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 .
Election/Restrictions
Applicant’s election without traverse of Invention I, claims 1-20, in the reply filed on May 19, 2026 is acknowledged. Claims 21-40 are withdrawn from consideration.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following figures mentioned in the description: Figures 18A and 18C. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 1, 2, 7, 10, 16, and 19 are objected to because of the following informalities:
In claim 1, line 10, “(neurodevelopmental/psychiatric)” should be removed.
In claim 2, line 1, “claim 1 comprising” should read “claim 1 further comprising”.
In claim 7, line 2, “via a display” should read “via the display”.
In claim 10, line 11, “(neurodevelopmental/psychiatric)” should be removed.
In claim 16, line 2, “via a display” should read “via the display”.
In claim 19, line 2, “embodied in a computer readable medium” should be removed.
In claim 19, line 11, “(neurodevelopmental/psychiatric)” should be removed.
Appropriate correction is required.
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, 10, and 19 recite a process, a computer system for performing the process, and a computer program product including the process, the process including the steps of obtaining user related information, wherein the user related information includes metrics derived from a user interacting with one or more applications executing on at least one user device; and generating, using the user related information, a user assessment report including a prediction value indicating a likelihood that the user has a neurodevelopmental or psychiatric (neurodevelopmental/psychiatric) disorder or risk for such a disorder and a prediction confidence value computed using relative contributions of the metrics to the prediction value. The recited steps, under their broadest reasonable interpretation, are obtaining user related information including metrics derived from a user interacting with one or more applications and generating a user assessment report including a prediction value and a prediction confidence value. The recited steps, as drafted, are a process that is a method of applying an abstract idea, specifically mental processes (evaluation (generating a user assessment report), observation (obtaining user related information)). If claim limitations, under their broadest reasonable interpretation, include a mental process, the limitations fall under the abstract ideas judicial exception and therefore recite ineligible subject matter. Accordingly, claims 1, 10, and 19 recite abstract ideas.
The judicial exception is not integrated into a practical application because the claims do not recite additional elements that are significantly more than the judicial exception or meaningfully limit the practice of the judicial exception. The additional elements are at least one user device; using a machine learning model; providing the user assessment report to a display or a data store; a computing platform including at least one processor and memory [claims 1 and 10]; and a non-transitory computer readable medium comprising computer executable instructions embodied in a computer readable medium that when executed by at least one processor of a computer cause the computer to perform the steps [claim 19]. The additional elements are insignificant extra-solution activity and instructions for applying the judicial exception with a generic computing device as, under their broadest reasonable interpretation, the additional step(s) is/are merely displaying, outputting or storing the results of the judicial exceptions by displaying or storing the report. The other additional elements of at least one user device, using a machine learning model, a computing platform including at least one processor and memory, and a NTCRM executed by at least one processor of a computer are generic computer components and instructions for performing the above method, per MPEP 2106.05(f). Under their broadest reasonable interpretation, the additional elements are generic components of a computing device used to apply the abstract idea. Further, the specification states the “computing platform 100 may include a mobile device, a smartphone, a tablet computer, a laptop computer, a computer, a user assessment device, or a medical device” which are interpreted as generic computing devices. With regard to the use of a machine learning model, the limitation is recited at a high level of generality and, under its broadest reasonable interpretation, is interpreted as a computer algorithm and instructions for applying the judicial exceptions with a generic computing device. As such, these additional elements are interpreted as merely instructions to apply the judicial exception. Accordingly, the additional elements and steps do not integrate the abstract idea into a practical application because they do not impose any meaningful limitations on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional step(s) of providing the user assessment report is/are insignificant extra-solution activity performed during the abstract idea. The additional elements of at least one user device, using a machine learning model, a computing platform including at least one processor and memory, and a NTCRM executed by at least one processor of a computer used to perform the process are generic computing components/device and instructions used to apply the judicial exception and therefore fall under the “apply it” limitation of the judicial exception and do not amount to significantly more per MPEP 2106.05(f). Further, the limitations, taken in combination, add nothing that is not already present when looking at the elements taken individually. As such, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, under their broadest reasonable interpretation, the additional elements do not meaningfully limit the practice of the abstract idea and do not amount to significantly more than the judicial exceptions. Therefore, claims 1, 10, and 19 are not directed to eligible subject matter as they are directed to abstract ideas without significantly more.
