SYSTEM AND METHOD FOR TRAINING A RESPONSE-GENERATING MODEL

Application No. 18/651,403

• Filed: Apr 30, 2024
• Priority: Jan 02, 2024 — provisional 63/616,970
  • Jan 26, 2024 — provisional 63/625,430
  • Mar 30, 2024 — provisional 63/572,222
• Examiner: DETERDING, GWYNEVERE AMELIA
• Assignee: State Farm Mutual Automobile Insurance Company
• OA Round: 1 (Non-Final)

Office Action

DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .

Information Disclosure Statement
The information disclosure statements (IDS) submitted on November 21, 2025, and July 15, 2026, are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.

Specification
The disclosure is objected to because of the following informalities:
[0001]: “generally relates to generally to” should read “generally relates to”
Appropriate correction is required.

Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.

Claims 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an
abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent
Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).

Claim 1
Step 1: The claim is directed to a computer-implemented method and thus is directed to the statutory category of processes.
Step 2A Prong 1: The claim recites, inter alia:
“generating… one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life population”: This limitation encompasses mentally generating one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life population.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the method is “computer-implemented…the method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media” and that the generating is performed “by a trained response-generating model”; however, these limitations amount to mere instructions to apply a judicial exception using a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)). The claim further recites “transmitting the one or more simulated responses to be displayed on a user interface on a user device” and “receiving, from the user device, user feedback for the one or more simulated responses”; however, these limitations amount to the insignificant extra solution activity of mere data gathering and outputting (MPEP 2106.05(g)). The claim additionally recites “re-training the trained response-generating model based upon the one or more simulated responses and the user feedback”; however, this limitation amounts to merely generally linking the use of the judicial exception to the field of use/technological environment of model training (MPEP 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The transmitting and receiving limitations, in addition to reciting insignificant extra-solution activity, are also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2. As an ordered whole, the claim is directed to a mentally performable process of generating one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life population. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.

Claim 2
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
“wherein the one or more simulated responses further comprise answers and at least one of reasons for the answers or recommendations”: This limitation further limits the generated simulated responses from claim 1, and encompasses a human mentally generating simulated responses that comprise answers and at least one of reasons for the answers or recommendations.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. No further additional elements are recited, see analysis of claim 1.
Step 2B: The claim does not contain significantly more than the judicial exception. No further additional elements are recited, see analysis of claim 1.

Claim 3
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
“determining the simulated population for the real-life population”: This limitation encompasses mentally determining the simulated population for the real-life population.
“determining simulated characters for population groups for the real-life population based upon member characteristics for the population groups, wherein each of the simulated characters is associated with: (a) one of the population groups, and (b) respective characteristic values corresponding to the member characteristics”: This limitation encompasses mentally determining simulated characters for population groups for the real-life population based upon member characteristics for the population groups.
“determining one or more matched simulated characters of the simulated characters for the one or more known members of the real-life population based upon the one or more known-member characteristic values associated with the one or more known members”: This limitation encompasses mentally determining one or more matched simulated characters of the simulated characters for the one or more known members of the real-life population based upon the one or more known-member characteristic values associated with the one or more known members.
“determining the simulated population for the real-life population based upon the one or more matched simulated characters”: This limitation encompasses mentally determining the simulated population for the real-life population based upon the one or more matched simulated characters.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “receiving, via the user device, an inquiry input from a user, wherein the inquiry input is associated with the inquiry and one or more known-member characteristic values for the one or more known members of the real-life population,” however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)).

Claim 4
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
“before determining the simulated population, determining one or more characteristic variations for the respective characteristic values for the one or more matched simulated characters, wherein determining the simulated population further comprises determining the simulated population further based upon the one or more characteristic variations”: This limitation encompasses mentally determining one or more characteristic variations for the respective characteristic values for the one or more matched simulated characters, and mentally determining the simulated population further based upon the one or more characteristic variations.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “receiving, via a second user device from a second user, the member characteristics for the population groups,” however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)).

Claim 5
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
“determining the simulated characters further based upon the model population”: This limitation encompasses mentally determining the simulated characters further based upon the model population.
“generating the simulated characters… based upon the member characteristics for the population groups”: This limitation encompasses mentally generating the simulated characters based upon the member characteristics for the population groups.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “retrieving, from a member database, a model population for the real-life population,” however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The retrieving limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv) Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)).

Claim 6
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the same judicial exception as claims 1, 3, and 5.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “when the model population is to be retrieved, retrieving the model population comprises retrieving the model population based upon the member characteristics for the population groups,” however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)). The claim additionally recites “when the trained character-simulating model is used, the method further comprises: after receiving the user feedback, re-training the trained character-simulating model based upon the simulated characters and the user feedback,” however this limitation amounts to merely generally linking the use of the judicial exception to the field of use/technological environment of model training (MPEP 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The retrieving limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv) Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)). Otherwise, the analysis at this step mirrors that of step 2A prong 2 above.

Claim 7
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
“determining the simulated population…based upon the one or more matched simulated characters”: This limitation encompasses mentally determining the simulated population based upon the one or more matched simulated characters.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the determining is performed “by a trained population-generating model,” however this limitation amounts to mere instructions to apply an exception on a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of Step 2A Prong 2 above.

Claim 8
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the same judicial exception as claims 1, 3, and 7.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “after receiving the user feedback, re-training the trained population-generating model based upon the simulated population and the user feedback,” however this limitation amounts to merely generally linking the use of the judicial exception to the field of use/technological environment of model training (MPEP 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of Step 2A Prong 2 above.

Claim 9
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
“wherein the real-life population comprises one or more of: customers of a retailer; owners of vehicles manufactured by an automobile manufacture; homeowners; or members of a target market”: This limitation further limits the real-life population from the generating step of claim 1, and generating one or more simulated responses is still mentally performable when the real-life population comprises one of these options.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. No further additional elements are recited, see analysis of claim 1.
Step 2B: The claim does not contain significantly more than the judicial exception. No further additional elements are recited, see analysis of claim 1.

Claim 10
Step 1: The claim is directed to a computer system and thus is directed to the statutory category of machines.
Step 2A Prong 1: The claim recites, inter alia:
“generating… one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life population”: This limitation encompasses mentally generating one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life population.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “a computer system… the computer system comprising: one or more processors; and one or more non-transitory computer-readable media storing computing instructions that, when run on the one or more processors, cause the one or more processors to perform the following operations” and that the generating is performed “by a trained response-generating model,” however these limitations amount to mere instructions to apply a judicial exception using a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)). The claim further recites “transmitting the one or more simulated responses to be displayed on a user interface on a user device” and “receiving, from the user device, user feedback for the one or more simulated responses,” however these limitations amount to the insignificant extra solution activity of mere data gathering and outputting (MPEP 2106.05(g)). The claim additionally recites “re-training the trained response-generating model based upon the one or more simulated responses and the user feedback,” however this limitation amounts to merely generally linking the use of the judicial exception to the field of use/technological environment of model training (MPEP 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The transmitting and receiving limitations, in addition to reciting insignificant extra-solution activity, are also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2. As an ordered whole, the claim is directed to a mentally performable process of generating one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life population. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.

Claim 11
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
“determining the simulated population for the real-life population”: This limitation encompasses mentally determining the simulated population for the real-life population.
“determining simulated characters for population groups for the real-life population based upon member characteristics for the population groups, wherein each of the simulated characters is associated with: (a) one of the population groups, and (b) respective characteristic values corresponding to the member characteristics”: This limitation encompasses mentally determining simulated characters for population groups for the real-life population based upon member characteristics for the population groups.
“determining one or more matched simulated characters of the simulated characters for the one or more known members of the real-life population based upon the one or more known-member characteristic values associated with the one or more known members”: This limitation encompasses mentally determining one or more matched simulated characters of the simulated characters for the one or more known members of the real-life population based upon the one or more known-member characteristic values associated with the one or more known members.
“determining the simulated population for the real-life population based upon the one or more matched simulated characters”: This limitation encompasses mentally determining the simulated population for the real-life population based upon the one or more matched simulated characters.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “receiving, via the user device, an inquiry input from a user, wherein the inquiry input is associated with the inquiry and one or more known-member characteristic values for the one or more known members of the real-life population,” however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)).

Claim 12
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
“before determining the simulated population, determining one or more characteristic variations for the respective characteristic values for the one or more matched simulated characters, wherein determining the simulated population further comprises determining the simulated population further based upon the one or more characteristic variations”: This limitation encompasses mentally determining one or more characteristic variations for the respective characteristic values for the one or more matched simulated characters, and mentally determining the simulated population further based upon the one or more characteristic variations.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “receiving, via a second user device from a second user, the member characteristics for the population groups,” however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)).

Claim 13
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
“determining the simulated characters further based upon the model population”: This limitation encompasses mentally determining the simulated characters further based upon the model population.
“generating the simulated characters… based upon the member characteristics for the population groups”: This limitation encompasses mentally generating the simulated characters based upon the member characteristics for the population groups.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “retrieving, from a member database, a model population for the real-life population,” however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The retrieving limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv) Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)).

Claim 14
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the same judicial exception as claims 10-13.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “when the model population is to be retrieved, retrieving the model population comprises retrieving the model population based upon the member characteristics for the population groups,” however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)). The claim additionally recites “when the trained character-simulating model is used, the computing instructions, when run on the one or more processors, further cause the one or more processors to perform: after receiving the user feedback, re-training the trained character-simulating model based upon the simulated characters and the user feedback,” however this limitation amounts to merely generally linking the use of the judicial exception to the field of use/technological environment of model training (MPEP 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The retrieving limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv) Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)). Otherwise, the analysis at this step mirrors that of step 2A prong 2 above.

Claim 15
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
“determining the simulated population…based upon the one or more matched simulated characters”: This limitation encompasses mentally determining the simulated population based upon the one or more matched simulated characters.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the determining is performed “by a trained population-generating model,” however this limitation amounts to mere instructions to apply an exception on a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)). The claim additionally recites “after receiving the user feedback, re-training the trained population-generating model based upon the simulated population, as determined, and the user feedback,” however this limitation amounts to merely generally linking the use of the judicial exception to the field of use/technological environment of model training (MPEP 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of Step 2A Prong 2 above.

Claim 16
Step 1: The claim is directed to a non-transitory computer readable storage medium and thus is directed to the statutory category of articles of manufacture.
Step 2A Prong 1: The claim recites, inter alia:
“generating… one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life population”: This limitation encompasses mentally generating one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life population.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “A non-transitory computer readable storage medium storing one or more computing instructions that direct processor operations on one or more processors, the one or more computing instructions, when run on one or more processors, cause the one or more processors to perform:” and that the generating is performed “by a trained response-generating model,” however these limitations amount to mere instructions to apply a judicial exception using a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)). The claim further recites “transmitting the one or more simulated responses to be displayed on a user interface on a user device” and “receiving, from the user device, user feedback for the one or more simulated responses,” however these limitations amount to the insignificant extra solution activity of mere data gathering and outputting (MPEP 2106.05(g)). The claim additionally recites “re-training the trained response-generating model based upon the one or more simulated responses and the user feedback,” however this limitation amounts to merely generally linking the use of the judicial exception to the field of use/technological environment of model training (MPEP 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The transmitting and receiving limitations, in addition to reciting insignificant extra-solution activity, are also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2. As an ordered whole, the claim is directed to a mentally performable process of generating one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life population. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.

