DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on June 24, 2026, has been entered.
Response to Amendment
Claims 1, 7, 8, 14, 15, and 18 have been amended. Claims 1-20 are pending and are provided to be examined upon their merits.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. A response is provided below in bold where appropriate.
Applicant notes claim objections, pg. 12 of Remarks:
Claims 1-18 are objected to because of informalities.
In view of the foregoing amendments, it is respectfully submitted the above objections have been overcome.
Withdrawn based on the claim amendments.
Applicant argues 35 USC §112 Rejection, pg. 12 of Remarks:
Claim Rejections under 35 U.S.C. § 112
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In view of the foregoing amendments, it is respectfully submitted the above objection has been overcome.
The prior rejection is withdrawn. However, the amendments have caused a new rejection.
Applicant argues 35 USC §101 Rejection, starting pg. 13 of Remarks:
Claim Rejections under 35 U.S.C. § 101
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claim invention is directed to an abstract idea without significantly more.
Under the 2019 Guidance, the first prong of Step 2A involves determining whether the claims fall within three "enumerated groupings of abstract ideas" found in previous guidelines: "mathematical concepts," "certain methods of organizing human activity," and "mental processes." If a claim does not "fall within" one of these categories, then a claim is likely directed to patentable subject matter. The Examiner contends that the claims represent a "mental process" and "certain methods of organizing human activity." Applicant respectfully disagrees.
Applicant disagrees with the Examiner's characterization of the claims and analysis under Step 2A. As amended the claims describe a process for obtaining, by a processor using a measurement system, a first set of user data; inputting, the first set of user data into a predictive model to determine a first score; determining, by the processor using the first score, an indicator associated with a mental state status of a user; determining, by the processor based on the indicator, a category associated with a self-regulation of the mental state status of the user, wherein the category indicates one or more of a plurality of zones for the user, wherein the plurality of zones include a first zone, a second zone, a third zone, and a fourth zone, wherein the first zone indicates that the user is in a first brain mode, wherein the second zone indicates that the user is in a second brain mode, wherein the third zone indicates that the user is in a third brain mode, wherein the fourth zone indicates that the user is in a fourth brain mode; determining, by the processor, a first gamification application for the user based on the category associated with the one or more of the plurality of zones; and presenting, by the processor using the measurement system, the category, and the first gamification application associated with the one or more of the plurality of zones to the user. None of these steps can be performed "in the human mind" as they are all specifically tied to concrete technologies. Further, the processing performed is explicitly performed by a computing device and thus there are indeed multiple elements of the claim that "preclude the [claim] from being performed in the human mind."
As claimed, using a computer is not enough to make abstract claims statutory. Further, a person can obtain data, analyze the data, and provide an output. For example, a physician can diagnose and treat a patient. That is an abstract concept as both certain methods of organizing human activity and mental processes.
Further, the claims are not directed toward "mental processes." The Office Action characterizes Claim 1 as being directed to a mental process. Applicant respectfully submits that this characterization is incorrect, particularly in view of the USPTO memorandum issued on August 4, 2025, which reminds examiners that the mental-process grouping is limited to claim limitations that can practically be performed in the human mind, and that a claim does not recite a mental process when it contains limitations that cannot practically be performed in the human mind because "the human mind is not equipped to perform the claim limitation(s)." The memorandum further cautions examiners not to expand the mental-process grouping to encompass AI-related claim limitations that cannot practically be performed in the human mind.
The independent claims do not recite artificial intelligence (AI). Claims 7 and 14 recite a neural network, at a high level of generality. Also, a person can predict a first score, as predict is using a neural network model at a high level of generality. Further, there is no improvement to AI technology itself.
Furthermore, these steps are not organizing "human activity." This category is primarily directed towards "economic" or "commercial" practices, as well as managing human relationships. As described in detail above, the claims are directed to a series of specific operations for obtaining, by a processor using a measurement system, a first set of user data; inputting, the first set of user data into a predictive model to determine a first score; determining, by the processor using the first score, an indicator associated with a mental state status of a user; determining, by the processor based on the indicator, a category associated with a self-regulation of the mental state status of the user, wherein the category indicates one or more of a plurality of zones for the user, wherein the plurality of zones include a first zone, a second zone, a third zone, and a fourth zone, wherein the first zone indicates that the user is in a first brain mode, wherein the second zone indicates that the user is in a second brain mode, wherein the third zone indicates that the user is in a third brain mode, wherein the fourth zone indicates that the user is in a fourth brain mode; determining, by the processor, a first gamification application for the user based on the category associated with the one or more of the plurality of zones; and presenting, by the processor using the measurement system, the category, and the first gamification application associated with the one or more of the plurality of zones to the user. Applicant further notes that there is no human directly involved in the claimed process, instead the communications are executed by the processor. Thus, there are no financial or economic elements in the claims.
Certain methods of organizing human activity are not limited to financial or economic elements. See MPEP 2106.04(a)(2) II C of “Managing Personal Behavior or Relationships or Interactions Between People.”
From MPEP 2106.04(a)(2) II…
“… Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the “certain methods of organizing human activity” grouping. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.
Therefore, a person interacting with a computer is included in certain methods of organizing human activity.
Thus, the claims do not fall into an Office-enumerated category of abstract idea and fail prong one of Step 2A. Since the claims to not pass prong one, the claims are per se not directed to an abstract idea.
Again, Applicant respectfully submits that the claims are not properly characterized as "mental processes" or "organizing human activity." As amended, the claims recite a processor-driven pipeline including: obtaining user data via a measurement system, inputting the data into a predictive model to generate scores, determining mental state indicators, mapping the indicators into self-regulation categories (zones), selecting a corresponding gamification application, and presenting the results through the measurement system.
