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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/08/2025 has been entered.
Status of Amendments
Claims 1-18 are currently pending in this case and have been examined and
addressed below. This communication is a Final Rejection in response to the
Amendment to the Claims and Remarks filed on 12/08/2025.
Claims 1- 2, 5, 12-14, 16, and 18 are amended claims.
Claims 3, 6, 8, 10, 15, and 17 are original claims.
Claims 4, 7, 9, and 11 are previously presented.
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 1-18 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1-18 are drawn to a method and an article of manufacture, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a method comprising wherein the first data set records a first medical diagnostics assessment of a patient and the second data set records identifying factors associated with the patient; generating, the course of mental health treatment, with a list of providers generated from vector embeddings derived from the first and second data sets.
Independent claim 13 recites an article of manufacture comprising wherein the first data set records a first medical diagnostics assessment of a patient and the second data set records identifying factors associated with the patient; generating, the course of mental health treatment, with a list of providers generated from vector embeddings derived from the first and second data sets.
These steps amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior; and managing human activity (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people – also note October 2019 Update: Subject Matter Eligibility on p. 5 and MPEP § 2106.04(a)(2)(II) stating certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping).
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claims 1 recites a health platform and a graphical user interface. Claim 13 recites a computer readable storage medium and a processor.
These elements are recited at a high-level of generality such that it amounts to
mere instructions to apply the exception because this is an example of applying the
abstract idea by use of general-purpose computer which does not integrate the abstract
idea into a practical application.
Claims 1 and 13 recite applying to a first data set and a second data set the machine learning model and processed with a large language model. This limitation amounts to mere instructions to apply the exception because a mathematical algorithm applied on a general-purpose computer has been found by the courts to be mere instruction to apply as in MPEP 2106.05(f)(2).
Claims 1 and 13 recite displaying the course of mental health treatment on a graphical user interface. This limitation is recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claims 1 recites a health platform and a graphical user interface. Claim 13 recites a computer readable storage medium and a processor. Claims 1 and 13 recite applying to a first data set and a second data set the machine learning model, processed with a large language model, displaying the course of mental health treatment on a graphical user interface.
Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation. For the reasons stated, these claims are consequently rejected under 35 U.S.C. § 101.
Analysis of Dependent Claims
Dependent claims 3 and 15 recite wherein generating, based on the applying the machine learning model to the first and second data sets, the course of mental health treatment, further comprises at least one of: recommending a healthcare provider to contact the patient, and suggesting a prescription for the patient.
Dependent claim 4 recites wherein generating, based on the applying the machine learning model to the first and second data sets, the course of mental health treatment, is based, at least in part, on one or more of: income, age, gender, sexual orientation, race, education level, military experience of the patient.
Dependent claims 5 and 16 recite wherein the course of mental health treatment comprises a type of care and a frequency of care.
Dependent claim 6 recites wherein the identifying factors comprise at least one of: age, gender, medical history, mental health history, fitness, past injuries, past surgeries, past diseases, education, marital status, income, alcohol and narcotics history, sexual orientation, race, ethnicity, height, and weight.
Dependent claims 7 and 17 recite wherein the first data set is stored in the vector embeddings.
Dependent claim 8 recites wherein a vector embedding comprises at least one numeric value within a range of values.
Dependent claim 9 recites wherein at least one numeric value represents a variable that is selectable from one of multiple options.
Dependent claim 10 recites wherein a vector embedding comprises at least one Boolean value.
Dependent claim 11 and 17 recite wherein the second data set is stored in the vector embeddings.
Each of these steps of the preceding dependent claims 2-11 and 14-17 only serve to further limit or specify the features of independent claims 1 or 13 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
Dependent claims 2 and 14 recite receiving, by the health platform, from a big data source, data related to a third data set from a second medical diagnostics assessment of the patient; applying, by the health platform, the machine learning model to the third data set and the data from the big data source; and updating, by the health platform, the course of mental health treatment. The limitations of receiving data related to a third data set from a second medical diagnostics assessment of the patient and updating the course of mental health treatment. The health platform and the big data source are additional elements, which is mere instructions to apply the exception and does not provide a practical application or significantly more for the same reasons. The limitation of applying, by the health platform, the machine learning model to the third data set and the data from the big data source amounts to mere instructions to apply the exception because a mathematical algorithm applied on a general-purpose computer has been found by the courts to be mere instruction to apply as in MPEP 2106.05(f)(2).