Claim 17
Step 1: An article of manufacture, as above.
Step 2A Prong 1: The claim recites, inter alia:
“determining the simulated population for the real-life population”: This limitation encompasses mentally determining the simulated population for the real-life population.
“determining simulated characters for population groups for the real-life population based upon member characteristics for the population groups, wherein each of the simulated characters is associated with: (a) one of the population groups, and (b) respective characteristic values corresponding to the member characteristics”: This limitation encompasses mentally determining simulated characters for population groups for the real-life population based upon member characteristics for the population groups.
“determining one or more matched simulated characters of the simulated characters for the one or more known members of the real-life population based upon the one or more known-member characteristic values associated with the one or more known members”: This limitation encompasses mentally determining one or more matched simulated characters of the simulated characters for the one or more known members of the real-life population based upon the one or more known-member characteristic values associated with the one or more known members.
“determining the simulated population for the real-life population based upon the one or more matched simulated characters”: This limitation encompasses mentally determining the simulated population for the real-life population based upon the one or more matched simulated characters.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “receiving, via the user device, an inquiry input from a user, wherein the inquiry input is associated with the inquiry and one or more known-member characteristic values for the one or more known members of the real-life population,” however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)).

Claim 18
Step 1: An article of manufacture, as above.
Step 2A Prong 1: The claim recites, inter alia:
“before determining the simulated population, determining one or more characteristic variations for the respective characteristic values for the one or more matched simulated characters, wherein determining the simulated population further comprises determining the simulated population further based upon the one or more characteristic variations”: This limitation encompasses mentally determining one or more characteristic variations for the respective characteristic values for the one or more matched simulated characters, and mentally determining the simulated population further based upon the one or more characteristic variations.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “receiving, via a second user device from a second user, the member characteristics for the population groups,” however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)).

Claim 19
Step 1: An article of manufacture, as above.
Step 2A Prong 1: The claim recites, inter alia:
“determining the simulated characters further based upon the model population”: This limitation encompasses mentally determining the simulated characters further based upon the model population.
“generating the simulated characters… based upon the member characteristics for the population groups”: This limitation encompasses mentally generating the simulated characters based upon the member characteristics for the population groups.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “retrieving, from a member database, a model population for the real-life population,” however this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)). The claim additionally recites “after receiving the user feedback, re-training the trained character-simulating model based upon the simulated characters and the user feedback,” however this limitation amounts to merely generally linking the use of the judicial exception to the field of use/technological environment of model training (MPEP 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The retrieving limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv) Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)).

Claim 20
Step 1: An article of manufacture, as above.
Step 2A Prong 1: The claim recites, inter alia:
“determining the simulated population…based upon the one or more matched simulated characters”: This limitation encompasses mentally determining the simulated population based upon the one or more matched simulated characters.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the determining is performed “by a trained population-generating model,” however this limitation amounts to mere instructions to apply an exception on a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)). The claim additionally recites “after receiving the user feedback, re-training the trained population-generating model based upon the simulated population and the user feedback,” however this limitation amounts to merely generally linking the use of the judicial exception to the field of use/technological environment of model training (MPEP 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of Step 2A Prong 2 above.

Claim 21
Step 1: The claim is directed to a computer-implemented method and thus is directed to the statutory category of processes.
Step 2A Prong 1: The claim recites, inter alia:
“generating… one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life market segment or other group”: This limitation encompasses mentally generating one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life market segment or other group.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the method is “computer-implemented…the method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media” and that the generating is performed “by a trained response-generating model,” however these limitations amount to mere instructions to apply a judicial exception using a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)). The claim further recites “transmitting the one or more simulated responses to be displayed on a user interface on a user device” and “receiving, from the user device, user feedback for the one or more simulated responses,” however these limitations amount to the insignificant extra solution activity of mere data gathering and outputting (MPEP 2106.05(g)). The claim additionally recites “re-training the trained response-generating model based upon the one or more simulated responses and the user feedback,” however this limitation amounts to merely generally linking the use of the judicial exception to the field of use/technological environment of model training (MPEP 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The transmitting and receiving limitations, in addition to reciting insignificant extra-solution activity, are also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2. As an ordered whole, the claim is directed to a mentally performable process of generating one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life population. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.

Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –

(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.

(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.

Claims 1-2, 10, 16, and 21 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Kiljanek (US20200118691).

Regarding claim 1, Kiljanek discloses “A computer-implemented method for training a model based upon simulated responses and user feedback, the method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media (Kiljanek, [0169]: “FIG. 15 illustrates an exemplary computing system 1500 that may be used to implement some aspects of the technology… The computing system 1500 of FIG. 15 includes one or more processors 1510 and memory units 1520”; [0171]: “Mass storage device 1530, which may be implemented with a magnetic disk drive or an optical disk drive, is a non-volatile storage device for storing data and instructions for use by processor unit 1510”), the computer-implemented method comprising:
generating, by a trained response-generating model, one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life population (Kiljanek, [0029]: “The simulated patient population dataset 140 includes multiple simulated patient datasets 145A-Z that each correspond to a simulated patient; [0063]: “The machine learning engine 210, once trained based on the training dataset 290 (e.g., the simulated patient population dataset 140 and optionally one or more additional simulated and/or real patient population datasets), may generate one or more artificial intelligence (AI) or machine learning (ML) models that the machine learning engine 210 may use to generate predicted outcomes 540 based on query datasets 510 as discussed further in FIG. 5”; [0079]: “The query dataset 510 may identify one or more features and one or more feature values for those features, as in the example query dataset 710 of FIG. 7A... the machine learning engine 210 generates a set of one or more predicted outcomes 540 based on the query dataset 510”; Examiner notes that the predicted outcomes 540 correspond to “one or more simulated responses”, “query dataset” corresponds to “an inquiry for one or more known members” (see Fig. 7A, query dataset 710 includes features describing a known patient including age, symptoms, etc.), and simulated patient population dataset 140 corresponds to “a simulated population for a real-life population”. The simulated responses (predicted outcomes) to an inquiry for one or more known members (query dataset) are generated based upon a simulated population for a real-life population (simulated patient population dataset 140) because the trained machine learning engine 210 that generates the predicted outcomes is trained on the training dataset 290 comprising simulated patient population dataset 140);
transmitting the one or more simulated responses to be displayed on a user interface on a user device (Kiljanek, [0080]: “The one or more predicted outcomes 540 are provided from the dataset analysis system 205 to the query device 520. Upon receipt of the one or more predicted outcomes 540, the query device 520 renders and displays the one or more predicted outcomes 540 for the one or more querying users 505 to review, optionally through the query UI 525”);
receiving, from the user device, user feedback for the one or more simulated responses (Kiljanek, [0080]: “In some cases, the one or more querying users 505 may input feedback 550 about the one or more predicted outcomes 540 into the query device 520 upon reviewing the one or more predicted outcomes 540, optionally through the query UI 525”); and
re-training the trained response-generating model based upon the one or more simulated responses and the user feedback” (Kiljanek, [0062]: “In some cases, the expert reputation score and/or the simulated patient population dataset reputation score may be increased or decreased after training, for example based on feedback 550 of a querying user 505 as in FIG. 5. In such situations, the training dataset 290 may optionally be re-generated, with the amount of simulated patient datasets pulled from a simulated patient population dataset optionally modified based on the increase or decrease in the expert reputation score and/or the simulated patient population dataset reputation score. The newly re-generated training dataset 290 may then be input back into the training module 215 to train the machine learning engine 210”).

Regarding claim 2, the rejection of claim 1 is incorporated. Kiljanek further discloses “wherein the one or more simulated responses further comprise answers and at least one of reasons for the answers or recommendations” (Kiljanek, [0093]: “FIG. 6 illustrates a sample format for or predicted outcomes”; [0094]: “The outcomes 600 of FIG. 6 provide a format that may be used to predicted outcomes 540”; [0095]: “The outcomes 600 may include likely diagnoses 610 with likelihood probabilities”; [0096]: “The outcomes 600 may include recommended tests 615 with recommendation strengths”; [0098]: “The outcomes 600 may include identifications of features 625 that factor most into a particular diagnosis (of the diagnoses 610) with levels of importance”).

Regarding claim 10, Kiljanek discloses “A computer system for training a model based upon simulated responses and user feedback, the computer system comprising: one or more processors; and one or more non-transitory computer-readable media storing computing instructions that, when run on the one or more processors, cause the one or more processors to perform the following operations (Kiljanek, [0169]: “FIG. 15 illustrates an exemplary computing system 1500 that may be used to implement some aspects of the technology…The computing system 1500 of FIG. 15 includes one or more processors 1510 and memory units 1520”; [0171]: “Mass storage device 1530, which may be implemented with a magnetic disk drive or an optical disk drive, is a non-volatile storage device for storing data and instructions for use by processor unit 1510”):
generating, by a trained response-generating model, one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life population (Kiljanek, [0029]: “The simulated patient population dataset 140 includes multiple simulated patient datasets 145A-Z that each correspond to a simulated patient; [0063]: “The machine learning engine 210, once trained based on the training dataset 290 (e.g., the simulated patient population dataset 140 and optionally one or more additional simulated and/or real patient population datasets), may generate one or more artificial intelligence (AI) or machine learning (ML) models that the machine learning engine 210 may use to generate predicted outcomes 540 based on query datasets 510 as discussed further in FIG. 5”; [0079]: “The query dataset 510 may identify one or more features and one or more feature values for those features, as in the example query dataset 710 of FIG. 7A... the machine learning engine 210 generates a set of one or more predicted outcomes 540 based on the query dataset 510”; Examiner notes that the predicted outcomes 540 correspond to “one or more simulated responses”, “query dataset” corresponds to “an inquiry for one or more known members” (see Fig. 7A, query dataset 710 includes features describing a known patient including age, symptoms, etc.), and simulated patient population dataset 140 corresponds to “a simulated population for a real-life population”. The simulated responses (predicted outcomes) to an inquiry for one or more known members (query dataset) are generated based upon a simulated population for a real-life population (simulated patient population dataset 140) because the trained machine learning engine 210 that generates the predicted outcomes is trained on the training dataset 290 comprising simulated patient population dataset 140);
transmitting the one or more simulated responses to be displayed on a user interface on a user device (Kiljanek, [0080]: “The one or more predicted outcomes 540 are provided from the dataset analysis system 205 to the query device 520. Upon receipt of the one or more predicted outcomes 540, the query device 520 renders and displays the one or more predicted outcomes 540 for the one or more querying users 505 to review, optionally through the query UI 525”);
receiving, from the user device, user feedback for the one or more simulated responses (Kiljanek, [0080]: “In some cases, the one or more querying users 505 may input feedback 550 about the one or more predicted outcomes 540 into the query device 520 upon reviewing the one or more predicted outcomes 540, optionally through the query UI 525”); and
re-training the trained response-generating model based upon the one or more simulated responses and the user feedback” (Kiljanek, [0062]: “In some cases, the expert reputation score and/or the simulated patient population dataset reputation score may be increased or decreased after training, for example based on feedback 550 of a querying user 505 as in FIG. 5. In such situations, the training dataset 290 may optionally be re-generated, with the amount of simulated patient datasets pulled from a simulated patient population dataset optionally modified based on the increase or decrease in the expert reputation score and/or the simulated patient population dataset reputation score. The newly re-generated training dataset 290 may then be input back into the training module 215 to train the machine learning engine 210”).

