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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/4/2026 has been entered.
Response to Arguments
Applicant’s arguments filed 5/4/2026 with respect to the rejection of Independent Claims 1, 12 and 20 under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2019/0180879 A1 to Jain et al. (“Jain”) in view of U.S. Patent Publication No. 2021/0098093 A1 to Shadid et al. (“Shadid”), Non-Patent Literature Anja Thieme, Danielle Belgrave, and Gavin Doherty. 2020. Machine Learning in Mental Health: A Systematic Review of the HCI Literature to Support the Development of Effective and Implementable ML Systems. ACM Trans. Comput.-Hum. Interact. 27, 5, Article 34 (October 2020), 53 pages (“Thieme”) and Non-Patent Literature Alexander Yanovski, Reuben E. Kron, Raymond R. Townsend, Virginia Ford, The Clinical Utility of Ambulatory Blood Pressure and Heart Rate Monitoring in Psychiatric Inpatients, American Journal of Hypertension, Volume 11, Issue 3, March 1998, Pages 309–315 (”Yanowski”) have been fully considered and are persuasive. The rejection has been withdrawn.
Claim 1 and the other independent claims have been amended to require that the second predetermined range “is defined based on expected physiological or behavioral changes in the user associated with an adequate response to the medication administered following the first notification” and that the follow-up is automatically scheduled “in response to the second risk score not being within the second predetermined range, thereby indicating that the medication administered following the first notification has not produced an adequate physiological or behavioral response in the user... automatically scheduling a follow up based on the physiological or behavioral changes in the user detected by the sensor of the wearable device that caused the second risk score to fall outside the second predetermined range.” The Examiner agrees that the combination of Jain, Shadid, Thieme and Yanowski does not reasonably teach, disclose or suggest either a second predetermined range so-defined or scheduling a follow-up visit on the basis recited.
Applicant’s arguments regarding the rejection of Dependent Claims 2-5, 7-11, 13-19 and 22 under 35 USC 103 are based on Applicant’s arguments regarding the independent Claims from which they respectively depend. Applicant’s arguments have been fully considered and are persuasive for the same reasons as explained above.
Applicant’s arguments regarding the rejection of Independent Claims 1, 12 and 20 under 35 USC 101 have been fully considered but are not persuasive. Applicant highlights that the independent claims have been amended to recite “retraining the predictive machine learning model based on additional training data sets associated with information corresponding to the wearable device users, wherein the additional training data sets include data indicative of whether the medication produced an adequate therapeutic response, and wherein retraining updates one or more parameters of the predictive machine learning model based on the data indicative of whether the medication produced an adequate therapeutic response.” With reference to the Examiner’s reasoning set forth at Para. 5 of the Final Office Action dated 2/10/2026 (the substance of which is incorporated herein by reference), Applicant argues that the above amendments constitute “the kind of structural specificity that the Desjardins panel found sufficient to establish an improvement to how the model itself operates, as opposed to the generic application of ML condemned in Recentive,” and as such render the independent Claims patent eligible. The Examiner respectfully disagrees. The highlighted amendments elaborate on what type of data the retraining is based on, but does not otherwise the “retraining.” The amendments thus pertain to the type of input used in the machine algorithm rather than to the algorithm itself. The Examiner re-iterates by reference the reasoning set forth in Para. 5 of the Final Office Action dated 2/10/2026. For those same reasons, Applicant’s arguments are not persuasive.
Applicant’s arguments regarding dependent Claims 2-5, 7-11, 13-20 and 21 are based on Applicant’s arguments regarding the independent Claims from which each respectively depends. Applicant’s arguments have been fully considered but are not persuasive for the same reasons explained above.
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 1-5 and 7-20 and 22 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Regarding Independent Claim 1, Claim 1 is ineligible.
Eligibility Step 1: Claim 1 is directed to “a method” (i.e., a process) and thus falls within one of the four statutory categories.
Eligibility Step 2A, Prong One: Claim 1 recites an abstract idea.
“training a predictive machine learning model to predict risk values for mental or neurological disorders based on training data sets…” recites an abstract idea (specifically, a mathematical calculation) when afforded its broadest reasonable interpretation. Under the broadest reasonable interpretation, the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111. The Present Specification states at Para. [0037] that “…the predictive model may comprise a machine learning model or regression model (e.g. a multiple logistic regression model)….” While the term “a predictive machine learning model” as recited in Claim 1 is broader than (and therefore not limited solely to) such a “multiple logistic regression model” as described at Para. [0037], one of ordinary skill in the art would understand from the Specification the claimed “predictive machine learning model” to mean such a model that calculates the probability of something happening based on multiple sets of variables. Accordingly, a mathematical calculation is recited. See Example 47 of July 2024 Subject Matter Eligibility Examples from the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence.
