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 .
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 1-20 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.
The term “expert clinical reviewer” in claim 1, 9, 11 and 19 is a relative term which renders the claim indefinite because it doesn’t provide who qualifies as an expert clinical reviewer. The term “expert clinical reviewer” 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. Dependent claims 2-8, 10, 12-18, and 20 are similarly rejected based on their dependency.
The term “higher difficulty levels” in claims 4, 6, 16 and 17 is a relative term which renders the claim indefinite because it is unclear what is objectively is considered difficult nor a scale of the difficulty level. The term “higher difficulty levels” 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.
Claims 3 and 13 recite the limitation "the frequency of each diagnostic criterion." There is insufficient antecedent basis for this limitation in the claims.
Claims 5 and 15 recite the limitation "the frequency of each identified behavior." There is insufficient antecedent basis for this limitation in the claim.
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.
Claim 1-20 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.
Claims 1-20 are directed to a method (i.e., a process) and system (i.e., machine), and therefore fall within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). In other words, Step 1 of the subject-matter eligibility analysis is “Yes.”
However, the claims are drawn to an abstract idea of evaluating whether sentences are indicative of a diagnostic criteria used to diagnose a mental disorder by analyzing diagnostic information based on sentence classification, which can be performed in the human mind (including an observation, evaluation, judgement or opinion).
Regardless, the claims are reasonably understood as either “certain methods of organizing human activity” or “mental processes,” which require the following limitations: storing sentences labeled by an expert clinical reviewer or machine learning model as being indicative or not indicative of one or more of the diagnostic criteria used to diagnose a mental disorder;
presents at least one of the sentences;
provides functionality for the user to indicate whether the presented sentence is indicative of one of the one or more of the diagnostic criteria used to diagnose a mental disorder;
and outputs an indication of whether the user correctly indicated whether the presented sentence is indicative of one of the one or more of the diagnostic criteria.
These limitations simply describe a process of data gathering and manipulation, which is partially analogous to “collecting information, analyzing it, and displaying certain results of the collection analysis.”
Furthermore, the claims do not include additional elements that either alone or in combination are sufficient to claim a practical application because to the extent that a “user interface” “machine learning model”, “natural language processing”, a “non-transitory computer readable storage media” and a “hardware computer processor.” are claimed, as these are merely claimed to add insignificant extra-solution activity to the judicial exception (e.g., data gathering) and/or do no more than generally link the use of a judicial exception to a particular technological environment or field of use. In other words, evaluating whether sentences are indicative of one or more diagnostic criteria used to diagnose a mental disorder by analyzing sentence classification. is not providing a practical application, thus Step 2A, Prong 2 of the subject-matter eligibility analysis is “No.”
Likewise, the claims do not include additional elements that either alone or in combination are sufficient to amount to significantly more than the judicial exception because to the extent that a “user interface,” a “machine learning model,” a “natural language processing,” a “non-transitory computer storage medium,” and a “hardware computer processor” are claimed these are all generic, well-known, and conventional computing elements. As evidence that these are generic, well-known, and conventional computing elements, Shriberg discloses a user interface [0437], a machine learning model [0285] and natural language processing [0150]. Furthermore, Applicant’s specification discloses these elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a), per MPEP § 2106.07(a) III (a), which satisfies the Examiner’s evidentiary burden requirement per the Berkheimer memo.
Specifically, the Applicant’s claimed “user interface” is described in para. [0004], as follows: “[0004] The application provides positive examples, via user interface, of behaviors labeled as being indicative of one of the diagnostic criteria used to diagnose autism as well as negative examples (e.g., randomly selected from electronic health records) that are not indicative of autism spectrum disorder.” A “machine learning model” in para. [0017], as follows: “[0017] labeled by a machine learning model 245 trained using reference data labeled by the expert clinical reviewer as being positive examples 286 indicative of one or more diagnostic criteria 240 used to diagnose and/or assess the severity of a mental disorder or other medical condition or negative examples 288 that are not indicative of any of the diagnostic criteria 240.” A “natural language processing” in para. [0022], as follows: “[0022] Accordingly, in some embodiments, the system 200 includes natural language processing 270 that classifies each natural language sentence 284 as indicative of a particular behavior 260. Again, because the disclosed system 200 includes a large dataset 280 of real examples, the system 200 may calculate the relative frequency of each identified behavior 260 (behavior frequencies 256).” a “non-transitory computer storage medium,” in para. [0014], as follows:” The server 140 stores data in non-transitory computer readable storage media 160. The server 140 may be any hardware computing device that stores instructions in memory 146 and includes at least one hardware computer processing unit 144 that executes those instructions to perform the functions described herein.” and a “hardware computer processor” in para. [0014], as follows: “[0014] The user devices 120 may include any hardware computing device having one or more hardware computer processors that perform the functions described herein. For example, the user devices 120 may include personal computers (desktop computers, notebook computers, etc.), tablet computers, smartphones, etc. The computer network(s) 150 may include any combination of wired and/or wireless communications networks, including local area networks, wide area networks (such as the internet), etc.”
