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 .
Status of Claims
Claims 1-4, 6, 7, 9-19, 21, 22, and 24-30, as recited in an RCE filed on November 7, 2025, were previously pending and subject to a non-final office action filed on December 12, 2025 (the “December 12, 2025 Non-Final Office Action”). On March 5, 2026, Applicant filed a Subject Matter Eligibility Declaration under 37 CFR § 1.132 to traverse the rejections without amending the claims (the “March 5, 2026 Amendment and Reply under 37 CFR § 1.132”). After consideration of the arguments and evidence provided in the March 5, 2026 Amendment and Reply under 37 CFR § 1.132, claims 1-4, 6, 7, 9-19, 21, 22, and 24-30, as recited in the March 5, 2026 Amendment and Reply under 37 CFR § 1.132, are currently pending and subject to the final office action below.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Response to Applicant’s Remarks
Response to Applicant’s Remarks Concerning Rejections under 35 U.S.C. § 101
Applicant’s arguments, see Applicant’s Remarks, pp. 9-14, Rejection Under § 101 Section, filed March 5, 2025, with respect to rejections of claim 1-4, 6, 7, 9-19, 21, 22, and 24-30 under 35 U.S.C. § 101 have been fully considered, but they are not persuasive. Further, in light of the 2019 Revised Patent Subject Matter Eligibility Guidance (available at MPEP § 2106) (the “2019 Revised PEG”), the § 101 rejections of claims 1-4, 6, 7, 9-19, 21, 22, and 24-30 are maintained in this office action.
Applicant argues that the claims are patent eligible, because of evidence submitted in the Belhadi Declaration providing tangible examples which prove that the claimed invention is not merely an abstract idea. See Applicant’s Remarks, at p. 9. Examiner respectfully disagrees with this assertion. As outlined in the December 4, 2025 Memorandum for Best Practices for Submission of Rule 132 Subject Matter Eligibility Declarations (SMEDs) released by the USPTO, “[f]or an evidentiary declaration to be relevant, there must be a nexus between the invention as claimed and the evidence provided in the declaration”, and “a SMED may demonstrate how one of ordinary skill in the art would interpret a specification that describes a technological improvement to show that the claimed invention is patent-eligible subject matter.” In the present case, the Belhadi Declaration fails to establish or demonstrate how one of ordinary skill in the art would interpret the Applicant’s specification as describing a technological improvement in order to show that the claimed invention is patent-eligible subject matter. For example, the declaration could show this by providing testimony on how one of ordinary skill in the art would interpret the disclosed invention as improving technology and the underlying factual basis for that conclusion. See MPEP § 2106.07(b).
In Appendix B and C of the Belhadi Declaration, Applicant provided a Declaration of Conformity submitted by Applicant to AFNOR (Appendix B in the Belhadi Declaration) and an Opinion on Application to the Committee for the Protection of Persons West IV (Appendix C in the Belhadi Declaration). However, neither of the evidence submitted in Appendix B or Appendix C demonstrate how one of ordinary skill in the art would interpret the Applicant’s specification as describing a technological improvement in the art. For example, there is no discussion of prior art systems or how Applicant’s claimed invention provides technological improvements, such as improvements to the machine learning architecture, reduced hallucination rates in the machine learning, enhanced inferencing efficiency, improved hardware utilization, better training convergence, etc. These references merely show that Applicant submitted applications to some regulatory bodies, but they fail to demonstrate that one of ordinary skill in the art would interpret Applicant’s claimed invention as providing a technological improvement. Therefore, this argument and the Belhadi Declaration are not persuasive for these reasons.
Next, Applicant argues that Ms. Belhadi explains in the Belhadi Declaration that it would not be possible to perform the steps of training the machine learning algorithm and generating a first score with the updated machine learning algorithm in one’s mind, because it would not be possible to execute a computation program mentally. See Applicant’s Remarks, at p. 10. First, the Examiner acknowledges that training a machine learning algorithm and generating a score with the updated machine learning algorithm was not deemed to be part of the abstract mental process. See p. 13 of the December 12, 2025 Non-Final Office Action (where these steps were deemed to be additional elements beyond the abstract mental process). Claims can recite a mental process even if they are claimed as being performed on a computer. MPEP § 2106.04(a)(2)(III)(C). The Supreme Court recognized this in Gottschalk v. Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. Id. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures “can be carried out in existing computers long in use, no new machinery being necessary.” Id.
In the present case, while Applicant describes the type of data that is used to train the algorithm, Applicant has not described how the model is trained (e.g., the specific steps, flow-charts, or algorithm) to make the predictions for threshold or generate the first score. Consequently, the claimed machine learning related features are deemed to be the equivalent of describing the input to a generic black-box software program and providing the output, without describing what happens during the analyses steps in between the process. Therefore, under Prong Two of Step 2A, the machine learning related features are deemed to be additional elements beyond the abstract idea that amount to no more than: (1) adding the words “apply it” (or is the equivalent of) with the judicial exception; mere instructions to implement an abstract idea on a computer; or merely uses a computer as a tool to perform an abstract idea; and (2) generally linking the abstract idea to a particular field of use or technological environment (in this case, the field of machine learning technologies). See MPEP §§ 2106.05(f), (h). Therefore, this argument is not persuasive for these reasons.