The limitation “retraining the predictive machine learning model” recites a mathematical calculation for the same reasons. The limitation “wherein the additional training data sets include…” elaborates on what type of data the retraining is based on, but does not otherwise limit the “retraining.”
“determine a first risk score corresponding to the type of mental disorder or neurological disorder” recites an abstract idea (specifically, a mental process) when afforded its broadest reasonable interpretation. Such determining as claimed could be performed in the human mind. For example, a human could observe data detected by the sensor (e.g., via reading a computer printout of the information) and exercise judgment to assign (i.e., “determine”) a risk score which corresponds to the type of mental disorder based on that information.
The similar limitation “determine a second risk score (a) corresponding to the type of mental disorder or neurological disorder” recites a mental process for the same reasons.
“determining whether the first risk score is within a first predetermined range” recites an abstract idea (specifically, a mental process) when afforded its broadest reasonable interpretation. The claimed “determining” is practically performable in the human mind. For example, a human could observe both the first score and the first predetermined range, and exercise judgment regarding whether the first risk score is within the first predetermined range.
The similar limitation “determining whether the second risk score is within a second predetermined range” recites a mental process for the same reasons.
The Examiner notes that the limitation “wherein the second predetermined range is defined based on…” imposes further limitations upon what is determined, but does not otherwise impact the recited “determining.”
Eligibility Step 2A, Prong Two: Claim 1 does not recite additional elements that integrate the judicial exception into a practical application.
“storing real-time patient information, corresponding to a user, and detected by a sensor of a wearable device, into a particular Electronic Medical Record (EMR) associated with the user” amounts to generally linking the use of a judicial exception to a particular technological environment or field of use. Storing data that is subsequently analyzed does not add a meaningful limitation to the analysis of such data.
The similar limitation “storing the additional real-time patient information into the particular EMR associated with the user” does not integrate the recited judicial exceptions into a practical application for the same reasons.
“applying the predictive machine learning model to the real-time patient information, from the particular EMR,” amounts to merely reciting the words “apply it” with the judicial exception, and as such is insufficient to integrate the recited abstract idea into a practical application. See MPEP 2106.04(d)(I). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The judicial exception “determine a first risk score” is performed by “applying the predictive machine learning model….” The predictive machine learning model is used generally to apply the abstract idea without placing any limits on how the predictive machine learning model functions, and does not provide any details regarding how the determining is accomplished. Instead, only the outcome of a first risk score being determined are recited.
The similar recitation “applying the predictive machine learning model at least to the additional real-time patient information, from the particular EMR” does not integrate the recited judicial exceptions into a practical application for the same reasons.
“automatically providing a first notification, wherein the user is administered a medication based on the first notification” amounts to necessary data outputting in conjunction with the recited mental process, and is insignificant extra-solution activity. The provided notification is the result of the mental process “determining whether the first risk score is within a first predetermined range,” and is necessary for use of the claimed “determining” as without it the determination would remain unknown. The claimed “…providing a notification…” thus does not (either on its own or in combination with the remainder of the claim) add a meaningful limitation, and is insufficient to integrate the claimed mental process into a practical application.
The portion of the limitation which states “wherein the patient is administered a medication based on the notification” does not modify the claimed “providing a notification.” Administration of a medication is not affirmatively recited. Instead, the patient “is” administered a medication “based on” the notification. Per the broadest reasonable interpretation of the claim, such administration could take place days later, and could be necessitated by factors other than that upon which the notification was based, provided the notification was considered in the decision to administer medication.
“receiving feedback from the wearable device during a defined monitoring period subsequent to the medication being administered to the user based on the first notification, the feedback comprising additional real-time patient information detected by the sensor of the wearable device, wherein the feedback indicated physiological or behavioral changes in the user following administration of the medication” amounts to necessary data gathering in conjunction with the recited mental process, and is insignificant extra-solution activity. The mental process of determining a second risk score relies on the “receiv[ed] feedback.” Receipt (i.e., “gathering”) of the feedback (i.e., “data”) is therefore required for use of the claimed abstract idea. The claimed “receiving feedback…” thus does not (either on its own or in combination with the remainder of the claim) add a meaningful limitation, and is insufficient to integrate the claimed mental process into a practical application.
Neither the fact that the feedback is received during a particular time nor what the feedback indicates change that receipt of such feedback amounts to necessary data gathering in conjunction with the recited mental process in the manner explained above.