Critically, the claimed invention is rooted in a technical measurement-and-inference architecture, in which the gamification application operates as an engineered input mechanism for extracting measurable mental features, and the predictive model is designed to learn latent mental patterns from those extracted features. The claimed process therefore requires computerized measurement, machine learning inference, and model-driven output generation that cannot practically be performed in the human mind.
Applicant’s above arguments are not commensurate with the scope of their claims. Further, the Office has provided examples (July 2024 SME) of using AI as not enough to make abstract claims statutory.
Thus, Applicant respectfully submits that the present claims reflect an integrated technical system where: (i) gameplay is purposefully structured to generate meaningful machine-learning-ready measurement data, and (ii) the machine learning model is specifically configured to learn and infer mental/self-regulation patterns from that data, resulting in improved technical functionality for detecting and communicating mental state status indicators and zone classifications.
The rejection is respectfully maintained but modified for the claim amendments based on the above response.
The Applicant hereby authorizes permission for Internet Communications. Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application via video conferencing, instant messaging, or electronic mail.
Applicant is requested to comply with MPEP 502.03 II for Internet communications (i.e. fill out and submit appropriate form). Examiner thanks Applicant in advance.
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 an abstract idea without significantly more.
Claims 1-20 are directed to a method, product, or system, which are statutory categories of invention. (Step 1: YES).
The Examiner has identified method Claim 1 as the claim that represnts the claimed invention for analysis and is similar to product Claim 8 and system Claim 15.
Claim 1 recites the limitations of:
A computer-implemented method, comprising:
obtaining, by a processor using a measurement system, a first set of user data;
inputting, by the processor, the first set of user data into a predictive model to determine a first score;
determining, by the processor using the first score, an indicator associated with a mental state status of a user;
determining, by the processor based on the indicator, a category associated with a self-regulation of the mental state status of the user, wherein the category indicates one or more of a plurality of zones for the user, wherein the plurality of zones include a first zone, a second zone, a third zone, and a fourth zone, wherein the first zone indicates that the user
is in a first brain mode,
wherein the second zone indicates that the user is
in a second brain mode,
wherein the third zone indicates that the user
is in a third brain mode,
wherein the fourth zone indicates that the user
is in a fourth brain mode;
determining, by the processor, a first gamification application for the user based on the category associated with the one or more plurality of zones; and
presenting, by the processor using the measurement system, the category, and the first gamification application associated with the one or more of the plurality of zones to the user.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. The claim recites elements, highlighted in bold above, which covers performance of the limitation as a managing personal behavior. Obtaining a first set of user data, inputting the first set of data into a predictive model to determine a first score, determining an indicator associated with a mental state status of the user, determining a gamification application for the user based on a category associated with zones, and presenting the category and first gamification application associated with zones to the user is determining the mental state of a user in order to determine an appropriate game application, is managing personal behavior by following rules and instructions and teaching. Also, obtaining information of a user, determining a category associated with self-regulation of mental state status, determining a first gamification application for the user, and presenting the category and first gamification application is diagnosing and treating a user, which is managing personal behavior. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a managing personal behavior, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Claims 8 and 15 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
In as much as the claims are obtaining data, determining an indicator, a category, and a gamification application, and presenting the category and gamification application, the claims are obtaining, analyzing and providing a result. The claims can be performed in the mind of a person and/or with pen and paper, and therefore are abstract under Mental Processes grouping of abstract ideas. See also MPEP 2106.04(a)(2) III C, where use of a computer for a mental process was still abstract.
This judicial exception is not integrated into a practical application. In particular, the claims only recite: computer, processor (Claim 1); non-transitory machine-readable medium, processor, memory (Claim 8); processor, memory (Claim 15). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The “measurement system” is not defined in terms of hardware and appears to be about receiving data and displaying the mental state of a user at a high level of generality (see para. [0032] of the specification). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1, 8 and 15 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware 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. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Steps such as obtaining (receiving) are steps that are considered insignificant extra solution activity and mere instructions to apply the exception using general computer components (see MPEP 2106.05(d), II). Thus claims 1, 8, and 15 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-7, 9-14, and 16-20 further define the abstract idea that is present in their respective independent claims 1, 8 and 15 and thus correspond to Certain Methods of Organizing Human Activity and Mental Processes and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. The claims themselves are abstract or further limit abstract elements. Claims 2, 5, 7, 9, 12, 14, 16, 19 apply models at a high level of generality. Claims 7 and 14 recite neural network for the predictive model at a high level of generality. Claims 2-5 also recite a generic processor at a high level of generality. Therefore, the claims 2-7, 9-14, and 16-20 are directed to an abstract idea or recite models or processor at a high level of generality. Thus, the claims 1-20 are not patent-eligible.
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 1-20 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.
Claim 1 recites “wherein the plurality of zones include a first zone, a second zone, a third zone, and a fourth zone, wherein the first zone indicates that the user is in a first brain mode, wherein the second zone indicates that the user is in a second brain mode, wherein the third zone indicates that the user is in a third brain mode, wherein the fourth zone indicates that the user is in a fourth brain mode;…” where there is no teaching of first, second third, and fourth zone and first, second third, and fourth brain mode.
From Applicant’s specification:
“The self-regulation game module 204 determines a category associated with a self-regulation of the mental state status of the user. The category may indicate a zone of regulation for the student. In some implementations, the zone of regulation includes a blue zone, a green zone, a red zone, and a yellow zone.” [0040]
Therefore, the specification only teaches blue, green, red and yellow zone, not first-fourth zones and first-fourth brain modes. Claims 8 and 15 have a similar problem.