Further, Applicant argues that the ultimate step in claim 1 directed to generating a severity depression map directly reduces the duration and/or severity of a condition. See Applicant’s Remarks, at p. 12. Examiner respectfully disagrees with this assertion. It is not clear how generating a map directly reduces the duration and/or severity of a mental health condition. Rather, this step is deemed to be part of the abstract mental process. The use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another. MPEP § 2106.04(a)(2)(III)(B). For instance, in CyberSource Corp. v. Retail Decisions, Inc., the Federal Circuit determined that the step of “constructing a map of credit card numbers” was a limitation that was able to be performed “by writing down a list of credit card transactions made from a particular IP address.” Id. In making this determination, the court looked to the specification, which explained that the claimed map was nothing more than a listing of several (e.g., four) credit card transactions. Id. The court concluded that this step was able to be performed mentally with a pen and paper, and therefore, it qualified as a mental process. Id.
Applicant’s step for generating the severity map is similar to the step of constructing a map of credit card numbers in the CyberSource case. Specifically, in the present case, Applicant’s step directed to generating a severity depression map also describes a step that is capable of being performed mentally with the aid of a pen and paper. Applicant’s specification explains that the generated map may be “circular in nature having radial lines extending from the center to the perimeter of the map”, and “may show data that is relevant to depressive state”. See Applicant’s specification as filed on January 23, 2025, paragraphs [0078] and [0083], and FIG. 5F. Based on the description in Applicant’s specification and the illustration in Figure 5F of Applicant’s drawings, it is clear that person is capable of drawing a circular map of depression-related data, with radial lines to show the relevance of each category of the depression-related data. Further, Applicant has shown how generating a map directly reduces the duration and/or severity of a condition. Rather, this step amounts to displaying data visually for a person to look at. Therefore, this argument is not persuasive for these reasons.
Lastly, Applicant argues that the claims should be deemed to be patent eligible, because the previous office action indicated that independent claims 1 and 16 are considered to be novel and non-obvious over the prior art. See Applicant’s Remarks, at p. 14. Examiner respectfully disagrees with this assertion. Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. MPEP § 2106.05(I). As made clear by the courts, the “‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter.” Id. In addition, the search for an inventive concept is different from an obviousness analysis under 35 U.S.C. 103. Specifically, lack of novelty under 35 U.S.C. § 102 or obviousness under 35 U.S.C. § 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Id. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. §§ 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. § 101. Therefore, the novelty and/or non-obviousness determination of the claims is irrelevant to the eligibility analysis, and this argument is not persuasive for these reasons.
As such, the rejections of claims 1-4, 6, 7, 9-19, 21, 22, and 24-30 under 35 U.S.C. § 101 are maintained in this office action. Please see the rejections under the Claim Rejections - 35 U.S.C. § 101 Section below, for further clarification and complete analysis.
Oath/Declaration
The Declaration of Nour Hakiki Belhadi, under 37 CFR § 1.132 and MPEP § 716 filed on March 5, 2026 (the “Belhadi Declaration”) is insufficient to overcome the rejections of claims 1-4, 6, 7, 9-19, 21, 22, and 24-30 based upon 35 U.S.C. § 101 as set forth in the last Office action, because: the Belhadi Declaration fails to set forth facts related to its arguments in order to overcome the rejections.
Despite Nour Belhadi’s assertions that the claimed invention cannot practically be performed in the human mind, Applicant’s claims are deemed to be directed to an abstract mental process which a person is capable of performing in their mind and/or with the aid of pen and paper, because a majority of the claim limitations describe various types of observations, evaluations, judgments and opinions analogous to “collecting information, analyzing it, and displaying certain results of the analysis”. See MPEP § 2106.04(a)(2)(III)(A) (citing Electric Power Group v. Alstom). As set forth in the December 12, 2025 Non-Final Office Action, Applicant’s claims are directed to an abstract mental process, namely, a method for monitoring mental health of a user, comprising: receiving first user data comprising first physiological data, first activity data, and/or first sleep data at a first time period; receiving survey data related to at least one depressive state of the user at the first time period; receiving second user data comprising second physiological data, second activity data, and/or second sleep data at a second time period; transforming the second user data into a vector; predicting a depression severity based on two threshold values at two different time points; generating a score using the vector, where the score is indicative of a degree of depression of the user; determining when the score exceeds a threshold indicating a depression relapse at a first time point; and generating a depression severity map representative of the depression severity at the first point in time.