“automatically scheduling a follow up…” amounts to necessary data outputting in conjunction with the recited mental process, and is insignificant extra-solution activity. The claimed “scheduling” is the result of the mental process “determining whether the second risk score is within the second predetermined range.” Contextually, the claimed “scheduling” is outputting the result of determining whether the administered medication was effective. Such outputting is necessary for use of the claimed “determining.” as without it the determination would remain unknown. The claimed “…scheduling a follow up…” thus does not add a meaningful limitation, and is insufficient to integrate the claimed mental process into a practical application.
That the recited “automatically scheduling a follow up” is done “in response to the second risk score not being within the second predetermined range…” does not change the fact that the claimed “scheduling” is the result of the above-noted mental process.
Similarly, the limitations “thereby indicating that…” and “based on the physiological or behavioral changes…” both describe what the “scheduling” is based on, but do not otherwise impact said “scheduling.”
Eligibility Step 2B: Claim 1 does not amount to significantly more than the abstract ideas recited therein.
“storing real-time patient information, corresponding to a user, and detected by a sensor of a wearable device, into a particular Electronic Medical Record (EMR) associated with the user” does not contribute an inventive concept. Such storing is recited at a high level of generality, and is well-understood, routine and conventional. See MPEP 2106.05(d)(II).
The similar limitation “storing the additional real-time patient information into the particular EMR associated with the user” does not contribute an inventive concept for the same reasons.
“applying the predictive machine learning model to the real-time patient information, from the particular EMR,” does not contribute an inventive concept. Such applying is a mere instruction to apply the judicial exception. Mere instructions to apply a judicial exception cannot provide an inventive concept. MPEP 2106.05(f).
The similar recitation “applying the predictive machine learning model at least to the additional real-time patient information, from the particular EMR” does not contribute an inventive concept for the same reasons.
“automatically providing a first notification, wherein the user is administered a medication based on the first notification” does not contribute an inventive concept. Providing a notification to a wearable-device-user upon sensing a problematic parameter is well-understood, routine and conventional in the art. For example, Non-Patent Literature S. C. Mukhopadhyay, “Wearable Sensors for Human Activity Monitoring: A Review,” in IEEE Sensors Journal, vol. 15, no. 3, pp. 1321-1330, March 2015 states that “wearable sensors have become very popular in many applications such as medical … fields,” (Pg. 1321, Left Column, First Paragraph after Abstract) and describes as part of their “basic architecture” the ability to “generate a warning message” based on the processed data (Pg. 1322, Right Column, First Paragraph).
“receiving feedback from the wearable device during a defined monitoring period subsequent to the medication being administered to the user based on the first notification, the feedback comprising additional real-time patient information detected by the sensor of the wearable device, wherein the feedback indicated physiological or behavioral changes in the user following administration of the medication” does not contribute an inventive concept. Receiving user information from a sensor of a wearable device is well-understood, routine and conventional. For example, Non-Patent Literature Sara E. Schaefer et al.; "A Feasibility Study of Wearable Activity Monitors for Pre-Adolescent School-Age Children;" May 22, 2014; Centers for Disease Control and Prevention; Preventing Chronic Disease, Volume 11 describes such sensors as “increasingly used in health research to provide more complete, accurate and objective information,” “becoming increasingly available commercially” (Pg. 1 of 8, Fifth and Sixth paragraphs)
“automatically scheduling a follow up…” does not contribute an inventive concept. As explained above, such “scheduling” is in effect a notification. Providing a notification to a wearable-device-user upon sensing a problematic parameter is well-understood, routine and conventional in the art. For example, Non-Patent Literature S. C. Mukhopadhyay, “Wearable Sensors for Human Activity Monitoring: A Review,” in IEEE Sensors Journal, vol. 15, no. 3, pp. 1321-1330, March 2015 states that “wearable sensors have become very popular in many applications such as medical … fields,” (Pg. 1321, Left Column, First Paragraph after Abstract) and describes as part of their “basic architecture” the ability to “generate a warning message” based on the processed data (Pg. 1322, Right Column, First Paragraph).
Regarding Claims 2-5 and 7-9, Claims 2-5 and 7-9 are ineligible. Claims 2-5 and 7-9 fall within one of the four statutory categories. Claims 2-5 and 7-9 further limit the abstract ideas recited in Claim 1. Claims 2-5 and 7-9 do not contain any additional elements, and therefore do not integrate the recited abstract ideas into a practical application and do not contribute an inventive concept.
Regarding Claim 10, Claim 10 is ineligible.
Eligibility Step 1: Claim 10 is directed to “a method” (i.e., a process) and thus falls within one of the four statutory categories.
