DETAILED ACTION
Claims 1-20 are currently pending and have been examined.
This action is in response to the amendment filed on 1/15/2026.
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 1-7 & 15-20 are directed to a method (i.e., a process); Claims 8-14 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 8 includes limitations that recite at least one abstract idea. Specifically, independent claim 8 recites:
8. A system comprising:
at least one computer processor; and
a memory containing instructions which, when executed by the at least one computer processor, cause the at least one computer processor to:
receive patient data of a patient;
continuously retraining a trained neural network model regularly with newly collected patient data;
wherein the retraining comprises modifying one or more weights or biases associated with connections between neurons in the trained neural network model;
execute a neural network model trained on numerous examples of text data from recovering addicts to predict, based on the patient data, an addiction relapse risk in the patient;
generate a recommendation based on the predicted addiction relapse risk; and
communicate the recommendation to a relevant party.
The Examiner submits that the foregoing underlined limitations constitute “methods of organizing human activity” because sending healthcare data from various parties, detecting a medical condition, generating and sending a notification 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 to prevent addiction relapse risk 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. Furthermore, the underlined limitations of retraining a trained neural network model by modifying one or more weights or biases associated with connections between neurons in the trained neural network model amounts to mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations). When given their broadest reasonable interpretation in light of the background, retraining a neural network by modifying weights or biases are mathematical calculations. The plain meaning of these terms are
optimization algorithms, which compute neural network parameters using a series of mathematical calculations. As explained in the MPEP, when a claim recites multiple abstract ideas that fall in the same or different groupings, examiners should consider the limitations together as a single abstract idea, rather than as a plurality of separate abstract ideas to be analyzed individually. Accordingly, independent claim 8 and analogous independent claims 1 & 15 recite at least one abstract idea.
Furthermore, dependent claims 2-7, 9-14, & 16-20 narrow the abstract idea described in the independent claims. Claims 2-3, 9-10, & 16-17 recites receiving different patient data from various sources; Claims 4, 11, & 18-19 recites preprocessing data; Claims 6, 13 recites generating a recommendation; & Claim 7, 14 recites generating different outputs based on the determined relapse risk. These limitations only serve to further limit the abstract idea and hence, are directed towards fundamentally the same abstract idea as independent claim 8 and analogous independent claims 1 & 15, 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”):
8. A system comprising:
at least one computer processor; and
a memory containing instructions which, when executed by the at least one computer processor, cause the at least one computer processor to:
receive patient data of a patient;
continuously retraining a trained neural network model regularly with newly collected patient data;
wherein the retraining comprises modifying one or more weights or biases associated with connections between neurons in the trained neural network model;
execute a neural network model trained on numerous examples of text data from recovering addicts to predict, based on the patient data, an addiction relapse risk in the patient;
generate a recommendation based on the predicted addiction relapse risk; and
communicate the recommendation to a relevant party.
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 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 limitations of a trained neural network model that is continuously trained via modifying weights/biases associated with connections between neurons, the Examiner submits that these limitations amount to no more than a recitation of the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer (see MPEP § 2106.05(f)).
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, independent claim 8 and analogous independent claims 1 & 15 do not recite additional elements that integrate the judicial exception into a practical application.
Accordingly, the claims recites 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 4, 11, & 18: These claims recite preprocessing data using various techniques including NLP, NER, and tokenization and amount to no more than a recitation of the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer (see MPEP § 2106.05(f)).
Claims 5, 12, & 20: These claims recite using generative AI to recommend an action and amount to no more than a recitation of the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer (see MPEP § 2106.05(f)).
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 10 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 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 limitations of a trained neural network model, the Examiner submits that these limitations amount to no more than a recitation of the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer (see MPEP § 2106.05(f)).
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, 8-11, 13, & 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Ciganic (US20240087750) in view of Gonzalez (WO2025010329A2).
As per claim 1, Ciganic teaches a method comprising:
receiving patient data of a patient (para. 45: patient data collected);
continuously retraining a trained neural network model regularly with newly collected patient data (para. 67: training system updates a previously trained model with new data);
predicting, by the trained neural network model trained on numerous examples of text data from recovering addicts (para. 61-65, 72: system uses training data including generate input features by selecting a set of predictors from a set of candidate predictors based on subject data from multiple different subjects), based on the patient data, an addiction relapse risk in the patient (para. 31, 53: risk of subject overdose predicted based on collected patient data using trained neural network model);
generating a recommendation based on the predicted addiction relapse risk (para. 31: recommendation output based on risk determined); and
communicating the recommendation to a relevant party (para. 59: output displayed to user).
Ciganic does not expressly teach wherein the retraining comprises modifying one or more weights or biases associated with connections between neurons in the trained neural network model.
