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 .
Priority
Under 35 U.S.C. 119(a) or (e), the claims in a U.S. application are entitled to the benefit of a foreign priority date or the filing date of a provisional application if the corresponding foreign application or provisional application supports the claims in the manner required by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph. Purdue Pharma LP v. Iancu, 767 Fed. Appx. 918, 923-24, 2019 USPQ2d 136363 (Fed. Cir. 2019); In re Ziegler, 992 F.2d 1197, 1200, 26 USPQ2d 1600, 1603 (Fed. Cir. 1993); Kawai v. Metlesics, 480 F.2d 880, 178 USPQ 158 (CCPA 1973); In re Gosteli, 872 F.2d 1008, 10 USPQ2d 1614 (Fed. Cir. 1989).
The present application claims priority to provisional application 63/433,602. However, independent claims 1, 5, and 9 recite limitations that are not supported by provisional application 63/433,602. For example, provisional application 63/433,602 does not disclose anything related to memory states of LLM agents.
Since the subject matter of independent claims 1, 5, and 9 are not supported by provisional application 63/433,602 under 35 U.S.C. 112(a), claims 1, 5, and 9 have an effective filing date of 6 February 2024. The remaining claims also have an effective filing date of 6 February 2024 for their dependence on one of the independent claims.
Claim Rejections - 35 USC § 102
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)(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.
Claim(s) 1, 3-5, 7-9 and 11-12 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Worthington (U.S. Patent Application Pub. No. 2025/0148558).
In regard to claim 1, Worthington discloses a method for determining a prediction of a population level response to presented information (Fig. 1, 100), comprising:
(a) conditioning at least one large language model (LLM) agent on a plurality of population or group features using in-weight training or in-context tokens (a plurality of juror-agents are defined using demographics data, social science survey data, social characteristics, political preferences, etc. through prompting of an LLM, paragraph [0015]);
(b) recording an initial memory state of the at least one LLM agent (each juror-agent is initialized with a memory 106, paragraph [0015]);
(c) retrieving one or more entries of an output of the at least one LLM agent from an
LLM agent memory to include in the next planning step (juror memories of current and past opinions are utilized, paragraph [0019]);
(d) planning a response of the at least one LLM agent to an environment for the presented information (rules governing deliberations between the synthetic juror-agents are established, paragraph [0019]);
(e) transmitting one or more conditioned intra-agent communications to a plurality of additional LLM agents (deliberations in the form of iterative interaction and communication among the synthetic juror agents in a synthetic deliberative discussion environment, paragraph [0019]);
(f) receiving the one or more conditioned intra-agent communications from the plurality of additional LLM agents;
(g) recording an updated memory state of the LLM agent based on the one or more sent and received conditioned intra-agent communications (juror-agent memories are updating according to the rules in response to the deliberations, paragraphs [0019-0020]); and
(h) generating the prediction based on the updated memory state (a decision prediction is generated based on the deliberation and repeated iterative updating of the synthetic jury model state and individual juror-agent states, paragraphs [0020-0021]).
In regard to claim 3, Worthington discloses the plurality of additional LLM agents are defined by the environment for the information (the rules of deliberation define the interactions allowed between the juror-agents, paragraph [0019]).
In regard to claim 4, Worthington discloses iterating procedures (a)-(h) for one or more additional time points (the simulations are repeated for multiple iterations, paragraphs [0029-0030]).
In regard to claim 5, Worthington discloses a system for determining a prediction of a population level response to presented information (Fig. 3, 300), comprising:
at least one processor (processing unit 302) configured to:
(a) condition at least one large language model (LLM) agent on a plurality of population or group features using in-weight training or in-context tokens (a plurality of juror-agents are defined using demographics data, social science survey data, social characteristics, political preferences, etc. through prompting of an LLM, paragraph [0015]);
(b) record an initial memory state of the at least one LLM agent (each juror-agent is initialized with a memory 106, paragraph [0015]);
(c) retrieve one or more entries of an output of the at least one LLM agent from an
LLM agent memory to include in the next planning step (juror memories of current and past opinions are utilized, paragraph [0019]);
(d) plan a response of the at least one LLM agent to an environment for the presented information (rules governing deliberations between the synthetic juror-agents are established, paragraph [0019]);
(e) transmit one or more conditioned intra-agent communications to a plurality of additional LLM agents (deliberations in the form of iterative interaction and communication among the synthetic juror agents in a synthetic deliberative discussion environment, paragraph [0019]);
(f) receive the one or more conditioned intra-agent communications from the plurality of additional LLM agents;
(g) record an updated memory state of the LLM agent based on the one or more sent and received conditioned intra-agent communications (juror-agent memories are updating according to the rules in response to the deliberations, paragraphs [0019-0020]); and
(h) generate the prediction based on the updated memory state (a decision prediction is generated based on the deliberation and repeated iterative updating of the synthetic jury model state and individual juror-agent states, paragraphs [0020-0021]).