Eligibility Step 2A, Prong One: Claim 10 recites an abstract idea.
“determining a plurality of significant influencing factors corresponding to the type of mental disorder or neurological disorder using the predictive machine learning model” recites an abstract idea, and more specifically a mental process. Such “determining” as claimed could be performed in the human mind. For example, a human could create a simple predictive model using responses from the validating questionnaire (e.g., a model that looks at whether the entire questionnaire was completed to assign an agreeability score to the user) and use that simple model to decide upon the presence of such significant influencing factors as claimed (e.g., “low agreeability is a significant influencing factor for x”).
The limitation “determining the first risk score further using the plurality of significant influencing factors” modifies the mental process “determine a first risk score…” recited in Claim 1.
Eligibility Step 2A, Prong Two: Claim 10 does not recite additional elements that integrate the judicial exception into a practical application.
“Receiving a response from the user to a validating questionnaire for the type of mental disorder or neurological disorder” amounts to necessary data gathering in conjunction with the recited mental process, and is insignificant extra-solution activity. The mental process of “determine a first risk score” recited in Claim 1 relies on (i.e., via the further limitation imposed on it by Claim 10) the “receiv[ed] response.” Receipt (i.e., “gathering”) of the information (i.e., “data”) is therefore required for use of the claimed abstract idea. The claimed “receiving a response…” thus does not (either on its own or in combination with the remainder of the claim) add a meaningful limitation, and is insufficient to integrate the claimed mental process into a practical application
Eligibility Step 2B: Claim 10 does not amount to significantly more than the abstract ideas recited therein.
“receiving a response from the user to a validating questionnaire for the type of mental disorder or neurological disorder” does not contribute an inventive concept. Such receipt is well-understood, routine and conventional. For example, Non-Patent Literature D. McMillan et al.; "Defining successful treatment outcome in depression using the PHQ-9: A comparison of methods;" December 2010; Journal of Affective Disorders, Vol. 127, Issues 1-3, Pgs. 122-129 describes such a questionnaire, which it states “is widely used in primary care” (Pg. 122, Background Section of Abstract).
Regarding Claim 11, Claim 11 is ineligible. Claim 11 falls within one of the four statutory categories. Claim 11 further limit the abstract ideas recited in Claim 10. Claim 11 does not contain any additional elements, and therefore do not integrate the recited abstract ideas into a practical application and do not contribute an inventive concept.
Regarding Independent Claim 12, Claim 12 is ineligible.
Eligibility Step 1: Claim 12 is directed to “a non-transitory computer-readable medium” (i.e., a machine) and thus falls within one of the four statutory categories.
Eligibility Step 2A, Prong One: Claim 12 recites an abstract idea.
“training a predictive machine learning model to predict risk values for mental or neurological disorders based on training data sets…” recites an abstract idea (specifically, a mathematical calculation) when afforded its broadest reasonable interpretation for the same reasons as explained above with respect to the similar limitation recited in Claim 1.
The limitation “retraining the predictive machine learning model” recites a mathematical calculation for the same reasons.
“determine a first risk score corresponding to the type of mental disorder or neurological disorder” recites an abstract idea (specifically, a mental process) when afforded its broadest reasonable interpretation for the same reasons as explained above with respect to the similar limitation recited in Claim 1.
The similar limitation “determine a second risk score (a) corresponding to the type of mental disorder or neurological disorder” recites a mental process for the same reasons.
“determining whether the first risk score is within a first predetermined range” recites an abstract idea (specifically, a mental process) when afforded its broadest reasonable interpretation for the same reasons as explained above with respect to the similar limitation recited in Claim 1.
The similar limitation “determining whether the second risk score is within a second predetermined range” recites a mental process for the same reasons.
Eligibility Step 2A, Prong Two: Claim 12 does not recite additional elements that integrate the judicial exception into a practical application.
“A non-transitory computer-readable storage medium having instructions embodied thereon” is (1) insignificant extra-solution activity insufficient to integrate the judicial exception into a practical application and (2) a generic computer structure for performing a generic computer function, and thus simply amounts to using a computer as a tool to implement the abstract idea.
“storing real-time patient information, corresponding to a user, and detected by a sensor of a wearable device, into a particular Electronic Medical Record (EMR) associated with the user” amounts to generally linking the use of a judicial exception to a particular technological environment or field of use. Storing data that is subsequently analyzed does not add a meaningful limitation to the analysis of such data.
The similar limitation “storing the additional real-time patient information into the particular EMR associated with the user” does not integrate the recited judicial exceptions into a practical application for the same reasons.
