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 .
This communication is in response to the amendment received on 04/14/2026. Claims 1-2, 5-10, 12-13 and 16-22 remain pending in this application.
The claim objection to claims 21 and 22 has been withdrawn in light of the amendments.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-2, 5-10, 12-13 and 16-22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
In particular, claims 1 and 10 have been amended to recite “training a machine learning model for evaluating a mental health state using the corpus of survey data, wherein the response data used to train the machine learning model is differentially weighted based on its associated static confidence vector”, which appears to constitute new matter.
In particular, Applicant does not point to, nor was the Examiner able to find, any support for this feature. The current specification recites “Such unreliable responses can lead to misdiagnoses of survey takers. However, consequences of unreliable responses can extend far beyond the correctness of a diagnosis of a given survey taker. Unreliable responses can render any statistical analysis or modeling of the corpus less accurate and less useful. Examples include analysis for population assessments, for monitoring, or for assessment of therapeutic treatments including medications. Examples also include Al systems that are trained to predict depression and that use the survey data as ground truth estimates for model training and evaluation. Some percentage of the survey data used for analysis, interpretation or machine learning based models will contain problems of the types just mentioned, resulting in suboptimal interpretations and suboptimal models.” in [0004].
As such, Applicant is respectfully requested to clarify the above issues and to specifically point out support for the newly added limitations in the originally filed specification and claims.
Applicant is required to cancel the new matter in the reply to this Office action.
Claims 2, 5-9, 12-13 and 16-22 incorporate the deficiencies of independent claims 1 and 10, through dependency, and are also rejected.
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 1-2, 5-10, 12-13 and 16-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-2, 5-10, 12-13 and 16-22 are drawn to a method which is within the four statutory categories (i.e. process).
Step 2A, Prong 1:
Claims 1-2, 5-10, 12-13 and 16-20 are provided below with markings separating abstract elements from the additional limitations, wherein the bolded represents the additional limitations beyond abstract idea, and remaining limitations are directed to the abstract idea as discussed below.
Claim 1. “A method for generating a corpus of survey data for training machine learning models from responses received from a human subject in a mental health survey for evaluating a mental health state, the method comprising:
(a) administering the mental health survey to the human subject to cause the human subject to generate response data in response to one or more prompts of the mental health survey and measuring a latency of the human subject before responding to the one or more prompts by selecting the one or more prompts from a plurality of prompts using a computer system and transmitting the one or more prompts to a client device so as to cause the client device to present the one or more prompts to the human subject;
(b) obtaining the response data and response metadata with the computer system, wherein the response data comprises a plurality of conditioning events and a plurality of conditioned events and the response metadata comprises a latency of the human subject before responding to the one or more prompts;
(c) determining, with the computer system, a first probability that a first conditioned event is present in the response data based in part on a presence of a first conditioning event in the response data using a machine learning model, wherein the first probability is based on analysis of the corpus of survey data by finding all surveys with the conditioning event and determining a probability of the conditioned event from all surveys with the conditioning event;
(d) repeating steps (b) and (c) for two or more other conditioned events and other conditioning events to generate a plurality of additional probabilities for a plurality of additional event pairs with the computer system using the machine learning model, wherein the additional probabilities are based in part on the response metadata including the latency of the human subject before responding to the one or more prompts, and wherein the additional probabilities are based on analysis of the corpus of survey data by finding a second plurality of surveys with other conditioning events and determining a probability of the other conditioned events from the second plurality of surveys with the other conditioning events;
(e) combining, with the computer system, one or more probabilities from the first probability and the plurality of additional probabilities to generate single source confidence vector data;
(f) determining, with the computer system, cross source confidence vector data by comparing the response data to responses across multiple passes;
(h) determining, with the computer system, metadata confidence vector data based in part on the response metadata including the latency of the human subject before responding to the one or more prompts;
(g) combining the single source, cross source, and metadata confidence vector data to produce a static confidence vector, wherein the static confidence vector represents a measure of confidence in the reliability of the human subject that generated the response data in response to the mental health survey;
(i) determining, with a computer system, that the static confidence vector is below a predetermined threshold;
(j) in response to (i), intervening, with the computer system, in (a) to improve a quality of the response data by: sending intervention data to the client device, wherein the intervention data causes a client device to present a message to the human subject to influence responses of the human subject to prompts; or terminating the survey without presenting prompts other than the presented prompts; and
(k) training a machine learning model for evaluating a mental health state using the corpus of survey data, wherein the response data used to train the machine learning model is differentially weighted based on its associated static confidence vector.
