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 .
Priority
Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2021-160732, filed on September 30, 2021.
Response to Amendment
In the amendment filed on July 18, 2025, the following has occurred: claim(s) 1, 5, 11-12 have been amended, claim(s) 14-15 have been added, and claim(s) 2, 6-10, 13 have been cancelled. Now, claim(s) 1, 4-5, 11-12, 14-15 are pending.
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.
Claim(s) 1, 4-5, 11-12, 14-15 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 4-5, 11: Step 2A Prong One
Claim 1 recites:
calculate an effect evaluation value regarding a plurality of medical decisions, the effect evaluation value including a subjective best arm probability and/or a subjective best arm probability difference;
calculate a fairness index value regarding the plurality of medical decisions based on the effect evaluation value and a target value with respect to the effect evaluation value set for a respective one of subjects including a subject target, wherein in a case where the effect evaluation value matches the target value, a first value meaning fairness is assigned to the fairness index value, and in a case where the effect evaluation value does not match the target value, a second value meaning unfairness is assigned to the fairness index value;
determine a medical decision to be assigned to the subject target based on the effect evaluation value and the fairness index value: determine a single random number according to a predetermined probability distribution; sample an assignment candidate from the plurality of medical decisions according to a predetermined policy based on the determined single random number; in a case where the fairness index value regarding the assignment candidate is a predetermined value, determine that the assignment candidate is the medical decision to be assigned; and in a case where the fairness index value regarding the assignment candidate is not the predetermined value, determine that a medical decision other than the assignment candidate is the medical decision to be assigned;
obtain observation data indicating an effect produced on the subject target by the assigned medical decision, wherein the effect is produced after performing the assigned medical decision on the subject target, and the assigned medical decision is one or more of whether to perform a medical procedure, whether to conduct a medical test, and whether to use a medication; and
based on the obtained data accumulated by repeating the acquiring, calculating, determining, and obtaining steps,
wherein set the target value based on a history of medical decisions regarding the subject target and/or a subject other than the subject target.
These limitations, as drafted, given the broadest reasonable interpretation, but for generic computer components, encompass managing interactions between people, including following rules or instructions (e.g., a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193 194-96 (CCPA 1982)), which is a subgrouping of Certain Methods of Organizing Human Activity. For example, but for the generic computer components (“processing circuitry configured to”, “…from a testing instrument or a computer,…”), the claim encompasses a user manually calculating an effect evaluation value, calculating a fairness index value regarding medical decisions, a user manually determining a medical decision to a subject target, a user manually obtaining observation data indicating an effect produced on the subject target by the assigned medical decision, and a user manually repeating the acquiring, calculating, determining, and obtaining steps. These steps encompass steps that could be performed manually by users following rules or instructions which constitute certain methods of organizing human activity. These steps could be carried out manually by individuals, such as doctors or clinical staff.
Claims 4-5, 11 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea. For example, claims 4-5 describe setting a target value. Finally, claim 11 describes calculating the effect evaluation value. Such steps encompass Certain Methods of Organizing Human Activity.
Claims 1, 4-5, 11: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract idea, and generally linking the abstract idea to a particular technological environment.
Claims 1, 4-5, 11, directly or indirectly, recite the following generic computer component configured to implement the abstract idea: “processing circuitry configured to”, “…from a testing instrument or a computer,…” (See Specification in Paragraph [0027]: The processing circuitry 11 includes one or more processors such as a central processing unit (CPU), a graphics processing unit (GPU), and so on.) As set forth in the MPEP 2106.04(d) "merely including instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claims recite “…according to a bandit algorithm, wherein the processing circuity is configured to:…”, “the effect evaluation value being calculated based on a machine-learning model formulated to calculate effect evaluation values for medical decisions” and “apply reinforcement learning to train a parameter of the machine-learning model” at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words “apply it” to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has not been integrated into a practical application.
Claims 1, 4-5, 11: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer
configured to perform above identified functions amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Alice 573 U.S. at 223 ("mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.")
Additionally, generally linking the abstract idea to a particular technological environment does not amount to significantly more than the abstract idea (See MPEP 2106.05(h) and Affinity Labs of Texas v. DirectTV, LLC, 838 F.3d 1253, 120 USP12d 1201 (Fed. Cir. 2016)).