“applying the predictive machine learning model to the real-time patient information, from the particular EMR,” amounts to merely reciting the words “apply it” with the judicial exception, and as such is insufficient to integrate the recited abstract idea into a practical application for the same reasons as explained above with respect to the similar limitation recited in Claim 1.
The similar recitation “applying the predictive machine learning model at least to the additional real-time patient information, from the particular EMR” does not integrate the recited judicial exceptions into a practical application for the same reasons.
“automatically providing a first notification, wherein the user is administered a medication based on the first notification” amounts to necessary data outputting in conjunction with the recited mental process, and is insignificant extra-solution activity for the same reasons as explained above with respect to the similar limitation recited in Claim 1.
“receiving feedback from the wearable device during a defined monitoring period subsequent to the medication being administered to the user based on the first notification, the feedback comprising additional real-time patient information detected by the sensor of the wearable device wherein the feedback indicates physiological or behavioral changes in the user following administration of the medication” amounts to necessary data gathering in conjunction with the recited mental process, and is insignificant extra-solution activity insufficient to integrate the judicial exception into a practical application for the same reasons as explained above with respect to the similar limitation recited in Claim 1.
“automatically scheduling a follow up…” amounts to necessary data outputting in conjunction with the recited mental process, and is insignificant extra-solution activity insufficient to integrate the claimed mental process into a practical application for the same reasons as explained above with respect to the similar limitation recited in Claim 1
Eligibility Step 2B: Claim 12 does not amount to significantly more than the abstract ideas recited therein.
“A non-transitory computer-readable storage medium having instructions embodied thereon” does not contribute an inventive concept. The claimed “non-transitory computer-readable storage medium” is recited at a high level of generality, and use of such a storage medium as claimed is well-understood, routine and conventional in the art. For example, Non-Patent Literature S. C. Mukhopadhyay, "Wearable Sensors for Human Activity Monitoring: A Review," in IEEE Sensors Journal, vol. 15, no. 3, pp. 1321-1330, March 2015 states that “wearable sensors have become very popular in many applications such as medical … fields,” (Pg. 1321, Left Column, First Paragraph after Abstract) and describes as such a storage as being part of their “basic architecture” (Pg. 1322, Left Column, Third Paragraph; Pg. 1322, Fig. 3, “Microcontroller”).
“storing real-time patient information, corresponding to a user, and detected by a sensor of a wearable device, into a particular Electronic Medical Record (EMR) associated with the user” does not contribute an inventive concept for the same reasons explained above with respect to the similar limitation recited in Claim 1.
The similar limitation “storing the additional real-time patient information into the particular EMR associated with the user” does not contribute an inventive concept for the same reasons.
“applying the predictive machine learning model to the real-time patient information, from the particular EMR,” does not contribute an inventive concept for the same reasons explained above with respect to the similar limitation recited in Claim 1.
The similar recitation “applying the predictive machine learning model at least to the additional real-time patient information, from the particular EMR” does not contribute an inventive concept for the same reasons.
“automatically providing a first notification, wherein the user is administered a medication based on the first notification” does not contribute an inventive concept for the same reasons explained above with respect to the similar limitation recited in Claim 1.
“receiving feedback from the wearable device during a defined monitoring period subsequent to the medication being administered to the user based on the first notification, the feedback comprising additional real-time patient information detected by the sensor of the wearable device wherein the feedback indicates physiological or behavioral changes in the user following administration of the medication” does not contribute an inventive concept for the same reasons explained above with respect to the similar limitation recited in Claim 1.
“automatically scheduling a follow up…” does not contribute an inventive concept for the same reasons explained above with respect to the similar limitation recited in Claim 1.
Regarding Claim 13, Claim 13 is ineligible. Claim 13 falls within one of the four statutory categories. Claim 13 further limits the abstract ideas recited in Claim 12. Claim 13 does not contain any additional elements, and therefore does not integrate the recited abstract ideas into a practical application and does not contribute an inventive concept.
Regarding Claim 14, Claim 14 is ineligible.
Eligibility Step 1: Claim 14 is directed to a “media” (i.e., a machine) and thus falls within one of the four statutory categories.
Eligibility Step 2A, Prong One: Claim 14 recites an abstract idea.
“determining a third risk score of the user” recites an abstract idea, and more specifically a mental process for the same reasons as explained above regarding the similar limitation of Claim 1.
Eligibility Step 2A, Prong Two: Claim 14 does not recite additional elements that integrate the judicial exception into a practical application.