The aforementioned claim limitations are analogous to concepts which are capable of being performed in the human mind or encompass a human performing the steps mentally with the aid of a pen and paper, because they merely recite limitations which encompass a person mentally and/or manually: (1) receiving first user data comprising first physiological data, first activity data, and/or first sleep data at a first time period (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could evaluate the physiological, activity, and/or sleep related data); (2) receiving survey data related to at least one depressive state of the user at the first time period (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could manually collected survey related data); (3) receiving second user data comprising second physiological data, second activity data, and/or second sleep data at a second time period (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could evaluate the physiological, activity, and/or sleep related data); (4) transforming the second user data into a vector (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could manually convert data from one format into another format, such as converting unstructured data into a numbered list); (5) predicting a depression severity based on two threshold values at two different time points (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could mentally make a prediction of the severity of depression based on two threshold values); (6) generating a score using the vector, where the score is indicative of a degree of depression of the user (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could mentally come up with a score for the user based on the vector); (7) determining when the score exceeds a threshold indicating a depression relapse at a first time point (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could mentally make a determination that a score exceeds a threshold); and (8) generating a depression severity map representative of the depression severity at the first point in time (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could manually generate a depression severity map). Therefore, the aforementioned underlined claim limitations may reasonably be interpreted as mental/manual observations, evaluations, judgments, and/or opinions made by a person, such as a healthcare professional.
While the claims recite steps for training a machine learning algorithm based on a plurality of user data and/or general sleep data and updating the machine learning algorithm by further training the machine learning algorithm using user data and survey data, Applicant’s claims and specification do not describe the specific steps, flow-charts, or algorithm for how the machine learning makes the prediction for the threshold or generates the first score. Consequently, the claimed machine learning related features are additional elements beyond the abstract idea that amount to no more than: (1) adding the words “apply it” (or is the equivalent of) with the judicial exception; mere instructions to implement an abstract idea on a computer; or merely uses a computer as a tool to perform an abstract idea; and (2) generally linking the abstract idea to a particular field of use or technological environment. See MPEP §§ 2106.05(f), (h).
Next, the Belhadi Declaration asserts that one of ordinary skill in the art would have understood the invention as describing an improvement on current technology, thereby integrating any judicial exception recited in the claims into a practical application. See the Belhadi Declaration, at pp. 4-7. However, Applicant’s claims merely describe a conventional process of collecting data, analyzing the data, and displaying certain results of the analysis with a computer and a machine learning model. Further, the Belhadi Declaration has not provided any evidence of an improvement to the machine learning model, the training process, or the prediction architecture itself; nor has the Belhadi Declaration provided any evidence to show how the machine learning model produces any measurable technical benefit. For example, the Belhadi Declaration does not provide any benchmark comparisons, ablation studies, or computational complexity analyses showing that the claimed invention produces measurable improvements over the prior art. Therefore, the machine learning features are not indicative of an improvement to the functioning of a computer or a technological field.
Lastly, the Belhadi Declaration asserts that claimed invention includes additional elements that are sufficient to amount to significantly more than the judicial exception, because one of ordinary skill in the art would have understood the unconventional arrangement of the additional elements. See the Belhadi Declaration, at pp. 6-8. When making a determination of whether the additional elements in a claim amount to significantly more than a judicial exception, the examiner should evaluate whether the elements define only well-understood, routine, conventional activity. MPEP § 2106.05(d). In this respect, the well-understood, routine, conventional consideration overlaps with other Step 2B considerations, particularly the improvement consideration (see MPEP § 2106.05(a)), the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)), and the insignificant extra-solution activity consideration (see MPEP § 2106.05(g)). Id. Thus, evaluation of those other considerations may assist examiners in making a determination of whether a particular element or combination of elements is well-understood, routine, conventional activity. Id.
In the present case, the machine learning model features are not described with any specificity. Again, the Belhadi Declaration fails to describe the machine learning model with any specificity, nor do the claims recite the specific steps that the machine learning model goes through in order to: (i) make the prediction of the first threshold value, or (ii) generate a first score indicative of a first degree of depression. Therefore, Applicant’s claims do not recite any additional elements which are deemed to provide significantly more than the abstract idea.
In view of the foregoing, when all of the evidence is considered, the totality of the rebuttal evidence of eligibility fails to outweigh the evidence of ineligibility.
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 1-4, 6, 7, 9-19, 21, 22, and 24-30 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more. See MPEP § 2106 (hereinafter referred to as the “2019 Revised PEG”).
Step 1 of the 2019 Revised PEG
Following Step 1 of the 2019 Revised PEG, claims 1-4, 6, 7, and 9-15 are directed to a method for monitoring mental health of a user, which is within one of the four statutory categories (i.e., a process). See MPEP § 2106.03. Claims 16-19, 21, 22, and 24-30 are directed to a system for monitoring mental health of a user, which is also within one of the four statutory categories (i.e., a machine or apparatus). See id.
Step 2A of the 2019 Revised PEG - Prong One
Following Prong One of Step 2A of the 2019 PEG, the claim limitations are to be analyzed to determine whether they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. See MPEP §2106.04. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: (1) Mathematical Concepts; (2) Certain Methods of Organizing Human Activity, and (3) Mental Processes. See MPEP § 2106.04(a).