Dependent claims 12 and 18 recite wherein displaying the course of mental health treatment on the graphical interface further comprises displaying reasons for generating the course of mental health treatment. This limitation is recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
Claims 13-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 13 is rejected because it does not sufficiently recite a non-transitory computer readable storage medium. The United States Patent and Trademark Office (USPTO) is obliged to give claims their broadest reasonable interpretation consistent with the specification during proceedings before the USPTO. See In re Zletz, 893 F.2d 319(Fed. Cir. 1989) (during patent examination the pending claims must be interpreted as broadly as their terms reasonably allow). The broadest reasonable interpretation of a claim drawn to a computer readable medium (also called machine readable medium and other such variations) typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. See MPEP 2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. §101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter) and Interim Examination Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C. §101, Aug. 24, 2009; p. 2.
The USPTO recognizes that applicants may have claims directed to computer readable media that cover signals per se, which the USPTO must reject under 35 U.S.C. §101 as covering both non-statutory subject matter and statutory subject matter. In an effort to assist the patent community in overcoming a rejection or potential rejection under 35 U.S.C. §101 in this situation, the USPTO suggests the following approach. A claim drawn to such a computer readable medium that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 U.S.C. §101 by adding the limitation "non-transitory" to the claim. Cf. Animals – Patentability, 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) (suggesting that applicants add the limitation "non-human" to a claim covering a multi-cellular organism to avoid a rejection under 35 U.S.C. §101). Such an amendment would typically not raise the issue of new matter, even when the specification is silent because the broadest reasonable interpretation relies on the ordinary and customary meaning that includes signals per se. The limited situations in which such an amendment could raise issues of new matter occur, for example, when the specification does not support a non-transitory embodiment because a signal per se is the only viable embodiment such that the amended claim is impermissibly broadened beyond the supporting disclosure. See, e.g., Gentry Gallery, Inc. v. Berkline Corp., 134 F.3d 1473 (Fed. Cir. 1998).
Claims 14-18 depend from Claim 13 and are rejected for the reasons noted in the rejection(s) of Claim 13, above.
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) 1, 3-4, 6-7, 11-13, 15, and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Peters (US 11049605 B1) in view of Hanley (US 20210334697 A1).
As per Claim 1, Peters teaches a method of executing using a machine learning model to recommend a course of mental health treatment with a graphical user interface, the method comprising:
applying, by the health platform, to a first data set and a second data set, the machine learning model, wherein the a first data set records a first medical diagnostics assessment of a patient and the second data set records identifying factors associated with the patient; ([Col. 3, Lines 50-57] transmit a final dataset by compiling the demographic data (i.e. identifying factors) and the mental health questionnaire data (i.e. medical diagnostics assessment) of the user through a data transmission module. The server is communicatively coupled to the memory over a network. The server is configured to process the final dataset received from the data transmission module by applying a machine learning module. [Col. 4 Lines 7-10] the questionnaire module presents one or more questions pertaining to the diagnosis of mental health disorders and a plurality of corresponding selectable answers. [Col. 8 Lines 66-67 and Col. 9, Lines 1-4] receive mental health questionnaire data from the user through a questionnaire module 212. In an embodiment, the questionnaire module presents one or more questions 402 pertaining to the diagnosis of mental health disorders and a plurality of corresponding selectable answers 404. [Col. 9, Lines 9-17] he user may furthermore be prompted to answer questions pertaining to the state of their mental health. Such questionnaires may include but are not limited to the Goldberg Depression Questionnaire, PHQ-9 Depression Test, Hamilton Depression Rating Scale (HAD-D), Hamilton Anxiety Rating Scale (HAM-A), Generalized Anxiety Disorder Questionnaire-IV, GAD7 Anxiety test questionnaire or other clinical questionnaire process used to assess mental health. [Col. 8, Lines 17-22] receive demographic data (i.e. identifying factors) pertaining to the user through a demography module 204. Demographic data such as gender, ethnicity, weight, and height (i.e. identifying factors) may be important in determining the efficacy of personalized medicine.)