Claims 2-7, 9-14, and 16-20 are further rejected as they depend from their respective independent claim.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “wherein the plurality of zones include a first zone, a second zone, a third zone, and a fourth zone, wherein the first zone indicates that the user is in a first brain mode, wherein the second zone indicates that the user is in a second brain mode, wherein the third zone indicates that the user is in a third brain mode, wherein the fourth zone indicates that the user is in a fourth brain mode;…” where it is indefinite as to the first-fourth zones and first-fourth brain modes. There is no teaching as to what these are. For examination purposes, they are interpreted as anything that a brain may think, feel, etc.
Claims 2-7, 9-14, and 16-20 are further rejected as they depend from their respective independent claim.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 4, 6-9, 11, 13-16, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Pub. No. US 2022/0157466 to Vaughan et al. in view of Pub. No. US 2022/0351855 to Ohiomoba and in view of Pub. No. US 2021/0133509 to Wall et al.
Regarding claims 1, 8, and 15
(clam 1) A computer-implemented method, comprising:
obtaining, by a processor using a measurement system, a first set of user data;
{
From Applicant’s specification on first data…
“Process 1000 may include additional implementations, such as any single implementation or any combination of implementations described below and/or in connection with one or more other processes described elsewhere herein. In a first implementation, the first data includes at least one: of survey data, cognitive data, creativity data, mindfulness data, or social behavior data.” [00124]
}
Vaughan et al. teaches:
System to collect, process, and evaluate provided data (measurement system)…
“In some instances, the system can be configured to use digital diagnostics and digital therapeutics. Digital diagnostics and digital therapeutics can comprise a system or methods for digitally collecting information and processing and evaluating the provided data to improve the medical, psychological, or physiological state of an individual. A digital therapeutic system can apply software based learning to evaluate user data, monitor and improve the diagnoses and therapeutic interventions provided by the system.” [0018]
Receiving (obtaining) subject (user) initial set of data…
“The categorical determination may comprise a presence of the cognitive function attribute and an absence of the cognitive function attribute. Receiving data from the subject may comprise receiving an initial set of data. Evaluating the data from the subject may comprise evaluating the initial set of data using a preliminary subset of tunable machine learning assessment models selected from the plurality of tunable machine learning assessment models to output a numerical score for each of the preliminary subset of tunable machine learning assessment models.” [0027]
Processors with software to access and capture (obtain) various patient interaction and feedback data…
“In one aspect, the digital personalized medicine system can comprise digital devices with processors and associated software that can be configured to: use data to assess and diagnose a patient; capture interaction and feedback data that identify relative levels of efficacy, compliance and response resulting from the therapeutic interventions; and perform data analysis. Such data analysis can include artificial intelligence, including for example machine learning, and/or statistical models to assess user data and user profiles to further personalize, improve or assess efficacy of the therapeutic interventions.” [0017]
Methods performed by computer processor….
“Methods as described herein can be implemented by way of machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system 801, such as, for example, on the memory 810 or electronic storage unit 815. The machine executable or machine readable code can be provided in the form of software. During use, the code can be executed by the processor 805. In some cases, the code can be retrieved from the storage unit 815 and stored on the memory 810 for ready access by the processor 805. In some situations, the electronic storage unit 815 can be precluded, and machine-executable instructions are stored on memory 810.” [0154]
inputting, by the processor, the first set of user data into a predictive model to determine a first score;
Evaluating (inputting) data to a machine (computer processor) learning assessment (predictive) model to assess (determine) an output a score…
“The categorical determination may comprise a presence of the cognitive function attribute and an absence of the cognitive function attribute. Receiving data from the subject may comprise receiving an initial set of data. Evaluating the data from the subject may comprise evaluating the initial set of data using a preliminary subset of tunable machine learning assessment models selected from the plurality of tunable machine learning assessment models to output a numerical score for each of the preliminary subset of tunable machine learning assessment models.” [0027]
determining, by the processor using the score, an indicator associated with a mental state status of the user;
Determining cognitive function attribute (mental state indicator) of the subject using the score…
“The method may further comprise: combining the numerical scores for each of the preliminary subset of assessment models to generate a combined preliminary output score; and mapping the combined preliminary output score to a categorical determination or to an inconclusive determination as to the presence or absence of the cognitive function attribute in the subject, wherein the ratio of inconclusive to categorical determinations can be adjusted.” [0029]
Processor for evaluating subject…
“In one aspect, a method of providing an evaluation of at least one cognitive function attribute of a subject may comprise: on a computer system having a processor and a memory storing a computer program for execution by the processor, the computer program comprising instructions for: receiving data of the subject related to the cognitive function attribute; evaluating the data of the subject using a machine learning model; and providing an evaluation for the subject, the evaluation selected from the group consisting of an inconclusive determination and a categorical determination in response to the data. The machine learning model may comprise a selected subset of a plurality of machine learning assessment models.” [0026]
determining, by the processor based on the indicator, a category associated with a self-regulation of the mental state status of the user,
{
From Applicant’s specification on self-regulation…
“The client devices 102 provide user data to the mental health prediction system 104 and launches personalized self-regulation games that help student center themselves and get ready to learn. User data may include daily emotion data, students' in-game behavior records, reward histories, and insightful feedback from educators.” [0037]
Therefore, self-regulation is related to a user centering themselves based on their mental state.