These elements are reasonably considered generic and conventional computer components. As such, the claimed limitations do not provide anything significantly more than the judicial exception. Therefore, Step 2B, of the subject-matter eligibility analysis is “No.”
In addition, dependent claims 2-8, 10-13, and 15-20 do not provide a practical application and are insufficient to amount to significantly more than the judicial exception. As such, dependent claims 2-8, 10-13, and 15-20 are also rejected under 35 U.S.C. § 101.
Therefore, claims 1-20 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
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.
Claim(s) 1-7, 9-13, 19 and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20210110894 to Shriberg et al.
Regarding claim 1, Shriberg teaches A method, comprising: storing sentences labeled by an expert clinical reviewer or machine learning model as being indicative or not indicative of one or more of the diagnostic criteria used to diagnose a mental disorder; (Shriberg teaches that target mental states can be selected from the group consisting of depression, anxiety, post-traumatic stress disorder (PTSD), schizophrenia, suicidality, and bipolar disorder [0009] and natural language processing (NLP) models predict such mental states from patient language [0285]).
and providing a user interface that: presents at least one of the sentences; provides functionality for the user to indicate whether the presented sentence is indicative of one of the one or more of the diagnostic criteria used to diagnose a mental disorder; (Shriberg teaches a method for processing speech data to generate one or more descriptors indicative of the mental state and generates a plurality of visual elements that can be displayed on a graphical user interface and is usable by the user to identify or assess [0067]).
and outputs an indication of whether the user correctly indicated whether the presented sentence is indicative of one of the one or more of the diagnostic criteria. (Shriberg teaches the assessment can comprise a score that indicates whether the subject is (i) more likely than others to experience at least one of the target mental states or (ii) more likely than others to experience at least one of the target mental states at a future point in time. [0011]).
Regarding claim 2, The method of claim 1, wherein at least some of the sentences are extracted from clinical notes of electronic health records of individuals diagnosed with the mental disorder labeled as being indicative of at least one of the diagnostic criteria used to diagnose the mental disorder. (Shriberg teaches extracting information from a text transcript of a patient’s speech for mental health assessment and analysis [0170]).
Regarding claim 3, The method of claim 2, further comprising: calculating the frequency of each diagnostic criterion identified in the electronic health records. (Shriberg teaches calculating the frequency of identified diagnostic criteria, specifically identifying diagnoses such as depression, anxiety, etc. [0207]. Shriberg further teaches determining the frequency of insomnia experienced by the patient, and (iii) what medication if any the patient is taking for the insomnia [0252]).
Regarding claim 4, The method of claim 3, further comprising: at each of a plurality of increasingly higher difficulty levels, selecting sentences indicative of diagnostic criterion that are increasingly less frequent. (Shriberg teaches question quality is measured in the informativeness of responses elicited by the question [0206], Shriberg further teaches Question quality logic 1102 includes a number of metric records 1106 and metric aggregation logic 1112. To measure the quality of a question, i.e., to measure how informative are the responses elicited by the question. Shriberg additionally teaches that “Depressed patients may also use a higher frequency of first-person singular pronouns (e.g., “I”, “me”) and a lower frequency of second- or third-person pronouns when compared to the general population.[0170]).
Regarding claim 5, The method of claim 4, further comprising: using natural language processing or a machine learning model to classify each sentence as describing a behavior; and calculating the frequency of each identified behavior in the electronic health records. (Shriberg teaches NLP model 1806 includes a number of text-based machine learning models to (i) predict depression, anxiety, and perhaps other health states directly from the words spoken by the patient and (ii) model factors that correlate with such health states [0285]).