Claim 12 recites the same functions as claim 1, but in method form.
Thus, these elements taken individually or together do not amount to significantly more than the abstract ideas themselves.
Claim 14: Step 2A Prong One
Claim 14 recite(s)
calculate an effect evaluation value regarding a plurality of medical decisions, the effect evaluation value including a subjective best arm probability and/or a subjective best arm probability difference;
calculate a fairness index value regarding the plurality of medical decisions based on the effect evaluation value and a target value with respect to the effect evaluation value set for a respective one of subjects including a subject target, wherein in a case where the effect evaluation value ranges from a threshold value to a lower limit, a first value is assigned to the fairness index value, and the first value meaning fairness is assigned to the fairness index value, and in a case where the effect evaluation value ranges from the threshold value to an upper limit, a second value is assigned to the fairness index value, the second value meaning unfairness is assigned to the fairness index value;
determine a medical decision to be assigned to the subject target based on the effect evaluation value and the fairness index value, determine a plurality of random numbers according toa predetermined probability distribution; sample a plurality of assignment candidates from the plurality of medical decisions according to a predetermined policy based on the determined plurality of random numbers; and determine that an assignment candidate having a greatest fairness index value among the plurality of assignment candidates is the medical decision to be assigned;
obtain observation data indicating an effect produced on the subject target by the assigned medical decision, wherein the effect is produced after performing the assigned medical decision on the subject target, and the assigned medical decision is one or more of whether to perform a medical procedure, whether to conduct a medical test, and whether to use a medication; and
based on the obtained data accumulated by repeating the acquiring, calculating, determining, and obtaining steps, wherein the processing circuitry is further configured to set the target value based on a history of medical decisions regarding the subject target and/or a subject other than the subject target.
These limitations, as drafted, given the broadest reasonable interpretation, but for generic computer components, encompass managing interactions between people, including following rules or instructions (e.g., a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193 194-96 (CCPA 1982)), which is a subgrouping of Certain Methods of Organizing Human Activity. For example, but for the generic computer components (“processing circuitry configured to”, “…from a testing instrument or a computer,…”), the claim encompasses a user manually calculating an effect evaluation value regarding a plurality of medical decisions, calculating a fairness index value regarding medical decisions, a user manually determining a medical decision to a subject target, a user manually obtaining observation data indicating an effect produced on the subject target by the assigned medical decision, and a user manually repeating the acquiring, calculating, determining, and obtaining steps. These steps encompass steps that could be performed manually by users following rules or instructions which constitute certain methods of organizing human activity. These steps could be carried out manually by individuals, such as doctors or clinical staff.
Claim 14: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract idea, and generally linking the abstract idea to a particular technological environment.
Claim 14, directly or indirectly, recites the following generic computer components configured to implement the abstract idea: “processing circuitry configured to”, “…from a testing instrument or a computer,…” (See Specification in Paragraph [0027]: The processing circuitry 11 includes one or more processors such as a central processing unit (CPU), a graphics processing unit (GPU), and so on.) As set forth in the MPEP 2106.04(d) "merely including instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claim recites “…the effect evaluation value being calculated based on a machine- learning model formulated to calculate effect evaluation values for medical decisions”, “…according to a bandit algorithm, wherein the processing circuitry is configured to:…”, and “…apply reinforcement learning to train a parameter of the machine-learning model,…” at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words “apply it” to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has not been integrated into a practical application.
Claim 14: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer
configured to perform above identified functions amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Alice 573 U.S. at 223 ("mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.")
Additionally, generally linking the abstract idea to a particular technological environment does not amount to significantly more than the abstract idea (See MPEP 2106.05(h) and Affinity Labs of Texas v. DirectTV, LLC, 838 F.3d 1253, 120 USP12d 1201 (Fed. Cir. 2016)).
Claim 15 recites the same functions as claim 14, but in method form.
Thus, these elements taken individually or together do not amount to significantly more than the abstract ideas themselves.
Subject Matter Free of the Prior Art
The following is an examiner’s statement of subject matter free of the prior art.
