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 .
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.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994)
The disclosure of the prior-filed applications, US Provisional Application 63/234,336 and US Application 17/889,415, fail to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application.
In particular, the disclosure of the prior-filed application fails to provide sufficient written description for “a method of reducing bias in analyzing a patient's mental state so that treatment of the patient can be improved, wherein the patient is undergoing treatment for a mental disorder, the method comprising: training a machine learning model with a dataset, the dataset comprising data from a population of patients who have previously undergone treatment for a mental disorder;… analyzing the patient data using the trained machine learning model; outputting a score quantifying the mental state of the patient; and determining whether the treatment should be modified based on the score” in claim 39 to show one of ordinary skill in the art that Applicant had possession of the claimed invention. Claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See MPEP 2161.01(I). In particular, the specification of the prior-filed application, at best, merely recites similar language as the claims without providing any substantive description for the claimed limitations identified above for the same reasons that the instant specification also fails as identified in the rejections of the claims under 35 USC 112(a) below for the same claim limitations.
Thus, examined claims 39-43 do not gain benefit of priority to US 63/234,336 and US 17/889,415. Therefore, examined claims 39-43 have an effective filing date of 04 April 2025.
Information Disclosure Statement
The information disclosure statement filed 13 November 2025 fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. In particular, no copies of many non-patent literature documents and foreign patent documents have not been provided. See the citations which are lined through. It has been placed in the application file, but the information referred to therein has not been considered.
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.
Claims 39-43 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.
Regarding claim 39, the preamble recites that the method is “of reducing bias in analyzing a patient’s mental state”. However, the body of the claim is silent regarding any language towards “reducing bias in analyzing a patient’s mental state”. This is of particular note since one of ordinary skill in the art would understand that bias can be in each step of the recited method. Thus, it is unclear how the claim is to a method of reducing bias in analyzing a patient’s mental state. The disclosure does not aid understanding as it is silent regarding “reducing bias in analyzing a patient’s mental state”, let alone any mention of bias. Therefore, one of ordinary skill in the art would not be apprised of the metes and bounds of the patent protection sought. For purposes of compact prosecution, prior art teaching the limitations in the body of the claim are construed as teaching the preamble. Dependent claims 40-43 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
Regarding claims 39 and 42, it is unclear whether “the patient data” recited in line 6 of claim 39 and ”the acquired patient data” in claim 42 are referring to “patient information” in “acquiring patient information” or whether “patient data” is separate and different from “patient information”. This is exacerbated with the use of both “information” and “data” throughout the claims causing it to be unclear whether there is a difference in meaning or not. Thus, one of ordinary skill in the art would not be apprised of the metes and bounds of the patent protection sought. For the purposes of compact prosecution, “data” is construed to be the same as “information” such that “patient information” provides antecedent basis for “patient data”. Dependent claims 40-43 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
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.
Claims 39-43 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.
Regarding claim 39, the originally filed disclosure is silent regarding “a method of reducing bias in analyzing a patient's mental state so that treatment of the patient can be improved, wherein the patient is undergoing treatment for a mental disorder” to show one of ordinary skill in the art that Applicant had possession of the claimed invention. There are two statutory provisions that prohibit the introduction of new matter. The first provision is 35 USC 132, which provides that no amendment shall introduce new matter into the disclosure of the invention. If new matter is added to the claims, the examiner should reject the claims under 35 USC 112(a) – written description requirement. See MPEP 2163.06. The disclosure is silent regarding “reducing bias in analyzing a patient’s mental state”. Thus, since this language was added via amendment, this is new matter. Dependent claims 40-43 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
Further regarding claim 39, the disclosure fails to provide sufficient written description for “training a machine learning model with a dataset, the dataset comprising data from a population of patients who have previously undergone treatment for a mental disorder” to show one of ordinary skill in the art that Applicant had possession of the claimed invention. There are two statutory provisions that prohibit the introduction of new matter. The first provision is 35 USC 132, which provides that no amendment shall introduce new matter into the disclosure of the invention. If new matter is added to the claims, the examiner should reject the claims under 35 USC 112(a) – written description requirement. See MPEP 2163.06. Claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See MPEP 2161.01(I). While the disclosure does include language generically referencing “training a machine learning model with a dataset”, the originally filed disclosure is silent regarding “the dataset comprising data from a population of patients who have previously undergone treatment for a mental disorder”. Thus, since this language was added via amendment, this is new matter. Regarding “training a machine learning model with a dataset”, the specification merely recites similar language as the claim without any further meaningful description. See, for example, at least para. 38-43, 46, 56, 60, and 70 of the specification which illustrate this. For instance, the disclosure recites a “mental state NLP classifier” is used to train and generate a machine learning model without providing any meaningful description of how the NLP classifier is for “mental states”, let alone how it is meaningfully used to train and generate a machine learning model. Furthermore, the disclosure is silent regarding what kind of machine learning model is generated which amounts to an unbounded list of machine learning models. Therefore, the disclosure at best merely recites that this limitation is performed in results-based language without providing the necessary description of the steps, calculations, or algorithms for performing the claimed functionality. Dependent claims 40-43 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
