector data after generation of each response such that each subsequent unanswered question is processed using contextual vector data that incorporates previously generated responses; a plurality of survey question fields and corresponding unfilled answer fields; populating, by the one or more computers, the unfilled answer fields of the structured digital survey artifact, wherein each generated response is written into one of the unfilled answer fields.
Invention II includes at least the following distinct features not required by Inventions I or III: survey question records (8 recitations), incomplete digital state to a completed digital state; accumulates previously generated responses for prior survey question records; configured for automated retrieval and downstream computational analysis by one or more external computing system. Invention III includes at least the following distinct features not required by Inventions I or II: neural network model, first/second feed-forward neural network branch, probability distributions, generate contextualized vector embeddings, generate contextualized vector embeddings from input textual data; and plurality of learned attention parameter matrices.
Since applicant has received an action on the merits for the originally presented invention, this invention has been constructively elected by original presentation for prosecution on the merits. Accordingly, claims 34-37 are withdrawn from consideration as being directed to a non-elected invention. See 37 CFR 1.142(b) and MPEP § 821.03.
The examiner has required restriction between subcombinations usable together. Where applicant elects a subcombination and claims thereto are subsequently found allowable, any claim(s) depending from or otherwise requiring all the limitations of the allowable subcombination will be examined for patentability in accordance with 37 CFR 1.104. See MPEP § 821.04(a). Applicant is advised that if any claim presented in a continuation or divisional application is anticipated by, or includes all the limitations of, a claim that is allowable in the present application, such claim may be subject to provisional statutory and/or nonstatutory double patenting rejections over the claims of the instant application.
Response to Amendment
The 35 U.S.C. §112(a) rejection of claims 1, 21, 25, and 27 is withdrawn in response to applicant’s amendment and remarks, however the §112(a) rejection of claim 28 is maintained.
The 35 U.S.C. §101 rejection of claims 1, 8-10, and 21-28 is withdrawn in response to applicant’s amendment and remarks.
Response to Arguments
Response to §112(a) arguments: Applicant’s remarks (Remarks at pgs. 1-6) with respect to the §112(a) rejection of claims 1, 21, 25, and 27-28 have been considered and found sufficient to show descriptive support for the subject matter in claims 1, 21, 25, and 27 to satisfy §112(a), however applicant’s arguments concerning the §112(a) rejection of claim 28 are not persuasive.
In the previous and current office action, claim 28 is rejected under §112(a) because the following limitation lacks descriptive support in the originally filed specification to show that applicant was in possession of the claimed invention: database is configured to support batch queries for simultaneously retrieving multiple simulated response sets for a plurality of different virtual respondents grouped by demographic cohorts. For example, the Specification does not mention or provide descriptive support showing possession of a database configured to support batch queries or for querying demographic cohorts.
In response, applicant refers to the parent provisional application (63/528,781) and suggests that descriptive support is provided via pages 16-17 of the ‘781 application’s description related to “generating simulated responses across multiple individuals by repeating the simulation process for demographic groups and generating entire simulated datasets” along with the flowcharts in Figs. 1-2 of the instant application depicting the storage of simulated response data in a repository and retrieving such data to augment survey facts, and by further arguing that “Batch retrieval of simulated response sets for demographic cohorts is a direct extension of the expressly disclosed process of generated simulated datasets across demographic groups and storing them in a repository for use” (Remarks at pg. 6). The Examiner respectfully disagrees.
In response, the Examiner emphasizes that generating simulates datasets across demographic groups and storing them in a repository does not show possession of a database specifically “configured to support batch queries” as recited in claim 28. Notably, one skilled in the art would recognize that virtually any database can store datasets in a repository for use, and that no inherent batch processing configuration is achieved as a result of merely storing datasets in a database to enable queries of such data, i.e., a database configured to support batch queries is reasonably expected to include something more. In particular, a database configured to support batch queries would be reasonably be expected to require some discernible details that, for example, optimizes high-throughput data processing and reduces network round-trips, provide details of some logical or structural configuration representing a batch mode execution framework, as well as memory, processor, and/or resource allocation that specifically supports a configuration to process batch queries. Applicant’s Specification is silent regarding batch processing or any features reasonably understood in the art as configuring a database for batch queries as required by claim 28. Applicant’s remarks and Specification fail to mention, describe, or inherently show support for the claim limitation requiring that the “database is configured to support batch queries for simultaneously retrieving multiple simulated response sets for a plurality of different virtual respondents grouped by demographic cohorts” as recited by claim 28, and the §112(a) rejection is therefore maintained due to lack of descriptive support for the claimed subject matter.
