DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Nonfinal Office Action is in response to the Application filed 10/04/2024. Claims 1-21 are currently pending and considered herein.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 recites as follows, wherein the abstract elements are not emboldened:
A computer-implemented method for mental health (MH) risk prediction comprising: receiving mental health risk input signals for a subject, the mental health risk input signals including: glucometer data for the subject including at least one blood glucose value, and demographic data for the subject; inputting the mental health risk input signals into a machine learning (ML) system previously trained with mental health risk input signals for a plurality of subjects and mental health status data for the plurality of subjects; and obtaining a prediction of mental health risk for the subject from the ML system.
Independent claims 11 and 21 recite substantially similar limitations. The above limitations of receiving mental health risk input signals comprising demographic data for a subject and glucometer data including blood glucose values, inputting the mental health risk signals with previously trained mental health risk input signals for a plurality of subjects and mental health status data for the plurality of subjects, and obtaining a prediction of mental health risk for the subject, as drafted, is a process that, under its broadest reasonable interpretation, is an abstract idea that covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than broadly reciting a “computer-implemented method” (claims 1-10) “system” (claims 11-20) and “computer-readable medium” (claim 21) and generic recitation of machine learning and “signals,” nothing in the claim elements precludes the steps from practically being performed in the mind. For example, but for the generic computer components and language and glucometer, a computer-implemented method or system, in the context of this claim, encompasses one skilled in the pertinent art to manually determine mental health risk based on a blood glucose reading and demographics when observing a patient. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
Additionally, the claims recite the abstract idea of a form of organizing human activity including following rules or instructions, and broadly amounts to interactions between a physician observing and diagnosing her patients. The claims appear to monopolize the diagnostic techniques of the physician making clinical assessments using blood glucose data and other patient data. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of using glucometer data, sending signals to/from a generic computer device and training a machine learning system as a function of mental health risk input signals and history. However, the use of a device output measurement and the machine learning system in these steps are recited at a high-level of generality (i.e., as a generic processor/server/storage/display performing a generic computer function of receiving inputs, analyzing the inputs, and displaying selected information) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements when considered separately and as an ordered combination do not integrate the judicial exception/abstract idea into a “practical application” of the judicial exception because they do not impose any meaningful limit on practicing the judicial exception. The claim is thus directed to an abstract idea.
The claims 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 including glucometer data for a subject and machine learning and sending and receiving signals amounts to no more than mere instructions to apply the exception using a computer component, namely data communication and training models. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The dependent claims do not remedy the deficiencies of the independent claims with respect to patent eligible subject matter. The dependent claims further limit the abstract idea, but do not overcome it. Claims 2 and 12 include “neural networks” and defines the machine learning system and comprises, like the machine learning it limits, mere instructions to apply the exception using a computer component. Claims 3 and 13 define glucometer data and limit the abstract idea. Claims 4 and 14 define demographic data. Claims 5 and 15 narrow the mental health status data including medications, assessments, insurance claims and interventions and merely limits the abstract the idea further. Claims 6-7 and 16-17 include additional training steps for a machine learning model, but does not amount to a technological improvement or practical application, and further limits the abstract idea. Claims 8-9 and 18-19 narrows the mental health input signals to include coaching data and further limits the abstract the idea. Claims 10 and 20 narrows the mental health risk signals and further limits the abstract idea. Thus, the additional limitations from dependent claims merely detail a type of data input or calculated from one source or another, and limits the abstract idea.
Therefore, claims 1-21 are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-2, 4, 6, 10-12, 14, 16 and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2023/0094344 A1 to Tara, hereinafter “Tara,” in view of U.S. 2024/0079145 A1 to Conward, hereinafter “Conward.”
Regarding claim 1, Tara discloses A computer-implemented method for mental health (MH) risk prediction comprising: receiving mental health risk input signals for a subject (See Tara at least at Abstract (collecting mental evidence nodes and other mental health data); Paras. [0046] (“[C]ommunication of data and/or signals between any of the components.”) [0333]-[0338], [0368] (signals), [0391]; Figs. 1, 2, 5, 8, 9), the mental health risk input signals including: glucometer data for the subject including at least one blood glucose value (See id. at least at Paras. [0010]-[0011] (blood glucose sensor data), [0040], [0048] (blood glucose sensor); Claim 1; Figs. 1, 2, 8), and demographic data for the subject (See id. at least at Paras. [0032] (demographic factors of the patient), [0035], [0042], [0107] (demographic and socioeconomic and genotypical data)).