Claims 2-9, 11-18, and 20 are dependent from claims 1, 10, and 19, respectively, and include all the limitations of the independent claims. Therefore, the dependent claims recite the same abstract idea. The limitations of the dependent claims fail to amount to significantly more than the judicial exception. For example:
The limitations of claims 2-5, 7, 11-14, 16, and 20 recite further abstract ideas including administering a therapy for treating (certain method of organizing human activity), computing an assessment administration quality value (evaluation MP), performing a model interpretability analysis (evaluation MP), generating normalized Shapley additive explanations (evaluation MP), generate an individualized summary report (evaluation MP), providing a survey and/or stimuli to the user (CMOHA), and generating the metrics (observation/evaluation MP). As the limitations are further abstract ideas, the limitations cannot meaningfully limit or amount to significantly more than the abstract ideas of the independent claims. The additional elements of the dependent claims are further insignificant extra-solution activities including gathering data. The limitations fail to provide any teaching that integrates the judicial exceptions into a practical application or amounts to significantly more than the judicial exceptions. For this reason, the analysis performed on the independent claims is also applicable on these claims.
The limitations of claims 6, 9, 15, and 18 recite the additional elements of including a multiple tree-based extreme gradient-boosting (XGBoost) algorithm and wherein the computing platform includes a mobile device, a smartphone, a tablet computer, a laptop computer, a computer, a user assessment device, or a medical device. As discussed above, the computing platform/devices are interpreted as generic computing devices for applying the judicial exceptions. Due to the high-level of generality of the recitation of an XGBoost and the lack of specific steps or uses of the XGBoost, the limitation is interpreted as mere computer code for performing the computer functions and falls under the instructions for applying an abstract idea. Therefore, the limitations fail to provide any teaching that integrates the judicial exceptions into a practical application or amount to significantly more than the judicial exception. For this reason, the analysis performed on the independent claims is also applicable on these claims.
The limitations of claims 8 and 17 recite clarification of the types of disorders determined/analyzed. The limitations, under their broadest reasonable interpretation, are merely defining/selecting a type of data to be manipulated which, per MPEP 2106.05(g), is insignificant extra-solution activity. Therefore, the limitations fail to provide any teaching that integrates the judicial exceptions into a practical application or amount to significantly more than the judicial exception. For this reason, the analysis performed on the independent claims is also applicable on these claims.
Accordingly, claims 2-9, 11-18, and 20are directed to abstract ideas without significantly more and are not drawn to eligible subject matter.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-4, 7-13, and 16-20 is/are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Vaughan et al. (US PGPub 20190019581), hereinafter referred to as Vaughan.
With regard to claims 1, 10, and 19, Vaughan teaches a method [claim 1] (Abstract; Paragraphs 0009, 0011; “method”) and a system [claim 10] (Abstract; Paragraphs 0009, 0011; “systems”) for detection of a neurodevelopmental or psychiatric disorder using scalable computational behavioral phenotyping [claims 1 and 10] (Paragraphs 0009, 0011, 0017 teach the system and method provide digital diagnostics based on collecting and analyzing user data including diagnosing neurological or mental health disorders), and a non-transitory computer readable medium comprising computer executable instructions embodied in a computer readable medium that when executed by at least one processor of a computer cause the computer to perform steps [claim 19] (Paragraph 0272 teaches the system can be embodied in programming including processor executable code stored in a non-transitory storage), the system, method, and steps comprising:
at a computing platform including at least one processor and memory (Paragraphs 0017, 0105, 0265 teach the method and system can execute using a processor wherein the system includes a CPU and memory):
obtaining user related information, wherein the user related information includes metrics derived from a user interacting with one or more applications executing on at least one user device (Paragraphs 0170, 0175, 0179, 0186, 0188, 0197-0198, 0217 teach various user data may be collected or gathered including user interaction data with features of the system wherein the data can include the features and feature values (metrics));
generating, using the user related information and a machine learning based model, a user assessment report (Paragraphs 0019, 0046, 0184, 0218, 0240, 0262 teach the system can output diagnostic data and assessment results from a diagnostic module which may comprise a machine learning model using the collected user data) including a prediction value indicating a likelihood that the user has a neurodevelopmental or psychiatric (neurodevelopmental/psychiatric) disorder or risk for such a disorder (Paragraphs 0078, 0221, 0223, 0229, 0240 teach the system can predict the likelihood and risk of a subject having a plurality of behavioral, neurological, or mental health disorders) and a prediction confidence value computed using relative contributions of the metrics to the prediction value generated using the machine learning based model (Paragraphs 0078, 0218, 0222-0223, 0228, 0240, 0246, 0253 teach the system can evaluate a plurality of features or characteristics of the subject and determine the contribution of each feature and feature value to the diagnosis including determine a prediction confidence value based on the contributions of the features to the diagnosis using an algorithm/machine learning model); and
providing the user assessment report to a display or a data store (Paragraphs 0193, 0218, 0240, 0262, 0268 teach the system can display the data and assessment results including an informative display and store the diagnostic data and results in a database).