}
Cognitive function attribute (mental state indicator) and classification of the subject (category)…
“The methods and apparatus disclosed herein can account for different subject-specific dimensions such as, for example, a subject's age, a geographic location associated with a subject, a subject's gender or any other subject-specific or demographic data associated with a subject. In particular, the methods and apparatus disclosed herein can take different subject-specific dimensions into account in identifying the subject as at risk of having one or more cognitive function attributes such as developmental conditions, in order to increase the sensitivity and specificity of evaluation, diagnosis, or classification of the subject. For example, subjects belonging to different age groups may be evaluated using different machine learning assessment models, each of which can be specifically tuned to identify the one or more developmental conditions in subjects of a particular age group. Each age group-specific assessment model may contain a unique group of assessment items (e.g., questions, video observations), wherein some of the assessment items may overlap with those of other age groups' specific assessment models.” [0015]
Digital therapeutics and feedback with interactions and games to the patient…
“Digital therapeutics can comprise instructions, feedback, activities or interactions provided to the patient or caregiver by the system. Examples include suggested behaviors, activities, games or interactive sessions with system software and/or third party devices.” [0023]
Example of self-assessment (self-regulation)…
“Additional datasets may be obtained from large archival data repositories as described herein, such as the Autism Genetic Resource Exchange (AGRE), Boston Autism Consortium (AC), Simons Foundation, National Database for Autism Research, and the like. Alternatively or in combination, additional datasets may comprise mathematically simulated data, generated based on archival data using various simulation algorithms. Alternatively or in combination, additional datasets may be obtained via crowd-sourcing, wherein subjects self-administer the assessment procedure as described herein and contribute data from their assessment. In addition to data from the self-administered assessment, subjects may also provide a clinical diagnosis obtained from a qualified clinician, so as to provide a standard of comparison for the assessment procedure.” [0196]
Processors to assess users data…
“In another aspect, a digital personalized medicine system as described herein comprises digital devices with processors and associated software configured to: receive data to assess and diagnose a patient; capture interaction and feedback data that identify relative levels of efficacy, compliance and response resulting from the therapeutic interventions; and perform data analysis, including at least one or machine learning, artificial intelligence, and statistical models to assess user data and user profiles to further personalize, improve or assess efficacy of the therapeutic interventions.” [0197]
Classifiers with therapy (category associated with mental status of user)…
“The classifiers as disclosed herein are particularly well suited for combination with this data to provide improved therapy and treatment. The data can be stratified and used with a feedback loop as described herein. For example, the feedback data can be used in combination with a drug therapy to determine differential responses and identify responders and non-responders. Alternatively or in combination, the feedback data can be combined with non-drug therapy, such as behavioral therapy.” [0212]
wherein the category indicates one or more of a plurality of zones for the user, wherein the plurality of zones include a first zone, a second zone, a third zone, and a fourth zone, wherein the first zone indicates that the user is in a first brain mode, wherein the second zone indicates that the user is in a second brain mode, wherein the third zone indicates that the user is in a third brain mode, wherein the fourth zone indicates that the user is in a fourth brain mode;
“The training module 110 can utilize a machine learning algorithm or other algorithm to construct and train an assessment model to be used in the assessment procedure, for example. An assessment model can be constructed to capture, based on the training data, the statistical relationship, if any, between a given feature value and a specific developmental disorder to be screened by the assessment procedure. The assessment model may, for example, comprise the statistical correlations between a plurality of clinical characteristics and clinical diagnoses of one or more developmental disorders. A given feature value may have a different predictive utility for classifying each of the plurality of developmental disorders to be evaluated in the assessment procedure. For example, in the aforementioned example of a feature comprising the ability of the subject to engage in imaginative or pretend play, the feature value of “3” or “no variety of pretend play” may have a high predictive utility for classifying autism, while the same feature value may have low predictive utility for classifying ADHD. Accordingly, for each feature value, a probability distribution may be extracted that describes the probability of the specific feature value for predicting each of the plurality of developmental disorders to be screened by the assessment procedure. The machine learning algorithm can be used to extract these statistical relationships from the training data and build an assessment model that can yield an accurate prediction of a developmental disorder when a dataset comprising one or more feature values is fitted to the model.” [0112]
See Zones below.
determining, by a processor, a first gamification application for the user based on the category associated with the one or more plurality of zones; and
Diagnosing and identifying (determining and classifying) cognitive function attribute (mental state indicator) to provide digital therapeutics (gamification) as needed…
“Additionally, it would be helpful if diagnostic methods and treatments could be applied to subjects to advance cognitive function for subjects with advanced, normal and decreased cognitive function. In light of the above, improved methods and systems of diagnosing and identifying subjects at risk for a particular cognitive function attribute such as a developmental disorder and for providing improved digital therapeutics are needed. Ideally such methods and apparatus would require fewer questions, decreased amounts of time, determine a plurality of cognitive function attributes, such as behavioral, neurological or mental health conditions or disorders, and provide clinically acceptable sensitivity and specificity in a clinical or nonclinical environment, which can be used to monitor and adapt treatment efficacy. Moreover, improved digital therapeutics can provide a customized treatment plan for a patient, receive updated diagnostic data in response to the customized treatment plan to determine progress, and update the treatment plan accordingly. Ideally, such methods and apparatus can also be used to determine the developmental progress of a subject, and offer treatment to advance developmental progress.” [0009]
Digital therapeutics include suggested (determining) games…
“Digital therapeutics as described herein can comprise of instructions, feedback, activities or interactions provided to the patient or caregiver by the system. Examples include suggested behaviors, activities, games or interactive sessions with system software and/or third party devices (for example, the Internet of Things “IoT” enabled therapeutic devices as understood by one of ordinary skill in the art).” [0217]
presenting, by the processor using the measurement system, the category, and the first gamification application associated with the one or more of the plurality of zones to the user.