“receiving further real-time patient information from the wearable device” amounts to necessary data gathering in conjunction with the recited mental process, and is insignificant extra-solution activity. The mental process of “determining a third risk score” relies on the received information. Receipt (i.e., “gathering”) of the information (i.e., “data”) is therefore required for use of the claimed abstract idea. The claimed “receiving further real-time patient information” thus does not (either on its own or in combination with the remainder of the claim) add a meaningful limitation, and is insufficient to integrate the claimed mental process into a practical application.
“automatically providing a third notification” amounts to necessary data outputting in conjunction with the recited mental process, and is insignificant extra-solution activity for the same reasons as explained above with respect to the similar limitation recited in Claim 1.
Eligibility Step 2B: Claim 14 does not amount to significantly more than the abstract ideas recited therein.
“receiving further real-time patient information from the wearable device” does not contribute an inventive concept. Such receipt is well-understood, routine and conventional. For example, Non-Patent Literature D. McMillan et al.; "Defining successful treatment outcome in depression using the PHQ-9: A comparison of methods;" December 2010; Journal of Affective Disorders, Vol. 127, Issues 1-3, Pgs. 122-129 describes such a questionnaire, which it states “is widely used in primary care” (Pg. 122, Background Section of Abstract).
“automatically providing a third notification” does not contribute an inventive concept for the same reasons as explained above with respect to the similar limitation recited in Claim 1.
Regarding Claim 15, Claim 15 is ineligible.
Eligibility Step 1: Claim 15 is directed to a “media” (i.e., a machine) and thus falls within one of the four statutory categories.
Eligibility Step 2A, Prong One: Claim 15 recites an abstract idea.
“determining a plurality of influencing factors corresponding to the type of mental disorder or neurological disorder using wearable device information from the wearable device users and their responses to the validating questionnaire” recites an abstract idea, and more specifically a mental process for the same reasons as explained above regarding the similar limitation of Claim 10.
“determining confidence scores for each of the plurality of influencing factors” recites an abstract idea, and more specifically a mental process. Such “determining” as claimed could be performed in the human mind. For example, a human could glance at each of the two influencing factors he had previously determined and arbitrarily assign each a confidence score. No specific manner of determination is recited, and the particulars of the how the determination is made are not claimed.
“determining the first risk score by additionally using at least one of the plurality of influencing factors having a confidence score above a threshold” recites an abstract idea, and more specifically a mental process for the same reasons as explained above regarding the similar limitation of Claim 1.
Eligibility Step 2A, Prong Two: Claim 15 does not recite additional elements that integrate the judicial exception into a practical application.
“receiving a response from the user to the validating questionnaire for the type of mental disorder or neurological disorder” amounts to necessary data gathering in conjunction with the recited mental process, and is insignificant extra-solution activity. The mental process of “determining the first risk score” relies on the received information. Receipt (i.e., “gathering”) of the information (i.e., “data”) is therefore required for use of the claimed abstract idea. The claimed “receiving further real-time patient information” thus does not (either on its own or in combination with the remainder of the claim) add a meaningful limitation, and is insufficient to integrate the claimed mental process into a practical application.
Eligibility Step 2B: Claim 15 does not amount to significantly more than the abstract ideas recited therein.
“receiving a response from the user to the validating questionnaire for the type of mental disorder or neurological disorder” does not contribute an inventive concept. Such receipt is well-understood, routine and conventional. For example, Non-Patent Literature D. McMillan et al.; "Defining successful treatment outcome in depression using the PHQ-9: A comparison of methods;" December 2010; Journal of Affective Disorders, Vol. 127, Issues 1-3, Pgs. 122-129 describes such a questionnaire, which it states “is widely used in primary care” (Pg. 122, Background Section of Abstract).
Regarding Claims 16-19, Claims 16-19 are ineligible. Claims 16-19 fall within one of the four statutory categories. Claims 16-19 further limit the abstract ideas recited in Claim 12. Claims 16-19 do not contain any additional elements, and therefore do not integrate the recited abstract ideas into a practical application and do not contribute an inventive concept.
Regarding Independent Claim 20, Claim 20 is ineligible.
Eligibility Step 1: Claim 20 is directed to “a system” (i.e., a machine) and thus falls within one of the four statutory categories.
Eligibility Step 2A, Prong One: Claim 20 recites an abstract idea.
“training a predictive machine learning model to predict risk values for mental or neurological disorders based on training data sets…” recites an abstract idea (specifically, a mathematical calculation) when afforded its broadest reasonable interpretation for the same reasons as explained above with respect to the similar limitation recited in Claim 1.
The limitation “retraining the predictive machine learning model” recites a mathematical calculation for the same reasons.