With regard to claims 2, 11, and 20, Vaughan further teaches further comprising: administering to the user a therapy for treating the neurodevelopmental/psychiatric disorder (Paragraphs 0188-0189, 0194, 0202, 0219 teach the system can recommend, provide, and administer treatment to the user/subject based on the diagnosis to treat the disorder or likely condition).
With regard to claims 3 and 12, Vaughan further teaches wherein the user assessment report includes an assessment administration quality value (Paragraphs 0240, 0262 teach the system can determine a confidence value for the assessment and diagnosis), wherein the assessment administration quality value indicates whether a user assessment should be readministered (Paragraphs 0240, 0262 teach the assessment steps should be repeated until the confidence exceeds a sufficient threshold) or wherein the assessment administration quality value is computed based on the metrics weighted by their relative contributions to the prediction value (Paragraphs 0218, 0246-0247, 0253 teach the confidence of the assessment and diagnosis can be based in part of the confidence and weighted importance of each feature value).
With regard to claims 4 and 13, Vaughan further teaches wherein computing the prediction confidence value includes performing a model interpretability analysis involving the metrics and the machine learning based model (Paragraphs 0193, 0224, 0228, 0241, 0243, 0246, 0253 teach the system using the algorithm/machine learning model to determine the importance or relevance of each feature and feature value and the relative sensitivity of each input/data to the diagnosis (model interpretability analysis)).
With regard to claims 7 and 16, Vaughan further teaches wherein obtaining the user related information includes providing a survey (Paragraph 0217 teaches the data collection can be through diagnostic tests or questionnaires) and/or stimuli to the user via a display (Paragraph 0260 teaches the user can be evaluated based on interactions including responses to presented stimuli), capturing user survey data and/or user interaction data using one or more input devices (Paragraphs 0176, 0179, 0186, 0217, 0260 teaches the system collected and gathers the user responses and interaction data input by sensors and user interaction with a mobile device), and generating the metrics (Paragraphs 0170, 0175, 0179, 0186, 0188, 0197-0198, 0217 teach various user data may be collected or gathered including user interaction data with features of the system wherein the data can include the features and feature values (metrics)), wherein the metrics relate to facial orientation, attention, social attention, facial expressions, head movements, eye movements, gaze, eyebrow movements, mouth movements, user responses to name, hand motor skills, visual motor skills, or any combinations thereof (Paragraphs 0172, 0180-0181, 0200, 0206, 0215, 0259 teach the data/features can include facial expressions, eye movements, gaze, eye fixations vs saccades (visual motor skills), eye focus, attention span, movement of the subject’s eyes, mouth, and hands, socialization, non-verbal communication, responding to a name).
With regard to claims 8 and 17, Vaughan further teaches wherein the neurodevelopmental/psychiatric disorder or risk for such a disorder comprises autism spectrum disorder (ASD), language or developmental delay, an attention deficient and hyperactivity disorder (ADHD), or any combination thereof (Paragraphs 0174, 0221 teach the diagnosable conditions and disorders include autism spectrum disorder, speech or language delay, learning disability, ADHD, or combinations of the disorders).
With regard to claims 9 and 18, Vaughan further teaches wherein the computing platform includes a mobile device (Paragraphs 0176, 0269; “mobile phone” or “mobile communication devices”), a smartphone (Paragraph 0269; “smart phones”), a tablet computer (Paragraph 0269; “tablet PC’s”), a laptop computer (Paragraph 0269; “portable PC”), a computer (Paragraph 0269; “personal computers”), a user assessment device (Paragraph 0269), or a medical device (Paragraph 0269; “medical device”).