Further regarding claim 39, the disclosure fails to provide sufficient written description for “analyzing the patient data using the trained machine learning model; outputting a score quantifying the mental state of the patient” to show one of ordinary skill in the art that Applicant had possession of the claimed invention. Claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See MPEP 2161.01(I). In particular, the specification merely recites generic language construed to be related to the claims without any meaningful description. See, for example, at least para. 47 of the specification which recites that the “system 10 can include an assessment of specific mental disorder severity and progression from patient generated text and audio, shown in FIGURE 4. The model input is a text field (or audio transcript) of random length. The model type is mental disorder severity NLP classifier. The model output is a score in range 0-100 representing severity of a specific mental disorder and its progression in time. The training data is texts and transcripts from therapy sessions, diaries, psychotherapy interventions, with severity annotations of given mental state (pathological or non-pathological) or bootstrapped public data with high correlations with specific mental states severity. The user flow is texts and audio generated by a first user (patient) that is enabled as an input to the mental disorder severity NLP classifier model. The model processes provided text in near-real time and identifies correlates and indications of severity of a specific mental disorder and/or it's progression, detected in the text.” Para. 61 recites nearly identical language as para. 47. Therefore, the disclosure at best merely recites that these limitations are performed in results-based language without providing the necessary description of the steps, calculations, or algorithms for performing the claimed functionality. Dependent claims 40-43 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
Further regarding claim 39, the disclosure fails to provide sufficient written description for “determining whether the treatment should be modified based on the score” to show one of ordinary skill in the art that Applicant had possession of the claimed invention. There are two statutory provisions that prohibit the introduction of new matter. The first provision is 35 USC 132, which provides that no amendment shall introduce new matter into the disclosure of the invention. If new matter is added to the claims, the examiner should reject the claims under 35 USC 112(a) – written description requirement. See MPEP 2163.06. Claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See MPEP 2161.01(I). In particular, the originally filed disclosure is silent regarding this new limitation. The closest language found in the specification is “The present invention provides for a method of analyzing clinical and diagnostic texts and audio transcripts… and offering a retrospective view or an overview of a patient’s treatment progress” or similar language. See para. 13, 14, 58, and 71. Thus, no evidence is found in the originally filed disclosure of even a contemplation of “determining whether the treatment should be modified”, let alone “modified based on the score”. Therefore, this is new matter. Dependent claims 40-43 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
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 39-43 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 including additional elements that are sufficient to amount to significantly more than the judicial exception itself.
Step 1
The instant claims are directed to a method which falls under the four statutory categories (STEP 1: YES).
Step 2A, Prong 1
Independent claim 1 recites:
A method of reducing bias in analyzing a patient's mental state so that treatment of the patient can be improved, wherein the patient is undergoing treatment for a mental disorder, the method comprising:
training a machine learning model with a dataset, the dataset comprising data from a population of patients who have previously undergone treatment for a mental disorder;
acquiring patient information during a treatment session wherein the patient data comprises at least one text-based source of patient information;
analyzing the patient data using the trained machine learning model;
outputting a score quantifying the mental state of the patient; and
determining whether the treatment should be modified based on the score.
All of the foregoing underlined elements identified above amount to the abstract idea grouping of a certain method of organizing human activity because they amount to managing personal behavior or interactions between people (including social activities, teaching, and following rules or instructions) as they recite collecting information, analyzing the information, and outputting the results of the collection and analysis. Additionally, the underlined elements identified above are interpreted as a series of steps that could reasonably be performed by mental processes with the aid of pen and paper or aid of a computer because the claims, under their broadest reasonable interpretation, cover performance of the limitations in the mind. See MPEP 2106.04(a)(2)(III)(C) - A Claim That Requires a Computer May Still Recite a Mental Process.
The dependent claims amount to merely further defining the judicial exception.
Therefore, the claims recite a judicial exception. (STEP 2A, PRONG 1: YES).
Step 2A, Prong 2
This judicial exception is not integrated into a practical application because the independent and dependent claims do not include additional elements that are sufficient to integrate the exception into a practical application under the considerations set forth in MPEP 2106.04(d). The elements of the claims above that are not underlined constitute additional elements.