Regarding claim 16, Kiljanek discloses “A non-transitory computer readable storage medium storing one or more computing instructions that direct processor operations on one or more processors, the one or more computing instructions, when run on one or more processors, cause the one or more processors to perform (Kiljanek, [0169]: “FIG. 15 illustrates an exemplary computing system 1500 that may be used to implement some aspects of the technology… The computing system 1500 of FIG. 15 includes one or more processors 1510 and memory units 1520”; [0171]: “Mass storage device 1530, which may be implemented with a magnetic disk drive or an optical disk drive, is a non-volatile storage device for storing data and instructions for use by processor unit 1510”):
generating, by a trained response-generating model, one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life population (Kiljanek, [0029]: “The simulated patient population dataset 140 includes multiple simulated patient datasets 145A-Z that each correspond to a simulated patient; [0063]: “The machine learning engine 210, once trained based on the training dataset 290 (e.g., the simulated patient population dataset 140 and optionally one or more additional simulated and/or real patient population datasets), may generate one or more artificial intelligence (AI) or machine learning (ML) models that the machine learning engine 210 may use to generate predicted outcomes 540 based on query datasets 510 as discussed further in FIG. 5”; [0079]: “The query dataset 510 may identify one or more features and one or more feature values for those features, as in the example query dataset 710 of FIG. 7A... the machine learning engine 210 generates a set of one or more predicted outcomes 540 based on the query dataset 510”; Examiner notes that the predicted outcomes 540 correspond to “one or more simulated responses”, “query dataset” corresponds to “an inquiry for one or more known members” (see Fig. 7A, query dataset 710 includes features describing a known patient including age, symptoms, etc.), and simulated patient population dataset 140 corresponds to “a simulated population for a real-life population”. The simulated responses (predicted outcomes) to an inquiry for one or more known members (query dataset) are generated based upon a simulated population for a real-life population (simulated patient population dataset 140) because the trained machine learning engine 210 that generates the predicted outcomes is trained on the training dataset 290 comprising simulated patient population dataset 140);
transmitting the one or more simulated responses to be displayed on a user interface on a user device (Kiljanek, [0080]: “The one or more predicted outcomes 540 are provided from the dataset analysis system 205 to the query device 520. Upon receipt of the one or more predicted outcomes 540, the query device 520 renders and displays the one or more predicted outcomes 540 for the one or more querying users 505 to review, optionally through the query UI 525”);
receiving, from the user device, user feedback for the one or more simulated responses (Kiljanek, [0080]: “In some cases, the one or more querying users 505 may input feedback 550 about the one or more predicted outcomes 540 into the query device 520 upon reviewing the one or more predicted outcomes 540, optionally through the query UI 525”); and
re-training the trained response-generating model based upon the one or more simulated responses and the user feedback” (Kiljanek, [0062]: “In some cases, the expert reputation score and/or the simulated patient population dataset reputation score may be increased or decreased after training, for example based on feedback 550 of a querying user 505 as in FIG. 5. In such situations, the training dataset 290 may optionally be re-generated, with the amount of simulated patient datasets pulled from a simulated patient population dataset optionally modified based on the increase or decrease in the expert reputation score and/or the simulated patient population dataset reputation score. The newly re-generated training dataset 290 may then be input back into the training module 215 to train the machine learning engine 210”).

Regarding claim 21, Kiljanek discloses “A computer-implemented method for training a model based upon simulated responses and user feedback, the method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media (Kiljanek, [0169]: “FIG. 15 illustrates an exemplary computing system 1500 that may be used to implement some aspects of the technology… The computing system 1500 of FIG. 15 includes one or more processors 1510 and memory units 1520”; [0171]: “Mass storage device 1530, which may be implemented with a magnetic disk drive or an optical disk drive, is a non-volatile storage device for storing data and instructions for use by processor unit 1510”), the computer-implemented method comprising:
generating, by a trained response-generating model, one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life population (Kiljanek, [0029]: “The simulated patient population dataset 140 includes multiple simulated patient datasets 145A-Z that each correspond to a simulated patient; [0063]: “The machine learning engine 210, once trained based on the training dataset 290 (e.g., the simulated patient population dataset 140 and optionally one or more additional simulated and/or real patient population datasets), may generate one or more artificial intelligence (AI) or machine learning (ML) models that the machine learning engine 210 may use to generate predicted outcomes 540 based on query datasets 510 as discussed further in FIG. 5”; [0079]: “The query dataset 510 may identify one or more features and one or more feature values for those features, as in the example query dataset 710 of FIG. 7A... the machine learning engine 210 generates a set of one or more predicted outcomes 540 based on the query dataset 510”; Examiner notes that the predicted outcomes 540 correspond to “one or more simulated responses”, “query dataset” corresponds to “an inquiry for one or more known members” (see Fig. 7A, query dataset 710 includes features describing a known patient including age, symptoms, etc.), and simulated patient population dataset 140 corresponds to “a simulated population for a real-life population”. The simulated responses (predicted outcomes) to an inquiry for one or more known members (query dataset) are generated based upon a simulated population for a real-life population (simulated patient population dataset 140) because the trained machine learning engine 210 that generates the predicted outcomes is trained on the training dataset 290 comprising simulated patient population dataset 140);
transmitting the one or more simulated responses to be displayed on a user interface on a user device (Kiljanek, [0080]: “The one or more predicted outcomes 540 are provided from the dataset analysis system 205 to the query device 520. Upon receipt of the one or more predicted outcomes 540, the query device 520 renders and displays the one or more predicted outcomes 540 for the one or more querying users 505 to review, optionally through the query UI 525”);
receiving, from the user device, user feedback for the one or more simulated responses (Kiljanek, [0080]: “In some cases, the one or more querying users 505 may input feedback 550 about the one or more predicted outcomes 540 into the query device 520 upon reviewing the one or more predicted outcomes 540, optionally through the query UI 525”); and
re-training the trained response-generating model based upon the one or more simulated responses and the user feedback” (Kiljanek, [0062]: “In some cases, the expert reputation score and/or the simulated patient population dataset reputation score may be increased or decreased after training, for example based on feedback 550 of a querying user 505 as in FIG. 5. In such situations, the training dataset 290 may optionally be re-generated, with the amount of simulated patient datasets pulled from a simulated patient population dataset optionally modified based on the increase or decrease in the expert reputation score and/or the simulated patient population dataset reputation score. The newly re-generated training dataset 290 may then be input back into the training module 215 to train the machine learning engine 210”).

Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.

The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.

Claims 3-4, 11-12, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Kiljanek in view of Eng et al. (US20250045591) (hereinafter “Eng”).

Regarding claim 3, the rejection of claim 1 is incorporated. Kiljanek further discloses “the method further comprising one or more of:
receiving, via the user device, an inquiry input from a user, wherein the inquiry input is associated with the inquiry and one or more known-member characteristic values for the one or more known members of the real-life population (Kiljanek, [0079]: “One or more querying users 505 interact with the query device 520 through a query user interface (UI) 525, providing a query dataset 510 to the query device 520 through a query user interface (UI) 525. The query dataset 510 may identify one or more features and one or more feature values for those features, as in the example query dataset 710 of FIG. 7A); or
determining the simulated population for the real-life population comprising:
determining simulated characters for population groups for the real-life population based upon member characteristics for the population groups, wherein each of the simulated characters is associated with: (a) one of the population groups, and (b) respective characteristic values corresponding to the member characteristics (Kiljanek, [0029]: “The expert device 110 passes the patient population source seed 120 and optionally metadata 130 on to the dataset generation system 135, which generates a simulated patient population dataset 140 based on the patient population source seed 120 and optionally on the metadata 130. The simulated patient population dataset 140 includes multiple simulated patient datasets 145A-Z that each correspond to a simulated patient. The patient population source seed 120 identifies feature parameters 122, which are discussed further below, and outcomes 125. All of the simulated patient datasets 145A-Z within the simulated patient population dataset 140 are associated with the same outcomes 125—that is, the outcomes 155A-Z are the outcomes 125, for each of the simulated patient datasets 145A-Z within the simulated patient population dataset 140. The feature parameters 122 then provide information about feature values that correspond to those outcomes 125, as discussed further below. If the expert 105 wishes to describe a different set of outcomes than the outcomes 125, the expert then inputs a different patient population source seed with that different set of outcomes, and a separate simulated patient population dataset based on that different set of outcomes is generated by the dataset generation system 135 based on the different patient population source seed”; [0031]: “The patient population source seed 120 may include one or more feature parameters 122 associated with one or more features. As discussed in further detail below, features may include a patient's physical characteristics, health data, biometric data, medical history, vital signs, symptoms, other signs, test results, and the like”);
…and
determining the simulated population for the real-life population based upon the one or more…simulated characters” (Kiljanek, [0029]: “The simulated patient population dataset 140 includes multiple simulated patient datasets 145A-Z that each correspond to a simulated patient”).
Kiljanek does not appear to explicitly disclose the further limitations of the claim.
However, Eng discloses “determining one or more matched simulated characters of the simulated characters for the one or more known members of the real-life population based upon the one or more known-member characteristic values associated with the one or more known members” (Eng, [0089]: “FIG. 6 depicts examples of how real user data might be used to create one or more synthetic user profiles. Specifically, FIG. 6 shows real user data 601, which indicates a real name (Joe Smith), an age (42), a location (New York City), and a vehicle (a sedan). In turn, FIG. 6 also shows three illustrative synthetic user profiles: a first synthetic user profile 602a, a second synthetic user profile 602b, and a third synthetic user profile 602c. Each of these synthetic user profiles is different from the real user data 601. For example, the first synthetic user profile 602a uses a different name (Bob Frank) and a different location (Chicago, IL), the second synthetic user profile 602b uses a different name (Jane Smith) and a different age (39), and the third synthetic user profile 602c uses a different name (Joe Thomas) and a different vehicle (a convertible)”; Examiner notes that Fig. 6 depicts a known member (real user data 601) matched to simulated characters (synthetic user profiles 1, 2, and 3) wherein the simulated characters share at least one characteristic value with the known member).
Eng and the instant application both relate to machine learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Kiljanek with the teachings of Eng to include “determining one or more matched simulated characters of the simulated characters for the one or more known members of the real-life population based upon the one or more known-member characteristic values associated with the one or more known members” and such that determining the simulated population for the real-life population is based upon the one or more matched simulated characters, and one would have been motivated to do so. Doing so would allow for acquiring data for machine learning that is substantially similar to a real-life population without disclosing personal information about real people (see Eng, [0005-0006]).