Claims 1-4, 6, 7, 9-19, 21, 22, and 24-30 are rejected under 35 U.S.C. § 101, because the claimed invention is directed to an abstract idea without significantly more. Representative independent claims 1 and 16 include limitations that recite an abstract idea. Specifically, independent claim 16 recites the following limitations:
A system for monitoring mental health of a user, the system comprising:
memory configured to store computer-executable instructions; and
at least one computer processor configured to access memory and execute the computer-executable instructions to:
train a machine learning algorithm using semi-supervised and/or self-supervised learning techniques and based on a plurality of general user data comprising general physiological, general activity and/or general sleep data each corresponding to a plurality of general users different than the user, the plurality of general user data comprising unlabeled user data, and the machine learning model adapted to generate a score indicative of a degree of depression;
receive a plurality of first user data from at least one user device, the plurality of first user data comprising first physiological, first activity, and/or first sleep data corresponding to the user and associated with a calibration phase;
receive survey data corresponding to at least one depressive state of the user and associated with the first time period;
update the machine learning algorithm by further training the machine learning algorithm using the plurality of first user data and the survey data resulting in an updated machine learning algorithm;
predict, based on the plurality of first user data and the survey data, at least one first threshold value corresponding to a depression severity at a first time point in a monitoring phase later in time than the calibration phase and at least one second threshold value corresponding to the depression severity at a second time point in the monitoring phase after the first time point, the first threshold value being larger than the second threshold value;
receive a plurality of second user data from at least one user device, the plurality of second user data corresponding to second physiological, second activity, and/or second sleep data corresponding to the user and associated with the first time point;
transform the plurality of second user data into at least one vector, the at least one vector representative of the plurality of second user data; and
generate a first score using the updated machine learning algorithm and the at least one vector, the first score indicative of a first degree of depression of the user at the first time point;
determine the first score exceeds the first threshold value to indicate a depression relapse at the first time period; and
generate a depression severity map visually representative of the depression severity at the first time point, the depression severity map configured to visually indicate a deviation from a baseline value for each of a plurality of data modalities at the first time point, the plurality of data modalities comprising at least two of physiological, activity, and/or sleep data.
However, the Examiner submits that the foregoing underlined limitations constitute a process that, under its broadest reasonable interpretation, falls within the “Mental Processes” grouping of abstract ideas. See 2019 Revised PEG. The Mental Processes category covers concepts which are capable of being performed in the human mind or encompasses a human performing the step(s) mentally with the aid of a pen and paper (including an observation, evaluation, judgment, or opinion) (i.e., a method for monitoring mental health of a user, comprising: receiving first user data comprising first physiological data, first activity data, and/or first sleep data at a first time period; receiving survey data related to at least one depressive state of the user at the first time period; receiving second user data comprising second physiological data, second activity data, and/or second sleep data at a second time period; transforming the second user data into a vector; predicting a depression severity based on two threshold values at two different time points; generating a score using the vector, where the score is indicative of a degree of depression of the user; determining when the score exceeds a threshold indicating a depression relapse at a first time point; and generating a depression severity map representative of the depression severity at the first point in time). See MPEP § 2106.04(a)(2)(III). That is, other than reciting some computer components and functions (the foregoing limitations in claim 16 which are not underlined), the context of claims 1 and 16 encompasses concepts that are capable of being performed in the human mind or encompasses a human performing the step(s) mentally with the aid of a pen and paper (including an observation, evaluation, judgment, and/or opinion) (i.e., a method for monitoring mental health of a user, comprising: receiving first user data comprising first physiological data, first activity data, and/or first sleep data at a first time period; receiving survey data related to at least one depressive state of the user at the first time period; receiving second user data comprising second physiological data, second activity data, and/or second sleep data at a second time period; transforming the second user data into a vector; predicting a depression severity based on two threshold values at two different time points; generating a score using the vector, where the score is indicative of a degree of depression of the user; determining when the score exceeds a threshold indicating a depression relapse at a first time point; and generating a depression severity map representative of the depression severity at the first point in time).
The aforementioned claim limitations described in claims 1 and 16 are analogous to claim limitations directed toward concepts which are capable of being performed in the human mind or encompasses a human performing the step(s) mentally with the aid of a pen and paper, because they merely recite limitations which encompass a person mentally and/or manually: (1) receiving first user data comprising first physiological data, first activity data, and/or first sleep data at a first time period (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could evaluate the physiological, activity, and/or sleep related data); (2) receiving survey data related to at least one depressive state of the user at the first time period (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could manually collected survey related data); (3) receiving second user data comprising second physiological data, second activity data, and/or second sleep data at a second time period (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could evaluate the physiological, activity, and/or sleep related data); (4) transforming the second user data into a vector (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could manually convert data from one format into another format, such as converting unstructured data into a numbered list); (5) predicting a depression severity based on two threshold values at two different time points (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could mentally make a prediction of the severity of depression based on two threshold values); (6) generating a score using the vector, where the score is indicative of a degree of depression of the user (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could mentally come up with a score for the user based on the vector); (7) determining when the score exceeds a threshold indicating a depression relapse at a first time point (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could mentally make a determination that a score exceeds a threshold); and (8) generating a depression severity map representative of the depression severity at the first point in time (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could manually generate a depression severity map).