Claim 2. The method of claim 1, wherein step is carried out using a machine learning model that is trained using a corpus of survey data.
Claim 5. The method of claim 1, wherein the metadata confidence vector is based on a comparison of a distribution of latencies of the human subject to an expected latency distribution.
Claim 6. The method of claim 1, wherein the metadata confidence vector is based on a comparison of a test duration to an expected test duration.
Claim 7. The method of claim 1, wherein the metadata confidence vector is further based on a difference between the latency of the human subject before responding to the prompts and an expected latency.
Claim 8. The method of claim 7, wherein the expected latency is based in part on a previous latency obtained when the human subject provided a previous response to a query in the survey.
Claim 9. The method of claim 7, wherein the expected latency is based in part on behavioral metadata.
Claim 10. A method for generating a corpus of survey data for training machine learning models from responses received from a human subject in a mental health survey for evaluating a mental health state, the method comprising:
(a) obtaining (i) a response to a query in a survey and (ii) metadata about the response, wherein the metadata comprises a latency for the response, wherein the survey is delivered to the human subject by sending the query as a prompt to a client device so as to cause the client device to present the prompt to the human subject, wherein the prompt is selected from a plurality of prompts by a computer system; and
(b) determining single source confidence vector data based on a plurality of probabilities that a plurality of conditioned events are present in the response data based on a presence of a plurality of conditioning events using a machine learning model;
(c) determining cross source confidence vector data by comparing the response data to responses across multiple passes;
(d) determining, based at least in part on a difference between the metadata and an expected latency, metadata confidence vector data, wherein the expected latency is based on analysis of the corpus of survey data;
(e) combining the single source, cross source, and metadata confidence vector data to produce a static confidence vector, wherein the static confidence vector represents a measure of confidence in the reliability of the human subject that generated the response data in response to the mental health survey;
(f) determining that the static confidence vector is below a predetermined threshold;
(g) in response to (f), intervening in the survey by at least one of: sending intervention data to the client device administering the survey, wherein the intervention data causes the client device to present a message to the human subject to influence responses of the human subject to prompts; or terminating the survey without presenting prompts other than the presented prompts; and
(h) training a machine learning model for evaluating a mental health state using the corpus of survey data, wherein the response data used to train the machine learning model is differentially weighted based on its associated static confidence vector.
Claim 12. The method of claim 10, wherein: the response to the query in the survey comprises a plurality of responses to a plurality of queries in the survey; the metadata comprises one or more latencies for one or more of the plurality of responses; the expected latency comprises a plurality of expected latencies; and determining the static confidence vector comprises determining the reliability of the plurality of responses to one or more prompts of the mental health survey.
Claim 13. The method of claim 10, wherein the expected latency is based in part on a previous latency obtained when the human subject provided a previous response to the query in the survey.
Claim 16. The method of claim 10, wherein the metadata confidence vector of the response is based on a difference between a latency of the human subject before responding to prompts associated with the survey and an expected latency.
Claim 17. The method of claim 16, wherein the expected latency is based in part on a previous latency obtained when the human subject provided a previous response.
Claim 18. The method of claim 10, wherein the metadata confidence vector of the response is based on a comparison of a distribution of a latency of the human subject to an expected latency distribution.
Claim 19. The method of claim 10, wherein the metadata confidence vector is based on a comparison of a test duration to an expected test duration.
Claim 20. The method of claim 10, wherein the expected latency is based in part on behavioral metadata.
Claim 21. The method of claim 1, wherein: the predetermined threshold comprises an intervention threshold and a termination threshold, upon determining that the static confidence vector is below the intervention threshold and at or above the termination threshold, the computer system sends the intervention data to the client device, and upon determining that the static confidence vector is below the termination threshold, the computer system terminates the survey without presenting the prompts other than the presented prompts and omits the response data from the corpus of survey data.
Claim 22. The method of claim 1, wherein: the predetermined threshold comprises an intervention threshold and a termination threshold, upon determining that the static confidence vector is below the intervention threshold and at or above the termination threshold, the computer system sends the intervention data to the client device, and upon determining that the static confidence vector is below the termination threshold, the computer system terminates the survey without presenting the prompts other than the presented prompts and omits the response data from the corpus of survey data.