Regarding claim 4, the rejection of claim 3 is incorporated. Kiljanek as modified by Eng further discloses “the method further comprising one or more of:
receiving, via a second user device from a second user, the member characteristics for the population groups (Kiljanek, [0029]: “The block diagram 100 of FIG. 1 includes an expert device 110 and a dataset generation system 135. One or more experts 105 interact with the expert device 110 through an expert user interface (UI) 115, providing various input data to the expert device 110 through the expert UI 115. The input data may include a patient population source seed 120 and optionally metadata 130. The expert device 110 passes the patient population source seed 120 and optionally metadata 130 on to the dataset generation system 135, which generates a simulated patient population dataset 140 based on the patient population source seed 120 and optionally on the metadata 130... The patient population source seed 120 identifies feature parameters 122, which are discussed further below, and outcomes 125”); or
before determining the simulated population, determining one or more characteristic variations for the respective characteristic values for the one or more matched simulated characters, wherein determining the simulated population further comprises determining the simulated population further based upon the one or more characteristic variations.”

Regarding claim 11, the rejection of claim 10 is incorporated. Kiljanek further discloses “wherein the computing instructions, when run on the one or more processors, further cause the one or more processors to perform one or more of:
receiving, via the user device, an inquiry input from a user, wherein the inquiry input is associated with the inquiry and one or more known-member characteristic values for the one or more known members of the real-life population (Kiljanek, [0079]: “One or more querying users 505 interact with the query device 520 through a query user interface (UI) 525, providing a query dataset 510 to the query device 520 through a query user interface (UI) 525. The query dataset 510 may identify one or more features and one or more feature values for those features, as in the example query dataset 710 of FIG. 7A); or
determining the simulated population for the real-life population comprising:
determining simulated characters for population groups for the real-life population based upon member characteristics for the population groups, wherein each of the simulated characters is associated with: (a) one of the population groups, and (b) respective characteristic values corresponding to the member characteristics (Kiljanek, [0029]: “The expert device 110 passes the patient population source seed 120 and optionally metadata 130 on to the dataset generation system 135, which generates a simulated patient population dataset 140 based on the patient population source seed 120 and optionally on the metadata 130. The simulated patient population dataset 140 includes multiple simulated patient datasets 145A-Z that each correspond to a simulated patient. The patient population source seed 120 identifies feature parameters 122, which are discussed further below, and outcomes 125. All of the simulated patient datasets 145A-Z within the simulated patient population dataset 140 are associated with the same outcomes 125—that is, the outcomes 155A-Z are the outcomes 125, for each of the simulated patient datasets 145A-Z within the simulated patient population dataset 140. The feature parameters 122 then provide information about feature values that correspond to those outcomes 125, as discussed further below. If the expert 105 wishes to describe a different set of outcomes than the outcomes 125, the expert then inputs a different patient population source seed with that different set of outcomes, and a separate simulated patient population dataset based on that different set of outcomes is generated by the dataset generation system 135 based on the different patient population source seed”; [0031]: “The patient population source seed 120 may include one or more feature parameters 122 associated with one or more features. As discussed in further detail below, features may include a patient's physical characteristics, health data, biometric data, medical history, vital signs, symptoms, other signs, test results, and the like”);
…and
determining the simulated population for the real-life population based upon the one or more…simulated characters” (Kiljanek, [0029]: “The simulated patient population dataset 140 includes multiple simulated patient datasets 145A-Z that each correspond to a simulated patient”).
Kiljanek does not appear to explicitly disclose the further limitations of the claim.
However, Eng discloses “determining one or more matched simulated characters of the simulated characters for the one or more known members of the real-life population based upon the one or more known-member characteristic values associated with the one or more known members” (Eng, [0089]: “FIG. 6 depicts examples of how real user data might be used to create one or more synthetic user profiles. Specifically, FIG. 6 shows real user data 601, which indicates a real name (Joe Smith), an age (42), a location (New York City), and a vehicle (a sedan). In turn, FIG. 6 also shows three illustrative synthetic user profiles: a first synthetic user profile 602a, a second synthetic user profile 602b, and a third synthetic user profile 602c. Each of these synthetic user profiles is different from the real user data 601. For example, the first synthetic user profile 602a uses a different name (Bob Frank) and a different location (Chicago, IL), the second synthetic user profile 602b uses a different name (Jane Smith) and a different age (39), and the third synthetic user profile 602c uses a different name (Joe Thomas) and a different vehicle (a convertible)”; Examiner notes that Fig. 6 depicts a known member (real user data 601) matched to simulated characters (synthetic user profiles 1, 2, and 3) wherein the simulated characters share at least one characteristic value with the known member).
Eng and the instant application both relate to machine learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Kiljanek with the teachings of Eng to include “determining one or more matched simulated characters of the simulated characters for the one or more known members of the real-life population based upon the one or more known-member characteristic values associated with the one or more known members” and such that determining the simulated population for the real-life population is based upon the one or more matched simulated characters, and one would have been motivated to do so. Doing so would allow for acquiring data for machine learning that is substantially similar to a real-life population without disclosing personal information about real people (see Eng, [0005-0006]).

Regarding claim 12, the rejection of claim 11 is incorporated. Kiljanek as modified by Eng further discloses “wherein the computing instructions, when run on the one or more processors, further cause the one or more processors to perform one or more of:
receiving, via a second user device from a second user, the member characteristics for the population groups (Kiljanek, [0029]: “The block diagram 100 of FIG. 1 includes an expert device 110 and a dataset generation system 135. One or more experts 105 interact with the expert device 110 through an expert user interface (UI) 115, providing various input data to the expert device 110 through the expert UI 115. The input data may include a patient population source seed 120 and optionally metadata 130. The expert device 110 passes the patient population source seed 120 and optionally metadata 130 on to the dataset generation system 135, which generates a simulated patient population dataset 140 based on the patient population source seed 120 and optionally on the metadata 130... The patient population source seed 120 identifies feature parameters 122, which are discussed further below, and outcomes 125”); or
before determining the simulated population, determining one or more characteristic variations for the respective characteristic values for the one or more matched simulated characters, wherein determining the simulated population further comprises determining the simulated population further based upon the one or more characteristic variations.”

Regarding claim 17, the rejection of claim 16 is incorporated. Kiljanek further discloses “wherein the one or more computing instructions, when run on the one or more processors, further cause the one or more processors to perform one or more of:
receiving, via the user device, an inquiry input from a user, wherein the inquiry input is associated with the inquiry and one or more known-member characteristic values for the one or more known members of the real-life population (Kiljanek, [0079]: “One or more querying users 505 interact with the query device 520 through a query user interface (UI) 525, providing a query dataset 510 to the query device 520 through a query user interface (UI) 525. The query dataset 510 may identify one or more features and one or more feature values for those features, as in the example query dataset 710 of FIG. 7A”); or
determining the simulated population for the real-life population comprising:
determining simulated characters for population groups for the real-life population based upon member characteristics for the population groups, wherein each of the simulated characters is associated with: (a) one of the population groups, and (b) respective characteristic values corresponding to the member characteristics (Kiljanek, [0029]: “The expert device 110 passes the patient population source seed 120 and optionally metadata 130 on to the dataset generation system 135, which generates a simulated patient population dataset 140 based on the patient population source seed 120 and optionally on the metadata 130. The simulated patient population dataset 140 includes multiple simulated patient datasets 145A-Z that each correspond to a simulated patient. The patient population source seed 120 identifies feature parameters 122, which are discussed further below, and outcomes 125. All of the simulated patient datasets 145A-Z within the simulated patient population dataset 140 are associated with the same outcomes 125—that is, the outcomes 155A-Z are the outcomes 125, for each of the simulated patient datasets 145A-Z within the simulated patient population dataset 140. The feature parameters 122 then provide information about feature values that correspond to those outcomes 125, as discussed further below. If the expert 105 wishes to describe a different set of outcomes than the outcomes 125, the expert then inputs a different patient population source seed with that different set of outcomes, and a separate simulated patient population dataset based on that different set of outcomes is generated by the dataset generation system 135 based on the different patient population source seed”; [0031]: “The patient population source seed 120 may include one or more feature parameters 122 associated with one or more features. As discussed in further detail below, features may include a patient's physical characteristics, health data, biometric data, medical history, vital signs, symptoms, other signs, test results, and the like”);
…and
determining the simulated population for the real-life population based upon the one or more…simulated characters” (Kiljanek, [0029]: “The simulated patient population dataset 140 includes multiple simulated patient datasets 145A-Z that each correspond to a simulated patient”).
Kiljanek does not appear to explicitly disclose the further limitations of the claim.
However, Eng discloses “determining one or more matched simulated characters of the simulated characters for the one or more known members of the real-life population based upon the one or more known-member characteristic values associated with the one or more known members” (Eng, [0089]: “FIG. 6 depicts examples of how real user data might be used to create one or more synthetic user profiles. Specifically, FIG. 6 shows real user data 601, which indicates a real name (Joe Smith), an age (42), a location (New York City), and a vehicle (a sedan). In turn, FIG. 6 also shows three illustrative synthetic user profiles: a first synthetic user profile 602a, a second synthetic user profile 602b, and a third synthetic user profile 602c. Each of these synthetic user profiles is different from the real user data 601. For example, the first synthetic user profile 602a uses a different name (Bob Frank) and a different location (Chicago, IL), the second synthetic user profile 602b uses a different name (Jane Smith) and a different age (39), and the third synthetic user profile 602c uses a different name (Joe Thomas) and a different vehicle (a convertible)”; Examiner notes that Fig. 6 depicts a known member (real user data 601) matched to simulated characters (synthetic user profiles 1, 2, and 3) wherein the simulated characters share at least one characteristic value with the known member).
Eng and the instant application both relate to machine learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Kiljanek with the teachings of Eng to include “determining one or more matched simulated characters of the simulated characters for the one or more known members of the real-life population based upon the one or more known-member characteristic values associated with the one or more known members” and such that determining the simulated population for the real-life population is based upon the one or more matched simulated characters, and one would have been motivated to do so. Doing so would allow for acquiring data for machine learning that is substantially similar to a real-life population without disclosing personal information about real people (see Eng, [0005-0006]).

Regarding claim 18, the rejection of claim 17 is incorporated. Kiljanek as modified by Eng further discloses “wherein the one or more computing instructions, when run on the one or more processors, further cause the one or more processors to perform one or more of:
receiving, via a second user device from a second user, the member characteristics for the population groups (Kiljanek, [0029]: “The block diagram 100 of FIG. 1 includes an expert device 110 and a dataset generation system 135. One or more experts 105 interact with the expert device 110 through an expert user interface (UI) 115, providing various input data to the expert device 110 through the expert UI 115. The input data may include a patient population source seed 120 and optionally metadata 130. The expert device 110 passes the patient population source seed 120 and optionally metadata 130 on to the dataset generation system 135, which generates a simulated patient population dataset 140 based on the patient population source seed 120 and optionally on the metadata 130... The patient population source seed 120 identifies feature parameters 122, which are discussed further below, and outcomes 125”); or
before determining the simulated population, determining one or more characteristic variations for the respective characteristic values for the one or more matched simulated characters, wherein determining the simulated population further comprises determining the simulated population further based upon the one or more characteristic variations.”

Claims 5-6, 13-14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kiljanek in view of Eng, and further in view of Barrett et al. (US20170300657) (hereinafter “Barrett”).

