DETAILED ACTION
Status of Claims
This action is in reply to the application filed on 11/18/2024.
Claims 1-20 are currently pending and have been examined.
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 .
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.
Step 1:
Claims 1-20 are directed to a method (i.e., a process). Accordingly, claims 1-20 are all within at least one of the four statutory categories.
Step 2A - Prong One:
An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Representative independent method claim 1 includes limitations that recite an abstract idea.
Specifically, independent claim 1 recites:
A method for providing an evaluation of input data of an individual obtained through a computer system, said method comprising:
(a) receiving said input data of said individual related to a disorder, delay, or impairment; and
(b) evaluating said input data using at least one machine learning model, thereby generating an evaluation result, wherein said evaluation result is a first categorical determination or a first inconclusive determination with respect to a presence or an absence of said disorder, delay, or impairment.
The Examiner submits that the foregoing underlined limitations constitute: (a) “certain methods of organizing human activity” because evaluating result as categorical determination or inconclusive determination with respect to a presence or an absence of a disorder, delay, or impairment are a part of a medical workflow, diagnosing and providing medical services from a health care provider, which are managing human behavior/interactions between people. These limitations constitute (b) “a mental process” because providing an evaluation of input data of an individual related to the disorder, delay, or impairment are observations/evaluations/analysis that can be performed in the human mind or with a pen and paper. Furthermore, these limitations constitute (c) “mathematical concepts” because evaluating input data using a machine learning model is a mathematical concept.
Accordingly, the claim describes at least one abstract idea.
In relation to claims 2-5, 7-8, 10-12 and 15-17, these claims merely recite determining steps such as: claim 2 –at least one threshold range for determining if an evaluation result is inconclusive is adjustable, claim 3 - a threshold range of said first categorical determination decreases when a threshold range of said first inconclusive determination increases, claim 4- an accuracy of said first categorical determination increases when said threshold range of said first categorical determinations decreases, claim 5 – the threshold range of said first categorical determination or said first inconclusive determination is based on an inclusion rate for said first categorical determination, claim 7 – the inclusion rate is no less than 70%, and wherein said first categorical determination results in a sensitivity of at least 80% or a specificity of at least 80%, claim 8 – the first categorical determination is selected from said presence or said absence of said disorder, delay, or impairment, claim 10 – comprising: (a) requesting additional data when said evaluation result comprises said first inconclusive determination; and (b) generating a second categorical determination or a second inconclusive determination based on said additional data using at least one additional machine learning model selected from said plurality of tunable machine learning models, claim 11 – comprising:(a) combining scores for each of said subset of said plurality of tunable machine learning models to generate a combined preliminary output score; and(b) mapping said combined preliminary output score to said first categorical determination or to said first inconclusive determination for said presence or absence of said disorder, delay, or impairment in said individual, claim 12 – the combined preliminary output score is based on a rule-based logic or a combinatorial technique for combining said scores, claim 15 – comprising generating a personal therapeutic treatment plan for said individual based on said evaluation result, claim 16 – the personal therapeutic treatment plan is generated using at least one statistical or machine learning model and claim 17 - receiving feedback data based on performance of said personal therapeutic treatment plan and updating said personal therapeutic treatment plan based on said feedback data.
In relation to claims 6, 9, 13-14 and 18-20, these claims merely recite specific kinds of input data, such as: claim 6 - the first categorical determination for said presence or absence of said disorder, delay, or impairment in said individual is based on a specified sensitivity, a specified specificity, a specified negative predictive value, or a specified positive predictive value, claim 9 - at least one machine learning model comprises a subset of a plurality of tunable machine learning models, claim 13 - the disorder, delay, or impairment comprises pervasive development disorder (PDD), autism spectrum disorder (ASD), social communication disorder, restricted repetitive behaviors, interests, and activities (RRBs), autism ("classical autism"), Asperger's Syndrome ("high functioning autism), PDD-not otherwise specified (PDD-NOS, "atypical autism"), attention deficit disorder (ADD), attention deficit and hyperactivity disorder (ADHD), speech and language delay, obsessive compulsive disorder (OCD), depression, schizophrenia, Alzheimer's disease, dementia, intellectual disability, or learning disability, claim 14 - the disorder, delay, or impairment is autism spectrum disorder or autism, claim 18 - the feedback data comprises at least one of efficacy, compliance, and response to said personal therapeutic treatment plan, claim 19 - the personal therapeutic treatment plan comprises a drug therapy and a non-drug therapy and claim 20 – the apply non-drug therapy comprises digital therapeutics.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The limitations of claim 1, as drafted is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting a computer system to perform the limitations, nothing in the claim elements precludes the steps from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation within a health care environment in the mind but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity”, “Mental Process” and “mathematical concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the system is recited at high levels of generality (i.e., as generic computer components performing generic computer functions of receiving data/inputs, determining and providing data) such that it amounts no more than mere instructions to apply the exception using the generic computer components.