Claims 1 and 10 are specifically directed to the abstract idea (See limitations not bolded above) of a mental process. The limitations of “administering a survey…, obtaining a response data…, determining a first probability…, repeating the steps…, combining two or more probabilities…, determining that the measure of confidence is below a threshold…”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components (a computer system). That is, other than reciting “with the computer system,” nothing in the claim element precludes the step from practically being performed in the mind, or a user manually (using pen and paper) to perform administering the survey, obtaining the response data, determining probabilities/measures, and generating steps. After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
Also, the limitations of “determining, with the computer system, a first probability that a first conditioned event is present in the response data based in part on a presence of a first conditioning event in the response data using a machine learning model; (d) repeating steps (b) and (c) for two or more other conditioned events and other conditioning events to generate a plurality of additional probabilities for a plurality of additional event pairs with the computer system using the machine learning model; training a machine learning model for evaluating a mental health state using the corpus of survey data, wherein the response data used to train the machine learning model is differentially weighted based on its associated static confidence vector” correspond to mathematical calculations, therefore the limitation falls within the “mathematical concept” grouping of abstract ideas.
The limitation of “training a machine learning model for evaluating a mental health state using the corpus of survey data” corresponds to mathematical relationships, which falls within the “mathematical concepts” grouping of abstract ideas.
Claims 2, 5-9, 12-13 and 16-22 are ultimately dependent from claims 1/10 and include all the limitations of claims 1/10. Therefore, claims 2, 5-9, 12-13 and 16-22 recite the same abstract idea. Claims 2, 5-9, 12-13 and 16-22 describe a further limitation regarding the basis for measuring degree of confidence in mental survey for a human. These are all just further describing the abstract idea recited in claims 1/10, without adding significantly more.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, claims recite the additional elements that are shown in bolded style above. These additional elements are directed to hardware and software elements, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of the abstract idea in a particular technological environment, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea (MPEP 2106.05(f) & (h)).
The computer system in claim steps is recited at a high-level of generality (i.e., as a generic computer system performing generic computer functions of determining and generating) such that they amount no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer system to perform both the determining and generating steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claims are not patent eligible.
Response to Arguments
Applicant's arguments filed 04/14/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed below in the order in which they appear.
Applicant argues that claim limitation of “training a machine learning model for evaluating a mental health state using the corpus of survey data, wherein the response data is weighed based on the static confidence vector” may involve mathematical concepts, but it does not recite any mathematical concepts.
In response, Examiner submits that this feature corresponds to mathematical calculations, since “training” explicitly recites performing mathematical calculations-response data used to train the machine learning model is differentially weighted based on its associated static confidence vector (see the 2024 Guidance update on Patent SME).
Applicant argues that claims reflect an improvement similar to claim 2 of Example 48 the way as described in the current specification [0006]-[0008].
In response, Examiner submits that the current specification recites “Given these confidence annotations, any analysis of behavioral health survey results, e.g., statistical analysis and computational modeling through artificial intelligence (AI) such as deep machine learning, can be significantly more accurate and useful. For example, in such analysis, survey results with lower confidence can be weighted less or disregarded altogether while survey results with higher confidence can be weighted more heavily.” In [0007]. Examiner submits that providing more accurate outcome by using artificial intelligence, such as deep machine learning, may provide an improvement to the outcome, however, does not provide an improvement to the technology. The trained machine learning model used to generally apply the abstract idea without limiting how the trained machine learning model functions. The machine learning model is described at a high level such that amounts to using a computer with a generic trained model to apply the abstract idea. The claim limitations do not recite how the outcomes are accomplished.
Applicant argues that the pending subject mater is novel and inventive over prior art, therefore the claims amount to significantly more than the judicial exception.
In response, Examiner submits that under current Office guidelines, patentability with respect to 35 U.S.C. 101 is not evaluated through the lens of 35 U.S.C. 103(a). Although novelty and non-obviousness may provide a useful clue in identifying limitations that are not conventional or routine in the field (79 Fed. Reg. 74624), claims that overcome rejections under 35 U.S.C. 102 or 103 are not necessarily statutory in nature.
Therefore, the arguments are not persuasive and claims are rejected under 35 U.S.C. §101 as being directed to non-statutory subject matter.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DILEK B COBANOGLU whose telephone number is (571)272-8295. The examiner can normally be reached 8:30-5:00 ET.
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/DILEK B COBANOGLU/Primary Examiner, Art Unit 3687
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