generating, based on the applying the machine learning model to the first and second data sets, the course of mental health treatment; ([Col. 3 Lines 57-58] generate the tailored medical recipes based on the final dataset processed by the machine learning module. [Col. 1 Lines 31-33] generating tailored medical recipes for mental health disorders including anxiety and/or depression by applying machine learning)
displaying the course of mental health treatment on the graphical user interface. ([Col. 7, Lines 46-50] a display 114 having a User Interface (UI) 116 that may be used by the user or an administrator to initiate a request to view the tailored medical recipe. [Col. 7, Lines 56-57] Display 114 may further be used to display tailored medical recipes to the users. )
Peters does not explicitly teach, however Hanley teaches
with a list of providers generated from vector embeddings derived from the first and second data sets and processed with a large language model; ([Para. 0035] The artificial intelligence recommendation computing entity 106 can be configured to provide an optimal recommendation of a provider entity from a set of provider entities. The embedding generation engine 110 of the artificial intelligence recommendation computing entity 106 can generate the embeddings data 116, for example, based on historical visit data (e.g., historical visit data 401 shown in FIG. 4).The recommendation engine 114 of the artificial intelligence recommendation computing entity 106 can employ the model data 118 (e.g., the one or more machine learning models) to generate the recommendation data 120. [Para. 0058] Historical visit data 401 (i.e. first data set) can be employed by the step/operation 402 to generate provider embeddings. For example, the step/operation 402 can generate the embeddings data 116 based on the historical visit data 401. The historical visit data 401 can be, for example, historical data related to one or more previous visits to one or more patient entities by one or more patients. In certain embodiments, the historical visit data 401 can include data related to one or more previous medical records, data related to historical patient data provider visits, data related to one or more previous medical claims, data related to one or more previous diagnosis, data related to one or more previous symptoms. [Para. 0067] The latent features (i.e. second data set) 706 can include one or more latent variables associated with the historical visits from the historical visit data 401. In a non-limiting example, the latent features 706 can include a location variable, a region variable, a prescription variable, a policy variable, a premium variable, a specialty variable, a claim count variable, an average charged amount variable, a patient count variable, an age distribution variable, a gender variable, a distribution variable, a size of clinic variable, a symptom variable, a quality variable, an assessment variable, a ratio of complained visits variable, a ranked diagnosis code variable, a repeated appointment variable, a score variable, and/or another type of variable related to the historical visit data 401.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of recommending a course of mental health treatment as taught by Peters and incorporate an artificial intelligence recommendation system as taught by Hanley, with the motivation of providing recommendations based on analysis of digital data in an accurate, computationally efficient and predictively reliable manner (Hanley Para. 0001).
As per Claim 3, Peters/ Hanley teach the method of claim 1, Peters further teaches wherein generating, based on the applying the machine learning model to the first and second data sets, the course of mental health treatment, further comprises at least one of: recommending a healthcare provider to contact the patient, and suggesting a prescription for the patient. ([Col. 11, Lines 32-41] Substances that may be included in the recipe generation process may include different doses of certified mental health medicines (i.e. suggesting a prescription )such as selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), atypical antidepressants such as bupropion (Wellbutrin XL, Wellbutrin SR, Aplenzin, Forfivo XL), mirtazapine (Remeron), nefazodone, trazodone and vortioxetine (Trintellix), tricyclic antidepressants and/or monoamine oxidase inhibitors (MAOIs).)
As per Claim 4, Peters/ Hanley teach the method of claim 1, Peters further teaches wherein generating, based on the applying the machine learning model to the first and second data sets, the course of mental health treatment, is based, at least in part, on one or more of: income, age, gender, sexual orientation, race, education level, military experience of the patient. ([Col. 3, Lines 50-58] transmit a final dataset by compiling the demographic data (i.e. identifying factors) and the mental health questionnaire data (i.e. medical diagnostics assessment) of the user through a data transmission module. The server is communicatively coupled to the memory over a network. The server is configured to process the final dataset received from the data transmission module by applying a machine learning module; generate the tailored medical recipes based on the final dataset processed by the machine learning module [Col. 1 Lines 31-33] generating tailored medical recipes for mental health disorders including anxiety and/or depression by applying machine learning. [Col. 8, Lines 17-22] receive demographic data (i.e. identifying factors) pertaining to the user through a demography module 204. Demographic data such as gender, ethnicity, weight, and height (i.e. identifying factors) may be important in determining the efficacy of personalized medicine.)
As per Claim 6, Peters/ Hanley teach the method of claim 1, Peters further teaches wherein the identifying factors comprise at least one of: age, gender, medical history, mental health history, fitness, past injuries, past surgeries, past diseases, education, marital status, income, alcohol and narcotics history, sexual orientation, race, ethnicity, height, and weight. ([Col. 8, Lines 17-22] receive demographic data (i.e. identifying factors) pertaining to the user through a demography module 204. Demographic data such as gender, ethnicity, weight, and height (i.e. identifying factors) may be important in determining the efficacy of personalized medicine.)