Regarding the additional limitations “using at least one machine learning”, the Examiner submits that this additional limitation amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)). Regarding the additional limitation “(a) receiving said input data of said individual ……” the Examiner submits that this additional limitation merely adds insignificant pre-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea (see MPEP § 2106.05(g)).
Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvements in the functioning of a computer or an improvement to another technology or technical field, apply or us the above-noted implement/use to above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see 2019 PEG and MPEP §2106.05). Their collective functions merely provide conventional computer implementation.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer component provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, in representative independent claim 1, regarding the additional limitations of the system, the Examiner submits that these limitations amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)).
Thus, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
The dependent claims no not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reason discussed above with respect to determining that the dependent claims do not integrate the at least abstract idea into a practical application.
Therefore, claims 1-20 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 102
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.
Claims 1-6 and 8-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Clark (US 2016/0180737 A1).
Claim 1:
Clark discloses A method for providing an evaluation of input data of an individual obtained through a computer system (See Fig. 1 using computing system 102 to evaluate a participant for characteristics of autism mentioned in P0015, P0018-P0020. Also, see Fig. 5-6.), said method comprising:
(a) receiving said input data of said individual related to a disorder, delay, or impairment (See Fig. 2-3 receiving collected conversations where the autism is a disorder mentioned in P0015, P0030.); and
(b) evaluating said input data using at least one machine learning model, thereby generating an evaluation result, wherein said evaluation result is a first categorical determination or a first inconclusive determination with respect to a presence or an absence of said disorder, delay, or impairment (See Fig. 2-3, P0016, P0029-P0032 using the machine learning model to determine whether the participant exhibits characteristics of autism and see collecting symptoms as classified first and second sets of conversations in P0035-P0036.).
Regarding claim 2, Clark discloses the method of claim 1, wherein at least one threshold range for determining if an evaluation result is inconclusive is adjustable (Besides diagnosis score in predetermined range mentioned in P0049-P0050, see P0057, P0067 where the participant can modify or adapt his behavior depending on inconclusive data in a monitored conversation.).
Regarding claim 3, Clark discloses the method of claim 2, wherein a threshold range of said first categorical determination decreases when a threshold range of said first inconclusive determination increases (Taught in P0015 where range of complex neurodevelopment disorders. Also, see diagnosis score in a predetermined range in P0049.).
Regarding claim 4, Clark discloses the method of claim 3, wherein an accuracy of said first categorical determination increases when said threshold range of said first categorical determinations decreases (See [P0067] the diagnosis system includes a threshold for taking action based on a probability in the determinations made by the coaching application in evaluating an ongoing conversation. After processing the text of the ongoing application, the diagnosis system may generate a measure of probability whether the conversation pattern of a participant exhibits a characteristic of autism (or violates a social norm). Also, see P0075.).
Regarding claim 5, Clark discloses the method of claim 2, wherein said threshold range of said first categorical determination or said first inconclusive determination is based on an inclusion rate for said first categorical determination (Taught in P0032-P0033, P0049 as diagnosis scoring.).
Regarding claim 6, Clark discloses the method of claim 5, wherein said first categorical determination for said presence or absence of said disorder, delay, or impairment in said individual is based on a specified sensitivity, a specified specificity, a specified negative predictive value, or a specified positive predictive value (See P0030, P0065, P0067 predicting autism and set of weights to construct ML model. Also, see confidence score based on the estimated accuracy of the diagnosis score mentioned in P0050.).
Regarding claim 8, Clark discloses the method of claim 1, wherein said first categorical determination is selected from said presence or said absence of said disorder, delay, or impairment (See Autism spectrum disorder (“ASD” or “autism”) and social impairments in P0015.).
Regarding claim 9, Clark discloses the method of claim 1, wherein said at least one machine learning model comprises a subset of a plurality of tunable machine learning models (See set of concepts in P0023, weight in P0030, P0040.).