In regard to claim 7, Worthington discloses the plurality of additional LLM agents are defined by the environment for the information (the rules of deliberation define the interactions allowed between the juror-agents, paragraph [0019]).
In regard to claim 8, Worthington discloses iterating procedures (a)-(h) for one or more additional time points (the simulations are repeated for multiple iterations, paragraphs [0029-0030]).
In regard to claim 9, Worthington discloses a computer readable medium which includes software thereon for determining a prediction of a population level response to presented information (Fig. 3, system memory 304 and/or removable storage 308 and/or non-removable storage 310), wherein, when at least one computer processor executes the software (processing unit 302), the computer processor is configured to perform the procedures, comprising:
(a) conditioning at least one large language model (LLM) agent on a plurality of population or group features using in-weight training or in-context tokens (a plurality of juror-agents are defined using demographics data, social science survey data, social characteristics, political preferences, etc. through prompting of an LLM, paragraph [0015]);
(b) recording an initial memory state of the at least one LLM agent (each juror-agent is initialized with a memory 106, paragraph [0015]);
(c) retrieving one or more entries of an output of the at least one LLM agent from an
LLM agent memory to include in the next planning step (juror memories of current and past opinions are utilized, paragraph [0019]);
(d) planning a response of the at least one LLM agent to an environment for the presented information (rules governing deliberations between the synthetic juror-agents are established, paragraph [0019]);
(e) transmitting one or more conditioned intra-agent communications to a plurality of additional LLM agents (deliberations in the form of iterative interaction and communication among the synthetic juror agents in a synthetic deliberative discussion environment, paragraph [0019]);
(f) receiving the one or more conditioned intra-agent communications from the plurality of additional LLM agents;
(g) recording an updated memory state of the LLM agent based on the one or more sent and received conditioned intra-agent communications (juror-agent memories are updating according to the rules in response to the deliberations, paragraphs [0019-0020]); and
(h) generating the prediction based on the updated memory state (a decision prediction is generated based on the deliberation and repeated iterative updating of the synthetic jury model state and individual juror-agent states, paragraphs [0020-0021]).
In regard to claim 11, Worthington discloses the plurality of additional LLM agents are defined by the environment for the information (the rules of deliberation define the interactions allowed between the juror-agents, paragraph [0019]).
In regard to claim 12, Worthington discloses iterating procedures (a)-(h) for one or more additional time points (the simulations are repeated for multiple iterations, paragraphs [0029-0030]).
Claim Rejections - 35 USC § 103
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.
Claim(s) 2, 6, and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Worthington, in view of Simmons et al. (Large Language Models as Subpopulation Representative Models: A Review, hereinafter “Simmons”).
In regard to claims 2, 6 and 10, Worthington discloses the synthetic jury may be applied to non-legal questions such as the evaluation of political candidates (paragraph [0016]) and further discloses the juror-agents may output a vote (paragraphs [0018-0019]). However, Worthington does not expressly disclose the prediction comprises an election outcome.
Simmons discloses a comparable method of using LLMs to estimate subpopulation representative models (SRMs), and discloses using SRMs to predict an election outcome (section 4.1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the voting juror-agents of Worthington to predict the outcome of an election as taught by Simmons by simply tallying the votes output by the juror-agents and this would predictably allow one to predict an election outcome by determining a candidate that received the most votes.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bouron et al., Breitweiser, Harrison et al., Hughes, Judith et al., Rosenberg et al., Aher et al., and Sanders et al. disclose additional methods of simulating populations using LLMs and/or AI agents.
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BLA 10/23/25
/BRIAN L ALBERTALLI/ Primary Examiner, Art Unit 2656