As per Claim 12, Peters/ Hanley teach the method of claim 1, Peters further teaches wherein displaying the generated course of mental health treatment on the graphical user interface comprises displaying reasons for generating the course of mental health treatment. ([Col. 7, Lines 46-50 and 56-57]A display 114 having a User Interface (UI) 116 that may be used by the user or an administrator to initiate a request to view the tailored medical recipes. Display 114 may further be used to display tailored medical recipes to the users. [Col. 10, Lines 66-67 and Col. 11, Lines 1-12] FIG. 8 illustrates a flow diagram 80 of generating the tailored medical recipes. At block 806, the final predictive model is used to predict efficacy (i.e. reasons for generating course of mental health treatment) in each iteration. At block 808, the medical recipe with the highest predicted efficacy is chosen as a final medical recipe for the user. For each patient recipes are created and the recipe, or an average of several recipes, with the highest predicted efficacy, may be selected as the final recipe to be produced for the patient.)
As per Claim 13, Peters teaches a computer readable medium tangibly encoded with a computer program to recommend a course of mental health treatment with a graphical user interface, the computer program executable by a processor to perform actions comprising:
applying to the a first data set and a second data sets, a machine learning model, wherein the a first data set records a first medical diagnostics assessment of a patient and the second data set records identifying factors associated with the patient; ([Col. 3, Lines 50-57] transmit a final dataset by compiling the demographic data (i.e. identifying factors) and the mental health questionnaire data (i.e. medical diagnostics assessment) of the user through a data transmission module. The server is communicatively coupled to the memory over a network. The server is configured to process the final dataset received from the data transmission module by applying a machine learning module. [Col. 4 Lines 7-10] The questionnaire module (i.e. first data set) presents one or more questions pertaining to the diagnosis of mental health disorders and a plurality of corresponding selectable answers. [Col. 9, Lines 9-17] The user may furthermore be prompted to answer questions pertaining to the state of their mental health. Such questionnaires may include but are not limited to the Goldberg Depression Questionnaire, PHQ-9 Depression Test, Hamilton Depression Rating Scale (HAD-D), Hamilton Anxiety Rating Scale (HAM-A), Generalized Anxiety Disorder Questionnaire-IV, GAD7 Anxiety test questionnaire or other clinical questionnaire process used to assess mental health. [Col. 8 Lines 17-22] Receive demographic data (i.e. second data set) pertaining to the user through a demography module 204. The user is prompted to enter his/her demographic data. Demographic data such as gender, ethnicity, weight, and height may be important in determining the efficacy of personalized medicine.)
generating, based on the applying the machine learning model to the first and second data sets, the course of mental health treatment; ([Col. 3 Lines 57-58] generate the tailored medical recipes based on the final dataset processed by the machine learning module. [Col. 1 Lines 31-33] generating tailored medical recipes for mental health disorders including anxiety and/or depression by applying machine learning)
and displaying the course of mental health treatment on the graphical user interface. ([Col. 7, Lines 46-50] a display 114 having a User Interface (UI) 116 that may be used by the user or an administrator to initiate a request to view the tailored medical recipe. [Col. 7, Lines 56-57] Display 114 may further be used to display tailored medical recipes to the users.)
Peters does not explicitly teach, however Hanley teaches
with a list of providers generated from vector embeddings derived from the first and second data sets and processed with a large language model; ([Para. 0035] The artificial intelligence recommendation computing entity 106 can be configured to provide an optimal recommendation of a provider entity from a set of provider entities. The embedding generation engine 110 of the artificial intelligence recommendation computing entity 106 can generate the embeddings data 116, for example, based on historical visit data (e.g., historical visit data 401 shown in FIG. 4).The recommendation engine 114 of the artificial intelligence recommendation computing entity 106 can employ the model data 118 (e.g., the one or more machine learning models) to generate the recommendation data 120. [Para. 0058] Historical visit data 401 (i.e. first data set) can be employed by the step/operation 402 to generate provider embeddings. For example, the step/operation 402 can generate the embeddings data 116 based on the historical visit data 401. The historical visit data 401 can be, for example, historical data related to one or more previous visits to one or more patient entities by one or more patients. In certain embodiments, the historical visit data 401 can include data related to one or more previous medical records, data related to historical patient data provider visits, data related to one or more previous medical claims, data related to one or more previous diagnosis, data related to one or more previous symptoms. [Para. 0067] The latent features (i.e. second data set) 706 can include one or more latent variables associated with the historical visits from the historical visit data 401. In a non-limiting example, the latent features 706 can include a location variable, a region variable, a prescription variable, a policy variable, a premium variable, a specialty variable, a claim count variable, an average charged amount variable, a patient count variable, an age distribution variable, a gender variable, a distribution variable, a size of clinic variable, a symptom variable, a quality variable, an assessment variable, a ratio of complained visits variable, a ranked diagnosis code variable, a repeated appointment variable, a score variable, and/or another type of variable related to the historical visit data 401.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of recommending a course of mental health treatment as taught by Peters and incorporate an artificial intelligence recommendation system as taught by Hanley, with the motivation of providing recommendations based on analysis of digital data in an accurate, computationally efficient and predictively reliable manner (Hanley Para. 0001).