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.
Claim(s) 5-6 and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vaughan in view of Maharjan et al. (US PGPub 20240120067), hereinafter referred to as Maharjan.
With regard to claims 5 and 14, Vaughan further teaches performing the model interpretability analysis (see prior art rejection of claims 4 and 13 above) and normalizing the training data (Paragraph 0225), but may not explicitly teach wherein the model interpretability analysis includes generating normalized Shapley additive explanations (SHAP) interaction values for the metrics and using the normalized SHAP interaction values for the metrics to generate an individualized summary report indicating how the metrics affected the prediction value. However, Maharjan teaches a system and method for recommending therapy for individuals with neurodevelopmental disorders using various data features and machine learning to predict a recommendation for the subject wherein feature importance can be evaluated using Shapley Additive Explanation and include a SHAP summary plot of the features for the model (Paragraphs 0052-0053, 0060, 0163-0164).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vaughan to incorporate the teachings of Maharjan by applying the technique of analyzing the features to generate SHAP values and a plot/summary of the features of Maharjan to the features and feature values of Vaughan, as both references and the claimed invention are directed to systems and methods for treatment and analysis of neurodevelopmental disorders of a subject. It would have been obvious to apply the SHAP technique of Maharjan to the features and feature values of Vaughan as part of the importance or relevance (interpretability) analysis of the features as the references are directed to similar systems and methods using feature based machine learning analysis to make predictions with regards to neurodevelopmental disorders and treatments. By applying the technique of Maharjan, one of ordinary skill in the art would expect Vaughan to be improved in the same way by presenting a summary of the most important features for the model and predictions. One of ordinary skill in the art would modify Vaughan by coding the system to evaluate the features and feature values further using Shapley Additive Explanation and generating SHAP values for each feature using the normalized data of Vaughan and presenting the values as a summary plot. Upon such modification, the method and system of Vaughan would include wherein the model interpretability analysis includes generating normalized Shapley additive explanations (SHAP) interaction values for the metrics and using the normalized SHAP interaction values for the metrics to generate an individualized summary report indicating how the metrics affected the prediction value. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Maharjan with Vaughan’s system and method in order to further determine and analyze the importance of each feature on the model outcome and present a summary of the most important features (Maharjan Paragraphs 0163-0164).
With regard to claims 6 and 15, Vaughan further teaches the machine learning /ensemble method may be optimized using a machine learning ensemble meta-algorithm such as boosting including AdaBoost, LPBoost, TotalBoost, etc. (Paragraphs 0229, 0236), but may not explicitly teach wherein the machine learning based model includes a multiple tree-based extreme gradient-boosting (XGBoost) algorithm. However, Maharjan further teaches the model can include/be an XGBoost model (Paragraphs 0112, 0153).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vaughan to incorporate the teachings of Maharjan by substituting the XGBoost model of Maharjan for the boosting algorithm of Vaughan, as both references and the claimed invention are directed to systems and methods for treatment and analysis of neurodevelopmental disorders of a subject. It would have been obvious to substitute the XGBoost model of Maharjan for the boosting algorithm of Vaughan as XGBoost is a known type of boosting model and would have been obvious to one of ordinary skill in the art. By substituting the model of Maharjan for the algorithm of Vaughan, one of ordinary skill in the art would expect Vaughan to work in the same way by applying the boost model in order to handle missingness in the data. One of ordinary skill in the art would modify Vaughan by coding the system to use an XGBoost algorithm/model to optimize and improve the machine learning model and algorithm. Upon such modification, the method and system of Vaughan would include wherein the machine learning based model includes a multiple tree-based extreme gradient-boosting (XGBoost) algorithm. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Maharjan with Vaughan’s system and method in order to improve Vaughan in the same way by improving model performance by better handling missingness in the data (Maharjan Paragraph 0153).
Conclusion
Accordingly, claims 1-20 are rejected and claims 21-40 are withdrawn from consideration.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CORRELL T FRENCH whose telephone number is (571)272-8162. The examiner can normally be reached M-Th 7:30am-5pm; Alt Fri 7:30am-4pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kang Hu can be reached at (571)270-1344. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CORRELL T FRENCH/Examiner, Art Unit 3715
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