Regarding claim 6, The method of claim 3, further comprising: at each of a plurality of increasingly higher difficulty levels, selecting sentences indicative of behaviors that are increasingly less frequent. (Shriberg teaches Question quality is measured in the informativeness of responses elicited by the question [0206]. Shriberg further teaches the frequency of a condition or behavior such as the frequency of insomnia [0252]).
Regarding claim 7, The method of claim 6, further comprising: calculating a similar score for each pair of sentences; and at each of a plurality of increasingly higher difficulty levels, selecting negative examples having increasingly higher similarity scores with respect to positive examples of one or more of the diagnostic criteria. (Shriberg teaches the angle determined in step 1312 is determined by question equivalence logic 1104 to be the measured similarity between the two questions [0234]. Shriberg further teaches the predetermined threshold is 0.98 such that two questions have a measured similarity of at least 0.98 are considered equivalent and are so represented in equivalence 910 (FIG. 9) for both questions [0235]).
Regarding claim 9, The method of claim 1, wherein at least some of the sentences are extracted from survey responses provided by laypersons and labeled by the expert clinical reviewer or machine learning model as being indicative or not indicative of one or more of the diagnostic criteria used to diagnose the mental disorder. (Shriberg teaches label data includes not only verified diagnosed patients, but inferred labels collected from particular user attributes or human annotation [0303]).
Regarding claim 10, The method of claim 1, wherein at least some of the sentences are generated by a language model in response to a prompt asking for examples indicative or not indicative of one or more of the diagnostic criteria used to diagnose the mental disorder. (Shriberg teaches the questions may be generated by, for example, a natural language processing (NLP) algorithm [0237]).
Regarding claim 11, Shriberg teaches a system, comprising: non-transitory computer readable storage media that stores sentences labeled by an expert clinical reviewer as being indicative or not indicative of one or more of the diagnostic criteria used to diagnose a mental disorder; (Shriberg teaches provides a non-transitory computer readable-medium comprising machine-executable instruction [0030]). and at least one hardware computer processor that provides a user interface that: presents one of the sentences; provides functionality for the user to indicate whether the presented sentence is indicative of one of the one or more of the diagnostic criteria used to diagnose a mental disorder; (Shriberg teaches IO 2041 enables the collection of response data (in the form of at least speech and video data) and labels from the clients 260a-n, and the presentation of prompting information (such as a question or topic) [0299]). And outputs an indication of whether the user correctly indicated whether the presented sentence is indicative of one of the one or more of the diagnostic criteria. (Shriberg teaches the final component of the web server 240 is a results and presentation module 2045 which collates the results from the model server(s) 230 and provides then to the clients 260a-n via the IO 2041, as well as providing feedback information to the interaction engine 2043 [0301]).
Regarding claim 12, The system of claim 11, wherein at least some of the sentences are extracted from clinical notes of electronic health records of individuals diagnosed with the mental disorder labeled as being indicative of at least one of the diagnostic criteria used to diagnose the mental disorder. (Shriberg teaches the clinical data portion may be compiled from the healthcare providers, and may include diagnoses, vital information (age, weight, height, blood chemistry, etc.), diseases, medications, lists of clinical encounters (hospitalizations, clinic visits, Emergency Department visits), clinician records, and the like. This clinical data may be compiled from one or more electronic health record (EHR) systems [0159]).
Regarding claim 13, The system of claim 12, wherein the at least one hardware computer processor is further configured to: calculate the frequency of each diagnostic criterion identified in the electronic health records. (Shriberg teaches depressed patients may use words or phrases that indicate dark, black, or morbid humor. They may talk about feeling worthless or feeling like failures, or use absolutist language, such as “always”, “never”, or “completely.” [0170]).
Regarding claim 19, The system of claim 11, wherein at least some of the sentences are extracted from survey responses provided by laypersons and labeled by the expert clinical reviewer or machine learning model as being indicative or not indicative of one or more of the diagnostic criteria used to diagnose the mental disorder. (Shriberg teaches the labels are based on surveys or questionnaires completed by patients rather than official clinical diagnoses, the quality of the labels may be determined to be lower, and the confidence level of the score may thus be lower. Shriberg further explains imputed label data is received by a manual review of a medical record and/or interaction record with a given client [0373]).