As per Claim 15, Peters/ Hanley teach the computer readable medium of claim 13, Peters further teaches wherein generating, based on the applying the machine learning model to the first and second data sets, the course of mental health treatment, further comprises at least one of: recommending a healthcare provider to contact the patient, and suggesting a prescription for the patient. ([Col. 11, Lines 32-41] Substances that may be included in the recipe generation process may include different doses of certified mental health medicines (i.e. suggesting a prescription )such as selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), atypical antidepressants such as bupropion (Wellbutrin XL, Wellbutrin SR, Aplenzin, Forfivo XL), mirtazapine (Remeron), nefazodone, trazodone and vortioxetine (Trintellix), tricyclic antidepressants and/or monoamine oxidase inhibitors (MAOIs).)
As per Claim 18, Peters/ Hanley teach the computer readable medium of claim 13, Peters further teaches wherein updating the graphical user interface to display the course of mental health treatment further comprises displaying reasons for generating the course of mental health treatment.([Col. 7, Lines 46-50 and 56-57] A display 114 having a User Interface (UI) 116 that may be used by the user or an administrator to initiate a request to view the tailored medical recipes. Display 114 may further be used to display tailored medical recipes to the users. [Col. 10, Lines 66-67 and Col. 11, Lines 1-12] FIG. 8 illustrates a flow diagram 80 of generating the tailored medical recipes. At block 806, the final predictive model is used to predict efficacy (i.e. reasons for generating course of mental health treatment) in each iteration. At block 808, the medical recipe with the highest predicted efficacy is chosen as a final medical recipe for the user. For each patient recipes are created and the recipe, or an average of several recipes, with the highest predicted efficacy, may be selected as the final recipe to be produced for the patient.)
Claim(s) 2 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Peters (US 11049605 B1) in view of Hanley (US 20210334697 A1) in view of Cray (US 20220028558 A1).
As per Claim 2, Peters/ Hanley teach the method of claim 1, Peters teaches further comprising:
applying, by the health platform, the machine learning model to the third data set and the data from the big data source; ([Col. 3, Lines 50-57] transmit a final dataset by compiling the voice data (i.e. third set and data from big source) of the user through a data transmission module. The server is communicatively coupled to the memory over a network. The server is configured to process the final dataset received from the data transmission module by applying a machine learning module.)
and updating, by the health platform, the course of mental health treatment. ([Col. 3 Lines 57-58] generate the tailored medical recipes based on the final dataset processed by the machine learning module. [Col. 1 Lines 31-33] generating tailored medical recipes for mental health disorders including anxiety and/or depression by applying machine learning. [Col. 11, Lines 2-8] Block 804 depicts the amount of each substance in the medical recipe is changed for each patient and the user summary statistics are kept constant. At block 806, the final predictive model is used to predict efficacy in each iteration. At block 808, the medical recipe with the highest predicted efficacy is chosen as a final medical recipe for the user.)
Peters does not explicitly teach, however Cray teaches
receiving, by the health platform, from a big data source, data related to a third data set from a second medical diagnostics assessment of the patient; ([Para. 0014] growing prevalence of available data that objectively measure a patient's physiological state, such as voice analysis (i.e. data related to the third data set). [Para. 0020] The functions performed by the plurality of devices may include at least: collecting patient assessments—the system 100 may support methods (see FIG. 1C) to enable clinics (of any type) to systematically assign standardized mental health assessments (see FIG. 2) to patients. Additional data associated with a specific assessment which may include clinic-gathered data such as voice analysis. Gathering assessment results in a structured, relational database, including calculated levels of mental health disorder. All collected data may be aggregated in a secure, relational database (i.e. big data source). Examiner interprets that the collection of assessment results of multiple patients which include the acquisition of voice data is indicative of receiving from a big data source data related to the third data set.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method for generating tailored medical recipes for mental health disorders as taught by Peters, an artificial intelligence recommendation system as taught by Hanley, and incorporate allocating resources in mental health treatment as taught by Cray, with the motivation of assessing patient status for specific mental health conditions (Cray Para. 0003).