Further, Applicant’s claims are similar to claims which have been held to recite an abstract mental process. For example, the Federal Circuit held the a claim directed to “collecting information, analyzing it, and displaying certain results of the collection and analysis”, where the data analysis steps are recited at a high level of generality amounted to steps that could practically be performed in the human mind. See MPEP § 2106.04(a)(2)(III)(A) (citing Electric Power Group g. Alstom, S.A.). Similarly, Applicant’s claims recite steps for collecting information (i.e., receiving the first and second user data and survey data); analyzing the data (training and updating the machine learning algorithm with the first and second user data and survey data, and transforming the second user data into a vector); and displaying certain results about the collection and analysis (i.e., generating the score indicative of the depression of the user and generating the depression severity map), at a high level of generality. Therefore, the aforementioned underlined claim limitations may reasonably be interpreted as mental/manual observations, evaluations, judgments, and/or opinions made by a person, such as a healthcare professional. If a claim limitation, under its broadest reasonable interpretation, covers concepts which are capable of being performed in the human mind or encompasses a human performing the step(s) mentally with the aid of a pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. See 2019 Revised PEG. Accordingly, claims 1 and 16 recite an abstract idea that falls within the Mental Processes category.
Furthermore, Examiner notes that dependent claims 2-4, 6, 7, 9-15, 17-19, 21, 22, and 24-30 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract) as set forth below. Examiner notes that: (1) dependent claims 2, 4, 6, 7, 9-12, 14, 17, 19, 21, 22, 24-27, and 29 include limitations that are deemed to be additional elements, and require further analysis under Prong Two of Step 2A; and (2) dependent claims 3, 13, 15, 18, 28, and 30 do not provide any limitations that are deemed to be additional elements which require further analysis under Prong Two of Step 2A. For example, claims 3, 13, 15, 18, 28, and 30 merely recite further steps for making determinations with the score; comparing the score to a threshold; or further limiting the type of data that is received and used to generate the score (i.e., these steps are deemed to be reasonably performed mentally or manually using a pen and paper, because they modify the data that is used for the observations, evaluations, judgments, and/or opinions).
Step 2A of the 2019 Revised PEG - Prong Two
Regarding Prong Two of Step 2A of the 2019 Revised PEG, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted in the 2019 Revised PEG, it must be determined whether any additional elements in the claims are indicative of integrating the abstract idea 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.” See MPEP § 2106.05(h).
In the present case, for independent claim 16, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A system (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)) for monitoring mental health of a user, the system comprising (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)):
memory configured to store computer-executable instructions (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)); and
at least one computer processor configured to access memory and execute the computer-executable instructions to (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)):
train a machine learning algorithm using semi-supervised and/or self-supervised learning techniques and based on a plurality of general user data comprising general physiological, general activity and/or general sleep data each corresponding to a plurality of general users different than the user, the plurality of general user data comprising unlabeled user data, and the machine learning model adapted to generate a score indicative of a degree of depression (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h));
receive a plurality of first user data from at least one user device (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)), the plurality of first user data comprising first physiological, first activity, and/or first sleep data corresponding to the user and associated with a calibration phase;
receive survey data corresponding to at least one depressive state of the user and associated with the first time period;
update the machine learning algorithm by further training the machine learning algorithm using the plurality of first user data and the survey data resulting in an updated machine learning algorithm (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h));
predict, based on the plurality of first user data and the survey data, at least one first threshold value corresponding to a depression severity at a first time point in a monitoring phase later in time than the calibration phase and at least one second threshold value corresponding to the depression severity at a second time point in the monitoring phase after the first time point, the first threshold value being larger than the second threshold value;
receive a plurality of second user data from at least one user device (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)), the plurality of second user data corresponding to second physiological, second activity, and/or second sleep data corresponding to the user and associated with the first time point;
transform the plurality of second user data into at least one vector, the at least one vector representative of the plurality of second user data; and
generate a first score using the updated machine learning algorithm (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)) and the at least one vector, the first score indicative of a first degree of depression of the user at the first time point;
determine the first score exceeds the first threshold value to indicate a depression relapse at the first time period; and
generate a depression severity map visually representative of the depression severity at the first time point, the depression severity map configured to visually indicate a deviation from a baseline value for each of a plurality of data modalities at the first time point, the plurality of data modalities comprising at least two of physiological, activity, and/or sleep data.
However, the recitation of these generic computer components and functions in claim 16 are recited at a high-level of generality (i.e., using generic computer devices to perform the abstract idea of: a method for monitoring mental health of a user, comprising: receiving first user data comprising first physiological data, first activity data, and/or first sleep data at a first time period; receiving survey data related to at least one depressive state of the user at the first time period; receiving second user data comprising second physiological data, second activity data, and/or second sleep data at a second time period; transforming the second user data into a vector; predicting a depression severity based on two threshold values at two different time points; generating a score using the vector, where the score is indicative of a degree of depression of the user; determining when the score exceeds a threshold indicating a depression relapse at a first time point; and generating a depression severity map representative of the depression severity at the first point in time), such that it amounts to no more than: (1) adding the words “apply it” (or is the equivalent of) with the judicial exception; mere instructions to implement an abstract idea on a computer; or merely uses a computer as a tool to perform an abstract idea; (2) adding insignificant extra-solution activity to the judicial exception; and (3) generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.05(f)-(h). For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
- The following is an example of court decisions that demonstrate merely applying instructions by reciting the computer structure as a tool to implement the claimed limitations (e.g., see MPEP § 2106.05(f)):
- Reciting only the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, e.g., see Intellectual Ventures I v. Symantec – similarly, the steps directed to: “training machine learning algorithm using semi-supervised and/or self-supervised learning techniques” and “updating the machine learning algorithm by further training the machine learning algorithm” merely recite the idea of a solution or outcome (i.e., training a machine learning algorithm/model) without reciting the necessary details to show how the machine learning algorithm is actually trained to perform the step of generating the first score.