Regarding claim 20, The system of claim 11, wherein at least some of the sentences are generated by a language model in response to a prompt asking for examples indicative or not indicative of one or more of the diagnostic criteria used to diagnose the mental disorder. (Shriberg teaches generating questions using a natural language processing algorithm [0237]. This interaction engine 2043 includes the ability to take a number of actions, including different prompts, questions, and other interactions. [0364] as well as a machine learning tool that generates Alternatively, a machine learned model is applied in lieu of a rule based decision model. This results in the output of a customized action that is supplied to the IO 2041 for communication to the client 260a-n [0369]).
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.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shriberg in view of US 11508251 B2 to He et al.
Regarding claim 8, Shriberg teaches a user interface that presents prompts and questions to a user that receives user responses while providing feedback to the user [0299, 0364 and 0369]. However, Shriberg lacks in providing functionality for the user to indicate which of the diagnostic criteria the presented sentence is indicative of and outputting whether the user correctly indicated which of the diagnostic criteria the presented sentence is indicate of. He teaches identify the question type for regions of the individual questions respectively by using the first model, and label the identified individual types of questions respectively, and label individual components of the corresponding type [See Column 4, lines 22-26]. He further teaches compared with the prior art, the present invention can automatically identifies and corrects test papers containing various types of questions, and is not limited to a single question type (for example, only limited to oral arithmetic type questions), thereby expanding the application range and improving the efficiency of assignments correction [See Column 5, line 45-51]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective date to modify Shriberg with identifying, labeling and improving efficiency taught by He in order to provide feedback regarding the correctness of the user.
Claim(s) 14-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Shriberg in view of US 20140342321 A1 to Wendt et al.
Regarding claim 14, Shriberg teaches identifying behaviors and analyzing patient responses including information based on frequency [0170]. However, Shriberg lacks in teaching selecting sentences indicative of behaviors that are less frequent at higher difficulty levels. Wendt teaches a respective relative difficulty level is automatically determined for each of the groups of words using the corresponding mastery-level data. In one example, words can be ranked by the average mastery level of each word across all learners, and the lowest average mastery level can correspond to the highest relative difficulty level. [0120]. It would have been obvious to modify Shriberg to present content according to difficulty levels.
Regarding claim 15, Shriberg teaches using natural language processing to perform semantic analysis on patient speech utterances [0150]. Shriberg further teaches depicting word or phrase frequency in such a way may be helpful as depressed patients commonly say particular words or phrases with larger frequencies than non-depressed patients [0170].
Regarding claim 16, Shriberg teaches identifying behaviors and analyzing patient speech by the words and phrases they say [0170]. However, Shriberg lacks in teaching selecting sentences indicative of behaviors that are less frequent at increasingly higher difficulty levels. Wendt teaches Words with relatively lower numbers of trials are considered to be less difficult (e.g., concrete object such as "cup") and words with relatively higher numbers of trials are considered to be more difficult (e.g., abstract action such as "point to"). [0121]. It would have been obvious to modify Shriberg to present content according to difficulty levels in order to provide a more efficient training experience and tailor the content to the users difficulty level.
Regarding claim 17, Shriberg teaches whether two questions are equivalent is determined by question equivalence logic 1104 (FIG. 11) by comparing similarity between the two questions to a predetermined threshold [0230]. Shriberg further teaches analyzing the patients speech based on the patients speech [0405]. However, Shriberg lacks teaching selecting negative examples negative examples having increasingly higher similarity scores with respect to positive examples of one or more of the diagnostic criteria. Wendt teaches a training sequence for the groups of words is determined based on the determined relative difficulty levels [0122]. It would have been obvious to modify Shriberg with the difficulty level in order to organize training content and improve the training experience.
Regarding claim 18, Shriberg teaches diagnostic criteria that is associated with mental disorders and analyzing speech data [0363]. However, Shriberg lacks in providing a user interface that allows a user to correctly indicate which diagnostic criterion a sentence is indicative of. Wendt teaches only when the learner activates action and object symbols in correct sequence within (e.g.) 4 s is the response considered correct. The system will not correct wrong answers or provide any prompts. [0106]. It would have been obvious to modify Shriberg to incorporate Wendt’s response evaluation in order to provide a training experience that shows the correct indication of the diagnostic criterion.
Conclusion
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/S.B./Examiner, Art Unit 3715
/WILLIAM H MCCULLOCH JR/Primary Examiner, Art Unit 3715
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