As per Claim 14, Peters/ Hanley teach the computer readable medium of claim 13, Peters further teaches wherein the actions further comprise:
applying the machine learning model to the third data set and the data from the big data source; ([Col. 3, Lines 50-57] transmit a final dataset by compiling the voice data (i.e. third set and data from big source) of the user through a data transmission module. The server is communicatively coupled to the memory over a network. The server is configured to process the final dataset received from the data transmission module by applying a machine learning module.)
and updating the generated course of mental health treatment. ([Col. 3 Lines 57-58] generate the tailored medical recipes based on the final dataset processed by the machine learning module. [Col. 1 Lines 31-33] generating tailored medical recipes for mental health disorders including anxiety and/or depression by applying machine learning. [Col. 11, Lines 2-8] Block 804 depicts the amount of each substance in the medical recipe is changed for each patient and the user summary statistics are kept constant. At block 806, the final predictive model is used to predict efficacy in each iteration. At block 808, the medical recipe with the highest predicted efficacy is chosen as a final medical recipe for the user.)
Peters does not explicitly teach, however Cray teaches
receiving, from a big data source, data related to a third data set from a second medical diagnostics assessment of the patient; ([Para. 0014] growing prevalence of available data that objectively measure a patient's physiological state, such as voice analysis (i.e. data related to the third data set). [Para. 0020] The functions performed by the plurality of devices may include at least: collecting patient assessments—the system 100 may support methods (see FIG. 1C) to enable clinics (of any type) to systematically assign standardized mental health assessments (see FIG. 2) to patients. Additional data associated with a specific assessment which may include clinic-gathered data such as voice analysis. Gathering assessment results in a structured, relational database, including calculated levels of mental health disorder. All collected data may be aggregated in a secure, relational database (i.e. big data source). Examiner interprets that the collection of assessment results of multiple patients which include the acquisition of voice data is indicative of receiving from a big data source data related to the third data set.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method for generating tailored medical recipes for mental health disorders as taught by Peters, an artificial intelligence recommendation system as taught by Hanley, and incorporate allocating resources in mental health treatment as taught by Cray, with the motivation of assessing patient status for specific mental health conditions (Cray Para. 0003).
Claim(s) 5 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Peters (US 11049605 B1) in view of Hanley (US 20210334697 A1) in view of Jain (US 11302448 B1).
As per Claim 5, Peters/ Redlus teach the method of claim 1, however Jain teaches wherein the course of mental health treatment comprises a type of care and a frequency of care. ([Col. 43, Lines 63-65] medications or other therapies to provide and the parameters for them (e.g., frequency, dosage or intensity, etc.). [Col. 47 Lines 65-57 and Col. 48 Lines 1-2] different categories or classes of drugs, specific drugs, different regimens or administrations of drugs (e.g., combinations of different parameters for dosage, frequency, administration with or without food, etc.))
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method for generating tailored medical recipes for mental health disorders as taught by Peters, an artificial intelligence recommendation system as taught by Hanley, and incorporate selecting digital therapeutics as taught by Jain, with the motivation of selecting or adjusting the digital therapeutics interventions for individuals to provide the appropriate intensity of treatment, interaction, or support (Jain Col. 22, Lines 62-65).
As per Claim 16, Peters/ Hanley teach the computer readable medium of claim 13, however Jain teaches wherein the course of mental health treatment, comprises a type of care and a frequency of care. ([Col. 43, Lines 63-65] medications or other therapies to provide and the parameters for them (e.g., frequency, dosage or intensity, etc.). [Col. 47 Lines 65-57 and Col. 48 Lines 1-2] different categories or classes of drugs, specific drugs, different regimens or administrations of drugs (e.g., combinations of different parameters for dosage, frequency, administration with or without food, etc.))
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method for generating tailored medical recipes for mental health disorders as taught by Peters, an artificial intelligence recommendation system as taught by Hanley, and incorporate selecting digital therapeutics as taught by Jain, with the motivation of selecting or adjusting the digital therapeutics interventions for individuals to provide the appropriate intensity of treatment, interaction, or support (Jain Col. 22, Lines 62-65).
Claim(s) 7, 11, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Peters (US 11049605 B1) in view of Hanley (US 20210334697 A1) in view of Redlus (US 20210134444 A1).