Tara may not specifically describe but Conward teaches inputting the mental health risk input signals into a machine learning (ML) system previously trained with mental health risk input signals for a plurality of subjects and mental health status data for the plurality of subjects (See Conward at least at Abstract (machine learning model using patient-generated data and biometric data); Paras. [0007]-[0012], [0019]-[0022] (ML and health risks including mental health risks), [0068], [0105]-[0107], [0115], [0145]; Claim 12; Figs. 2-7); and obtaining a prediction of mental health risk for the subject from the ML system (See id. at least at Paras. [0012], [0019]-[0022] (ML and predictions), [0061]-[0062], [0073]-[0080] (predictions and diagnoses), [0105]-[0106]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tara to incorporate the teachings of Conward and provide machine learning for various health inputs. Conward is directed to systems for deriving health indicators from various content, generating priorities using machine learning. (See Conward at Abstract). Incorporating the machine learning techniques as in Conward with the methods including multiple sensors for data related to a physical or mental health of a patient as in Tara would thereby increase the applicability, utility, and efficacy of the claimed method and system for mental health risk detection using glucometer data.
Regarding claim 2, Tara as modified by Conward teaches all the limitations of claim 1 and Conward further teaches wherein the ML system comprises a neural network (See Conward at least at Paras. [0019] (“[A] machine learning platform may process health data using one or more approaches including, but not limited to, neural networks, decision tree learning, deep learning, etc.”), [0039]-[0042]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tara to incorporate the teachings of Conward and provide machine learning comprising neural networks. Conward is directed to systems for deriving health indicators from various content, generating priorities using machine learning. (See Conward at Paras. [0039]-[0042]). Incorporating the machine learning and neural network as in Conward with the methods including multiple sensors for data related to a physical or mental health of a patient as in Tara would thereby increase the applicability, utility, and efficacy of the claimed method and system for mental health risk detection using glucometer data.
Regarding claim 4, Tara as modified by Conward teaches all the limitations of claim 1 and Tara further discloses wherein the demographic data includes one or more of: age; body mass index (BMI); gender; race; diabetes status; and smoking status. (See id. at least at Abstract (smoking status); Paras. [0010]-[0011], [0032], [0051], [0053], [0309]).
Regarding claim 6, Tara as modified by Conward teaches all the limitations of claim 1 and Tara further discloses for each subject of a first set of subjects, collecting mental health risk input signals including: glucometer data including a glucose value, demographic data, and mental health status data value (See id. at least at Paras. [0010]-[0011], [0032], [0040], [0048], [0309]-[0311]; Claim 1; Figs. 1, 2). While Conward teaches creating a training set comprising the glucometer data, demographic data, and mental health status data for each subject of the first set of subjects; and training the ML system in a training stage using the training set to create an ML model (See Conward at least at Paras. [0039]-[0044], [0064]-[0068]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tara to incorporate the teachings of Conward and provide particular training for machine learning models including different health inputs. Conward is directed to systems for deriving health indicators from various content, generating priorities using machine learning. (See Conward at Abstract). Incorporating the machine learning techniques as in Conward with the methods including multiple sensors for data related to a physical or mental health of a patient as in Tara would thereby increase the applicability, utility, and efficacy of the claimed method and system for mental health risk detection using glucometer data.
Regarding claim 10, Tara as modified by Conward teaches all the limitations of claim 1 and Tara further discloses wherein the mental health risk input signals further include event data relating to one or more of frequency, duration, interactivity, and consistency of interaction sessions associated with use by the subject of a mobile application or web portal (See Tara at least at Paras. [0049], [0125], [0334]-[0336]).
Regarding claim 11, claim 11 recites substantially the same limitations as included in independent claim 1 except for an input device and an output device. Thus, claim 11 is rejected under the same grounds of rejection and for the same reasoning as applied to claim 1, above and wherein Tara further teaches an input device for receiving mental health risk input signals for a subject (See Tara at least at Abstract; Paras. [0036]-[0037]; [0271], Fig. 1 (102)) and an output device for providing a prediction of mental health risk for the subject output by the ML system (See id. at Para. [0372]-[0373]; Fig. 1 (124), (128); Fig. 14).
