DETAILED ACTION
Notice to Applicant
This communication is in response to amendment and remarks dated 6/8/2026. Claims 2, 13, and 17 have been canceled. Claims 21 and 22 have been added. Claims 1, 3-9, 11, 14-16, 18-20 have been amended. Claims 1, 3-12, 14-16 and 18-22 are now 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.
Claims 1, 3-12, 14-16 and 18-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 3-12, 14-15 and 21 are drawn to a method for using machine learning to guide health care service referral and treatment, which is within the four statutory categories (i.e. process). Claims 16, 18-20 and 22 are drawn to a computer program product for using machine learning to guide health care service referral and treatment, which is within the four statutory categories (i.e. article of manufacture).
Representative independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
A method, comprising:
accessing a first patient referral of a first patient to a first healthcare service;
determining, based on the first patient referral, a first referral condition of the first patient;
generating a first prediction indicating for each respective referral outcome of a plurality of referral outcomes a respective probability that the first patient will have the respective referral outcome if the first patient referral is accepted by the first healthcare service based on processing the first referral condition and an identifier of the first healthcare service using a first machine learning model;
facilitating acceptance of the first patient referral to the first healthcare service based on the first prediction.
These recited underlined limitations fall within the "Certain Methods of Organizing Human Activities" grouping of abstract ideas as it relates to managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II). The limitations are directed to evaluating, accepting, declining, routing, and otherwise managing patient referrals. The claimed prediction is used to facilitate the administrative and operational determination of whether a particular healthcare service should accept a particular patient. These limitations, as drafted and detailed above, are steps that, under its broadest reasonable interpretation, recites steps for organizing human interactions. The claim recites accessing a patient referral, determining a referral condition, generating, using a machine learning model, probabilities that the patient will experience respective referral outcomes if the referral is accepted by a particular healthcare service, and facilitating acceptance of the referral based on the prediction.
That is other than reciting “machine learning” language, nothing in the claim element precludes the steps from practically being performed between people or by a person. If a claim limitation, under its broadest reasonable interpretation, covers interactions between people or managing personal behavior or relationships then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
In the present case, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A method, comprising:
accessing a first patient referral of a first patient to a first healthcare service;
determining, based on the first patient referral, a first referral condition of the first patient;
generating a first prediction indicating for each respective referral outcome of a plurality of referral outcomes a respective probability that the first patient will have the respective referral outcome if the first patient referral is accepted by the first healthcare service based on processing the first referral condition and an identifier of the first healthcare service using a first machine learning model;
facilitating acceptance of the first patient referral to the first healthcare service based on the first prediction.
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.
The additional elements (i.e. the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f).
the recitation of using machine learning to indicate referral outcomes recites only the idea of a solution or outcome (i.e. claim fails to recite details of how a solution to a problem is accomplished).
in order to transform a judicial exception into a patent-eligible application, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’".
Examiner submits that these limitations amount to merely using software to tailor information and provide it to the user on a generic computer. Claim 1 only recites the training of the model, however recites the training in a generic manner. Applicant does not provide adequate evidence or technical reasoning on how the process improves the efficiency of the computer and is beyond conventional use of components, as opposed to the efficiency of the process, or of any other technological aspect of the computer.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Independent claim 1 does not include additional elements that are sufficient to amount to “significantly more” than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and generally linking the abstract idea to a particular technological environment or field of use and the same analysis applies with regards to whether they amount to “significantly more.” Therefore, the additional elements do not add significantly more to the at least one abstract idea.
As per claims 9 and 16, the claims teach limitations similar to claim 1 and the same abstract idea (“certain methods of organizing human activity”) for the same reasons as stated above. Claim 16 further teaches computer readable media comprising computer-executable instructions, when executed by one or more processors perform the functionality taught by claim 1. These limitations of a processor and computer readable media as generally recited, amount to mere instructions to apply an exception, see MPEP 2106.05(f) and generally link the abstract idea to a particular technological environment or field of use, see MPEP 2106.05(h). Independent claims 9 and 16 are directed to an abstract idea.
Furthermore, for similar reasons as representative independent claim 1, analogous independent claims 9 and 16 do not recite additional elements that integrate the judicial exception into a practical application nor add significantly more.
The following dependent claims further the define the abstract idea or are also directed to an abstract idea itself:
Dependent claims 4, 12, and 20 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract).
In relation to claims 6-8, 19 these claims specify processing a referral condition to indicate a probability; which is a mental process as it is an evaluation that can, at the currently claimed high level of generality, be practically performed in the human mind.