- A commonplace business method or mathematical algorithm being applied on a general purpose computer, e.g., see Alice Corp. Pty. Ltd. v. CLS Bank Int’l – similarly, the current invention implements the commonplace medical business method of collecting user data and generating a health related score using generic computing devices (i.e., the Examiner submits that the additional elements directed to the system, comprising a memory; executable instructions, and processor; and at least one user device, are generic computer devices).
- The following are examples of generally linking the use of a judicial exception to a particular technological environment or field of use (e.g., see MPEP § 2106.05(h)):
- (1) Specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, FairWarning v. Iatric Sys.; (2) Specifying that the abstract idea of using advertising as currency is used on the Internet, because this narrowing limitation is merely an attempt to limit the use of the abstract idea to a particular technological environment, Ultramercial, Inc. v. Hulu; and (3) Requiring that the abstract idea of creating a contractual relationship that guarantees performance of a transaction (a) be performed using a computer that receives and sends information over a network, or (b) be limited to guaranteeing online transactions, because these limitations simply attempted to limit the use of the abstract idea to computer environments, buySAFE Inc. v. Google, Inc. - similarly, the limitations directed to “training machine learning algorithm using semi-supervised and/or self-supervised learning techniques” and “updating the machine learning algorithm by further training the machine learning algorithm” amounts to limiting the abstract idea to the field of machine learning technologies. See MPEP § 2106.05(h).
Thus, the additional elements in independent claims 1 and 16 are not indicative of integrating the judicial exception into a practical application. Similarly, dependent claims 3, 13, 15, 18, 28, and 30 do not recite any additional elements outside of those identified as being directed to the abstract idea described above. Examiner notes that dependent claims 2, 4, 6, 7, 9-12, 14, 17, 19, 21, 22, 24-27, and 29 recite the following additional elements identified in bold font below (with limitations deemed to be part of the above identified abstract idea identified in underlined font):
wherein the updated machine learning algorithm comprises an input layer, a plurality of feature extraction layers, a plurality of recurrent layers, and at least one dense layer that is a fully connected layer (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)) (as described in claims 2 and 17);
wherein at least the dense layer processes the historical data and the updated machine learning algorithm generates the first score based on both the historical data and the plurality of second user data (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)) (as described in claims 4 and 19);
wherein the updated machine learning algorithm is a recurrent deep neural network (RDNN) (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)) (as described in claims 6 and 21);
wherein the updated machine learning algorithm comprises a regression model and a classification model (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)) (as described in claims 7 and 22);
further comprising sending, based on the first score exceeding the first threshold value, an alert to a user device and/or a health care provider device corresponding to the presence of the depression relapse at the second time period (the Examiner submits that this additional element amounts to adding insignificant extra-solution activity as noted below, see MPEP § 2106.05(g); the Examiner further submits that such steps are not unconventional as they merely consist of receiving data over a network, as evidenced by the Intellectual Ventures v. Symantec case, as noted below in the Step 2B Analysis Section, see MPEP § 2106.05(d)) (as described in claims 9 and 24);
wherein the alert is sent to the user device (the Examiner submits that this additional element amounts to adding insignificant extra-solution activity as noted below, see MPEP § 2106.05(g); the Examiner further submits that such steps are not unconventional as they merely consist of receiving data over a network, as evidenced by the Intellectual Ventures v. Symantec case, as noted below in the Step 2B Analysis Section, see MPEP § 2106.05(d)), the method further comprising receiving feedback data from the user device after the alert is sent to the user device, the feedback data rejecting the first score and/or the alert (the Examiner submits that this additional element amounts to adding insignificant extra-solution activity as noted below, see MPEP § 2106.05(g); the Examiner further submits that such steps are not unconventional as they merely consist of receiving data over a network, as evidenced by the Intellectual Ventures v. Symantec case, as noted below in the Step 2B Analysis Section, see MPEP § 2106.05(d)) (as described in claims 10 and 25);
further comprising updating the updated machine learning algorithm by further training the updated machine learning algorithm using the feedback data (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)) (as described in claims 11 and 26);
determining, based on the first score exceeding the first threshold value, a corrective action corresponding to the presence of the depression relapse; and sending instructions corresponding to the corrective action to the user device (the Examiner submits that this additional element amounts to adding insignificant extra-solution activity as noted below, see MPEP § 2106.05(g); the Examiner further submits that such steps are not unconventional as they merely consist of receiving data over a network, as evidenced by the Intellectual Ventures v. Symantec case, as noted below in the Step 2B Analysis Section, see MPEP § 2106.05(d)) (as described in claims 12 and 27); and
generating, automatically, a report comprising the plurality of second user data and the first score; and sending the report to a healthcare provider device (the Examiner submits that this additional element amounts to adding insignificant extra-solution activity as noted below, see MPEP § 2106.05(g); the Examiner further submits that such steps are not unconventional as they merely consist of receiving data over a network, as evidenced by the Intellectual Ventures v. Symantec case, as noted below in the Step 2B Analysis Section, see MPEP § 2106.05(d)) (as described in claims 14 and 29).
