DETAILED ACTION
Claims 1-20 are currently pending and have been examined.
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 .
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 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Subject Matter Eligibility Criteria - Step 1:
Claims 1-10 are directed to a method (i.e., a process) & Claims 11-20 are directed to a system (i.e., a machine). Accordingly, claims 1-20 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Representative independent claim 11 includes limitations that recite at least one abstract idea. Specifically, independent claim 11 recites:
11. A system for medical decision making, comprising:
a hardware processor; and
a memory that stores a computer program which, when executed by the hardware processor, causes the hardware processor to:
select a strategy from a strategy library, expressed in natural language;
select an improvement from an improvement library, expressed in natural language;
combine the strategy with the improvement using a large language model (LLM) to generate an improved strategy;
evaluate the improved strategy to generate feedback;
update the strategy library and the improvement library based on the feedback; and
perform an action based on the improved strategy.
The Examiner submits that the foregoing underlined limitations constitute “methods of organizing human activity” because selecting and combining a strategy and an improvement to create an improved strategy, evaluating the improved strategy to generate feedback, updating based on the feedback, and performing an action are associated with managing personal behavior or relationships or interactions between people. For example, but for the system, this claim encompasses a person facilitating data access, receiving data, and outputting data in the manner described in the identified abstract idea. The Examiner notes that “method of organizing human activity” includes a person’s interaction with a computer – see MPEP 2106.04(a)(2)(II)(C). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Accordingly, independent claim 11 and analogous independent claim 1 recite at least one abstract idea.
Furthermore, dependent claims 2-10 & 12-20 further narrow the abstract idea described in the independent claims. Claims 2 & 12 recite adding a new improvement; Claims 3 & 13 recite generating dialogues; Claims 4-5 & 14-15 recite evaluating the improved strategy; Claims 6 & 16 recite the improvement library with scores; Claims 7 & 17 recite the strategy and a treatment action; Claims 10 & 20 recite selecting an improvement. These limitations only serve to further limit the abstract idea and hence, are directed towards fundamentally the same abstract idea as independent claim 11 and analogous independent claim 1, even when considered individually and as an ordered combination.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP §2106.04(II)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
11. A system for medical decision making, comprising:
a hardware processor; and
a memory that stores a computer program which, when executed by the hardware processor, causes the hardware processor to:
select a strategy from a strategy library, expressed in natural language;
select an improvement from an improvement library, expressed in natural language;
combine the strategy with the improvement using a large language model (LLM) to generate an improved strategy;
evaluate the improved strategy to generate feedback;
update the strategy library and the improvement library based on the feedback; and
perform an action based on the improved strategy.
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the processor, memory, system; the Examiner submits that these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitation of using a large language model, the Examiner submits that these additional claim limitations amount to an attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result and is equivalent to the words “apply it”. See MPEP 2106.05(f)(1).
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, representative independent claim 11 and analogous independent claim 1 do not recite additional elements that integrate the judicial exception into a practical application.
Accordingly, the claims recite at least one abstract idea.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claims 8-9 & 18-19: These claims recite using a machine learning model and evaluating a strategy using a monte carlo tree search and amount to an attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result and is equivalent to the words “apply it”. See MPEP 2106.05(f)(1).
Thus, taken alone, any additional elements do not integrate the at least one abstract idea into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claim 11 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
As discussed above, Regarding the additional limitations of the processor, memory, system; the Examiner submits that these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)). Regarding the additional limitation of using a large language model, the Examiner submits that these additional claim limitations amount to an attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result and is equivalent to the words “apply it”. See MPEP 2106.05(f)(1).
The dependent claims also do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Therefore, claims 1-20 are ineligible under 35 USC §101.
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.
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 1-4, 6-8, 10-14, 16-18, & 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ghose (US20250095642) in view of Mohan (US20260142029).
As per claim 1, Ghose teaches a computer-implemented method for medical decision making, comprising:
selecting a strategy from a strategy library, expressed in natural language (para. 45-48, 91: system uses natural language to output clinically explainable information to user);
selecting an improvement from an improvement library, expressed in natural language (para. 45-48, 91: system uses natural language to output recommendation to user including next steps).
Ghose does not expressly teach combining the strategy with the improvement using a large language model (LLM) to generate an improved strategy; evaluating the improved strategy to generate feedback; updating the strategy library and the improvement library based on the feedback; and performing an action based on the improved strategy.
Mohan, however, teaches to machine learning model such as an LLM used to compiles the inferred diagnose(s), potential treatment(s), recommended test(s), and/or wellness recommendations into a user report (para. 43, 69). Mohan also teaches to a feedback loop that updates the AI models based on new data and clinician feedback (para. 75). Mohan further teaches to using feedback from users and/or medical professionals that may indicate the accuracy of an inferred diagnosis, and/or the effectiveness of the recommended treatment or wellness suggestions (para. 69).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the aforementioned features in Mohan with Ghose based on the motivation of leverages machine learning models, trained on vast datasets of medical records and outcomes, to perform advanced diagnostics and treatment planning autonomously (Mohan – para. 6).
As per claim 2, Ghose and Mohan teach the method of claim 1. Ghose teaches further comprising adding a new improvement to the improvement library by prompting the LLM to suggest an improvement for the strategy (para. 37: using RAG when submitting the prompt, a performance of the LLM and an accuracy of a result returned by the LLM may be increased.).
As per claim 3, Ghose and Mohan teach the method of claim 1. Ghose teaches wherein the action includes generating dialogue using the LLM in accordance with the improved strategy (para. 96: If the recommended treatment matches the predicted treatment, LLM outputs and delivers the response to the care provider).