Regarding claim 10, Clark discloses the method of claim 9, further comprising: (a) requesting additional data when said evaluation result comprises said first inconclusive determination; and (b) generating a second categorical determination or a second inconclusive determination based on said additional data using at least one additional machine learning model selected from said plurality of tunable machine learning models (Taught as pre-recorded conversations in P0030, P0074 and diagnosis score as supplemental data in P0049.).
Regarding claim 11, Clark discloses the method of claim 9, further comprising: (a) combining scores for each of said subset of said plurality of tunable machine learning models to generate a combined preliminary output score; and(b) mapping said combined preliminary output score to said first categorical determination or to said first inconclusive determination for said presence or absence of said disorder, delay, or impairment in said individual (Taught in P0032-P0033, P0049 as diagnosis scoring. Also, P0032-P0033, P0049 as diagnosis scoring.).
Regarding claim 12, Clark discloses the method of claim 11, wherein said combined preliminary output score is based on a rule-based logic or a combinatorial technique for combining said scores (See confidence score based on the estimated accuracy of the diagnosis score mentioned in P0050.).
Regarding claim 12, Clark discloses the method of claim 1, wherein said disorder, delay, or impairment comprises pervasive development disorder (PDD), autism spectrum disorder (ASD), social communication disorder, restricted repetitive behaviors, interests, and activities (RRBs), autism ("classical autism"), Asperger's Syndrome ("high functioning autism), PDD-not otherwise specified (PDD-NOS, "atypical autism"), attention deficit disorder (ADD), attention deficit and hyperactivity disorder (ADHD), speech and language delay, obsessive compulsive disorder (OCD), depression, schizophrenia, Alzheimer's disease, dementia, intellectual disability, or learning disability (See evaluating the participant for characteristics of autism mentioned in P0015, P0018-P0020. Also, see Fig. 5-6 and P0077.).
Regarding claim 14, Clark discloses the method of claim 13, wherein said disorder, delay, or impairment is autism spectrum disorder or autism (See [P0015] Autism spectrum disorder (“ASD” or “autism”) is a range of complex neurodevelopment disorders, characterized by social impairments.).
Regarding claim 15, Clark discloses, the method of claim 1, further comprising generating a personal therapeutic treatment plan for said individual based on said evaluation result (See [P0049] the diagnosis score may be used as the primary diagnosis for a patient in order to determine a treatment plan.).
Regarding claim 16, Clark discloses the method of claim 15, wherein said personal therapeutic treatment plan is generated using at least one statistical or machine learning model (See greater weight and scoring relative to the ML model in P0048-P0049.).
Regarding claim 17, Clark discloses the method of claim 15, further comprising receiving feedback data based on performance of said personal therapeutic treatment plan and updating said personal therapeutic treatment plan based on said feedback data (Taught in P0018, P0057 as suggesting a corrective action or sympathetic response to the participant.).
Regarding claim 18, Clark discloses the method of claim 17, wherein said feedback data comprises at least one of efficacy, compliance, and response to said personal therapeutic treatment plan (Taught in P0018, P0057 as suggesting a corrective action or sympathetic response to the participant. Also, see determining treatment plan in P0049.).
Regarding claim 19, Clark discloses the method of claim 15, wherein said personal therapeutic treatment plan comprises a drug therapy and a non-drug therapy (See P0058-P0059, where coaching the participant servs as a non-drug therapy.).
Regarding claim 20, Clark discloses the method of claim 19, wherein said non-drug therapy comprises digital therapeutics (See Fig. 9A-9B, GUI coaching mentioned in P0076-P0077 serve as a non-drug therapy comprising digital therapeutics.).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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 7 is rejected under 35 U.S.C. 103 as being unpatentable over Clark (US 2016/0180737 A1) in view of Official Notice.
Regarding claim 7, although Clark discloses the method of claim 5 mentioned above. Clark does not disclose “wherein said inclusion rate is no less than 70%, and wherein said first categorical determination results in a sensitivity of at least 80% or a specificity of at least 80%”. However, the Examiner takes Official Notice that it is old and well-known in the computer arts for evaluating mental disorders when the inclusion rate is no less than 70%, and wherein said first categorical determination results in a sensitivity of at least 80% or a specificity of at least 80%. It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Clark’s criteria for a threshold range of the first categorical determination or first inconclusive determination because this would better diagnose patient’s presence or an absence of a disorder, delay, or impairment for accuracy.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TERESA S WILLIAMS whose telephone number is (571)270-5509. The examiner can normally be reached Mon-Fri, 8:30 am -6:30 pm.
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/T.S.W./Examiner, Art Unit 3687 06/02/2026
/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687
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