User’s interaction with (presenting) games…
“Types of data collected and utilized by the system can include patient and caregiver video, audio, responses to questions or activities, and active or passive data streams from user interaction with activities, games or software features of the system, for example. Such data can also include meta-data from patient or caregiver interaction with the system, for example, when performing recommended activities. Specific meta-data examples include data from a user's interaction with the system's device or mobile app that captures aspects of the user's behaviors, profile, activities, interactions with the software system, interactions with games, frequency of use, session time, options or features selected, and content and activity preferences. Data can also include data and meta-data from various third party devices such as activity monitors, games or interactive content.” [0022]
Output based on categorical determination…
“In another aspect, digital therapeutic system to treat a subject with a personal therapeutic treatment plan may comprise: one or more processors comprising software instructions; a diagnostic module to receive data from the subject and output diagnostic data for the subject, the diagnostic module comprising one or more classifiers built using machine learning or statistical modeling based on a subject population to determine the diagnostic data for the subject, and wherein the diagnostic data comprises an evaluation for the subject, the evaluation selected from the group consisting of an inconclusive determination and a categorical determination in response to data received from the subject; and a therapeutic module to receive the diagnostic data and output the personal therapeutic treatment plan for the subject, the therapeutic module comprising one or more models built using machine learning or statistical modeling based on at least a portion the subject population to determine and output the personal therapeutic treatment plan of the subject, wherein the diagnostic module is configured to receive updated subject data from the subject in response to therapy of the subject and generate updated diagnostic data from the subject and wherein the therapeutic module is configured to receive the updated diagnostic data and output an updated personal treatment plan for the subject in response to the diagnostic data and the updated diagnostic data.” [0038]
Provide categorial determinations…
“FIG. 15 shows an exemplary questionnaire screening algorithm configured to provide only categorical determinations of a developmental condition as described herein. In particular, the questionnaire screening algorithm depicted in FIG. 15 shows an alternating decision tree classifier that outputs a determination indicating only the presence or the absence of autism. The different shading depicts the total population of children who are autistic and not autistic and who are evaluated via the questionnaire. Also depicted are the results of the classifier, showing the correctly and incorrectly diagnosed children populations for each of the two categorical determinations.” [0189]
See Zones below.
See Present Category and Gamification below.
Zones
The combined references teach categories. They do not specifically teach four zones.
Ohiomoba also in the business of categories teaches:
Different moods (zones)…
“… The computing system may use a first machine learning model to predict a first mental state of the first user based on the obtained first electronic data and predict respective second mental states of the second users based on the obtained second electronic data. In some embodiments, a mental state may be generally defined as a distribution of mood values (or, simply, moods) over time. For example, mood values may include “angry,” “sad,” “happy,” and/or other predefined or otherwise generally understood moods. Accordingly, it will be appreciated that, in some embodiments, a mood value is discrete, while a mental state is contiguous. The computing system, based on the predicted mental state(s) of the first user and/or the second users, can intelligently predict a match between the first user and one or more of the second users using another machine learning model. For example, the computing system can provide the predicted mental state(s) of the first user and/or the second users to the machine learning model, and the machine learning model can output a value indicative of a successful or unsuccessful match.” [0033]
Categories…
“As discussed elsewhere herein, mental states typically are not predefined. For example, while the machine learning-based state prediction engine 212 may recognize and/or define general categories of mental state (e.g., depressed, bipolar, and/or the like), the predicted mental states themselves may be unique. Accordingly, two different users may have different mental states (e.g., as indicated by their respective mappings) but fall within the same category of mental state (e.g., depressed). The visualization engine 214 may organize the subset of graphical elements based on the predicted mental state of the user, and not necessarily upon associated category of mental state. Thus, two different users that may be predicted to fall into a “depressed” mental category, may nonetheless be presented with a different organization of graphical elements.” [0091]
Mental states with first-fourth mood regions (zones)…
“As shown, the three-dimensional coordinate system 550 includes three-axes (e.g., the x-axis, the y-axis, and the z-axis). The plotted points (e.g., first plotted point 560, second plotted point 570, and third plotted point 580) may represent respective moods at different times for an individual. For example, one individual may be associated with multiple points (e.g., first plotted point 562 and second plotted point 572) that each represent a particular mood at a particular point in time. The mental state may comprise the set of those plotted points associated with that individual. The points may be plotted in various mood regions of the three-dimensional coordinate system 550. For example, the mood regions may include a first mood region 552 (e.g., a happy mood region), a second mood region 554 (e.g., a sad mood region), a third mood region 556 (e.g., an angry mood region), and a fourth mood region 558. Each point may be associated with a magnitude value (e.g., 1.3 on a scale of 0.0 to 10.0, with 10.0 being the highest value indicating the strongest mood) and a radius indicating an uncertainty value associated with the plotted point. For example, a longer radius may indicate a higher uncertainty in the predicted mood and/or plotted point, and a short radius may indicate a lower uncertainty. In some examples, a plotted point may effectively overlap multiple mood regions based on the associated uncertainty value. For example, the second plotted point 570 has a magnitude value 572 of 9.5, and a radius 574 that extends into the second mood region.” [0114]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the Vaughn et al. the ability to have four regions (zones) as taught by Ohiomoba since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Ohiomoba who teaches the importance of evaluating different moods.
Present Category and Gamification
The combined references teach category and games. They also teach four zones with moods. They do not specifically teach presenting category and game.