The limitations in independent claims 1, 12, 14-15 states: “determine a medical decision to be assigned to the subject target based on the effect evaluation value and the fairness index value according to a bandit algorithm, wherein the processing circuitry is configured to: determine a single random number according to a predetermined probability distribution; sample an assignment candidate from the plurality of medical decisions according to a predetermined policy based on the determined single random number; in a case where the fairness index value regarding the assignment candidate is a predetermined value, determine that the assignment candidate is the medical decision to be assigned; and in a case where the fairness index value regarding the assignment candidate is not the predetermined value, determine that a medical decision other than the assignment candidate is the medical decision to be assigned;” and “apply reinforcement learning to train a parameter of the machine-learning model, based on the obtained data accumulated by repeating the acquiring, calculating, determining, and obtaining steps, wherein the processing circuitry is further configured to set the target value based on a history of medical decisions regarding the subject target and/or a subject other than the subject target”. The closest prior art (Slotman (U.S. Patent Pre-Grant Publication No. 2017/0276676), Huttin (U.S. Patent Pre-Grant Publication No. 2005/0182659), Chen et al. (U.S. Patent Pre-Grant Publication No. 2020/0357515), Baronov et al. (U.S. Patent Pre-Grant Publication No. 2021/0090742)), describes a system for identifying, monitoring and matching patients with appropriate treatments using a systemic mediator-associated physiologic test profile, combined with a method of selecting a treatment decision, and system that is configured to predict treatment decisions, the system is operative to predict treatment decisions using an index selected from one or more of a cost sensitivity index, a quality index or a risk index, combined with a method and apparatus include receiving, by a device, medical information associated with a user, inquiry information is determined based on the medical information associated with the user and a reinforcement learning model, combined with a physiology observer module in the system utilizes multiple measurements to estimate Probability Density Functions (PDF) of a number of Internal State Variables (ISVs) that describe components of the physiology relevant to the patient treatment and condition. However, the prior art does not describe the particular steps of “determine a medical decision to be assigned to the subject target based on the effect evaluation value and the fairness index value according to a bandit algorithm, wherein the processing circuitry is configured to: determine a single random number according to a predetermined probability distribution; sample an assignment candidate from the plurality of medical decisions according to a predetermined policy based on the determined single random number; in a case where the fairness index value regarding the assignment candidate is a predetermined value, determine that the assignment candidate is the medical decision to be assigned; and in a case where the fairness index value regarding the assignment candidate is not the predetermined value, determine that a medical decision other than the assignment candidate is the medical decision to be assigned”. Therefore claims 1, 4-5, 11-12, 14-15 are free of the prior art.
Response to Arguments
In the Remarks filed on July 18, 2025, the Applicant argues that the newly amended and/or added claims overcome the 35 U.S.C. 101 rejection(s) and 35 U.S.C. 103 rejection(s). The Examiner does acknowledge that the newly added and/or amended claims overcome the 35 U.S.C. 103 rejection(s). The Examiner does not acknowledge that the newly added and/or amended claims overcome the 35 U.S.C. 101 rejection(s).
The Applicant argues that:
(1) Applicant respectfully submits that at least the features of (1) calculating an effect evaluation value based on a machine-learning model, (2) determining a medical decision to be assigned to the subject target based on the effect evaluation value and the fairness index value according to a bandit algorithm, (3) obtaining observation data indicating an effect produced on the subject target by the assigned medical decision from a testing instrument or a computer, and (4) applying reinforcement learning to train a parameter of the machine-learning model, based on the obtained data accumulated by repeating the acquiring, calculating, determining, and obtaining steps are not mental processes and cannot be carried out manually by individuals. For example, as described in paragraph [0045] of the specification, bandit algorithms such as epsilon greedy, Thompson sampling, linear Thompson sampling, posterior sampling for reinforcement learning (PSRL), and Bayesian deep Q-networks (BDQN) are applied to use the effect evaluation value and the fairness index value to determine the medical decision to be assigned. These algorithms go far beyond mental processes, instead, they involve complex computational procedures, including probabilistic modeling, Bayesian updating, stochastic sampling, and/or deep learning-based value function approximation. These computations cannot be practically performed by a human mind, but need to be implemented through algorithms executed on processing circuitry such as specialized computational infrastructure. Furthermore, in the present case, evaluating the claim language "as a whole," including the "combination of elements" recited, demonstrates that the claims are clearly directed to a "practical application" as they provide technical advantages in a particular (medical) environment. According to the present invention, a