“determine a first risk score corresponding to the type of mental disorder or neurological disorder” recites an abstract idea (specifically, a mental process) when afforded its broadest reasonable interpretation for the same reasons as explained above with respect to the similar limitation recited in Claim 1.
The similar limitation “determine a second risk score (a) corresponding to the type of mental disorder or neurological disorder” recites a mental process for the same reasons.
“determining whether the first risk score is within a first predetermined range” recites an abstract idea (specifically, a mental process) when afforded its broadest reasonable interpretation for the same reasons as explained above with respect to the similar limitation recited in Claim 1.
The similar limitation “determining whether the second risk score is within a second predetermined range…” recites a mental process for the same reasons.
Eligibility Step 2A, Prong Two: Claim 20 does not recite additional elements that integrate the judicial exception into a practical application.
“one or more processors;” is (1) insignificant extra-solution activity insufficient to integrate the judicial exception into a practical application and (2) a generic computer structure for performing a generic computer function, and thus simply amounts to using a computer as a tool to implement the abstract idea.
“one or more computer storage media storing computer-useable instructions” is (1) insignificant extra-solution activity insufficient to integrate the judicial exception into a practical application and (2) a generic computer structure for performing a generic computer function, and thus simply amounts to using a computer as a tool to implement the abstract idea.
“storing real-time patient information, corresponding to a user, and detected by a sensor of a wearable device, into a particular Electronic Medical Record (EMR) associated with the user” amounts to generally linking the use of a judicial exception to a particular technological environment or field of use. Storing data that is subsequently analyzed does not add a meaningful limitation to the analysis of such data.
The similar limitation “storing the additional real-time patient information into the particular EMR associated with the user” does not integrate the recited judicial exceptions into a practical application for the same reasons.
“applying the predictive machine learning model to the real-time patient information, from the particular EMR,” amounts to merely reciting the words “apply it” with the judicial exception, and as such is insufficient to integrate the recited abstract idea into a practical application for the same reasons as explained above with respect to the similar limitation recited in Claim 1.
The similar recitation “applying the predictive machine learning model at least to the additional real-time patient information, from the particular EMR” does not integrate the recited judicial exceptions into a practical application for the same reasons.
“automatically providing a first notification, wherein the user is administered a medication based on the first notification” amounts to necessary data outputting in conjunction with the recited mental process, and is insignificant extra-solution activity for the same reasons as explained above with respect to the similar limitation recited in Claim 1.
“receiving feedback from the wearable device during a defined monitoring period subsequent to the medication being administered to the user based on the first notification, the feedback comprising additional real-time patient information detected by the sensor of the wearable device wherein the feedback indicates physiological or behavioral changes in the user following administration of the modification” amounts to necessary data gathering in conjunction with the recited mental process, and is insignificant extra-solution activity insufficient to integrate the judicial exception into a practical application for the same reasons as explained above with respect to the similar limitation recited in Claim 1.
“automatically scheduling a follow up…” amounts to necessary data outputting in conjunction with the recited mental process, and is insignificant extra-solution activity insufficient to integrate the claimed mental process into a practical application for the same reasons as explained above with respect to the similar limitation recited in Claim 1
Eligibility Step 2B: Claim 20 does not amount to significantly more than the abstract ideas recited therein.
“one or more processors;” does not contribute an inventive concept. The claimed processors are recited at a high level of generality, and use of such processors as claimed is well-understood, routine and conventional in the art. For example, Non-Patent Literature S. C. Mukhopadhyay, "Wearable Sensors for Human Activity Monitoring: A Review," in IEEE Sensors Journal, vol. 15, no. 3, pp. 1321-1330, March 2015 states that “wearable sensors have become very popular in many applications such as medical … fields,” (Pg. 1321, Left Column, First Paragraph after Abstract) and describes as such a processor as being part of their “basic architecture” (Pg. 1322, Left Column, Third Paragraph; Pf. 1322, Fig. 1).
“one or more computer storage media storing computer-useable instructions” does not contribute an inventive concept. The claimed “computer storage medium” is recited at a high level of generality, and use of such a storage medium as claimed is well-understood, routine and conventional in the art. For example, Non-Patent Literature S. C. Mukhopadhyay, "Wearable Sensors for Human Activity Monitoring: A Review," in IEEE Sensors Journal, vol. 15, no. 3, pp. 1321-1330, March 2015 states that “wearable sensors have become very popular in many applications such as medical … fields,” (Pg. 1321, Left Column, First Paragraph after Abstract) and describes as such a storage as being part of their “basic architecture” (Pg. 1322, Left Column, Third Paragraph; Pg. 1322, Fig. 3, “Microcontroller”).