Wall et al. also in the business of category and games teaches:
Improve state of the individual…
“In some instances, the system can be configured to use digital diagnostics and digital therapeutics. Digital diagnostics and digital therapeutics, in some embodiments, together comprise a device or methods for digitally collecting information and processing and evaluating the provided data to improve the medical, psychological, or physiological state of an individual. A digital therapeutic system can apply software based learning to evaluate user data, monitor and improve the diagnoses and provide therapeutic interventions. In some embodiments, a digital therapy is configured to improve social reciprocity in individuals with autism or autism spectrum disorder by helping them identify expressions of emotion in real time while they interact with a person or a virtual image that expresses the emotion.” [0023]
Match an emotional expression (category)…
“An emotion guessing game stores previous images that the child has evaluated mixed with stock face images (from pre-reviewed sources). The goal of this activity is to (a) review images that were not evaluated correctly by the children and have the caregiver correct it and (b) reinforce and remind the child of their correct choices to improve retention. The child can then try to correctly match or label the emotional expressions displayed in the images. The goal from this EGG is to reinforce the learnings from the augmented reality unstructured play session in a different, 2D environment. It also provides additional social interaction opportunities between caregiver and child to review and discuss the emotions together.” [0670]
Image with game…
(A) A game shows three images that the patient has collected (may be mixed with stock images) that have been classified as showing three different emotions: happy, sad, and angry. The game provides a visual and audio prompt asking the patient to select the image that shows the “happy” emotion. The patient selects a image, and is then given feedback based on whether the selection is correct. The patient proceeds to complete several of these activities using various images that have been collected.” [0672]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to present category and game as taught by Wall et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Wall et al. who teaches the advantages of using games to improve the psychological state of an individual.
Regarding claims 2, 9, and 16
(claim 2) The method of claim 1, further comprising:
Obtaining, by the processor, a second set of user data;
Vaughan et al. teaches:
Capture interaction (obtaining second set of data) of a patient (user)…
“In one aspect, the digital personalized medicine system can comprise digital devices with processors and associated software that can be configured to: use data to assess and diagnose a patient; capture interaction and feedback data that identify relative levels of efficacy, compliance and response resulting from the therapeutic interventions; and perform data analysis. Such data analysis can include artificial intelligence, including for example machine learning, and/or statistical models to assess user data and user profiles to further personalize, improve or assess efficacy of the therapeutic interventions.” [0017]
Inputting, by the processor, the second set of user data into the predictive model to determine a second score; and
Capture interaction (inputting second data) for models….
“In another aspect, a digital personalized medicine system as described herein comprises digital devices with processors and associated software configured to: receive data to assess and diagnose a patient; capture interaction and feedback data that identify relative levels of efficacy, compliance and response resulting from the therapeutic interventions; and perform data analysis, including at least one or machine learning, artificial intelligence, and statistical models to assess user data and user profiles to further personalize, improve or assess efficacy of the therapeutic interventions.” [0197]
Prediction model obtain additional input values….
“…If the new data comprises data collected in real time from the subject or caretaker during the prediction process, such that the dataset is updated with each new input data value provided to the prediction module and each updated dataset is fitted to the assessment model, the prediction module may be able to query the subject for additional feature values. If the prediction module has already obtained data for all features included in the assessment module, the prediction module may output “no diagnosis” as the predicted classification of the subject, as shown in step 440. If there are features that have not yet been presented to the subject, as shown in step 435, the prediction module may obtain additional input data values from the subject, for example by presenting additional questions to the subject. The updated dataset including the additional input data may then be fitted to the assessment model again (step 415), and the loop may continue until the prediction module can generate an output.” [0124]
Comparing, by the processor, the second score to the first score to determine feedback score.
Relative (comparing) levels of efficacy and compliance (feedback score) based on updated data (second score)…
“… The personal therapeutic treatment plan may comprise digital therapeutics. The digital therapeutics may comprise instructions, feedback, activities or interactions provided to the subject or caregiver. The digital therapeutics may be provided with a mobile device. The diagnostics data and the personal therapeutic treatment plan may be provided to a third-party system. The third-party system may comprise a computer system of a health care professional or a therapeutic delivery system. The diagnostic module may be configured to receive updated subject data from the subject in response to a feedback data of the subject and generate updated diagnostic data. The therapeutic module may be configured to receive the updated diagnostic data and output an updated personal treatment plan for the subject in response to the diagnostic data and the updated diagnostic data. The updated subject data may be received in response to a feedback data that identifies relative levels of efficacy, compliance and response resulting from the personal therapeutic treatment plan…” [0101]
Regarding claims 4 and 11
(claim 4) The method of claim 2, wherein the second score is less than the first score, further comprising:
Removing, by the processor, the first gamification application from the category; and
Vaughan et al. teaches:
Suggest changes to (removing) digital treatment (game)…
“…Once a treatment has been initiated, the questions, symptoms, or observations can be repeated or different questions, symptoms, or observations can be used to more accurately monitor progress and suggest changes to the digital treatment. The relevance of a next question, symptom or observation can also depend on the variance of the ultimate assessment among different answer choices of the question or potential options for an observation. For example, a question for which the answer choices might have a significant impact on the ultimate assessment down the line can be deemed more relevant than a question for which the answer choices might only help to discern differences in severity for one particular condition, or are otherwise less consequential.” [0025]
Score with threshold, where score is mapped to categorical determination…
“The combinatorial model output score can then be subjected to thresholds determined during the model training phase as described herein. In particular, as shown in FIG. 14, these thresholds are indicated by the dashed regions that partition the range of numerical scores 1460 into three segments corresponding to a negative determination output 1470, an inconclusive determination output 1480, and a positive determination output 1490. This effectively maps the combined numerical score to a categorical determination, or to an inconclusive determination if the output is within the predetermined inconclusive range.” [0187]
determining, by the processor, a second gamification application.