Gonzalez, however, teaches to machine learning-based methods and systems for predicting risk based on multi-modal health data where a neural network model is trained by tuning node weights and/or biases between connections (para. 66).
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 Gonzalez with Ciganic based on the motivation of optimize treatment decision-making, aid in earlier detection and intervention, and ultimately improve survival (Gonzalez - para. 3).
As per claim 2, Ciganic and Gonzalez teach the method of claim 1. Ciganic teaches wherein the patient data is received from one or more of:
a wearable device; a mobile device; a mobile application; and a telemedicine system (para. 47: subject data obtained from various sources including EHR database).
As per claim 3, Ciganic and Gonzalez teach the method of claim 1. Ciganic teaches wherein the patient data comprises one or more of:
heart rate; heart rate variability; skin conductance; blood pressure; sleep patterns; activity level; mood; stress level; geographic location; a therapy session transcript; textual patient assessment; qualitative patient assessment; and quantitative patient assessment (para. 45: subject data includes different data such as assessment data).
As per claim 4, Ciganic and Gonzalez teach the method of claim 1. Ciganic teaches comprising preprocessing the patient data, wherein the preprocessing comprises one or more of:
natural language processing (NLP); named entity detection (NER); and tokenization (para. 45: natural language processing used on specific data for use in system).
As per claim 6, Ciganic and Gonzalez teach the method of claim 1. Ciganic teaches wherein the recommendation comprises one or more of:
a request for additional patient data; an intervention; a stress management technique; and a personalized message (para. 75-76: treatment recommendation output to user).
As per claim 8, Ciganic discloses a system comprising:
at least one computer processor (abstract: processor); and
a memory containing instructions which, when executed by the at least one computer processor, cause the at least one computer processor to:
receive patient data of a patient (para. 45: patient data collected);
continuously retraining a trained neural network model regularly with newly collected patient data (para. 67: training system updates a previously trained model with new data);
execute a neural network model trained on numerous examples of text data from recovering addicts (para. 61-65, 72: system uses training data including generate input features by selecting a set of predictors from a set of candidate predictors based on subject data from multiple different subjects) to predict, based on the patient data, an addiction relapse risk in the patient (para. 31, 53: risk of subject overdose predicted based on collected patient data using trained neural network model);
generate a recommendation based on the predicted addiction relapse risk (para. 31: recommendation output based on risk determined); and
communicate the recommendation to a relevant party (para. 59: output displayed to user).
Ciganic does not expressly teach wherein the retraining comprises modifying one or more weights or biases associated with connections between neurons in the trained neural network model.
Gonzalez, however, teaches to machine learning-based methods and systems for predicting risk based on multi-modal health data where a neural network model is trained by tuning node weights and/or biases between connections (para. 66).
The motivations to combine the above mentioned references are discussed in the rejection of claim 1, and incorporated herein.
Claims 9-11 & 13 recite substantially similar limitations as those already addressed in claims 2-4 & 6, and, as such, are rejected for similar reasons as given above.
As per claim 15, Ciganic teaches a method for predicting relapse in a patient, the method comprising:
continuously retraining a neural network model regularly with newly collected patient data (para. 67: training system updates a previously trained model with new data) relating to a plurality of patients (para. 31, 53: risk of subject overdose predicted based on collected patient data using trained neural network model);
wherein the trained neural network model is trained on numerous examples of text data from recovering addicts to predict relapse risk of patients (para. 61-65, 72: system uses training data including generate input features by selecting a set of predictors from a set of candidate predictors based on subject data from multiple different subjects);
predicting, by the retrained neural network model, based on a set of runtime patient data, a relapse risk of the patient (para. 31, 53: risk of subject overdose predicted based on collected patient data using trained neural network model);
recommending an action based on the predicted relapse risk (para. 31: recommendation output based on risk determined); and
notifying the patient or a predefined contact of the predicted relapse risk and recommended action (para. 59: output displayed to user).
Ciganic does not expressly teach wherein the retraining comprises modifying one or more weights or biases associated with connections between neurons in the trained neural network model.
Gonzalez, however, teaches to machine learning-based methods and systems for predicting risk based on multi-modal health data where a neural network model is trained by tuning node weights and/or biases between connections (para. 66).
The motivations to combine the above mentioned references are discussed in the rejection of claim 1, and incorporated herein.
Claims 16-18 recite substantially similar limitations as those already addressed in claims 2-4, and, as such, are rejected for similar reasons as given above.
As per claim 19, Ciganic and Gonzalez teach the method of claim 18. Ciganic teaches wherein the preprocessing is used to extract features relevant to relapse risk contexts (para. 50, 63: system generates input features using the subject data) and correspondingly label the patient data relating to the plurality of patients for training the neural network model (para. 50, 63: system also generates labels for the sets of input features to be used as training data).