Regarding claim 5, the rejection of claim 3 is incorporated. Kiljanek does not appear to explicitly disclose the further limitations of the claim.
However, Eng discloses “wherein determining the simulated characters comprises one or more of: … (ii) generating the simulated characters by a trained character-simulating model based upon… [characteristics]” (Eng, [0026]: “As an example of how the present disclosure may operate, a user might want to shop for a car loan. The user might provide user data, such as their name, address, the car they want to purchase, and the like. That data might be provided to a first trained machine learning model that has been trained to generate synthetic user profiles. In turn, the first trained machine learning model might output a plurality of different synthetic user profiles: one might have a slightly different address, another might have a slightly different model year of vehicle, and the like”).
Eng and the instant application both relate to machine learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Kiljanek with the teachings of Eng such that determining the simulated characters comprises “generating the simulated characters by a trained character-simulating model based upon the member characteristics for the population groups”, and one would have been motivated to do so. Doing so would allow for acquiring data for machine learning that is substantially similar to a real-life population without disclosing personal information about real people (see Eng, [0005-0006]).
Neither Kiljanek nor Eng appear to explicitly disclose the further limitations of the claim.
However, Barrett discloses “wherein determining the simulated characters comprises one or more of:
retrieving, from a member database, a model population for the real-life population (Barrett, [0081]: “At block 304, server 126 can receive attributes 136 of (e.g., designating or defining) a synthetic population (referred to as “synth. pop.,” “s. p.,” or “SP” throughout this description and figures); [0083]: “At block 306, server 126 can select a synthetic-population (SP) graph 308 from the data library 122 based at least in part on the attributes 136. This can be performed, e.g., by the subpopulation-services or subpopulation data manager components shown in FIG. 35. The SP graph 308 can include nodes representing synthetic entities, and edges between at least some of the nodes”; [0348] The synthetic population includes synthetic entities that may represent entities in a real geographic area (e.g., the United States)); and
determining the simulated characters further based upon the model population” (Barrett, [0083]: “The SP graph 308 can include nodes representing synthetic entities, and edges between at least some of the nodes”; [0087]: “At block 316, server 126 can simulate the course of the epidemic in the SP graph 308”).
Barrett and the instant application both relate to population simulation and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified the combination of Kiljanek/Eng with the teachings of Barrett to include “retrieving, from a member database, a model population for the real-life population; and determining the simulated characters further based upon the model population,” and one would have been motivated to do so. Doing so would achieve realistic synthetic populations for performing simulations (see Barrett, [0184]).

Regarding claim 6, the rejection of claim 5 is incorporated. Kiljanek discloses “after receiving the user feedback, retraining the… [trained response-generating model] based upon… the user feedback” (see rejection of claim 1) but does not appear to explicitly disclose the further limitations of the claim.
However, Eng discloses “when the trained character-simulating model is used, the method further comprises: … re-training the trained character-simulating model based upon the simulated characters…” (Eng, [0057]: “In step 406, the computing device may receive, from the one or more quote providers, quotes for the synthetic user profiles”; [0058]: “In step 407, the computing device may determine an average quote based on the quotes received in step 406. The average quote may be determined in a manner that averages one or more aspects of the quotes and which reflects the fact that the quotes are based on synthetic user profiles that are designed to be, in some ways, different from the real user data”; [0067]: “In step 409, the computing device may determine an amount paid by a user. The amount paid by the user may comprise an actual amount paid by the user (e.g., on a periodic basis, in total) for the good and/or service to which the average quote pertains”; [0071]: “In step 410, the computing device may determine whether a difference between the average quote determined in step 407 and the amount paid determined in step 409 satisfies a threshold”; [0072]: “In step 411, the computing device may further train the machine learning model based on the difference from step 410. In this manner, the computing device may train the machine learning model to generate synthetic user profiles that better emulate the real user and thus potentially result in a more accurate average quote”).
Eng and the instant application both relate to machine learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Kiljanek with the teachings of Eng to include “when the trained character-simulating model is used, the method further comprises: after receiving the user feedback, re-training the trained character-simulating model based upon the simulated characters and the user feedback” and one would have been motivated to do so. Doing so would allow for acquiring data for machine learning that is substantially similar to a real-life population without disclosing personal information about real people (see Eng, [0005-0006]).
Neither Kiljanek nor Eng appear to explicitly disclose the further limitations of the claim.
However, Barrett discloses “wherein: when the model population is to be retrieved, retrieving the model population comprises retrieving the model population based upon the member characteristics for the population groups” (Barrett, [0081]: “At block 304, server 126 can receive attributes 136 of (e.g., designating or defining) a synthetic population (referred to as “synth. pop.,” “s. p.,” or “SP” throughout this description and figures); [0083]: “At block 306, server 126 can select a synthetic-population (SP) graph 308 from the data library 122 based at least in part on the attributes 136”).
Barrett and the instant application both relate to population simulation and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified the combination of Kiljanek/Eng with the teachings of Barrett to include “when the model population is to be retrieved, retrieving the model population comprises retrieving the model population based upon the member characteristics for the population groups,” and one would have been motivated to do so. Doing so would provide users an increased degree of control at simulating specific situations (see Barrett, [0082]).

Regarding claim 13, the rejection of claim 12 is incorporated. Kiljanek does not appear to explicitly disclose the further limitations of the claim.
However, Eng discloses “wherein determining the simulated characters comprises one or more of: … (ii) generating the simulated characters by a trained character-simulating model based upon… [characteristics]” (Eng, [0026]: “As an example of how the present disclosure may operate, a user might want to shop for a car loan. The user might provide user data, such as their name, address, the car they want to purchase, and the like. That data might be provided to a first trained machine learning model that has been trained to generate synthetic user profiles. In turn, the first trained machine learning model might output a plurality of different synthetic user profiles: one might have a slightly different address, another might have a slightly different model year of vehicle, and the like”).
Eng and the instant application both relate to machine learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Kiljanek with the teachings of Eng such that determining the simulated characters comprises “generating the simulated characters by a trained character-simulating model based upon the member characteristics for the population groups”, and one would have been motivated to do so. Doing so would allow for acquiring data for machine learning that is substantially similar to a real-life population without disclosing personal information about real people (see Eng, [0005-0006]).
Neither Kiljanek nor Eng appear to explicitly disclose the further limitations of the claim.
However, Barrett discloses “wherein determining the simulated characters comprises one or more of:
retrieving, from a member database, a model population for the real-life population (Barrett, [0081]: “At block 304, server 126 can receive attributes 136 of (e.g., designating or defining) a synthetic population (referred to as “synth. pop.,” “s. p.,” or “SP” throughout this description and figures); [0083]: “At block 306, server 126 can select a synthetic-population (SP) graph 308 from the data library 122 based at least in part on the attributes 136. This can be performed, e.g., by the subpopulation-services or subpopulation data manager components shown in FIG. 35. The SP graph 308 can include nodes representing synthetic entities, and edges between at least some of the nodes”; [0348] The synthetic population includes synthetic entities that may represent entities in a real geographic area (e.g., the United States)); and
determining the simulated characters further based upon the model population” (Barrett, [0083]: “The SP graph 308 can include nodes representing synthetic entities, and edges between at least some of the nodes”; [0087]: “At block 316, server 126 can simulate the course of the epidemic in the SP graph 308”).
Barrett and the instant application both relate to population simulation and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified the combination of Kiljanek/Eng with the teachings of Barrett to such that determining the simulated characters includes “retrieving, from a member database, a model population for the real-life population; and determining the simulated characters further based upon the model population,” and one would have been motivated to do so. Doing so would achieve realistic synthetic populations for performing simulations (see Barrett, [0184]).

Regarding claim 14, the rejection of claim 13 is incorporated. Kiljanek discloses “after receiving the user feedback, retraining the… [trained response-generating model] based upon… the user feedback” (see rejection of claim 10) but does not appear to explicitly disclose the further limitations of the claim.
However, Eng discloses “when the trained character-simulating model is used, the computing instructions, when run on the one or more processors, further cause the one or more processors to perform: … re-training the trained character-simulating model based upon the simulated characters…” (Eng, [0057]: “In step 406, the computing device may receive, from the one or more quote providers, quotes for the synthetic user profiles”; [0058]: “In step 407, the computing device may determine an average quote based on the quotes received in step 406. The average quote may be determined in a manner that averages one or more aspects of the quotes and which reflects the fact that the quotes are based on synthetic user profiles that are designed to be, in some ways, different from the real user data”; [0067]: “In step 409, the computing device may determine an amount paid by a user. The amount paid by the user may comprise an actual amount paid by the user (e.g., on a periodic basis, in total) for the good and/or service to which the average quote pertains”; [0071]: “In step 410, the computing device may determine whether a difference between the average quote determined in step 407 and the amount paid determined in step 409 satisfies a threshold”; [0072]: “In step 411, the computing device may further train the machine learning model based on the difference from step 410. In this manner, the computing device may train the machine learning model to generate synthetic user profiles that better emulate the real user and thus potentially result in a more accurate average quote”).
Eng and the instant application both relate to machine learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Kiljanek with the teachings of Eng to include “when the trained character-simulating model is used, the method further comprises: after receiving the user feedback, re-training the trained character-simulating model based upon the simulated characters and the user feedback” and one would have been motivated to do so. Doing so would allow for acquiring data for machine learning that is substantially similar to a real-life population without disclosing personal information about real people (see Eng, [0005-0006]).
Neither Kiljanek nor Eng appear to explicitly disclose the further limitations of the claim.
However, Barrett discloses “wherein: when the model population is to be retrieved, retrieving the model population comprises retrieving the model population based upon the member characteristics for the population groups” (Barrett, [0081]: “At block 304, server 126 can receive attributes 136 of (e.g., designating or defining) a synthetic population (referred to as “synth. pop.,” “s. p.,” or “SP” throughout this description and figures); [0083]: “At block 306, server 126 can select a synthetic-population (SP) graph 308 from the data library 122 based at least in part on the attributes 136”).
Barrett and the instant application both relate to population simulation and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified the combination of Kiljanek/Eng with the teachings of Barrett to include “when the model population is to be retrieved, retrieving the model population comprises retrieving the model population based upon the member characteristics for the population groups,” and one would have been motivated to do so. Doing so would provide users an increased degree of control at simulating specific situations (see Barrett, [0082]).

