DETAILED ACTION
Status of Claims
In the response filed December 4, 2025, Applicant amendment claims 1 and 6. Claims 1-10 are pending in the current application.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 December 4, 2025 has been entered.
Response to Arguments
Applicant's amendments with respect to the rejection under 35 U.S.C. 101 have been fully considered but they are not persuasive. First, Applicant asserts that the claims do not recite an abstract concept under Prong One because the features recited are indicative of hardware, which requires a processor coupled with a computer memory and could not allow for metal processes. Examiner respectfully disagrees. Claims can recite a mental process even if they are claimed as being performed on a computer (see MPEP 2106.04 (III)(C)).The claims, under their broadest reasonable interpretation, cover performance of the limitations in the mind (including observation, evaluation, judgement or opinion) but for the recitation of generic computer components. That is, other than reciting processor, memory, and medical devices, nothing in the claim elements precludes the steps form practically being performed in the mind. For example, the identified limitations encompass a healthcare professional collecting patient data through patient questionnaires, comparing to a medical standard, determining a rating or score based on any changes to health status of the patient, and relaying recommendations based on the score. Additionally, continuously monitoring effectiveness of the recommendations can be implemented using flowsheets or patient charts (see MPEP 2106.04(III)(B)). Furthermore, the limitation, “automatically convert…matrix form” involves a mental process because the Applicant’s specification does not provide details on how the conversion is executed. Thus, the Broadest Reasonable Interpretation (BRI) of this limitation cannot be construed as complex mathematical transformation into matrix form. Second, Applicant asserts that the claims integrate the judicial exception into a practical application because the additional elements include an improvement to technology or technical field by “processing efficiency and diagnostic throughput by enabling real-time fusion of heterogenous data streams in real-time.” Examiner respectfully disagrees. In order to determine whether the claim improve the functioning of a computer or technology, the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement (MPEP 2106.04(d)(1)). The Applicant’s specification discloses, “ The clinical interventions may use additional tests to improve the diagnostic and recommendations accuracy. The therapeutic/clinical interventions may include food, medicine, meditation, music, panchakarma, acupuncture and the like (Pg. 14)” and “However, such modern medical practices lack personalization as they cannot be structured specifically for any patient. Sometimes, such modern medicine has proven to be not hundred percent effective in curing a disease. Personalization is needed to predict actionable interventions for patients that further improves long-term health and wellness. Actionable interventions as described here may be referred to yoga, medicine, and the like (Pg. 1).” In this case, the improvements disclosed in the specification are not to the technology, but to the patient’s well-being and health. The analysis includes the Specification paragraphs cited by Applicant. Moreover, the artificial intelligence and machine learning model (e.g., graphical user interface of conversational artificial intelligence and adaptive questioning) in the claims recites conventional AI/machine learning models without specific improvements to the technology itself because simply applying generic machine learning techniques to diagnosis a medical condition without improving the underlying technology is insufficient for patent eligibility. Furthermore, the graphical user interface with adaptive questioning and dynamic. Third, the Applicant asserts that the claims amount to significantly more than the abstract idea under 2B. As stated in earlier in the Prong Two response, the improvements disclosed in the specification are not to the technology, but to the patient’s well-being and health. Finally, Applicant asserts that the claims are analogous to ex. 47 of the July 2024 AI SME update. Examiner respectfully disagrees. Using the broadest reasonable interpretation, the claims are analogous to claim 2 of ex. 47 because the claims encompass a mental process by collecting and analyzing information. The rejection is maintained.
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 1-10 are rejected under 35 U.S.C. 101 because the claims are not directed to patent eligible subject matter.
Claims 1-10 do fall within at least one of the four categories of patent eligible subject matter because the claims recite a machine (i.e., system) and process (i.e., a method).
Although claims 1-10 fall under at least one of the four statutory categories, it should be determined whether the claim wholly embraces a judicially recognized exception, which includes laws of nature, physical phenomena, and abstract ideas, or is it a particular practical application of a judicial exception (See MPEP 2106 I and II).
Claims 1-10 are directed to a judicial exception (i.e., a law of nature, natural phenomenon, or abstract idea) without significantly more.
Part I: Step 2A, Prong One: Identify the Abstract Idea
Under step 2A, Prong One of the Alice framework, the claims are analyzed to determine if the claims are directed to a judicial exception. MPEP §2106.04(a). The determination consists of a) identifying the specific limitations in the claim that recite an abstract idea; and b) determining whether the identified limitations fall within at least one of the three subject matter groupings of abstract ideas (i.e., mathematical concepts, mental processes, and certain methods of organizing human activity).
