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 .
Claims 1-3 are pending. Examiner notes that due to the objection of claim 3, claim is not being treated on the merits.
Claim Objections
Claims 1 and 3 are objected to.
Claim 1 is objected to because of the following informalities:
Claim 1 recites “HIPAA protected health information” but does not first spell out what the letters in HIPAA stand for. For purposes of examination, “Health Insurance Portability and Accountable Act (HIPAA) protected health information”
Claim 1 recites: “unique artificial (AI) training datasets.” It appears that the word “intelligence” is missing. The “unique artificial (AI) training datasets” will be interpreted as “unique artificial intelligence (AI) training datasets” for purposes of examination.
Claim 3 is objected to under 37 CFR 1.75(c) as being in improper form because a multiple dependent claim should refer to other claims in the alternative only. See MPEP § 608.01(n). Accordingly, the claim 3 has not been further treated on the merits.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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 1-2 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 “training custom Large Language Models (LLM) from such propriety clinical datasets composed of such user responses….” It is unclear if the preceding the training datasets are comprised of the datasets curated in the preceding limitation or are datasets that are similar to those datasets as the claims say “from such propriety clinical datasets.” Claim 1 also states “by employing such LLMs,” rendering the claim indefinite. It is unclear if the use of such means the LLMs in the preceding limitations or LLMs similar to those above.
The term “useful” in claim 1 in the limitations “clinically useful insights,” “clinically useful interventions” and “clinically useful longitudinal mental health summaries” is a relative term which renders the claim indefinite. The term “useful” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For instance, what is useful to one clinician might not be useful to another.
Claim 2 is rejected as it depends from claim 1.
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 1-2 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
Claims 1-2 are drawn to a method for a comprehensive digital platform for mental health assessment, which is within the four statutory categories (i.e. process).
Step 2A | Prong One
Claims 1-2 (Group I) recite a computer-implemented method, comprising the steps of:
receiving user responses over a network from a recorded mental health screening interview (insignificant extra-solution activity, MPEP § 2106.05(g));
curating Health Insurance Portability and Accountable Act (HIPAA) protected health information from the screenings into unique artificial intelligence (AI) training datasets intended to produce unprecedented diagnostic behavior;
training custom Large Language Models (LLM) from such proprietary clinical datasets composed of such user responses to create clinically useful insights;
generating clinically useful insights from the user screenings for diagnostic purposes by employing such LLMs;
recommending useful interventions from external libraries to users by analyzing the generated insights and using AI searching algorithms;
improving the clinical accuracy of such insights LLMs by means of a graphical user interface supporting Reinforcement Learning through Human Feedback as part of a continuous AI clinical training engine;
and generating clinically useful longitudinal mental health summaries by using a custom LLM to analyze the aforementioned generated insights.
The bolded limitations, given the broadest reasonable interpretation, cover a mathematical concept and/or a certain method of organizing human activity because it recites mathematical relationships, formulas, equations, and/or mathematical calculations and/or fundamental economic practices, commercial or legal interactions, and/or managing personal behavior or relationships or interactions between people. Any limitations not identified above as part of abstract idea are deemed “additional elements,” and will be discussed in further detail below.
Dependent Claim 2 includes other limitations, for example Claim 2 recites assessing Stress and Burnout by means of a clinically-validated visual scale presented to users through a graphical questionnaire; training AI models to accurately predict Stress and Burnout from clinically gathered data; predicting Stress and Burnout risk scores and insights by analyzing a video received over the network of a user answering questions and using the trained AI models, but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent Claim 1.
Step 2A | Prong Two
Furthermore, Claims 1-2 are not integrated into a practical application because the additional elements (i.e. the limitations not identified as part of the abstract idea) amount to no more than limitations which:
add insignificant extra-solution activity to the abstract idea – for example, the recitation of receiving user responses over a network from a recorded mental health screening interview, which amounts to mere data gathering, see MPEP 2106.05(g).
Step 2B
Furthermore, the Claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e. the elements other than the abstract idea) amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification expressly disclosing that the additional elements are well-understood, routine, and conventional in nature:
paragraphs [0212] of the Specification discloses that the additional elements (i.e. computer) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e. receive data) that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. healthcare);
Relevant court decisions: The following are examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention receives user response data, and transmits the data over a network;
Dependent Claim 2 does not include any additional elements beyond those recited in claim 1.
Thus, taken alone, the additional elements do not amount to “significantly more” than the above-identified abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-2 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
Claims 1-2 are rejected under 35 U.S.C. 103 as being unpatentable over Shriberg (U.S. Pub. No. 2021/0110895 A1) in view of Cordell (U.S. Pub. No. 2024/0029850 A1).
Regarding claim 1, Shriberg discloses a computer-implemented method, comprising the steps of:
receiving user responses over a network from a recorded mental health screening interview (Paragraphs [0073], [0185] and [0564] discuss receiving recorded data from an interactive screening for mental health.);
curating HIPAA protected health information from the screenings into unique artificial intelligence (AI) training datasets intended to produce unprecedented diagnostic behavior (Paragraph [0153] and [0165] discuss curating data to be HIPAA compliant which is used for the modeling system and [0303] discusses a training data filter using data from the assessments which is then filtered to be fed into models.);
training custom artificial intelligence models from such proprietary clinical datasets composed of such user responses to create clinically useful insights (Paragraphs [0375-0376] discuss using the filtered training data to generate and train machine learning models.);
generating clinically useful insights from the user screenings for diagnostic purposes by employing such artificial intelligence models (Paragraphs [0376-0378] discuss the models using the training are used for future patients.);
recommending useful interventions from external libraries to users by analyzing the generated insights and using AI searching algorithms (Paragraphs [0302] and [0480-0481] discuss generating recommendations based on specific treatment protocols using the models and external data.);
improving the clinical accuracy of such insights AI models by means of a graphical user interface supporting Reinforcement Learning through Human Feedback as part of a continuous AI clinical training engine (Paragraph [0509] discusses using a reinforcement learning mechanisms.); and
generating clinically useful longitudinal mental health summaries by using a custom AI model to analyze the aforementioned generated insights (Paragraphs [0170-0171] discuss generating a report that includes a visual graphical element showing the progression of the patient’s score over time.);
but Shriberg does not explicitly disclose wherein:
the artificial intelligence models are Large Language Models (LLM).
Cordell teaches wherein the artificial intelligence models are large language models (Paragraphs [0016] and [0023] discuss using large language models to evaluate patient records.)
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare before the effective filing date of the claimed invention to modify the models of Shriberg to be large language models, as taught by Cordell, in order to “automate the receipt and processing of information about patients from a range of sources beyond standard diagnostic and patient intake forms (Cordell, Paragraph [0016]).”
Regarding claim 2, Shriberg discloses:
assessing Stress and Burnout by means of a clinically-validated visual scale presented to users through a graphical questionnaire (Paragraphs [0351] and [0418] discusses assessing stress and exhaustion, construed as burnout, via the questionnaire.);
training AI models to accurately predict Stress and Burnout from clinically gathered data (Paragraph [0508] discusses the training of models may be continuous.);
predicting Stress and Burnout risk scores and insights by analyzing a video received over the network of a user answering questions and using the trained AI models (Paragraphs [0017], [0351] and [0418] and [0507-0510] discuss predicting scores for different conditions, including stress and exhaustion, using the patient assessments which include video.).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rachelle Reichert whose telephone number is (303)297-4782. The examiner can normally be reached M-F 9-5 MT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Dunham can be reached at (571)272-8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686