As per claim 4, Ghose and Mohan teach the method of claim 1. Ghose teaches wherein evaluating the improved strategy includes updating information about a state of another agent in a scenario (para. 38: historical patient records may be analyzed to determine a set of probabilities that treatments recommended by recommendation model have a highest probability of success; set of probabilities used to train the prediction model to predict a treatment for a given patient scenario with a highest probability of success. The prediction model may be used to verify an output of the LLM on the same patient scenario).
As per claim 6, Ghose and Mohan teach the method of claim 1. Ghose teaches wherein the improvement library includes a set of improvements, each associated with a score that reflects how it affects performance (para. 38: historical patient records may be analyzed to determine a set of probabilities that treatments recommended by recommendation model have a highest probability of success).
As per claim 7, Ghose and Mohan teach the method of claim 1. Ghose teaches wherein the strategy relates to treating a medical condition and wherein the action includes automatically performing a treatment action on a patient (para. 20: clinical recommendation system may receive a natural language query from a care provider (e.g., a question about a course of action regarding a patient), and submit the natural language query to the LLM as a prompt. The LLM may output a response to the prompt, which may include the suggested treatment).
As per claim 8, Ghose and Mohan teach the method of claim 1. Ghose teaches wherein the LLM is implemented using a machine learning model (para. 18: various models may include AI models, such as a machine learning (ML) or deep learning (DL) models).
As per claim 10, Ghose and Mohan teach the method of claim 1. Ghose teaches wherein selecting the improvement includes selecting a plurality of improvements, and wherein combining the strategy includes combining the strategy with all of the plurality of improvements (para. 58-59: one or more recommended treatments for patients presenting with a clinical presentation are generated).
Claims 12-14, 16-18, & 20 recite substantially similar limitations as those already addressed in claims 2-4, 6-8, & 10, and, as such, are rejected for similar reasons as given above.
As per claim 11, Ghose teaches a system for medical decision making, comprising:
a hardware processor (para. 24: computer with processor); and
a memory that stores a computer program which, when executed by the hardware processor, causes the hardware processor to (para. 24-25: memory):
select a strategy from a strategy library, expressed in natural language (para. 45-48, 91: system uses natural language to output clinically explainable information to user);
select an improvement from an improvement library, expressed in natural language (para. 45-48, 91: system uses natural language to output recommendation to user including next steps).
Ghose does not expressly teach combining the strategy with the improvement using a large language model (LLM) to generate an improved strategy; evaluating the improved strategy to generate feedback; updating the strategy library and the improvement library based on the feedback; and performing an action based on the improved strategy.
Mohan, however, teaches to machine learning model such as an LLM used to compiles the inferred diagnose(s), potential treatment(s), recommended test(s), and/or wellness recommendations into a user report (para. 43, 69). Mohan also teaches to a feedback loop that updates the AI models based on new data and clinician feedback (para. 75). Mohan further teaches to using feedback from users and/or medical professionals that may indicate the accuracy of an inferred diagnosis, and/or the effectiveness of the recommended treatment or wellness suggestions (para. 69).
Claims 5 & 15 are rejected under 35 U.S.C. 103 as being unpatentable over Ghose (US20250095642) in view of Mohan (US20260142029) as applied to claim 3 above, and in further view of Haggai (US20260066103).
As per claim 5, Ghose and Mohan teach the method of claim 3, but do not expressly teach wherein evaluating the improved strategy includes generating a scenario prompt that includes agent goals.
Sheba, however, teaches to an evaluation LLM-based agent configured to evaluate the output of one or more LLM-based agents with respect to content structure and linguistic integration using guardrails implemented to ensure that the LLM-based agent operates safely, ethically and in accordance with the LLM-based agent's defined objectives (para. 49).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the aforementioned features in Haggai with Ghose and Mohan based on the motivation of provide a medical history, an anamnesis and recommending treatment in a rapid yet reliable manner, thereby minimizing physicians-computer time allowing maximal physician-patient time (Haggai – para. 5).
Claim 15 recites substantially similar limitations as those already addressed in claim 5, and, as such, is rejected for similar reasons as given above.
Claims 9 & 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ghose (US20250095642) in view of Mohan (US20260142029) as applied to claim 3 above, and in further view of Sandholm (US20140039913).
As per claim 9, Ghose and Mohan teach the method of claim 3, but do not expressly teach wherein evaluating the improved strategy includes performing a Monte Carlo tree search over a strategy tree.
Sandholm, however, teaches to utilizing a monte carlo tree search with modeling a course of treatment over time as a game and evaluating a course of treatment (para. 26).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the aforementioned features in Sandholm with Ghose and Mohan based on the motivation of providing improved methods of identifying and designing pharmaceutical or other courses of treatment are desirable (Sandholm – para. 4).
Claim 19 recites substantially similar limitations as those already addressed in claim 9, and, as such, is rejected for similar reasons as given above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chu (WO 2025037772 A1) teaches to controls an LLM model to be trained on the basis of result data constructed by matching international classification of diseases (hereinafter, ICD) and the consultation data, inputs the consultation data to the trained LLM model to perform inference, and generates condition prediction information of the patient on the basis of an inference result.
Bulut (US 20210241909 A1) teaches to obtaining a virtual model of a patient, wherein the virtual model uses input data, providing information on characteristics of the patient, to generate output data; receiving treatment information, the treatment information indicating a potential treatment strategy for the patient; processing the treatment information and the virtual model to predict an effect of the potential treatment strategy on the virtual model of the patient; and generating, based on the processing, effect information that indicates the predicted effect of the potential treatment strategy on the virtual model of the patient.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jonathan K Ng whose telephone number is (571)270-7941. The examiner can normally be reached M-F 8 AM - 5 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anita Coupe can be reached at 571-270-7949. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Jonathan Ng/Primary Examiner, Art Unit 3619
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