DETAILED ACTION
Notices to Applicant
This communication is a final rejection. Claims 1-20, as filed 09/09/2026, are currently pending and have been considered below.
Priority is generally acknowledged to 63/599,912 which was filed 11/16/2023.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon and the rationale supporting the rejection would be the same under either status.
Claim Objections
The amendment filed 09/09/2026 is objected to under 35 U.S.C. 132(a) and 37 CFR 1.121(f) because it introduces new matter into the disclosure. 35 U.S.C. 132(a) states that no amendment shall introduce new matter into the disclosure of the invention. The added material which is not supported by the original disclosure is as follows: “brain mode,” “neuro-state of regulation,” “current state of regulation,” “MEandMine Regulation Spectrum,” “The Fog Mode,” “The Learning Mode,” “The Guard mode,” and “The Spark Mode.” None of these terms appears in the specification as filed.
Applicant is required to cancel the new matter in the reply to this Office Action.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a process, machine, and/or article of manufacture which recite:
1. A computer-implemented method, comprising:
obtaining, by a processor, using a first measurement system, a first data;
inputting, by the processor, the first data into a predictive model to determine a first score; (additional element – insignificant extra-solution activity; mere data-gathering; applying the abstract idea with a computer)
determining, by the processor, using the first score, an indicator associated with a mental state status of a user; (mental process because a therapist can review various patient data including scores to determine mental state of a patient; to the extent that the processor is not abstract, it amounts to merely applying the abstract idea with a computer)
obtaining, by the processor, using a second measurement system, a second data;
obtaining, by the processor, using a third measurement system, a third data; (additional element – insignificant extra-solution activity; mere data-gathering)
updating, by the processor, the predictive model based on the obtained second data and obtained third data; (abstract idea – mental process)
determining, by the processor, using the updated predictive model, a wellbeing index to identify at-risk user; and (mental process because a therapist can review various patient data including scores to determine mental state of a patient)
sending, to a provider, a notification if the user is the at-risk user (additional element – insignificant extra-solution activity; data output).
Claim 1 is presented as an exemplary claim but the same analysis applies to the other claims 9 and 17.
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mental processes such as judgments a school counselor or therapist makes about a student. For example, but for the predictive model and updating the predictive model, determining a wellbeing index in the context of this claim is analogous to steps a counselor would make while reading a student’s intake questionnaire, forming a judgment of the student’s wellbeing, and deciding that the student is at-risk and needs additional attention.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims. For example, claims 2-4 recite additional types of data which amount to insignificant extra-solution activity and applying the abstract idea with a computer. Claim 5 recites CNNs at a high level such that it amounts to generally linking to a technical field. Claims 6-8 recite at a high level displaying data which amounts to applying the abstract idea with a computer. The same logic applies to the other dependent claims.
Step 2A Prong Two
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements:
amount to mere instructions to apply an exception. For example, “using the predictive model” and updating the predictive model amount to invoking computers as a tool to perform the abstract idea, see applicant’s specification [0034]-[0035], see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea. For example, obtaining first, second, and third data amounts to mere data gathering and selecting a particular data source or type of data to be manipulated, see MPEP 2106.05(g))
generally link the abstract idea to a particular technological environment or field of use such as a deep CNN in claim 5, see MPEP 2106.05(h))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. For example, claims 6-8 and 14-16 recite elements that amount to invoking computers as a tool to perform the abstract idea and types of data that are obtained and generally link the abstract idea to a particular field of use. 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 functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as amounts to receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i), performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii), electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii), and/or storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv). With respect to the deep CNN of claim 5, this technology is well-understood, routine, and conventional as set forth in the Muffat reference and the “Convolutional neural network” Wikipedia article. This conclusion is further supported by the fact that the specification merely invokes this technology at a high level without describing technical implementation details. Such a recitation is only enabled under 112(a) because this type of model is well-known in the field.
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. 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 functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Misra (US12380988B1) in view of Tyler (US20250182901A1).