Response to §101 arguments: Applicant’s amendments and supporting remarks (Remarks at pgs. 7-12) with respect to the §101 rejection of claims 1, 21, 25, and 27-28 have been considered and found sufficient to overcome the §101 rejection.
In particular, the Examiner agrees that applying the claimed computer implemented algorithm and trained machine learning model for iteratively updating the contextual vector data after generation of each response such that each subsequent unanswered question is processed using contextual vector data that incorporates previously generated responses, and automatically populating, by the one or more computers, the unfilled answer fields of the structured digital survey artifact, wherein each generated response is written into one of the unfilled answer fields of the structured digital survey artifact producing a completed machine-readable survey dataset, as recited and arranged with the other limitations of claim 1, is sufficient to apply or use the judicial exception in a meaningful way beyond generally linking its use to a particular technological environment.
Specification - Objection
The disclosure is objected to because of the following informalities: Paragraph [00105] includes a grammatical error in the form of an incomplete sentence lacking substance and proper punctuation in the segment at the end of the paragraph:
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Appropriate correction is required. Applicant is respectfully reminded that, if the above-underlined segment is amended, any inserted text must not add new matter to the disclosure.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 28 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
The first paragraph of 35 U.S.C. 112 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).
Claim 28 recites the following limitation that lacks descriptive support in the originally filed specification to show that applicant was in possession of the claimed invention:
database is configured to support batch queries for simultaneously retrieving multiple simulated response sets for a plurality of different virtual respondents grouped by demographic cohorts – For example, the Specification does not mention or provide descriptive support showing possession of a database configured to support batch queries or for querying demographic cohorts. Paragraphs 25, 48-49, and 105-108 generally refer to a queryable computer database, however Applicant’s Specification is silent regarding batch query processing or any structure, technique, feature, example or other showing reasonably understood by one skilled in the art as showing possession of a feature by which the claimed invention is specifically configured with a database to support batch queries in the manner required by this limitation.
Accordingly, claim 28 fails to satisfy the written description requirement of §112(a) because there is no evidence of a complete specific application or embodiment to satisfy the requirement that the description is set forth “in such full, clear, concise, and exact terms” to show possession of the claimed invention. See Fields v. Conover, 443 F.2d 1386, 1392, 170 USPQ 276, 280 (CCPA 1971).
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 8-10, and 21-29 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 1 recites the limitation(s) of “trained machine learning model (virtual response model),” and subsequently recites both “trained machine learning model” and “virtual response model.” It is unclear whether the “virtual response model” is intended as the same limitation as “trained machine learning model” or whether these two expressions are distinct, and if they are intended as describing the same claim feature, the claim scope is nevertheless rendered ambiguous as a result of shifting between the two expressions. Appropriate correction is required.
Claims 8-10 and 21-29 depend from claim 1 and fails to cure the deficiency noted above, and are therefore indefinite based on their inheritance of the deficiency of parent claim 1.