Change (therefore, determine second) digital treatment (game)…
“…Once a treatment has been initiated, the questions, symptoms, or observations can be repeated or different questions, symptoms, or observations can be used to more accurately monitor progress and suggest changes to the digital treatment. The relevance of a next question, symptom or observation can also depend on the variance of the ultimate assessment among different answer choices of the question or potential options for an observation. For example, a question for which the answer choices might have a significant impact on the ultimate assessment down the line can be deemed more relevant than a question for which the answer choices might only help to discern differences in severity for one particular condition, or are otherwise less consequential.” [0025]
Regarding claims 6, 13, and 20
(claim 6) The method of claim 2, wherein the first set of user data and the second set of user data include at least one of:
survey data,
cognitive data,
Vaughan et al. teaches:
Cognitive function attribute (data)…
“In another aspect, a system to evaluate of at least one cognitive function attribute of a subject may comprise: a processor configured with instructions that when executed cause the processor to: present a plurality of questions from a plurality of chains of classifiers, the plurality of chains of classifiers comprising a first chain comprising a social/behavioral delay classifier and a second chain comprising a speech & language delay classifier. The social/behavioral delay classifier may be operatively coupled to an autism & ADHD classifier. The social/behavioral delay classifier may be configured to output a positive result if the subject has a social/behavioral delay and a negative result if the subject does not have the social/behavioral delay. The social/behavioral delay classifier may be configured to output an inconclusive result if it cannot be determined with a specified sensitivity and specificity whether or not the subject has the social/behavioral delay. The social/behavioral delay classifier output may be coupled to an input of an Autism and ADHD classifier and the Autism and ADHD classifier may be configured to output a positive result if the subject has Autism or ADHD…” [0040]
creativity data,
mindfulness data, or
social behavior data.
Social/behavioral delay (data)…
“In another aspect, a system to evaluate of at least one cognitive function attribute of a subject may comprise: a processor configured with instructions that when executed cause the processor to: present a plurality of questions from a plurality of chains of classifiers, the plurality of chains of classifiers comprising a first chain comprising a social/behavioral delay classifier and a second chain comprising a speech & language delay classifier. The social/behavioral delay classifier may be operatively coupled to an autism & ADHD classifier. The social/behavioral delay classifier may be configured to output a positive result if the subject has a social/behavioral delay and a negative result if the subject does not have the social/behavioral delay. The social/behavioral delay classifier may be configured to output an inconclusive result if it cannot be determined with a specified sensitivity and specificity whether or not the subject has the social/behavioral delay. The social/behavioral delay classifier output may be coupled to an input of an Autism and ADHD classifier and the Autism and ADHD classifier may be configured to output a positive result if the subject has Autism or ADHD…” [0040]
Regarding claims 7 and 14
(claim 7) The method of claim 1, wherein the predictive model is a deep neural network model.
Vaughan et al. teaches:
Convolutional neural nets (deep learning)…
“The training module may comprise feature selection. One or more feature selection algorithms (such as support vector machine, convolutional neural nets) may be used to select features able to differentiate between individuals with and without certain developmental disorders. Different sets of features may be selected as relevant for the identification of different disorders. Stepwise backwards algorithms may be used along with other algorithms. The feature selection procedure may include a determination of an optimal number of features.” [0118]
Regarding claim 18
(claim 4) The system of claim 16, wherein the second score is less than the first score, further comprising:
removing the first gamification application from the category; and
Vaughan et al. teaches:
Suggest changes to (removing) digital treatment (game)…
“…Once a treatment has been initiated, the questions, symptoms, or observations can be repeated or different questions, symptoms, or observations can be used to more accurately monitor progress and suggest changes to the digital treatment. The relevance of a next question, symptom or observation can also depend on the variance of the ultimate assessment among different answer choices of the question or potential options for an observation. For example, a question for which the answer choices might have a significant impact on the ultimate assessment down the line can be deemed more relevant than a question for which the answer choices might only help to discern differences in severity for one particular condition, or are otherwise less consequential.” [0025]
Score with threshold, where score is mapped to categorical determination…
“The combinatorial model output score can then be subjected to thresholds determined during the model training phase as described herein. In particular, as shown in FIG. 14, these thresholds are indicated by the dashed regions that partition the range of numerical scores 1460 into three segments corresponding to a negative determination output 1470, an inconclusive determination output 1480, and a positive determination output 1490. This effectively maps the combined numerical score to a categorical determination, or to an inconclusive determination if the output is within the predetermined inconclusive range.” [0187]
determining a second gamification application.
Change (therefore, determine second) digital treatment (game)…
“…Once a treatment has been initiated, the questions, symptoms, or observations can be repeated or different questions, symptoms, or observations can be used to more accurately monitor progress and suggest changes to the digital treatment. The relevance of a next question, symptom or observation can also depend on the variance of the ultimate assessment among different answer choices of the question or potential options for an observation. For example, a question for which the answer choices might have a significant impact on the ultimate assessment down the line can be deemed more relevant than a question for which the answer choices might only help to discern differences in severity for one particular condition, or are otherwise less consequential.” [0025]
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over the combined references in section (10) above in further view of Pub. No. US 2025/0118442 to Guttikonda et al.
Regarding claims 3, 10 and 17
(claim 3) The method of claim 2, wherein the second score is greater to or equal than the first score, further comprising:
retaining, by the processor, the first gamification application in the category.
Vaughan et al. teaches:
Score with threshold, where score is mapped to categorical determination…
“The combinatorial model output score can then be subjected to thresholds determined during the model training phase as described herein. In particular, as shown in FIG. 14, these thresholds are indicated by the dashed regions that partition the range of numerical scores 1460 into three segments corresponding to a negative determination output 1470, an inconclusive determination output 1480, and a positive determination output 1490. This effectively maps the combined numerical score to a categorical determination, or to an inconclusive determination if the output is within the predetermined inconclusive range.” [0187]
Example of additional data to discern conclusive output (second score greater than first score)…
“In the case of an inconclusive output, the evaluation module can determine that additional data should be obtained from the subject in order to load and run additional models beyond the preliminary or initial set of models. The additional models might be well suited to discern a conclusive output in cases where the preliminary models might not. This outcome can be realized by training additional models that are more sophisticated in nature, more demanding of detailed input data, or more focused on the harder-to-classify cases to the exclusion of the straightforward ones.” [0188]
The combined reference teaches digital therapeutic as games. They also teach train. They do not teach retrain.