Regarding claim 1, Misra discloses: A computer-implemented method, comprising:
--obtaining, by a processor, using a first measurement system, a first data (“receiving patient user input at the platform from the GUI interface displayed to the patient user device,” Abstract; “the behavioral health engagement platform 200 may be accessed by the child patient on an IOS/Android app 205 via a smartphone/tablet or a web-based app 206 via a computer or laptop,” col. 14 lines 5-10; “a processor to execute the instructions at the behavioral health engagement platform” col. 25 lines 22-23);
--inputting, by the processor, the first data into a predictive model to determine a first score (“applying the patterns of answers by the child patient to predict values for future sessions by keeping clinicians informed of favorable outcome data,” col. 16 lines 23-26; “Scoring regulator 160 operates to capture and implement the manner by which points are awarded, accumulated, tracked, and calculated” col. 12 lines 11-14);
--determining, by the processor, using the first score, an indicator associated with a mental state status of a user (“predict mental health states of the patient based on user response and inputs at the GUI (e.g., displayed to the patient's user device) responsive to questions and emotions recommending content and constructive stress coping mechanisms,” col. 27 lines 19-23);
--obtaining, by the processor, using a second measurement system, a second data (“Therapists/clinicians may also input compiled information from these various parties into the platform and standardize their assessment process to quantify and track the efficacy of the behavioral therapies they utilize through various metrics including biometric data from the child,” col. 6 lines 37-42; “The embodiments allow children, therapists, parents, other healthcare providers and school staff to contribute behavioral health data for tracking and analysis to promote positive behavioral health outcomes, implemented and facilitated by a behavioral health engagement platform 101,” col. 13 lines 34-39);
--obtaining, by the processor, using a third measurement system, a third data (“The platform allows parents and therapists to have more open and frequent communication, for example, by parents reporting information to therapists via questionnaires ahead of therapy sessions with the child instead of using valuable time during therapy sessions to do so. Therapists and parents may also receive alerts when the child reports a threshold of consecutive negative emotions over a period of sessions in which the child uses the platform,” col. 7 lines 29-36);
--determining(“creating alert notifications at the platform based on one or more of: (i) the patient user input meeting or exceeding pre-set thresholds,” col. 8 lines 16-18; “users may be notified of symptoms or episodes of the child's behavioral health warranting their attention” col. 13 lines 47-49); and
--sending, by the processor, to a provider, a notification if the user is the at-risk user (“sending the alert notification to the therapist GUI interface for display at a therapist user device and a parent GUI interface for display at a parent user device; and monitoring and promoting positive behavioral health outcomes,” col. 25 lines 49-52).
Misra does not expressly disclose but Tyler teaches:
--updating, by the processor, the predictive model based on the obtained second data and the obtained third data; [and] determining, by the processor, using the updated predictive model (“The system can update the one or more models based at least in part on newly received data from: (i) the one or more data sources,” [0178]; “the third party input comprises data from the user's doctor(s), family, friends, co-workers, therapists, counselors, teachers, professors,” [0018]; “the user specific parameters can be updated in the one or more models based on the data as it is received to ensure a real-time, near real-time, or dynamic model of the user's well-being,” [0178]).
It would have been obvious to a POSITA before the effective filing date to include the model updates of Tyler within the machine learning analysis of patient data of Misra because this would keep the model up to date with the user’s wellbeing rather than fix it in time, thus resulting in a mor effective platform for identifying at-risk patients and alerting caregivers (see Tyler [0002] and [0178]).
Additionally, each element is taught by either Misra or Tyler. Tyler’s model updates do not affect the normal functioning of the elements of the claim which are taught by Misra. Because the elements do not affect the normal functioning of each other, the results of their combination would have been predictable. Therefore, before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Tyler with the teachings of Misra since the result is merely a combination of old elements, and, since the elements do not affect the normal functioning of each other, the results of the combination would have been predictable.
Regarding claim 2, Misra discloses: wherein the first data includes at least one of: survey data,
cognitive data, creativity data, mindfulness data, or social behavior data (“responses to questions about the patient's emotional state,” col. 26 lines 33-34; “questionnaires to be distributed in order to better understand mental health development and similarly to improve and identify the most significant psychological symptoms in a specific diagnosis” col. 13 lines 64-67).
Regarding claim 3, Misra discloses: wherein the second data includes at least one of: school reports; clinician reports; or family reports (“Therapists/clinicians may also input compiled information from these various parties into the platform and standardize their assessment process to quantify and track the efficacy of the behavioral therapies they utilize through various metrics including biometric data from the child,” col. 6 lines 37-42; “The embodiments allow children, therapists, parents, other healthcare providers and school staff to contribute behavioral health data for tracking and analysis to promote positive behavioral health outcomes, implemented and facilitated by a behavioral health engagement platform,” col. 13 lines 33-38).
Regarding claim 4, Misra discloses: wherein the third data includes at least one of: user reports;
educator reports; or counsellor reports (“provides the opportunity for the child patient to report changes in their behavior and overall health in real time to these various parties and to promote engagement between the various parties outside of therapy sessions,” col. 6 lines 29-33; “input about the patient is received from subsequent users such as other healthcare providers and school staff,” col. 27 lines 60-61).