“storing real-time patient information, corresponding to a user, and detected by a sensor of a wearable device, into a particular Electronic Medical Record (EMR) associated with the user” does not contribute an inventive concept for the same reasons explained above with respect to the similar limitation recited in Claim 1.
The similar limitation “storing the additional real-time patient information into the particular EMR associated with the user” does not contribute an inventive concept for the same reasons.
“applying the predictive machine learning model to the real-time patient information, from the particular EMR,” does not contribute an inventive concept for the same reasons explained above with respect to the similar limitation recited in Claim 1.
The similar recitation “applying the predictive machine learning model at least to the additional real-time patient information, from the particular EMR” does not contribute an inventive concept for the same reasons.
“automatically providing a first notification, wherein the user is administered a medication based on the first notification” does not contribute an inventive concept for the same reasons explained above with respect to the similar limitation recited in Claim 1.
“receiving feedback from the wearable device during a defined monitoring period subsequent to the medication being administered to the user based on the first notification, the feedback comprising additional real-time patient information detected by the sensor of the wearable device wherein the feedback indicates physiological or behavioral changes in the user following administration of the modification” does not contribute an inventive concept for the same reasons explained above with respect to the similar limitation recited in Claim 1.
“automatically scheduling a follow up…” does not contribute an inventive concept for the same reasons explained above with respect to the similar limitation recited in Claim 1.
Regarding Claim 22, Claim 22 is ineligible.
Eligibility Step 1: Claim 22 is directed to “a method” (i.e., a process) and thus falls within one of the four statutory categories.
Eligibility Step 2A, Prong One: Claim 22 recites an abstract idea.
“combining the first risk score with the predisposition probability value to calculate a probability of mental health” recites a mental process and a mathematical calculation. The claimed computing is practically performable in the human mind. No particular manner of computing is required, and the specifics of how the claimed computing is done are not recited. Additionally, the claimed computing is a mathematical calculation.
Eligibility Step 2A, Prong Two: Claim 22 does not recite any additional elements.
Eligibility Step 2B: Claim 22 does not amount to significantly more than the abstract ideas recited therein.
Allowable Subject Matter
Claims 1-5,7-20 and 22 stand rejected under 35 USC 101, but would be allowable over prior art were that rejection to be overcome.
The following is a statement of reasons for the indication of allowable subject matter relative to the prior art:
Independent Claim 1 recites a method comprising training a machine learning model to predict risk values in a particular manner using particular data. The method includes comparing the risk scores to a first and a second predetermined range, and scheduling a follow-up medical visit. Claim 1 requires that the second predetermined range “is defined based on expected physiological or behavioral changes in the user associated with an adequate response to the medication administered following the first notification” and that the follow-up is automatically scheduled “in response to the second risk score not being within the second predetermined range, thereby indicating that the medication administered following the first notification has not produced an adequate physiological or behavioral response in the user... automatically scheduling a follow up based on the physiological or behavioral changes in the user detected by the sensor of the wearable device that caused the second risk score to fall outside the second predetermined range.” The prior art does not fairly teach, disclose or suggest either a second predetermined range so-defined or scheduling a follow-up visit on the basis recited.
The closest prior art is U.S. Patent Publication No. 2021/0098093 A1 to Shadid et al. (“Shadid”). Shadid describes “Systems and methods … for integrated healthcare management… [which] include establishing a medical record data, accessing information about a treatment related to the medical record data, obtaining biometric data related to the treatment, processing the biometric data, determining whether the treatment is appropriate with respect to the biometric data, in response to the treatment being appropriate with respect to the biometric data, administering medication, and in response the treatment not being appropriate with respect to the biometric data, determining a new treatment, and administering new medication based on the new treatment.” Shadid describes use of a similar risk score at Paras. [0008], [0019], and [0084], and similarly scheduling a follow-up at Paras. [0116] and [0119]. However, Shadid’s risk score is not “defined based on expected physiological or behavioral changes in the user associated with an adequate response to the medication administered following the first notification,” and Shadid’s follow-up is not scheduled in the manner recited. It would not have been obvious for a person of ordinary skill to modify Shadid to arrive at the teachings of Claim 1 being doing so would require substantial redesign, and would serve no benefit.
Independent Claims 12 and 20 recite similar limitations to Claim 1.
Dependent Claims 2-5,7-11,13-19 and 22 depend from and further limit Independent Claims 1, 12 and 20, respectively.
Conclusion
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/C.J.M./Examiner, Art Unit 3796
/LYNSEY C Eiseman/Primary Examiner, Art Unit 3796
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