As per Claim 7, Peters/ Hanley teach the method of claim 1, however Redlus further teaches wherein the first data set is stored in the vector embeddings. ([Para. 0052] A feature vector is formed using the indicators for health characteristics. [Para. 0053] The feature vectors incorporate patient-specific. Examples of patient-specific factors include age, gender, as well as other information retrieved from a health assessment (i.e. first data set), such as a measure of treatability, medical history (e.g., pre-existing conditions, and/or family history), and diagnosed diseases (e.g., diagnosed with depression, cancer, a broken leg) (i.e. first data set). )
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of recommending a course of mental health treatment as taught by Peters, an artificial intelligence recommendation system as taught by Hanley, and incorporate vectors of patient health characteristics as taught by Redlus, with the motivation of to providing effective healthcare referrals with a high likelihood of compatibility and treatment success of a patient (Redlus Para. 0005--0006).
As per Claim 11, Peters/ Hanley teach the method of claim 1, however Redlus further teaches wherein the second data set is stored in the vector embeddings. ([Para. 0052] A feature vector is formed using the indicators for health characteristics. [Para. 0053] The feature vectors incorporate patient-specific. Examples of patient-specific factors include age (i.e. second data set), gender (i.e. second data set), as well as other information retrieved from a health assessment, such as a measure of treatability, medical history (e.g., pre-existing conditions, and/or family history), and diagnosed diseases (e.g., diagnosed with depression, cancer, a broken leg).)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of recommending a course of mental health treatment as taught by Peters, an artificial intelligence recommendation system as taught by Hanley, and incorporate vectors of patient health characteristics as taught by Redlus, with the motivation of to providing effective healthcare referrals with a high likelihood of compatibility and treatment success of a patient (Redlus Para. 0005--0006).
As per Claim 17, Peters/ Hanley teach the computer readable medium of claim 13, however Redlus further teaches wherein the first data set and the second data set is stored as vector embeddings. ([Para. 0052] A feature vector is formed using the indicators for health characteristics. [Para. 0053] The feature vectors incorporate patient-specific. Examples of patient-specific factors include age (i.e. second data set), gender (i.e. second data set), as well as other information retrieved from a health assessment (i.e. first data set), such as a measure of treatability, medical history (e.g., pre-existing conditions, and/or family history), and diagnosed diseases (e.g., diagnosed with depression, cancer, a broken leg) (i.e. first data set).)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of recommending a course of mental health treatment as taught by Peters, an artificial intelligence recommendation system as taught by Hanley, and incorporate vectors of patient health characteristics as taught by Redlus, with the motivation of to providing effective healthcare referrals with a high likelihood of compatibility and treatment success of a patient (Redlus Para. 0005--0006).
Claim(s) 8-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Peters (US 11049605 B1) in view of Hanley (US 20210334697 A1) in view of Hur (WO 2022260293 A1).
As per Claim 8, Peters/ Hanley teach the method of claim 7, however Hur teaches wherein a vector embedding comprises at least one numeric value within a range of values. ([Pg. 10, Para. 73] when a patient is diagnosed to diagnose a disease, a diagnosis name/diagnosis code is described in the variable data table. In this case, when some of the features included in the input data are the diagnosis number COUNT of I20, I21, and E11 of the diagnosis name/diagnosis code, the vectorization unit 170 May convert the diagnosis codes I20, I21, E11 and E11 into [1,1,0].)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating tailored medical recipes for mental health disorders as taught by Peters, an artificial intelligence recommendation system as taught by Hanley, and incorporate generating conversion data by applying at least one designated vectorization function to the feature characteristics as taught by Hur, with the motivation of converting medical data into vectorization functions (Hur Pg.2, Para. 5).
As per Claim 9, Peters/ Hanley teach the method of claim 7, however Hur teaches wherein at least one numeric value represents a variable that is selectable from one of multiple options. ([Pg. 10, Para. 73] when a patient is diagnosed to diagnose a disease, a diagnosis name/diagnosis code is described in the variable data table. In this case, when some of the features included in the input data are the diagnosis number COUNT of I20, I21, and E11 of the diagnosis name/diagnosis code, the vectorization unit 170 May convert the diagnosis codes I20, I21, E11 and E11 into [1,1,0].)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating tailored medical recipes for mental health disorders as taught by Peters, an artificial intelligence recommendation system as taught by Hanley, and incorporate generating conversion data by applying at least one designated vectorization function to the feature characteristics as taught by Hur, with the motivation of converting medical data into vectorization functions (Hur Pg.2, Para. 5).
As per Claim 10, Peters/ Hanley teach the method of claim 7, however Hur teaches wherein a vector embedding comprises at least one Boolean value. ([Pg.3, Para. 8] The variable metadata storage may store a variable type of each variable extracted from the medical data, and the variable type may be at least one of categorical, numerical, time delta, Boolean, and date/time.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating tailored medical recipes for mental health disorders as taught by Peters, providing patients with clinician referrals based on health assessment from users as taught by Redlus, and incorporate generating conversion data by applying at least one designated vectorization function to the feature characteristics as taught by Hur, with the motivation of converting medical data into vectorization functions (Hur Pg.2, Para. 5).