Claims 5, 12, & 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ciganic (US20240087750) and Gonzalez (WO2025010329A2) as applied to claims 1 & 8 in view of Troy (WO2024216072A1).
As per claim 5, Ciganic and Gonzalez the method of claim 1, wherein generating a recommendation based on the predicted addiction relapse risk (Ciganic - para. 75-76: treatment recommendation output to user).
Ciganic and Gonzalez do not expressly teach generating the recommendation using generative artificial intelligence.
Troy, however, teaches to using a coaching system for users rehabilitating from addiction or mental health where the coaching system can use generative AI (para. 22, 39).
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 Troy with Ciganic and Gonzalez based on the motivation of promote healthier lifestyles, utilizing the power of Al to help individuals improve their health outcomes (Troy - para. 13).
Claims 12 & 20 recite substantially similar limitations as those already addressed in claim 5, and, as such, are rejected for similar reasons as given above.
Claims 7 & 14 are rejected under 35 U.S.C. 103 as being unpatentable over Ciganic (US20240087750) and Gonzalez (WO2025010329A2) as applied to claims 1 and 8 above, and in further view of Hamalainen (US20180140241) and Troy (WO2024216072A1).
As per claim 7, Ciganic and Gonzalez teach the method of claim 1, wherein the addiction relapse risk is categorized as none, low, or high (Ciganic - para. 74-76: risk levels determined including low, medium or high), wherein if the addiction relapse risk is categorized as high, the method comprises communicating the recommendation to the patient and a therapist of the patient (Ciganic - para. 59: output displayed via GUI to user and clinician).
Ciganic and Gonzalez do not expressly teach wherein if the addiction relapse risk is categorized as low, the method comprises communicating the recommendation to the patient, wherein the recommendation comprises a request for additional patient data.
Hamalainen, however, teaches to determining a risk of relapse of addictive behavior of a user and where the system outputs a request for additional information via a questionnaire if the risk is below a threshold (para. 55, 63-71)
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 Hamalainen with Ciganic and Gonzalez based on the motivation of aid in determining if an individual is at risk for a relapse of an addictive behavior (Hamalainen - para. 33).
Ciganic, Gonzalez, and Hamalainen do not expressly teach wherein the recommendation comprises a suggested intervention generated by a generative artificial intelligence, and wherein the suggested intervention optionally comprises input from the therapist.
Troy, however, teaches to using a coaching system for users rehabilitating from addiction or mental health where the coaching system can use generative AI (para. 22, 39).
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 Troy with Ciganic, Gonzalez, and Hamalainen based on the motivation of promote healthier lifestyles, utilizing the power of Al to help individuals improve their health outcomes (Troy - para. 13).
Claim 14 recites substantially similar limitations as those already addressed in claim 7, and, as such, is rejected for similar reasons as given above.
Response to Arguments
Applicant’s arguments with respect to the 35 U.S.C. § 101 rejection on pages 7-10 in regards to claims 1-3, 6-9, 11, and 13-16 have been considered but are not persuasive. Applicant argues that:
The claims are not directed to organizing human activity but is directed to a continual learning technique that updates a trained model over time.
See updated 101 rejection above.
Similar to McRo, the claims provide a technical improvement by improving a neural network model that updates over time.
The Examiner asserts that the claimed invention is unlike McRo and the holding of McRo actually supports the Examiner's position. McRo held that an improvement may be one that “that improve[s] computer-related technology by allowing computer performance of a function not previously performable by a computer.” There is nothing in the Applicant’s invention that allows a computer to perform functions not previously performable by a computer. The distinction is “not previously performable by a computer.” Whether or not the claimed invention was previously known (i.e., is novel/non-obvious) is not the test. The test is where a computer could not physically perform the identified functions prior to the invention and whether the claimed invention solved this problem. This is not the case in the present invention. The Examiner asserts that the training of the machine learning model and use of the machine learning model is claimed at a high level of generality and merely use computers as a tool to perform the recited abstract idea.
Applicant’s arguments on pages 11-12 regarding claims 1-20 being rejected under 35 USC § 102 & 103(a) have been fully considered but they are moot in view of the new grounds of rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Williams (US20180176727) teaches determine, through one or more communications networks, the location of an addict and the context of the addict at the location; evaluate a risk of relapse by the addict in relation to the location and/or the context; facilitate one or more actions and/or activities to mitigate the risk.
Balian (US20200411191) teaches to predicting an individual's likelihood of addiction or relapse to pharmaceuticals that include controlled or addictive substances. The prediction can be used to generate an alert that, in some aspects, prevents care provider actions in an electronic health record system that are related to the addictive pharmaceutical until the prediction is acknowledged.
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 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