Allowable over the prior art
Claims 1, 8-10, and 21-29 are allowable over the prior art. The closest prior art reference of record, Barrett et al. (US 2004/0088392), is directed to a computer-implemented population mobility generator and simulator. Barrett et al. and the other prior art of record collectively teach several features of independent claim 1, including computer-implemented features for initializing, by one or more computers executing instructions, a virtual respondent…, the virtual respondent configured to infer likely responses to survey queries based on inputs (Barrett et al. at pars. 57, 253, 271, and 428); obtaining demographic feature data of a virtual respondent (Barrett et al. at pars. 58-59, 88, 119, and Figs. 1-6); and transforming, by the one or more computers, the demographic feature data to demographic vector data (Barrett et al. at pars. 59, 119, and 413). However, Barrett et al. and the other prior art of record do not teach or render obvious the combination of limitations directed to computing, via the one or more computers executing the shared transformer layer of the virtual response model, question vector data based on an input of a text data of the given unanswered question to the shared transformer layer, wherein the question vector data comprises a structured vector representation of the given unanswered question enabling a classification of the plurality of distinct response options to the given unanswered question based on semantic features, ordinal or nominal features, and contextual features of the given unanswered question; selecting one of a nominal response generation layer or an ordinal response generation layer based on a question type associated with the given unanswered question; in response to the selection, routing the question vector data from the shared transformer layer of the trained machine learning model to the selected one of the nominal response generation layer or the ordinal response generation layer of the trained machine learning model; wherein if the question type of the given unanswered question comprises the ordinal type: inputting a combination of (a) the demographic vector data and (b) the question vector data to the ordinal response generation layer; generating, by the one or more computers executing the ordinal response generation layer, an ordinal response inference based on the inputted combination, wherein the ordinal response inference indicates a probability that a given ordinal response option of the plurality of distinct response options responds to the given unanswered question, or wherein if the question type of the given question comprises the nominal type: inputting a combination of (a) the demographic vector data, (b) the question vector data, and (c) vector data of the plurality of distinct response options associated with the given unanswered question to the nominal response generation layer; generating, by the one or more computers executing the nominal response generation layer, a nominal response inference based on the inputted combination, wherein the nominal response inference indicates a probability that a given nominal response option of the plurality of distinct response options responds to the given unanswered question; and iteratively updating the contextual vector data after generation of each response such that each subsequent unanswered question is processed using contextual vector data that incorporates previously generated responses; automatically populating, by the one or more computers, the unfilled answer fields of the structured digital survey artifact, wherein each generated response is written into one of the unfilled answer fields of the structured digital survey artifact producing a completed machine-readable survey dataset, as recited and arranged with the other limitations recited by independent claim 1, thus rendering independent claim 1 and dependent claims 8-10 and 21-29 as allowable over the prior art. Although claims 1, 8-10, and 21-29 are allowable over the prior art, these claims are not allowed because they stand rejected under 35 USC §112(a) (claim 28) and §112(b) (claims 1, 8-10, and 21-29).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
AND THE SURVEY SAYS...: AI models of actual consumers could be the next great wave of market research. Neff, Jack. Advertising Age 89.15: 26. Crain Communications, Incorporated. (Jul 9, 2018): discloses features for constructing simulated survey participants to simulate focus groups and panel surveys including applying machine learning to large amounts of data and computer-generated models of real people to facilitate market research and analytics.
Bouron et al. (US 2003/0154092): discloses features for simulating consumer behavior in a virtual marketplace, including extracting characteristics of attitudes related to consumption of potential consumers on the basis of surveys (at least par. 49).
Froman et al. (US 2021/0090097): discloses automated market research using automation and virtualization, including virtual audiences simulating a model of a customer segment (pars. 18-19 and 152), generating a machine learning model to simulate a human response to a particular question or stimuli and simulated feedback in real-time (pars. 30-31, 68, 153, 162).
Stevens et al. (US 2017/0032394): discloses automated testing of processor-based surveys, including answering questions using simulated data (at least par. 26).
Kilar et al. (US 2012/0110619): discloses a population mobility generator and simulator, including an interface that presents the unanswered survey questions (pars. 151, 257, and 258).
Harris et al. (US 2021/0264448): discloses computer implemented consumer simulation features for predicting variables and behaviors
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Timothy A. Padot whose telephone number is 571.270.1252. The Examiner can normally be reached on Monday-Friday, 8:30 - 5:30. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Brian Epstein can be reached at 571.270.5389. The fax phone number for the organization where this application or proceeding is assigned is 571- 273-8300.
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/TIMOTHY PADOT/
Primary Examiner, Art Unit 3625
06/24/2026
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