Regarding claims 12, 14, 16 and 20, claims 12, 14 and 16 and 20 recite substantially the same limitations as included in claims 2, 4, 6 and 10, respectively. Thus, claims 12, 14, 16 and 20 are rejected under the same grounds of rejection and for the same reasoning as applied to claims 2, 4, 6 and 10, above.
Regarding claim 21, claim 21 recites substantially the same limitations as included in independent claim 1 except for a computer readable medium and processor. Thus, claim 21 is rejected under the same grounds of rejection and for the same reasoning as applied to claim 1, above and further Tara teaches A computer-readable medium including program instructions that, when executed by a processor, cause the processor to perform a method for mental health (mental health) risk prediction (See Tara at least at Abstract; Para. [0060], [0062]).
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Tara, in view of Conward and further in view of U.S. 2020/0375549 A1 to Wexler et al., hereinafter “Wexler.”
Regarding claim 3, Tara as modified by Conward teaches all the limitations of claim 1. Tara and Conward may not specifically describe but Wexler teaches wherein the glucometer data includes one or more of: a total number of blood glucose checks performed within a particular time interval; a proportion of days with blood glucose checks; a minimum blood glucose value; a mean blood glucose; a maximum blood glucose value; a standard deviation of blood glucose values; a proportion of blood glucose values below 70 mg/dl; proportion of blood glucose values above 180 mg/dl; a proportion of blood glucose checks with wellness indicated; a proportion of blood glucose checks with unwell state indicated; a proportion of blood glucose checks without a reported feeling tag; and a proportion of blood glucose checks with exercise indicated. (See Wexler at least at Para. [0020]-[0027] (predicting and assessing blood glucose levels at fixed intervals), [0039], [0042], [0054]-[0057], [0063]; Figs. 1-3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tara and Conward to incorporate the teachings of Wexler and provide blood glucose measurements and inputs. Wexler is directed to systems for biomonitoring and blood glucose forecasting. (See Wexler at Abstract). Incorporating the biomonitoring and blood glucose data as in Wexler with the machine learning techniques of Conward and the multiple sensors for data related to a physical or mental health of a patient as in Tara would thereby increase the applicability, utility, and efficacy of the claimed method and system for mental health risk detection using glucometer data.
Regarding claim 13, claim 13 recites substantially the same limitations as included in claims 3. Thus, claims 13 is rejected under the same grounds of rejection and for the same reasoning as applied to claim 3, above.
Claims 8-9 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Tara, in view of Conward and further in view of U.S. 2011/0223574 A1 to Crawford et al., hereinafter “Crawford.”
Regarding claim 8, Tara as modified by Conward teaches all the limitations of claim 1. Tara and Conward may not specifically describe but Crawford teaches wherein the mental health risk input signals further include coaching data relating to contacts between the subject and a coach (See Crawford at least at Abstract (“The virtual coach works alongside assigned human coaches to assist participants.”); Paras. [0047], [0055], [0072]-[0073]; Claim 3; Figs. 1, 7).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tara and Conward to incorporate the teachings of Crawford and provide coaching and interaction records and tasks. Crawford is directed to a directed collaboration platform for online virtual coaching. (See Crawford at Abstract). Incorporating the coaching, contacts and assessment techniques as in Crawford with the machine learning techniques of Conward and the multiple sensors for data related to a physical or mental health of a patient as in Tara would thereby increase the applicability, utility, and efficacy of the claimed method and system for mental health risk detection using glucometer data.
Regarding claim 9, Tara as modified by Conward and Crawford teaches all the limitations of claim 8, and Crawford further teaches wherein the coaching data includes one or more of: a number of coaching alerts triggered; a number of successful coach-subject contacts (See Crawford at Abstract; Paras. [0072]-[0073]; Claim 3 (virtual coach and alerts and sending messages and encouragement), Claim 6; Figs. 1-7); a number of successful coach-subject contacts by phone; a number of attempted unsuccessful coach-subject contacts by phone; a number of successful coach-subject contacts by text (See id.); a number of attempted coach-subject unsuccessful contacts by text; a number of successful coach-subject contacts by email; a number of attempted unsuccessful coach-subject contacts by email; a number of successful coach-subject contacts by glucometer; a number of attempted unsuccessful contacts by glucometer; a number of coaching sessions where subjects took scheduled a future coaching session; and average minutes spent on a coaching alert interaction (See id.).