The following additional elements, both individually and as a whole, merely generally link the judicial exception to a particular technological environment or field of use: using the trained machine learning model (claim 39) and a visual interface (claim 40). This is evidenced by the manner in which these elements are disclosed. For instance, no particular machine learning model is disclosed. Only generic references to training and using a machine learning model with generalized examples of types of training data. See, for example, at least para. 38, 41-70, and 74. Additionally, para. 38 and 44-48 of the specification merely provide limited recitations of generic computer hardware and software components in no particular order. Additionally, the drawings illustrate all of the elements with non-descript black boxes and stock icons. Furthermore, this also evidences that the computer components are merely an attempt to link the abstract idea to a particular technological environment, but do not result in an improvement to the technology or computer functions employed. It should be noted that because the courts have made it clear that the mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of the computing device and associated hardware does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty Ltd. v. CLS Bank Int’l, 573 US 208, 224-26 (2014). It is also noted that the only additional element is not recited to actively perform any part of the claimed method. It is merely recited to be passively used. Thus, the method is reasonably construed as entirely performed by a human. The claims do not recite any specific rules with specific characteristics that improve the functionality of a computer system. None of the hardware offer a meaningful limitation beyond generally linking the performance of the steps to a particular technological environment, that is, implementation via computers. Again, this is evidenced by the manner in which these elements are disclosed in the instant specification and drawings as identified above. Additionally, the claims do not apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition nor do they apply or use a judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. In particular, the claims are silent regarding any specific treatment or prophylaxis for any specific disease or medical condition. Further evidence is found in at least para. 43 of the specification which assert that the “systems and methods of the present invention can be used with any type of therapy, for any type of disorder of the patient, and in combination with any type of medication.” Not only does this identify that the claims do not recite any particular treatment or prophylaxis, they also identify that claims are silent regarding any specific rules with any specific characteristics. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to the judicial exception. (STEP 2A, PRONG 2: NO).
Step 2B
The independent and dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under the considerations set forth in MPEP 2106.05. As identified in Step 2A, Prong 2, above, the claimed process does not require the use of a particular machine, nor does it result in the transformation of an article. This is at least evidenced by the manner in which this is disclosed 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 USC 112(a) as identified in Step 2A, Prong 2, above. Furthermore, as identified in Step 2A, Prong 2 above, the additional element is merely an attempt to link the abstract idea to a particular technological environment, but do not result in an improvement to the technology or computer functions employed. The claims do not recite any specific rules with specific characteristics that improve the functionality of the computer system. None of the hardware offer a meaningful limitation beyond generally linking the performance of the steps to a particular technological environment, that is, implementation via computers. Again, this is evidenced by the manner in which these elements are disclosed in the instant specification. Viewed as a whole, this additional claim element does not provide meaningful limitation to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea of itself (STEP 2B: NO).
Therefore, the claims are rejected under 35 USC 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.
(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.
Claims 39-43 are rejected under 35 U.S.C. 102(a)(1), or alternatively under 35 U.S.C. 102(a)(2) in the event that the claims gain benefit of priority to US Provisional Application 63/234,336, as being anticipated by Vaughan (US 2022/0254461).
Regarding claim 39, Vaughan teaches a method of reducing bias in analyzing a patient's mental state so that treatment of the patient can be improved, wherein the patient is undergoing treatment for a mental disorder, the method comprising:
training a machine learning model with a dataset (Vaughan, para. 114, “The procedure can comprise an assessment model that has been trained using a large set of clinically validated data to learn the statistical relationship between a feature of a subject and clinical diagnosis of one or more behavioral, neurological or mental health disorders.”), the dataset comprising data from a population of patients who have previously undergone treatment for a mental disorder (Vaughan, para. 14, “The diagnostic classifier may be based on data for a subject population to determine the diagnostic data for the subject… The therapeutic classifier may be based on the data for the subject population to determine the timing or amount of the dose of the therapeutic agent for the subject.” Para. 15, “The data for the subject population may comprise answers to the plurality of questions related to cognitive function of members of the subject population.”);
acquiring patient information during a treatment session wherein the patient data comprises at least one text-based source of patient information (Vaughan, para. 190, “The diagnostic or therapeutic module may further comprise a second diagnostic or therapeutic classifier that can assess a patient's behavior or performance. The assessment may be based directly on answers to a plurality of questions related to a cognitive function of the patient or can be based in combination with passive data obtained from or related to the patient or data obtained or collected from third parties.”);
analyzing the patient data using the trained machine learning model (Vaughan, para. 189, “The classifier of the therapeutic module or the diagnostic module may be configured to produce one or more cognitive function scores, as described herein… A magnitude of the cognitive score may be indicative of the severity of a behavioral disorder at a particular moment in time, for example. A change in the magnitude of the cognitive score may be indicative of a change in state of the behavioral disorder, such as may occur in response to a therapeutic intervention, a treatment, or a progression of the disorder. For example the score may be related to where the subject falls on the autism spectrum, e.g. from autism to Asperger's syndrome.”” para. 190, “The second classifier may assign a numerical score to the patient's behavior or performance.”);
outputting a score quantifying the mental state of the patient (Vaughan, para. 189, “A magnitude of the cognitive score may be indicative of the severity of a behavioral disorder at a particular moment in time, for example. A change in the magnitude of the cognitive score may be indicative of a change in state of the behavioral disorder, such as may occur in response to a therapeutic intervention, a treatment, or a progression of the disorder. For example the score may be related to where the subject falls on the autism spectrum, e.g. from autism to Asperger's syndrome.” para. 190, “The second classifier may assign a numerical score to the patient's behavior or performance.”); and
determining whether the treatment should be modified based on the score (Vaughan, para. 190, “Based on this comparison or matching of a patient's score or assessment metric to similar cohorts, which may be made for example at particular milestones, the therapeutic module may determine and output a personal therapeutic treatment plan for the patient.”).