Response to Arguments
Applicant’s arguments, see pg. 6 “IV. Rejection under 35 U.S.C. 112”, filed 12/08/2025, with respect to Claims 1-18 have been fully considered and are persuasive. The rejection of the claims has been withdrawn.
Applicant's arguments, see pgs. 6-10 “V. Rejections under 35 U.S.C 101” filed 12/08/2025 have been fully considered but they are not persuasive.
Applicant submits that the independent claims do not recite the abstract grouping of organizing human activity. Examiner is not persuaded. The claims recite Independent claim 1 recites a method comprising wherein the first data set records a first medical diagnostics assessment of a patient and the second data set records identifying factors associated with the patient; generating, the course of mental health treatment, with a list of providers generated from vector embeddings derived from the first and second data sets. These limitations recite collecting patient data to determine optimal courses of treatment for the patient and recommend healthcare providers. These steps organize patients and healthcare providers by determining optimal courses of treatment for the patient and the associated healthcare providers to provide the courses of treatment. Because these limitations determine optimal courses of treatment and recommendation for the healthcare providers to implement following the analysis of patient data, they constitute the management of personal behavior on part of healthcare providers and hospital administrative staff. Accordingly, the claims fall under “Certain Methods of Organizing Human Activity” grouping of abstract, and, thus, recite an abstract idea.
Claims 1 and 13 recite applying to a first data set and a second data set the machine learning model and processed with a large language model. This limitation amounts to mere instructions to apply the exception because a mathematical algorithm applied on a general-purpose computer has been found by the courts to be mere instruction to apply as in MPEP 2106.05(f)(2).
Claims 1 and 13 recite displaying the course of mental health treatment on a graphical user interface. This limitation is recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
Applicant submits that the amended claim integrates the alleged abstract idea into a practical application. The machine learning model processes structured diagnostic and demographic data to produce vector embeddings. These embeddings are further processed by a large language model to generate a list of providers tailored to the patient's needs. The output is displayed on a graphical user interface, enabling real-time interaction and delivery of personalized treatment recommendations. The claim reflects a specific implementation of machine learning and natural language processing techniques to solve a problem in mental health treatment. The combination of structured data transformation, model inference, and dynamic display forms a technological solution that improves the delivery and personalization of care. Examiner is not persuaded. An improvement to the abstract idea of improving the delivery and personalization of care does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG,921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”). There is no indication in the instant disclosure that the involvement of a computer assists in improving the technology for the outlined problem statement. Here, the improvement is to displaying data. The instant application and claim language fail to detail how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient.
Applicant asserts that the amended claim includes specific limitations that go beyond generic implementation. The generating step recites generating a course of mental health treatment with a list of providers derived from vector embeddings processed with a large language model. The claim also recites displaying the generated treatment course on a graphical user interface. The combination of machine learning and large language model processing applied to structured diagnostic and demographic data is not well-understood, routine, or conventional. The use of vector embeddings and natural language inference to generate provider recommendations tailored to individual patients reflects a novel and non-conventional application of computational techniques. The dynamic display of treatment recommendations on a graphical user interface further supports the claim's implementation in a specific technological context. Examiner is not persuaded. The consideration under Step 2B is if the additional elements, alone or in combination, are well-understood, routine, and conventional in the field – the novelty of the abstract idea is not considered relevant under the Step 2B analysis. Here, the additional elements of Claim 1 reciting a health platform and a graphical user interface and Claim 13 reciting a computer readable medium tangibly encoded with a computer program to recommend a course of mental health treatment with a graphical user interface, the computer program executable by a processor, and a health platform, alone or in combination, amount to instruction to implement the abstract idea using a general-purpose computer. The implementation of a machine learning model and processing data with a large language model amount to mere instructions to apply the exception because a mathematical algorithm applied on a general-purpose computer has been found by the courts to be mere instruction to apply as in MPEP 2106.05(f)(2). The displaying the course of mental health treatment on a graphical user interface is recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
Applicant’s arguments, see pgs. 10-12 “VI. Rejections under 35 U.S.C 103”, filed 12/08/2025, with respect to Claims 1-18 have been fully considered and are persuasive regarding the newly added limitations. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Hanley, as per the rejection above.
Conclusion
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/P.K.E./Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682