Guttikonda et al. also in the business of digital therapeutics teaches:
Re-train to modify second digital therapeutic content (therefore, game)…
“In some embodiments, the computing system may re-train the ML model using the one or more portions to modify the second digital therapeutic content. In some embodiments, the computing system may modify a prompt applied to a generative transformer model used to output the first digital therapeutic content, responsive to determining the non-compliance for the second digital therapeutic content, wherein the generative transformer model is a part of or separate from the ML model. In some embodiments, the computing system may retrain a generative transformer model used to generate the first digital therapeutic content, based on the indication of one of non-compliance or compliance for the second digital therapeutic content.” [0033]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined reference the ability to retrain digital therapeutics as taught by Guttiknnda et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Gottikonda et al. who teaches the advantages of retraining digital content.
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over the combined references in section (10) above in further view of Pub. No. US 2017/0004260 to Moturu et al. in view of Pub. No. US 2022/0304604 to Costea
Regarding claims 5, 12, and 19
(claim 5) The method of claim 1, further comprising:
obtaining, by the processor, additional data including school reports, clinician reports, family reports, user reports, educator reports, and counsellor reports; and
Vaughan et al. teaches:
Clinician answers (reports)…
“As described herein, the features of interest relevant to identifying one or more developmental disorders may be evaluated in a subject in many ways. For example, the subject or caretaker or clinician may be asked a series of questions designed to assess the extent to which the features of interest are present in the subject. The answers provided can then represent the corresponding feature values of the subject. The user interface may be configured to present a series of questions to the subject (or any person participating in the assessment procedure on behalf of the subject), which may be dynamically selected from a set of candidate questions as described herein. Such a question-and-answer based assessment procedure can be administered entirely by a machine, and can hence provide a very quick prediction of the subject's developmental disorder(s).” [0142]
Family member assessment (report)…
“…The diagnostic module may be configured for an adult to perform an assessment or provide data for an assessment of a child or juvenile. The diagnostic module may be configured for a caregiver or family member to perform an assessment or provide data for an assessment of the subject.” [0101]
Capture interaction (obtaining second set of data) of a patient (user)…
“In one aspect, the digital personalized medicine system can comprise digital devices with processors and associated software that can be configured to: use data to assess and diagnose a patient; capture interaction and feedback data that identify relative levels of efficacy, compliance and response resulting from the therapeutic interventions; and perform data analysis. Such data analysis can include artificial intelligence, including for example machine learning, and/or statistical models to assess user data and user profiles to further personalize, improve or assess efficacy of the therapeutic interventions.” [0017]
See Educator and Counsellor Reports below.
See School Report below.
updating the predictive model based on the additional data.
Update prediction model with new data…“… If the prediction module cannot fit the data to any specific developmental disorder within a confidence interval at or exceeding the designated threshold value, the prediction module may determine, in step 430, whether there are any additional features that can be queried. If the new data comprises a previously-collected, complete dataset, and the subject cannot be queried for any additional feature values, “no diagnosis” may be output as the predicted classification, as shown in step 440. If the new data comprises data collected in real time from the subject or caretaker during the prediction process, such that the dataset is updated with each new input data value provided to the prediction module and each updated dataset is fitted to the assessment model, the prediction module may be able to query the subject for additional feature values. If the prediction module has already obtained data for all features included in the assessment module, the prediction module may output “no diagnosis” as the predicted classification of the subject, as shown in step 440. If there are features that have not yet been presented to the subject, as shown in step 435, the prediction module may obtain additional input data values from the subject, for example by presenting additional questions to the subject. The updated dataset including the additional input data may then be fitted to the assessment model again (step 415), and the loop may continue until the prediction module can generate an output.” [0124]
Educator and Counsellor Reports:
The combined references teach providing data. They do not teach educator and counsellor.
Moturu et al. also in the business of providing data teaches:
Receive data provided by care giver…
“As shown in FIGS. 1A-1B, 2A, and 3A-3B, Block S125 recites: receiving a care provider dataset in association with a time period, which functions to receive active data provided in association with a care provider, for use in selecting and/or promoting a therapeutic intervention, generating and/or dynamically modifying a dynamic care plan, and/or any other purpose.” [0064]
Care giver as coach (educator) and guardian, friend (counsellor)…
“In relation to Block S125, a care provider can include any one or more of: a psychiatrist, physician, healthcare professional, health coach, therapist, guardian, friend, and/or any suitable provider of care for one or more patients.” [0065]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to provide educator and counsellor reports as taught by Moturu et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Moturu et al. who teaches the advantages of information provided by care givers that can include coach (educator) and guardian/friend (counsellor).
School Report
The combined references teach report and behavior. They do not teach school report.
Costea also in the business of report and behavior teaches:
School Report card…
“Therefore, a method, device and system addressing the key goals of behavior parent training, should support attending to child's misbehavior while optimizing the achievement of positive behavior. Such a system is directed towards automating the implementation of Home Token Economy and using a School Daily Report Card while indirectly facilitating the achievement of other goals for behavior parent training such as managing non-compliant behavior when in public and anticipating future misconduct.” [0006]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to provide school reports as taught by Costea since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Costea who teaches the advantages of using school report information for managing behavior.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNETH BARTLEY whose telephone number is (571)272-5230. The examiner can normally be reached Mon-Fri: 7:30 - 4:00 EST.
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/KENNETH BARTLEY/Primary Examiner, Art Unit 3684
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