Regarding claim 19, the rejection of claim 18 is incorporated. Kiljanek discloses “after receiving the user feedback, retraining the… [trained response-generating model] based upon… the user feedback” (see rejection of claim 10) but does not appear to explicitly disclose the further limitations of the claim.
However, Eng discloses “wherein determining the simulated characters comprises one or more of: … (ii) generating the simulated characters by a trained character-simulating model based upon… [characteristics]; and … re-training the trained character-simulating model based upon the simulated characters…” (Eng, [0026]: “As an example of how the present disclosure may operate, a user might want to shop for a car loan. The user might provide user data, such as their name, address, the car they want to purchase, and the like. That data might be provided to a first trained machine learning model that has been trained to generate synthetic user profiles. In turn, the first trained machine learning model might output a plurality of different synthetic user profiles: one might have a slightly different address, another might have a slightly different model year of vehicle, and the like”; [0057]: “In step 406, the computing device may receive, from the one or more quote providers, quotes for the synthetic user profiles”; [0058]: “In step 407, the computing device may determine an average quote based on the quotes received in step 406. The average quote may be determined in a manner that averages one or more aspects of the quotes and which reflects the fact that the quotes are based on synthetic user profiles that are designed to be, in some ways, different from the real user data”; [0067]: “In step 409, the computing device may determine an amount paid by a user. The amount paid by the user may comprise an actual amount paid by the user (e.g., on a periodic basis, in total) for the good and/or service to which the average quote pertains”; [0071]: “In step 410, the computing device may determine whether a difference between the average quote determined in step 407 and the amount paid determined in step 409 satisfies a threshold”; [0072]: “In step 411, the computing device may further train the machine learning model based on the difference from step 410. In this manner, the computing device may train the machine learning model to generate synthetic user profiles that better emulate the real user and thus potentially result in a more accurate average quote”).
Eng and the instant application both relate to machine learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Kiljanek with the teachings of Eng such that determining the simulated characters comprises “generating the simulated characters by a trained character-simulating model based upon the member characteristics for the population groups; and after receiving the user feedback, re-training the trained character-simulating model based upon the simulated characters and the user feedback” and one would have been motivated to do so. Doing so would allow for acquiring data for machine learning that is substantially similar to a real-life population without disclosing personal information about real people (see Eng, [0005-0006]).
Neither Kiljanek nor Eng appear to explicitly disclose the further limitations of the claim.
However, Barrett discloses discloses “wherein determining the simulated characters comprises one or more of:
(i) retrieving, from a member database, a model population for the real-life population (Barrett, [0081]: “At block 304, server 126 can receive attributes 136 of (e.g., designating or defining) a synthetic population (referred to as “synth. pop.,” “s. p.,” or “SP” throughout this description and figures); [0083]: “At block 306, server 126 can select a synthetic-population (SP) graph 308 from the data library 122 based at least in part on the attributes 136. This can be performed, e.g., by the subpopulation-services or subpopulation data manager components shown in FIG. 35. The SP graph 308 can include nodes representing synthetic entities, and edges between at least some of the nodes”; [0348] The synthetic population includes synthetic entities that may represent entities in a real geographic area (e.g., the United States)); and
determining the simulated characters further based upon the model population…” (Barrett, [0083]: “The SP graph 308 can include nodes representing synthetic entities, and edges between at least some of the nodes”; [0087]: “At block 316, server 126 can simulate the course of the epidemic in the SP graph 308”).
Barrett and the instant application both relate to population simulation and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified the combination of Kiljanek/Eng with the teachings of Barrett such that determining the simulated characters includes “retrieving, from a member database, a model population for the real-life population; and determining the simulated characters further based upon the model population,” and one would have been motivated to do so. Doing so would achieve realistic synthetic populations for performing simulations (see Barrett, [0184]).

Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Kiljanek in view of Eng, and further in view of Graham (US20220101970) (hereinafter “Graham”).

Regarding claim 7, the rejection of claim 3 is incorporated. Kiljanek as modified by Eng discloses “determining the simulated population…based upon the one or more matched simulated characters” (see rejection of claim 3) but does not appear to explicitly disclose the further limitations of the claim.
However, Graham discloses “determining the simulated population, by a trained population-generating model…” (Graham, [0027]: “FIG. 1 is a simplified block diagram of a synthetic EHR system 100, in accordance with the example embodiments. Synthetic EHR system 100 may use machine learning techniques to train one or more machine learning models to recognize patterns, provide predictions, and determine weights and measures during transition states of those predictions. Such machine learning models may be trained automatically or by acting on tuning parameters via interaction with an operator. The resulting trained machine learning models may be utilized to generate representative synthetic EHR data”; [0030]: “State machine 104A may include processes to simulate one or more synthetic lives. These processes may include generating a synthetic population from input population data, simulating synthetic lives for each synthetic person in the synthetic population, and outputting health details (i.e. synthetic EHR data) of each synthetic life. To simulate one or more synthetic lives, state machine 104A may be configured with one or more machine learning models, including, but not limited to: a neural network, a Bayesian network, a hidden Markov model, a Markov decision process, set/graph theory models, or other similar graphical models”).
Graham and the instant application both relate to machine learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified the combination of Kiljanek and Eng with the teachings of Graham to include “wherein determining the simulated population further comprises determining the simulated population, by a trained population-generating model, based upon the one or more matched simulated characters”, and one would have been motivated to do so. Doing so would allow for improving the representativeness of the simulated population to a real-life population of interest (see Graham, [0025] and [0111]).

Regarding claim 8, the rejection of claim 7 is incorporated. Neither Kiljanek nor Eng appear to explicitly disclose the further limitations of the claim.
However, Graham discloses “after receiving… feedback, re-training the trained population-generating model based upon the simulated population and the… feedback” (Graham, [0111]: “Synthetic EHR data 902 may represent synthetic EHR data generated from state machine 916 to represent a population of interest. Actual EHR data 904 may represent actual EHR data recorded from the same population of interest. Both synthetic EHR data 902 and actual EHR data 904 are fed into statistical comparison engine 908, which may be configured as a component of model evaluator 906. The output of statistical comparison 906 may then be scored and used to decide whether to output discovered model 910 or apply tuning engine 912 to update synthetic EHR data 902”; [0117]: “If the single output score does not meet or exceed the threshold score, model tuning process 900 may indicate that synthetic EHR data 902 is not representative of actual EHR data 904. This may result in model tuning process 900 requesting tuning engine 912 to update knowledge models 914. In example embodiments, tuning engine 912 may utilize prepare phase 912A, sequence phase 912B, inference phase 912C, and interpret phase 912D to update knowledge models 914. In turn, knowledge models 914 may reconfigure one or more clinical pathways of state machine 916 to generate more representative synthetic EHR data, in accordance to the embodiments described herein”).
Graham and the instant application both relate to machine learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified the combination of Kiljanek and Eng with the teachings of Graham to include “after receiving the user feedback, re-training the trained population-generating model based upon the simulated population and the user feedback”, and one would have been motivated to do so. Doing so would allow for improving the representativeness of the simulated population to a real-life population of interest (see Graham, [0025] and [0111]).

Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Kiljanek in view of Low et al. (US20250165678) (hereinafter “Low”).

Regarding claim 9, the rejection of claim 1 is incorporated. Kiljanek does not appear to explicitly disclose the further limitations of the claim.
However, Low discloses “wherein the real-life population comprises one or more of: customers of a retailer; owners of vehicles manufactured by an automobile manufacture; homeowners; or members of a target market” (Low, [0033]: “In some cases, the population of synthetic users may represent individual users of a content streaming platform. In some cases, the population of synthetic users may represent candidate users of a content streaming platform (e.g., a population of human users having a particular demographic). In some cases, the population of synthetic users may have a demographic distribution that matches a demographic distribution of a (general) population of human users”).
Low and the instant application both relate to machine learning for generating simulated responses and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Kiljanek with the teachings of Low such that the real life population comprises members of a target market, and one would have been motivated to do so. Doing so would increase speed and lower costs of performing customer research (see Low, [0022-0023]).

Claims 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kiljanek in view of Eng and Barrett, and further in view of Graham.

Regarding claim 15, the rejection of claim 14 is incorporated. Kiljanek as modified by Eng and Barrett discloses “determining the simulated population…based upon the one or more matched simulated characters” (see rejection of claim 11) but does not appear to explicitly disclose the further limitations of the claim.
However, Graham discloses “determining the simulated population, by a trained population-generating model… ;and after receiving… feedback, re-training the trained population-generating model based upon the simulated population, as determined, and the… feedback” (Graham, [0030]: “State machine 104A may include processes to simulate one or more synthetic lives. These processes may include generating a synthetic population from input population data, simulating synthetic lives for each synthetic person in the synthetic population, and outputting health details (i.e. synthetic EHR data) of each synthetic life. To simulate one or more synthetic lives, state machine 104A may be configured with one or more machine learning models”; [0111]: “Both synthetic EHR data 902 and actual EHR data 904 are fed into statistical comparison engine 908, which may be configured as a component of model evaluator 906. The output of statistical comparison 906 may then be scored and used to decide whether to output discovered model 910 or apply tuning engine 912 to update synthetic EHR data 902”; [0117]: “If the single output score does not meet or exceed the threshold score, model tuning process 900 may indicate that synthetic EHR data 902 is not representative of actual EHR data 904. This may result in model tuning process 900 requesting tuning engine 912 to update knowledge models 914... In turn, knowledge models 914 may reconfigure one or more clinical pathways of state machine 916 to generate more representative synthetic EHR data, in accordance to the embodiments described herein”).
Graham and the instant application both relate to machine learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified the combination of Kiljanek/Eng/Barrett with the teachings of Graham to include “wherein determining the simulated population further comprises: determining the simulated population, by a trained population-generating model, based upon the one or more matched simulated characters; and after receiving the user feedback, re-training the trained population-generating model based upon the simulated population, as determined, and the user feedback”, and one would have been motivated to do so. Doing so would allow for improving the representativeness of the simulated population to a real-life population of interest (see Graham, [0025] and [0111]).

Regarding claim 20, the rejection of claim 19 is incorporated. Kiljanek as modified by Eng and Barrett discloses “determining the simulated population…based upon the one or more matched simulated characters” (see rejection of claim 17) but does not appear to explicitly disclose the further limitations of the claim.
However, Graham discloses “determining the simulated population by a trained population-generating model…; and after receiving… feedback, re-training the trained population-generating model based upon the simulated population and the… feedback” (Graham, [0030]: “State machine 104A may include processes to simulate one or more synthetic lives. These processes may include generating a synthetic population from input population data, simulating synthetic lives for each synthetic person in the synthetic population, and outputting health details (i.e. synthetic EHR data) of each synthetic life. To simulate one or more synthetic lives, state machine 104A may be configured with one or more machine learning models”; [0111]: “Both synthetic EHR data 902 and actual EHR data 904 are fed into statistical comparison engine 908, which may be configured as a component of model evaluator 906. The output of statistical comparison 906 may then be scored and used to decide whether to output discovered model 910 or apply tuning engine 912 to update synthetic EHR data 902”; [0117]: “If the single output score does not meet or exceed the threshold score, model tuning process 900 may indicate that synthetic EHR data 902 is not representative of actual EHR data 904. This may result in model tuning process 900 requesting tuning engine 912 to update knowledge models 914... In turn, knowledge models 914 may reconfigure one or more clinical pathways of state machine 916 to generate more representative synthetic EHR data, in accordance to the embodiments described herein”).
Graham and the instant application both relate to machine learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified the combination of Kiljanek/Eng/Barrett with the teachings of Graham to include “wherein determining the simulated population further comprises: determining the simulated population by a trained population-generating model based upon the one or more matched simulated characters; and after receiving the user feedback, re-training the trained population-generating model based upon the simulated population and the user feedback”, and one would have been motivated to do so. Doing so would allow for improving the representativeness of the simulated population to a real-life population of interest (see Graham, [0025] and [0111]).

Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.

Claims 1, 3, 7, 8, 10, 11, 16, 17 and 21 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 9, 16, and 22 of copending Application No. 18/651,375 (reference application).
Although the claims at issue are not identical, they are not patentably distinct from each other because reference claim 9 anticipates instant claims 1, 3, 7, 8, and 21, reference claim 16 anticipates instant claims 10 and 11, and reference claim 22 anticipates instant claims 16 and 17. See the claim charts below.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.

Application No. 18/651,375 (reference)
Instant Application
1. A computer-implemented method of generating simulated responses via simulated characters, the method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer- readable media, the computer-implemented method comprising: determining simulated characters for population groups for a real-life population based upon member characteristics for the population groups, wherein each of the simulated characters is associated with: (a) one of the population groups, and (b) respective characteristic values corresponding to the member characteristics for the population groups; determining one or more matched simulated characters of the simulated characters for one or more known members of the real-life population based upon one or more known- member characteristic values associated with the one or more known members; generating a simulated population of a pre-determined population size for the real-life population based upon the one or more matched simulated characters, comprising scaling the simulated population to the pre-determined population size, wherein one or more members of the simulated population generated from scaling the simulated population to the pre-determined population size are based at least on member characteristic values for at least two of (a) the one or more matched simulated characters, (b) a model population for the real-life population, or (c) one or more characteristic variations for the respective characteristic values for the one or more matched simulated characters; generating one or more simulated responses to an inquiry for the one or more known members based upon the simulated population; and transmitting the one or more simulated responses to be displayed on a user interface on a user device.

8. The computer-implemented method of claim 1, wherein one or more of: determining the simulated population comprises determining the simulated population by a trained population-generating model based upon the one or more matched simulated characters; or generating the one or more simulated responses comprises generating the one or more simulated responses by a trained response-generating model.

9. The computer-implemented method of claim 8, further comprising, after transmitting the one or more simulated responses: receiving, from the user device, user feedback for the one or more simulated responses; when the trained population-generating model is used, re-training the trained population- generating model based upon the simulated population and the user feedback; and when the trained response-generating model is used, re-training the trained response- generating model based upon the one or more simulated responses and the user feedback.
1. A computer-implemented method for training a model based upon simulated responses and user feedback, the method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media, the computer-implemented method comprising: generating, by a trained response-generating model, one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life population; transmitting the one or more simulated responses to be displayed on a user interface on a user device; receiving, from the user device, user feedback for the one or more simulated responses; and re-training the trained response-generating model based upon the one or more simulated responses and the user feedback.

3. The computer-implemented method of claim 1, the method further comprising one or more of: receiving, via the user device, an inquiry input from a user, wherein the inquiry input is associated with the inquiry and one or more known-member characteristic values for the one or more known members of the real-life population; or determining the simulated population for the real-life population comprising: determining simulated characters for population groups for the real-life population based upon member characteristics for the population groups, wherein each of the simulated characters is associated with: (a) one of the population groups, and (b) respective characteristic values corresponding to the member characteristics; determining one or more matched simulated characters of the simulated characters for the one or more known members of the real-life population based upon the one or more known-member characteristic values associated with the one or more known members; and determining the simulated population for the real-life population based upon the one or more matched simulated characters.

7. The computer-implemented method of claim 3, wherein determining the simulated population further comprises determining the simulated population, by a trained population-generating model, based upon the one or more matched simulated characters.

8. The computer-implemented method of claim 7, further comprising, after receiving the user feedback, re-training the trained population-generating model based upon the simulated population and the user feedback.

Application No. 18/651,375 (reference)
Instant Application
11. A computer system for generating simulated responses via simulated characters, the computer system comprising: one or more processors; and one or more non-transitory computer-readable media storing computing instructions that, when run on the one or more processors, direct the one or more processors to: determine simulated characters for population groups for a real-life population based upon member characteristics for the population groups, wherein each of the simulated characters is associated with: (a) one of the population groups, and (b) respective characteristic values corresponding to the member characteristics for the population groups; determine one or more matched simulated characters of the simulated characters for one or more known members of the real-life population based upon one or more known- member characteristic values associated with the one or more known members; generate a simulated population of a pre-determined population size for the real-life population based upon the one or more matched simulated characters, comprising scaling the simulated population to the pre-determined population size, wherein one or more members of the simulated population generated from scaling the simulated population to the pre-determined population size are based at least on member characteristic values for at least two of (a) the one or more matched simulated characters, (b) a model population for the real-life population, or (c) one or more characteristic variations for the respective characteristic values for the one or more matched simulated characters; generate one or more simulated responses to an inquiry for the one or more known members based upon the simulated population; and transmit the one or more simulated responses to be displayed on a user interface on a user device.

15. The computer system of claim 11, wherein one or more of: determining the simulated population comprises determining the simulated population by a trained population-generating model based upon the one or more matched simulated characters; or generating the one or more simulated responses comprises generating the one or more simulated responses by a trained response-generating model.

16. The computer system of claim 15, wherein the computing instructions, when run on the one or more processors, further direct the one or more processors to perform the following operations, after transmitting the one or more simulated responses: receiving, from the user device, user feedback for the one or more simulated responses; when the trained population-generating model is used, re-training the trained population- generating model based upon the simulated population and the user feedback; and when the trained response-generating model is used, re-training the trained response- generating model based upon the one or more simulated responses and the user feedback.
10. A computer system for training a model based upon simulated responses and user feedback, the computer system comprising: one or more processors; and one or more non-transitory computer-readable media storing computing instructions that, when run on the one or more processors, cause the one or more processors to perform the following operations: generating, by a trained response-generating model, one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life population; transmitting the one or more simulated responses to be displayed on a user interface on a user device; receiving, from the user device, user feedback for the one or more simulated responses; and re-training the trained response-generating model based upon the one or more simulated responses and the user feedback.

11. The computer system of claim 10, wherein the computing instructions, when run on the one or more processors, further cause the one or more processors to perform one or more of: receiving, via the user device, an inquiry input from a user, wherein the inquiry input is associated with the inquiry and one or more known-member characteristic values for the one or more known members of the real-life population; or determining the simulated population for the real-life population comprising: determining simulated characters for population groups for the real-life population based upon member characteristics for the population groups, wherein each of the simulated characters is associated with: (a) one of the population groups, and (b) respective characteristic values corresponding to the member characteristics; determining one or more matched simulated characters of the simulated characters for the one or more known members of the real-life population based upon the one or more known-member characteristic values associated with the one or more known members; and determining the simulated population for the real-life population based upon the one or more matched simulated characters.

Application No. 18/651,375 (reference)
Instant Application
17. A non-transitory computer readable storage medium storing one or more computing instructions that direct determining simulated responses from simulated characters, the one or more computing instructions directing one or more processors to perform the following operations: determining simulated characters for population groups for a real-life population based upon member characteristics for the population groups, wherein each of the simulated characters is associated with: (a) one of the population groups, and (b) respective characteristic values corresponding to the member characteristics for the population groups; determining one or more matched simulated characters of the simulated characters for one or more known members of the real-life population based upon one or more known- member characteristic values associated with the one or more known members; generating a simulated population of a pre-determined population size for the real-life population based upon the one or more matched simulated characters, comprising scaling the simulated population to the pre-determined population size, wherein one or more members of the simulated population generated from scaling the simulated population to the pre-determined population size are based at least on member characteristic values for at least two of (a) the one or more matched simulated characters, (b) a model population for the real-life population, or (c) one or more characteristic variations for the respective characteristic values for the one or more matched simulated characters; generating one or more simulated responses to an inquiry for the one or more known members based upon the simulated population; and transmit the one or more simulated responses to be displayed on a user interface on a user device.

21. The non-transitory computer readable storage medium of claim 17, wherein the one or more computing instructions, when run on the one or more processors, further cause the one or more processors to perform one or more of the following operations: determining the simulated population comprises determining the simulated population by a trained population-generating model based upon the one or more matched simulated characters; or generating the one or more simulated responses comprises generating the one or more simulated responses by a trained response-generating model.

22. The non-transitory computer readable storage medium in claim 21, wherein the one or more computing instructions, when run on the one or more processors, further cause the one or more processors to perform, after transmitting the one or more simulated responses: receiving, from the user device, user feedback for the one or more simulated responses; when the trained population-generating model is used, re-training the trained population- generating model based upon the simulated population and the user feedback; and when the trained response-generating model is used, re-training the trained response- generating model based upon the one or more simulated responses and the user feedback.
16. A non-transitory computer readable storage medium storing one or more computing instructions that direct processor operations on one or more processors, the one or more computing instructions, when run on one or more processors, cause the one or more processors to perform: generating, by a trained response-generating model, one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life population; transmitting the one or more simulated responses to be displayed on a user interface on a user device; receiving, from the user device, user feedback for the one or more simulated responses; and re-training the trained response-generating model based upon the one or more simulated responses and the user feedback.

17. The non-transitory computer readable storage medium of claim 16, wherein the one or more computing instructions, when run on the one or more processors, further cause the one or more processors to perform one or more of: receiving, via the user device, an inquiry input from a user, wherein the inquiry input is associated with the inquiry and one or more known-member characteristic values for the one or more known members of the real-life population; or determining the simulated population for the real-life population comprising: determining simulated characters for population groups for the real-life population based upon member characteristics for the population groups, wherein each of the simulated characters is associated with: (a) one of the population groups, and (b) respective characteristic values corresponding to the member characteristics; determining one or more matched simulated characters of the simulated characters for the one or more known members of the real-life population based upon the one or more known-member characteristic values associated with the one or more known members; and determining the simulated population for the real-life population based upon the one or more matched simulated characters.

Application No. 18/651,375 (reference)
Instant Application
1. A computer-implemented method of generating simulated responses via simulated characters, the method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer- readable media, the computer-implemented method comprising: determining simulated characters for population groups for a real-life population based upon member characteristics for the population groups, wherein each of the simulated characters is associated with: (a) one of the population groups, and (b) respective characteristic values corresponding to the member characteristics for the population groups; determining one or more matched simulated characters of the simulated characters for one or more known members of the real-life population based upon one or more known- member characteristic values associated with the one or more known members; generating a simulated population of a pre-determined population size for the real-life population based upon the one or more matched simulated characters, comprising scaling the simulated population to the pre-determined population size, wherein one or more members of the simulated population generated from scaling the simulated population to the pre-determined population size are based at least on member characteristic values for at least two of (a) the one or more matched simulated characters, (b) a model population for the real-life population, or (c) one or more characteristic variations for the respective characteristic values for the one or more matched simulated characters; generating one or more simulated responses to an inquiry for the one or more known members based upon the simulated population; and transmitting the one or more simulated responses to be displayed on a user interface on a user device.

8. The computer-implemented method of claim 1, wherein one or more of: determining the simulated population comprises determining the simulated population by a trained population-generating model based upon the one or more matched simulated characters; or generating the one or more simulated responses comprises generating the one or more simulated responses by a trained response-generating model.