As such, the additional elements in claims 1, 2, 4, 6, 7, 9-12, 14, 16, 17, 19, 21, 22, 24-27, and 29 are not indicative of integrating the judicial exception 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, unlike the claims that have been held as a whole to be directed to an improvement or otherwise directed to something more than the abstract idea, claims 1-4, 6, 7, 9-19, 21, 22, and 24-30: (1) are not directed to improvements to the functioning of a computer, or to any other technology or technical field similar to the Enfish, LLC v. Microsoft Corp. case (see MPEP § 2106.05(a)); (2) do not apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see MPEP § 2106.04(d)(2)); (3) do not apply the judicial exception with, or by use of, a particular machine (see MPEP § 2106.05(b)); (4) do not effect a transformation or reduction of a particular article to a different state or thing (see MPEP § 2106.05(c)); nor do they (5) 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 whole is more than a drafting effort designed to monopolize the exception (see MPEP § 2106.05(e) and MPEP § 2106.04(d)(2)). For these reasons, claims 1-4, 6, 7, 9-19, 21, 22, and 24-30 do not recite additional elements that integrate the judicial exception into a practical application.
Step 2B of the 2019 Revised PEG
Regarding Step 2B of the 2019 Revised PEG, claims 1-4, 6, 7, 9-19, 21, 22, and 24-30 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 abstract idea into a practical application, the additional elements of claims 1, 2, 4, 6, 7, 9-12, 14, 16, 17, 19, 21, 22, 24-27, and 29 amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). Further the additional elements, other than the abstract idea per se, when considered both individually and as an ordered combination, amount to no more than limitations consistent with what the courts recognize, or those having ordinary skill in the art would recognize, to be well-understood, routine, and conventional computer components. See MPEP § 2106.05 (d).
Specifically, the Examiner submits that the additional elements of claims 1, 2, 4, 6, 7, 9-12, 14, 16, 17, 19, 21, 22, 24-27, and 29, as recited, the system; memory configured to store computer-executable instructions; computer process; machine learning algorithm; at least one user device; healthcare provider device; and the steps directed to: “training a machine learning algorithm using semi-supervised and/or self-supervised learning techniques and based on a plurality of general user data comprising general physiological, general activity and/or general sleep data each corresponding to a plurality of general users different than the user, the plurality of general user data comprising unlabeled user data, and the machine learning model adapted to generate a score indicative of a degree of depression”; “updating the machine learning algorithm by further training the machine learning algorithm using the plurality of first user data and the survey data resulting in an updated machine learning algorithm”; “wherein the updated machine learning algorithm comprises an input layer, a plurality of feature extraction layers, a plurality of recurrent layers, and at least one dense layer that is a fully connected layer”; “wherein at least the dense layer processes the historical data and the updated machine learning algorithm generates the first score based on both the historical data and the plurality of second user data”; “wherein the updated machine learning algorithm is a recurrent deep neural network (RDNN)”; “wherein the updated machine learning algorithm comprises a regression model and a classification model”; “further comprising sending, based on the first score exceeding the first threshold value, an alert to a user device and/or a health care provider device corresponding to the presence of the depression relapse at the second time period”; “wherein the alert is sent to the user device”; “the method further comprising receiving feedback data from the user device after the alert is sent to the user device, the feedback data rejecting the first score and/or the alert”; “further comprising updating the updated machine learning algorithm by further training the updated machine learning algorithm using the feedback data”; “sending instructions corresponding to the corrective action to the user device”; and “sending the report to a healthcare provider device”, are well-understood, routine, and conventional functions. See MPEP § 2106.05(d)(II).
- In regard to the system; memory configured to store computer-executable instructions; computer process; machine learning algorithm; at least one user device; healthcare provider device; and the steps of: “training a machine learning algorithm using semi-supervised and/or self-supervised learning techniques and based on a plurality of general user data comprising general physiological, general activity and/or general sleep data each corresponding to a plurality of general users different than the user, the plurality of general user data comprising unlabeled user data, and the machine learning model adapted to generate a score indicative of a degree of depression”; “updating the machine learning algorithm by further training the machine learning algorithm using the plurality of first user data and the survey data resulting in an updated machine learning algorithm”; “wherein the updated machine learning algorithm comprises an input layer, a plurality of feature extraction layers, a plurality of recurrent layers, and at least one dense layer that is a fully connected layer”; “wherein at least the dense layer processes the historical data and the updated machine learning algorithm generates the first score based on both the historical data and the plurality of second user data”; “wherein the updated machine learning algorithm is a recurrent deep neural network (RDNN)”; “wherein the updated machine learning algorithm comprises a regression model and a classification model”; and “further comprising updating the updated machine learning algorithm by further training the updated machine learning algorithm using the feedback data”, these additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than well-understood, routine, and conventional activities previously known to the industry, because:
- Applicant’s disclosure supports this assertion. For example, Applicant discloses that: (1) the HCP [healthcare provider] device may be one or more electronic and/or computing devices with one or more processors (see Applicant’s original specification, as filed on January 23, 2025, paragraph [0039]); (2) the user devices may include one or more physiological devices, and may be any electrical and/or computer device (see Applicant’s original specification, as filed on January 23, 2025, paragraph [0038]); (3) the memory may include volatile memory, such as RAM, ROM, and so forth; or persistent storage, such as removable storage, optical disk storage, etc. (see Applicant’s original specification, as filed on January 23, 2025, paragraphs [0090] and [0091]); (4) the system may be implemented by special-purpose, hardware-based computer systems that perform the specified functions (see Applicant’s original specification, as filed on January 23, 2025, paragraph [0106]); and (5) the machine learning module(s) any include computer-executable instructions, code, or the like such as an embedding, classification, regression RDNN, convolutional neural network (CNN) or any other type of machine learning model (see Applicant’s original specification, as filed on January 23, 2025, paragraph [0102]). The Examiner submits that these devices represent well-understood, routine, and conventional computer devices which are known in the medical industry.