Regarding claim 40, Vaughan teaches the method of claim 39, further comprising presenting the score via a visual interface (Vaughan, para. 25, “The processor may be configured with instructions to display a score result indicative of the user's response to the treatment.” Para. 109, “An informative display can provide symptoms of the disorder that can be displayed as a graph depicting covariance of symptoms displayed by the subject and symptoms displayed by the average population. A list of characteristics associated with a particular diagnosis can be displayed with confidence values, correlation coefficients, or other means for displaying the relationship between a subject's performance and the average population or a population comprised of those with a similar disorders.”).
Regarding claim 41, Vaughan teaches the method of claim 39, wherein the at least one text-based source of information includes the patient's diary, journal, worry script, transcript of patient-clinical staff discussions, written/transcribed answers to a structured set of questions, clinical staff’s clinical notes from patient's treatment and therapy, initial assessment notes, progress notes, non-clinical notes from patient's treatment and therapy, drug administration notes, clinical and research staff notes, treatment plans, prescriptions, or a combination thereof (Vaughan, Fig. 4, Data Inputs –Diagnostic Questions Data 505; para. 108, “Data can comprise information collected through diagnostic tests, diagnostic questions, or questionnaires (505). In some instances, data from diagnostic tests (505) can comprise data collected from a secondary observer (e.g. a parent, guardian, or individual that is not the subject being analyzed).”).
Regarding claim 42, Vaughan teaches the method of claim 39, wherein the acquired patient data further includes at least one audio-based source (Vaughan, Fig. 4, Data Inputs – Passive Data 501, Diagnostic Questions Data 505, Active Data 510; para. 63, “the active sources can include audio feed data source such as speech patterns, lexical/syntactic patterns (for example, size of vocabulary, correct/incorrect use of pronouns, correct/incorrect inflection and conjugation, use of grammatical structures such as active/passive voice etc., and sentence flow), higher order linguistic patterns (for example, coherence, comprehension, conversational engagement, and curiosity),” Para. 77, “Types of data collected and utilized by the system can include subject 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.” Para. 106, “Passive data sources can including for example, data collected from… smart devices that measure any single or combination of the following: subject's speech patterns,… prosody, lexical analysis”; para. 108, “Data can comprise information collected through diagnostic tests, diagnostic questions, or questionnaires (505). In some instances, data from diagnostic tests (505) can comprise data collected from a secondary observer (e.g. a parent, guardian, or individual that is not the subject being analyzed). Data can include active data sources (510), for example data collected from devices configured for tracking eye movement, or measuring or analyzing speech patterns.”).
Regarding claim 43, Vaughan teaches the method of claim 39, wherein the acquired patient information and score, become part of the dataset used to train the machine learning model (Vaughan, para. 91, “In step 216, the new data is fitted to the assessment model to generate an updated assessment model. This assessment model may comprise an initial diagnosis for a previously untreated subject, or an updated diagnosis for a previously treated subject.” Para. 132, “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”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
John et al. (US 2022/0016413) also anticipates the pending claims under 35 USC 102(a)(1)/102(a)(2).
Parkland (WO 2014/042942) is the primary reference used to reject the claims of the parent application.
Moturu et al. (US 2016/0196389) discloses using machine learning to rate severity of a mental condition in a patient based on patient data.
Colley et al. (US 2021/0090694) discloses an identical process, but for cancer. However, it incorporates by reference a provisional application towards mental disorder.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL LANE whose telephone number is (303)297-4311. The examiner can normally be reached Monday - Friday 8:00 - 4:30 MT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xuan Thai can be reached at (571) 272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL LANE/Examiner, Art Unit 3715
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