Regarding claim 5, Misra uses machine learning but does not expressly disclose a deep convolutional neural network model. Tyler teaches this (“In some embodiments, the machine learning models may comprise a deep neural network (DNN). The deep neural network may comprise a convolutional neural network (CNN). The CNN may be, for example, U-Net, ImageNet, LeNet-5, Alex Net, ZFNet, GoogleNet, VGGNet, ResNet18 or ResNet, etc,” [0171]). The motivation to combine is the same as in claim 1.
Regarding claim 6, Misra discloses: wherein the first measurement system includes a user dashboard (“Still further, the child may interact with a child GUI transmitted to the child device 106C from the dashboard GUI generator 195 which permits the child to interact with various interactive media and content as specified by the therapist or interact with content selected by the platform's consent selector 180 based on a therapist determined diagnosis or modality, etc,” col. 12 lines 41-46).
Regarding claim 7, Misra discloses: wherein the second measurement system includes an educator dashboard (“According to another embodiment of method 1500-1501, input about the patient is received from subsequent users such as other healthcare providers and school staff, wherein the alert notifications are also sent to GUI interfaces for such users to be displayed at user devices for such users,” col. 27 lines 60-63).
Regarding claim 8, Misra discloses: wherein the third measurement system includes a third party dashboard (“the parent may observe a parent dashboard to check on the status of therapy for the child or to schedule appointments or to correspond electronically with the therapist,” col. 12 lines 40-43; “Third parties such as insurance companies may also use the reported metrics that the platform provides for coverage and billing purposes,” col. 7 lines 42-44).
Claims 9-12 and 17-20 are substantially similar to claims 1-4 and are rejected with the same reasoning.
Claims 13-16 are substantially similar to claims 5-8 and are rejected with the same reasoning.
Response to arguments
Applicant's arguments filed 09/09/2026 have been fully considered and are discussed below.
The 112(b) rejections are withdrawn.
Regarding the subject matter ineligibility rejections, Applicant argues that the claimed invention is not directed to a mental process (Step 2A Prong One) because “[n]one of these steps can be performed ‘in the human mind’ as they are all specifically tied to concrete technologies,” and because a therapist “would not thereby perform the claimed processor operation of updating a computer-implemented predictive model based on data obtained from two separate measurement systems”. Remarks pages 18-20. The is not persuasive because the evaluative steps, i.e., determining an indicator and determining a wellbeing index, are judgments that the therapist could make mentally. Recitation of other features such as a processor are analyzed as additional elements. The processor, for example, is not abstract but amounts to implementing the abstract idea with a computer.
Applicant argues that the claimed invention integrates any abstract idea into a practical application (Step 2A Prong Two) because “the newly recited second and third data have a functional role in the claimed computer process” and “alter the predictive model that performs the subsequent determination”. Remarks page 22. The is not persuasive because better determinations of wellbeing achieved by feeding more of the evaluative data back into the same evaluation is an improvement to the abstract idea itself rather than an improvement to technology. MPEP 2106.05(a). All the computer features function in their typical way and amount to a straightforward computer implementation of the abstract idea. The models used in the claims are off-the-shelf architectures rather than anything inventive or otherwise serving to improve the model: “the AI models consist of advanced neural network architectures, including convolutional neural networks (CNN), deep neural networks (DNN), recurrent neural networks (RNN), deep residual convolutional neural networks, among others.” Specification [0039].
Applicant argues that the claimed invention amounts to significantly more than any abstract idea (Step 2B) because “[n]or does the Office Action establish that the newly added model-updating sequence was well-understood, routine, and conventional in the relevant field.” Remarks page 26. This is not persuasive. Applicant appears to take the position that the Examiner must establish that every element of a claim (alone and in combination) was well-understood, routine, and conventional, but this is not the inquiry of Step 2B. If Applicant’s reasoning held, then the 101 analysis would collapse into the anticipation/obviousness analysis. Instead, Step 2B focuses on additional elements which in this case amount to using computers as tools to implement the abstract idea, receiving and transmitting data over a network, electronic recordkeeping, and storing and retrieving information in memory, not the claimed determinations or updating of the model as described in the rejection above.
Regarding the prior art rejections, Applicant’s arguments are moot in view of the new ground of rejection applied above including Tyler.
Conclusion
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office Action (See MPEP 706.07(a)). Accordingly, 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 extension fee 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 JOSHUA BLANCHETTE whose telephone number is (571)272-2299. The examiner can normally be reached on Monday - Thursday 7:30AM - 6:00PM, EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shahid Merchant, can be reached on (571) 270-1360. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOSHUA B BLANCHETTE/ Primary Examiner, Art Unit 3624
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