Regarding claims 18-19, claims 18-19 recite substantially the same limitations as included in claims 8-9, respectively. Thus, claims 18-19 are rejected under the same grounds of rejection and for the same reasoning as applied to claims 8-9, above.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Tara, in view of Conward and further in view of U.S. 2016/0019813 A1 to Mullen, hereinafter “Mullen.”
Regarding claim 5, Tara as modified by Conward teaches all the limitations of claim 1. Tara and Conward may not specifically describe but Mullen teaches wherein the mental health status data includes one or more of: mental health medications prescribed for one or more of the plurality of subjects; mental health assessments made for one or more of the plurality of subjects; mental health insurance claims reported for one or more of the plurality of subjects; and mental health interventions provided for one or more of the plurality of subjects. (See Mullen at least at Paras. [0004], [0027]-[0029], [0038],[0052]-[0060] (mental health history inputs, interventions, medications, goals, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tara and Conward to incorporate the teachings of Mullen and provide inputs including medications, assessments and lifestyle choices. Mullen is directed systems for monitoring and treating individuals with sensory processing issues. (See Mullen at Abstract). Incorporating the mental health inputs and interventions as in Mullen with the machine learning techniques of Conward and the multiple sensors for data related to a physical or mental health of a patient as in Tara would thereby increase the applicability, utility, and efficacy of the claimed method and system for mental health risk detection using glucometer data.
Regarding claim 15, claim 15 recites substantially the same limitations as included in claims 5. Thus, claims 15 is rejected under the same grounds of rejection and for the same reasoning as applied to claim 5, above.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Tara, in view of Conward and further in view of U.S. 2021/0287131 A1 to Bhide et al., hereinafter “Bhide.”
Regarding claim 7, Tara as modified by Conward teaches all the limitations of claim 6. Tara further discloses for each subject of a second set of subjects, collecting mental health risk input signals including: glucometer data including a glucose value, demographic data, and mental health status data (See Tara. at least at Paras. [0010]-[0011], [0032], [0040], [0048], [0309]-[0311]; Claim 1; Figs. 1, 2). While Conward teaches creating a validation set comprising the glucometer data, demographic data, and mental health status data for each subject of the second set of subjects; validating the ML model in a validation stage using the validation set; (See Conward at least at Paras. [0019], [0028], [0040]-[0041], [0046], [0052], [0055]; Figs. 1, 2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tara to incorporate the teachings of Conward and provide machine learning validation for various health inputs. Conward is directed to systems for deriving health indicators from various content, generating priorities using machine learning. (See Conward at Abstract). Incorporating the machine learning techniques as in Conward with the methods including multiple sensors for data related to a physical or mental health of a patient as in Tara would thereby increase the applicability, utility, and efficacy of the claimed method and system for mental health risk detection using glucometer data.
Tara as modified by Conward may not specifically describe but Bhide teaches updating the ML model in response to one or more validation errors (See Bhide at least at Paras. [0034]-[0040], [0048]-[0053]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tara and Conward to incorporate the teachings of Bhide and provide an updated machine learning model in response to errors. Bhide is directed to machine learning model accuracy fairness. (See Bhide at Abstract). Incorporating the updated machine learning model validation inputs as in Bhide with the machine learning techniques of Conward and the multiple sensors for data related to a physical or mental health of a patient as in Tara would thereby increase the applicability, utility, and efficacy of the claimed method and system for mental health risk detection using glucometer data.
Regarding claim 17, claim 17 recites substantially the same limitations as included in claims 7. Thus, claims 17 is rejected under the same grounds of rejection and for the same reasoning as applied to claim 7, above.
Conclusion
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/WILLIAM T. MONTICELLO/ Examiner, Art Unit 3681
/MICHAEL I EZEWOKO/Primary Examiner, Art Unit 3681