- The Examiner submits that these limitations amount to merely using a computer or other machinery as tools for performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f) and analysis of these limitations under Step 2A, Prong Two above).
- The Examiner submits that these limitations generally link the use of the judicial exception to a particular technological environment or field of use – for example, the limitations directed to: “training a machine learning algorithm using semi-supervised and/or self-supervised learning techniques and based on a plurality of general user data comprising general physiological, general activity and/or general sleep data each corresponding to a plurality of general users different than the user, the plurality of general user data comprising unlabeled user data, and the machine learning model adapted to generate a score indicative of a degree of depression”; “updating the machine learning algorithm by further training the machine learning algorithm using the plurality of first user data and the survey data resulting in an updated machine learning algorithm”; “wherein the updated machine learning algorithm comprises an input layer, a plurality of feature extraction layers, a plurality of recurrent layers, and at least one dense layer that is a fully connected layer”; “wherein at least the dense layer processes the historical data and the updated machine learning algorithm generates the first score based on both the historical data and the plurality of second user data”; “wherein the updated machine learning algorithm is a recurrent deep neural network (RDNN)”; “wherein the updated machine learning algorithm comprises a regression model and a classification model”; and “further comprising updating the updated machine learning algorithm by further training the updated machine learning algorithm using the feedback data”, amounts to limiting the abstract idea to the field of machine learning (see MPEP § 2106.05(h) and analysis of these limitations under Step 2A, Prong Two above).
Therefore, these limitations are also deemed to be well-understood, routine, and conventional under Step 2B for similar reasons since they are claimed in a generic manner.
- Regarding the steps and features directed to: “further comprising sending, based on the first score exceeding the first threshold value, an alert to a user device and/or a health care provider device corresponding to the presence of the depression relapse at the second time period”; “wherein the alert is sent to the user device”; “the method further comprising receiving feedback data from the user device after the alert is sent to the user device, the feedback data rejecting the first score and/or the alert”; “sending instructions corresponding to the corrective action to the user device”; and “sending the report to a healthcare provider device” - The following represents an example that courts have identified to be well-understood, routine, and conventional activities (e.g., see MPEP § 2106.05(d)):
- Receiving or transmitting data over a network, e.g., see Intellectual Ventures v. Symantec – the aforementioned limitations directed to: “further comprising sending, based on the first score exceeding the first threshold value, an alert to a user device and/or a health care provider device corresponding to the presence of the depression relapse at the second time period”; “wherein the alert is sent to the user device”; “the method further comprising receiving feedback data from the user device after the alert is sent to the user device, the feedback data rejecting the first score and/or the alert”; “sending instructions corresponding to the corrective action to the user device”; and “sending the report to a healthcare provider device”, are similarly deemed to be well-understood, routine, and conventional activity in the medical field, because they also represent mere collection and transmission of data over a network (i.e., “receiving/sending” the alerts, feedback data, and report are the equivalent of receiving or transmitting data over a network).
Therefore, the additional limitations described in claims 1, 2, 4, 6, 7, 9-12, 14, 16, 17, 19, 21, 22, 24-27, and 29 are deemed to be additional elements which do not amount to significantly more than the abstract idea identified above.
Thus, taken alone, the additional elements of claims 1, 2, 4, 6, 7, 9-12, 14, 16, 17, 19, 21, 22, 24-27, and 29 do not amount to significantly more than the above-identified judicial exception (the abstract idea). Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functionality of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation. Therefore, whether taken individually or as an ordered combination, claims 1, 2, 4, 6, 7, 9-12, 14, 16, 17, 19, 21, 22, 24-27, and 29 are nonetheless rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Additionally, dependent claims 3, 13, 15, 18, 28, and 30 (which depend on claims 1 and 16 due to their respective chains of dependency), do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Examiner notes that claims 3, 13, 15, 18, 28, and 30 do not include any additional elements beyond those identified as well-understood, routine, and conventional components as described above in the subject matter eligibility rejections of independent claims 1 and 16. Dependent claims 3, 13, 15, 18, 28, and 30 merely add limitations that further narrow the abstract idea described in independent claims 1 and 16. Therefore, claims 1-4, 6, 7, 9-19, 21, 22, and 24-30 are also nonetheless rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
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/N.A.A./Examiner, Art Unit 3686
/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686
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