In relation to claims 3, 5, 10, 11, 14-15, and 18 these claims specify accessing demographics/generating a prediction; facilitating declination of a referral; updating parameters; which are certain methods of organizing human activity, under its broadest reasonable interpretation, covers interactions between people or managing personal behavior or relationships
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Dependent claims 21-22 recite updating one or more parameters of the first machine learning model based on a difference. This amounts to mere instructions to apply an exception and does not amount to a practical application.
The dependent claims further 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, 3-12, 14-16 and 18-22 are ineligible under 35 USC §101.
Subject Matter free from Prior Art
The closest prior art of record, including Cha (US 2020/0005900) and Roots (US 2019/0043606), teaches machine-learning prediction of patient risks/outcomes and machine-learning-based matching of patients with healthcare providers. However, the cited art does not teach or suggest generating, for a plurality of referral outcomes, respective probabilities that a patient will experience the outcomes if accepted by a particular healthcare service based on processing both the referral condition and an identifier of that healthcare service, as recited in independent claims 1 and 16.
The cited art further does not teach or suggest the training and deployment arrangement of independent claim 9, in which the machine-learning model is trained using referral conditions, a healthcare-service identifier, and post-acceptance/post-transition outcome data, and subsequently processes new referral conditions together with the healthcare-service identifier to generate predicted referral outcomes.
No final decision on patentability has been made in light of pending rejections.
Response to Arguments
Applicant's arguments filed 6/826 have been fully considered but they are not persuasive.
Applicant begins arguments on pg. 10 of the 6/8/26 Remarks traversing the rejection of the claims under 35 USC 101. Applicant argues on pgs. 11-12 the claims are not directed to a mental process. Examiner did not characterize the claims as being a mental process in the 3/6/26 Non-Final Office Action nor does Examiner currently characterize the amended claims as being directed to a mental process. Therefore, these arguments are moot and unpersuasive.
Next starting on pg. 12, Applicant argues that even if a judicial exception was recited the exception is integrated into a practical application because the claims reflect an improvement to the functioning of a computer or to another technology or technical field. To support this position, Applicant states on pg. 14 of the Remarks that the claims provide for improved referral evaluation and outcome prediction using machine learning. Applicant further states that generating service-specific probabilities using a machine-learning model as claimed helps solves technical problems. Although this may improve the quality or accuracy of the referral decision, the claims do not recite a particular improvement to the functioning of the computer or machine-learning model itself. The service identifier operates as additional information supplied to the model, and the claim does not recite a specific model architecture, training technique, calibration mechanism, or other technological improvement responsible for improved computer or machine-learning functionality.
Further Applicant argues that the dependent claims incorporate technical improvements from the specification. Specifically, Applicant argues that claims 10, 21, and 22 integrate the alleged abstract idea into a practical application because the claimed feedback operation updates parameters of the machine-learning model and thereby improves prediction accuracy. The argument is not persuasive. Unlike the claims in Ex parte Desjardins, which recited a particular training mechanism that addressed the technical problem of catastrophic forgetting and produced identified technological benefits including reduced storage and system complexity, the present claims broadly recite determining a difference between a predicted and actual outcome and updating model parameters based on that difference. Such limitations merely describe conventional feedback-based model training at a high level and do not recite a particular technological mechanism that improves how the machine-learning model functions. The specification’s statement that such updating may maintain or improve prediction accuracy does not, without more, establish a technological improvement reflected in the claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Redlus (CA3095006A1) the closest foreign prior art of record teaches a method that provides patients with clinician referrals based on health assessment from users. The method applies a trained model to the feature vector to generate a list of candidate treating clinicians who have optimally treated patients whose clinician selection characteristics and determined health characteristics correlate with the health assessment from the user.
Abdel-Hafez (Abdel-Hafez A, Jones M, Ebrahimabadi M, Ryan C, Graham S, Slee N and Whitfield B. "Artificial intelligence in medical referrals triage based on Clinical Prioritization Criteria." Front. Digit. Health 5:1192975. doi: 10.3389/fdgth.2023.1192975. (2023)) the closest non-patent literature of record teaches artificial intelligence in medical referrals triage based on Clinical Prioritization Criteria.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 LINH GIANG MICHELLE LE whose telephone number is (571)272-8207. The examiner can normally be reached Mon- Fri 8:30am - 5:30pm PST.
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LINH GIANG "MICHELLE" LE
PRIMARY EXAMINER
Art Unit 3686
/LINH GIANG LE/Primary Examiner, Art Unit 3686 8/26/26
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