medical decision is assigned using the fairness index value, so that exploration can be conducted while ensuring fairness, and as a result, the calculation accuracy of the effect evaluation value of the model can be improved. Therefore, the invention of Claim 1 is directed to a practical application that efficiently optimizes medical decisions without performing randomized controlled trials (RCT). Therefore, Applicant respectfully submits that any abstract idea is integrated into a practical application in Claim 1. Even further, it is respectfully submitted that the pending claims also amount to "significantly more" than an abstract idea, at least due to the technical improvement and because the claimed features are not well-understood, routine and conventional in the field. Accordingly, in view of the present amendment and the above discussion, Applicant kindly requests the §101 rejection be withdrawn; and
(2) Applicant respectfully submits that the present invention is configured to assign a medical decision using the effect evaluation value corresponding to the subjective best arm probability and/or the subjective best arm probability difference, the fairness index value, and the random number, in accordance with a bandit algorithm. The present invention is further configured to update a model regarding calculation of the effect evaluation value based on observation data presenting the effects caused in a subject by a medical decision assigned through the process recited in Claim 1. The effect evaluation value of the present invention corresponds to the subjective best arm probability and/or the subjective best arm probability difference, and the fairness index value of the present invention is determined based on the effect evaluation value and the target value. Furthermore, a medical decision is assigned according to a bandit algorithm. However, none of the cited references discloses or suggest these features. Therefore, any proper combination of the teachings of Slotman, Huttin, Chen, and
Baronov fails to disclose or otherwise suggest the claimed features. Accordingly, Applicant respectfully submits that the cited references do not describe, suggest, or render obvious all of the features of amended Claim 1. Additionally, it is respectfully noted that the cited references do not describe, suggest, or render obvious the features of independent Claims 12, 14, or 15, for reasons similar to those discussed above. Accordingly, Applicant respectfully requests the rejection of independent Claims 1, 12, 14, and 15, and claims depending respectively therefrom, be reconsidered and withdrawn. Therefore, Applicant respectfully submits that the pending claims are allowable.
In response to argument (1), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner does not acknowledge that the limitations, as drafted, given the broadest reasonable interpretation, but for generic computer components, encompass managing interactions between people, including following rules or instructions (e.g., a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193 194-96 (CCPA 1982)), which is a subgrouping of Certain Methods of Organizing Human Activity. The “bandit algorithm” is a type of reinforcement learning algorithm used in decision-making problems with unknown rewards, the Examiner maintains that the bandit algorithm as recited in the claims amount to no more than generally linking the abstract idea to a particular technical environment. The Examiner maintains that the recitation of “…according to a bandit algorithm, wherein the processing circuity is configured to:…”, “the effect evaluation value being calculated based on a machine-learning model formulated to calculate effect evaluation values for medical decisions” and “apply reinforcement learning to train a parameter of the machine-learning model” is similar to adding the words “apply it” to the abstract idea. The Examiner maintains that a person following rules or instructions could follow the steps claimed. The Examiner maintains that that the abstract idea is not integrated into a practical application as the additional elements amount to no more than general purpose computer components programmed to perform the abstract idea, and generally linking the abstract idea to a particular technological environment, and does not recite “significantly more”. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (2), the Examiner finds the Applicant’s argument(s) persuasive and after further search and additional consideration has withdrawn the 35 U.S.C. 103 rejection(s).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Frist, Jr. (U.S. Patent Publication No. 10,872,700), describes a method may include configuring each problem data object, observation data object, or action data object of the evaluation data object with a scoring rubric.
Ludviksson et al. (U.S. Pre-Grant Patent Publication No. 2016/0259899), describes a clinical decision support system for diagnosis and monitoring of a disease of at least one patient includes a computing system, a storage media, in communication with the computer system, configured to store medical data sets from two or more different medical training data sources.
Birriel ("Surrogate Decision-Making in the Context of Critical Illness"), describes the process of surrogate decision-making for critically ill adult patients, including surrogate decision-makers' cognitive and moral decision-making processes and to develop a model explaining the process.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bennett S Erickson whose telephone number is (571)270-3690. The examiner can normally be reached Monday - Friday: 9:00am - 5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached at (571) 272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Bennett Stephen Erickson/Primary Examiner, Art Unit 3683