9. The computer-implemented method of claim 8, further comprising, after transmitting the one or more simulated responses: receiving, from the user device, user feedback for the one or more simulated responses; when the trained population-generating model is used, re-training the trained population- generating model based upon the simulated population and the user feedback; and when the trained response-generating model is used, re-training the trained response- generating model based upon the one or more simulated responses and the user feedback.
21. A computer-implemented method for training a model based upon simulated responses and user feedback, the method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media, the computer-implemented method comprising: generating, by a trained response-generating model, one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life market segment or other group; transmitting the one or more simulated responses to be displayed on a user interface on a user device; receiving, from the user device, user feedback for the one or more simulated responses; and/or re-training the trained response-generating model based upon the one or more simulated responses and the user feedback.

Claims 1, 2, 10, 16, and 21 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 7, 14, and 19 of copending Application No. 18/651,387 (reference application).
Although the claims at issue are not identical, they are not patentably distinct from each other because reference claim 7 anticipates instant claims 1, 2, and 21, reference claim 14 anticipates instant claim 10, and reference claim 19 anticipates instant claim 16. See the claim charts below.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.

Application No. 18/651,387 (reference)
Instant Application
1. A computer-implemented method for associating simulated characters with characteristics and/or population groups, the method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non- transitory computer-readable media, the computer-implemented method comprising: determining a respective simulated population for each simulated character of multiple simulated characters based upon respective characteristic values associated with each simulated character, comprising: determining a respective associated population group of multiple population groups for a real-life population based upon each simulated character;
determining respective member characteristics for the respective associated population group; generating each simulated member of the respective simulated population, comprising:
associating each simulated member with one or more respective altered characteristic values altered based upon (a) the respective characteristic values associated with each simulated character and (b) the respective member characteristics for the respective associated population group; determining one or more respective characteristic variations for each simulated character of the multiple simulated characters based at least on respective statistics data for the respective associated population group for each simulated character; and altering the one or more respective altered characteristic values for each simulated member based upon the one or more respective characteristic variations for each simulated character of the multiple simulated characters;
receiving, from a user device for a user, user feedback for the respective simulated population; and re-training a trained population-generating model based upon the respective simulated population and the user feedback.

4. The computer-implemented method of claim 1, the method further comprising after determining the respective simulated population: generating one or more simulated responses to an inquiry for the real-life population based upon the respective simulated population for each simulated character of the multiple simulated characters; and transmitting the one or more simulated responses to be displayed on a user interface on a second user device for a second user.

6. The computer-implemented method of claim 4, wherein, when the one or more simulated responses are generated, one or more of:
the one or more simulated responses further comprise answers and at least one of reasons for the answers or recommendations; and
generating the one or more simulated responses comprises generating the one or more simulated responses by a trained response-generating model.

7. The computer-implemented method of claim 6, further comprising, after transmitting the one or more simulated responses: receiving, from the second user device, additional user feedback for the one or more simulated responses; and
when the trained response-generating model is used, further re-training the trained response-generating model based upon the one or more simulated responses and the additional user feedback.
1. A computer-implemented method for training a model based upon simulated responses and user feedback, the method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media, the computer-implemented method comprising: generating, by a trained response-generating model, one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life population; transmitting the one or more simulated responses to be displayed on a user interface on a user device; receiving, from the user device, user feedback for the one or more simulated responses; and re-training the trained response-generating model based upon the one or more simulated responses and the user feedback.

2. The computer-implemented method of claim 1, wherein the one or more simulated responses further comprise answers and at least one of reasons for the answers or recommendations.

Application No. 18/651,387 (reference)
Instant Application
10. A computer system for associating simulated characters with characteristics and/or population groups, the computer system comprising: one or more processors; and one or more non-transitory computer-readable media storing computing instructions that, when run on the one or more processors, direct the one or more processors to perform the following operations:
determining a respective simulated population for each simulated character of multiple simulated characters based upon respective characteristic values associated with each simulated character, comprising: determining a respective associated population group of multiple population groups for a real-life population based upon each simulated character; determining respective member characteristics for the respective associated population group; generating each simulated member of the respective simulated population, comprising: associating each simulated member with one or more respective altered characteristic values altered based upon (a) the respective characteristic values associated with each simulated character and (b) the respective member characteristics for the respective associated population group; determining one or more respective characteristic variations for each simulated character of the multiple simulated characters based at least on respective statistics data for the respective associated population group for each simulated character; and altering the one or more respective altered characteristic values for each simulated member based upon the one or more respective characteristic variations for each simulated character of the multiple simulated characters;
receiving, from a user device for a user, user feedback for the respective simulated population; and re-training a trained population-generating model based upon the respective simulated population and the user feedback.

12. The computer system of claim 10, wherein the computing instructions, when run on the one or more processors, further cause the one or more processors to perform after determining the respective simulated population: generating one or more simulated responses to an inquiry for the real-life population based upon the respective simulated population for each simulated character of the multiple simulated characters; and transmitting the one or more simulated responses to be displayed on a user interface on a second user device for a second user.

14. The computer system of claim 12, wherein: when the one or more simulated responses are generated, the one or more simulated responses further comprise answers and at least one of reasons for the answers or recommendations; and
generating the one or more simulated responses comprises generating the one or more simulated responses by a trained response-generating model; and the operations further comprise, after transmitting the one or more simulated responses: receiving, from the second user device, additional user feedback for the one or more simulated responses; and when the trained response-generating model is used, further re-training the trained response-generating model based upon the one or more simulated responses and the additional user feedback.

10. A computer system for training a model based upon simulated responses and user feedback, the computer system comprising: one or more processors; and one or more non-transitory computer-readable media storing computing instructions that, when run on the one or more processors, cause the one or more processors to perform the following operations:
generating, by a trained response-generating model, one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life population; transmitting the one or more simulated responses to be displayed on a user interface on a user device; receiving, from the user device, user feedback for the one or more simulated responses; and re-training the trained response-generating model based upon the one or more simulated responses and the user feedback.

Application No. 18/651,387 (reference)
Instant Application
16. A non-transitory computer readable storage medium storing one or more computing instructions that direct processor operations of one or more processors, the one or more computing instructions, when run on the one or more processors, cause the one or more processors to perform: determining a respective simulated population for each simulated character of multiple simulated characters based upon respective characteristic values associated with each simulated character, comprising: determining a respective associated population group of multiple population groups for a real-life population based upon each simulated character; determining respective member characteristics for the respective associated population group; generating each simulated member of the respective simulated population, comprising: associating each simulated member with one or more respective altered characteristic values altered based upon (a) the respective characteristic values associated with each simulated character and (b) the respective member characteristics for the respective associated population group; determining one or more respective characteristic variations for each simulated character of the multiple simulated characters based at least on respective statistics data for the respective associated population group for each simulated character; and altering the one or more respective altered characteristic values for each simulated member based upon the one or more respective characteristic variations for each simulated character of the multiple simulated characters;
receiving, from a user device for a user, user feedback for the respective simulated population; and re-training a trained population-generating model based upon the respective simulated population and the user feedback.

17. The non-transitory computer readable storage medium of claim 16, wherein generating each simulated member of the respective simulated population for each simulated character of the multiple simulated characters further comprises: determining, by the trained population-generating model, each simulated member based upon the respective characteristic values associated with each simulated character.

18. The non-transitory computer readable storage medium of claim 17, wherein the one or more computing instructions that, when run on the one or more processors, further cause the one or more processors to perform after determining the respective simulated population: generating one or more simulated responses to an inquiry for the real-life population based upon the respective simulated population for each simulated character of the multiple simulated characters; and transmitting the one or more simulated responses to be displayed on a user interface on a second user device for a second user.

19. The non-transitory computer readable storage medium of claim 18, wherein: determining the one or more respective characteristic variations comprises retrieving, from a database, the one or more respective characteristic variations; and receiving, via a third user device from a third user, the one or more respective characteristic variations; and when the one or more simulated responses are generated, the one or more simulated responses further comprise answers and at least one of reasons for the answers or recommendations; and generating the one or more simulated responses comprises generating the one or more simulated responses by a trained response-generating model; receiving, from the second user device, additional user feedback for the one or more simulated responses; and when the trained response-generating model is used, further re-training the trained response-generating model based upon the one or more simulated responses and the additional user feedback.
16. A non-transitory computer readable storage medium storing one or more computing instructions that direct processor operations on one or more processors, the one or more computing instructions, when run on one or more processors, cause the one or more processors to perform:
generating, by a trained response-generating model, one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life population; transmitting the one or more simulated responses to be displayed on a user interface on a user device; receiving, from the user device, user feedback for the one or more simulated responses; and re-training the trained response-generating model based upon the one or more simulated responses and the user feedback.

Application No. 18/651,387 (reference)
Instant Application
1. A computer-implemented method for associating simulated characters with characteristics and/or population groups, the method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non- transitory computer-readable media, the computer-implemented method comprising: determining a respective simulated population for each simulated character of multiple simulated characters based upon respective characteristic values associated with each simulated character, comprising: determining a respective associated population group of multiple population groups for a real-life population based upon each simulated character;
determining respective member characteristics for the respective associated population group;
generating each simulated member of the respective simulated population, comprising:
associating each simulated member with one or more respective altered characteristic values altered based upon (a) the respective characteristic values associated with each simulated character and (b) the respective member characteristics for the respective associated population group;
determining one or more respective characteristic variations for each simulated character of the multiple simulated characters based at least on respective statistics data for the respective associated population group for each simulated character; and altering the one or more respective altered characteristic values for each simulated member based upon the one or more respective characteristic variations for each simulated character of the multiple simulated characters;
receiving, from a user device for a user, user feedback for the respective simulated population; and re-training a trained population-generating model based upon the respective simulated population and the user feedback.

4. The computer-implemented method of claim 1, the method further comprising after determining the respective simulated population: generating one or more simulated responses to an inquiry for the real-life population based upon the respective simulated population for each simulated character of the multiple simulated characters; and transmitting the one or more simulated responses to be displayed on a user interface on a second user device for a second user.

6. The computer-implemented method of claim 4, wherein, when the one or more simulated responses are generated, one or more of:
the one or more simulated responses further comprise answers and at least one of reasons for the answers or recommendations; and
generating the one or more simulated responses comprises generating the one or more simulated responses by a trained response-generating model.

7. The computer-implemented method of claim 6, further comprising, after transmitting the one or more simulated responses: receiving, from the second user device, additional user feedback for the one or more simulated responses; and
when the trained response-generating model is used, further re-training the trained response-generating model based upon the one or more simulated responses and the additional user feedback.
21. A computer-implemented method for training a model based upon simulated responses and user feedback, the method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media, the computer-implemented method comprising:
generating, by a trained response-generating model, one or more simulated responses to an inquiry for one or more known members based upon a simulated population for a real-life market segment or other group; transmitting the one or more simulated responses to be displayed on a user interface on a user device; receiving, from the user device, user feedback for the one or more simulated responses; and/or re-training the trained response-generating model based upon the one or more simulated responses and the user feedback.

Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GWYNEVERE A DETERDING whose telephone number is [phone]. The examiner can normally be reached Mon-Fri. 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar can be reached at [phone]. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/G.A.D./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125

Prosecution Timeline

• Apr 30, 2024: Application Filed
• Sep 08, 2026: Non-Final Rejection mailed — §101, §102, §103 (current)