The identified limitations of independent claim 1 (representative of independent claim 6) recite (in bold and italics):
a hardware processor;
a memory coupled to the hardware processor, wherein the memory comprises a set of program instructions executable by the hardware processor to:
collect patient information and phenotypic features associated with a patient from a plurality of medical devices, computer vision techniques, and from a conversational artificial intelligence questionnaire, wherein the phenotypic features comprise anatomic features, physical, physiological features, psychological features;
present, in real time, the conversational artificial intelligence questionnaire through a graphical user interface comprising an adaptive questionnaire framework and an indicator, wherein the adaptive questionnaire framework is to dynamically select subsequent questions based on replies to previous questions, wherein the indicator comprises calculated scores for traditional medicine system parameters as numerical values that update automatically after response to each question, wherein the graphical user interface is to optimize question presentation by terminating questioning sequence when successive differences in calculated scores fall below a pre-set threshold value, and wherein layout of the graphical user interface is modified based on the calculated scores;
automatically convert responses from the conversational artificial intelligence questionnaire into categorical variables in matrix form;
capture physiological sensor data and automatically compute digital biomarkers from the physiological sensor data, including pulse rate variability in time-domain, frequency-domain, geometric and nonlinear forms, pulse transmit time, and pulse morphology descriptors, while compensating for noise using signals from accelerometer and gyro meter sensors;
apply the collected patient information and phenotypic features associated with the patient the categorical variables, and the digital biomarkers on to a trained machine learning model or a deep learning model;
generate baseline health status of the patient based on the results of the application of the trained machine learning model or the deep learning model, wherein the generated baseline health status comprises a set of integrative medicine system parameters, wherein the set of integrative medicine system parameters comprise a set of traditional medicine system and a set of modern medicine system parameters of the patient, and wherein the traditional medicine system comprises Ayurveda, traditional Chinese medicine system, Siddha, and Unani;
obtain a combination of responses for a set of adaptive and conversational artificial intelligence-based questionnaire in real time from the patient: one or more real time patient information from the plurality of medical devices associated with the patient, one or more real time spatiotemporal information data associated with the patient, and one or more real time inputs from biochemical markers, clinical markers and multi-omics markers, wherein to obtain the responses for the set of adaptive and conversational artificial intelligence-based questionnaire, the adaptive and conversational artificial intelligence-based questionnaire is to be presented through a graphical user interface comprising an adaptive questionnaire framework, wherein the adaptive questionnaire framework is to dynamically select subsequent questions based on replies to previous questions, wherein the graphical user interface is to optimize question presentation by terminating questioning sequence when successive differences in calculated scores fall below a pre-set threshold value, and wherein layout of the graphical user interface is modified based on the calculated scores;
convert the responses into real time categorical variables in matrix form;
estimate real time set of integrative medicine system parameters by applying the real time categorical variables, the physiological sensor data, the real-time patient information, the real-time spatiotemporal information, and the real-time inputs from the biochemical, clinical, and multi-omics markers onto the trained machine learning model or the deep learning model;
determine changes in the generated baseline health status of the patient by comparing the estimated real time set of integrative medicine system parameters with the estimated set of integrative medicine system parameters associated with the generated baseline health status by using artificial intelligence-based comparison model comprising machine learning or deep learning techniques;
compute a health, wellness, and disease risk score of the patient based on determined changes in the generated baseline health status of the patient, and generate, via the graphical user interface, explainable outputs including disease probabilities and feature importance values that contributed to the health, wellness, and disease risk score; and
continuously monitor effectiveness of the recommended interventions and dynamically update the trained machine learning model based on the monitored effectiveness of the personalized recommendation message and new patient data; and
a display to provide the graphical user interface.
The identified limitations, under their broadest reasonable interpretation, cover performance of the limitations in the mind (including observation, evaluation, judgement or opinion) but for the recitation of generic computer components. That is, other than reciting processor, memory, medical devices, and display, nothing in the claim elements precludes the steps form practically being performed in the mind. For example, the identified limitations encompass a healthcare professional collecting patient data through patient questionnaires, comparing to a medical standard, determining a rating or score based on any changes to health status of the patient, and relaying recommendations based on the score. The claim limitations fall within the Mental Processes groupings of abstract ideas. Thus, the claimed invention recites a judicial exception.
Part I: Step 2A, prong two: additional elements that integrate the judicial exception into a practical application
Under step 2A, Prong Two of the Alice framework, the claims are analyzed to determine whether the claims recite additional elements that integrate the judicial exception into a practical application. In particular, the claims are evaluated to determine if there are additional elements or a combination of elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claims are more than a drafting effort designed to monopolize the judicial exception.
This judicial exception is not integrated into a practical application. The claim recites the additional elements of “apply the collected patient information and phenotypic features associated with the patient the categorical variables, and the digital biomarkers on to a trained machine learning model or a deep learning model,” “generate baseline health status of the patient based on the results of the application of the trained machine learning model or the deep learning model,” “optimize question presentation by terminating questioning sequence when successive differences in calculated scores fall below a pre-set threshold value,” and “the physiological sensor data, the real-time patient information, the real-time spatiotemporal information, and the real-time inputs from the biochemical, clinical, and multi-omics markers onto the trained machine learning model.” These limitations, along with the processor, memory, medical devices, and display, are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer components. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Dependent claims 2-5 and 7-10, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea:
claims 2 and 8, wherein the phenotypic features of the patient are obtained using plurality of physiological sensors, computer vision and voice-based techniques and various biochemical, clinical and multi-omics markers.
claims 3 and 7, wherein the phenotypic feature comprises digital markers extracted from pulse electrocardiography (ECG) data, photo plethysmograph (PPG) data, electroencephalogram (EEG) data, bioimpedance sensor data, galvanic skin response data, multispectral reflectance data, transmittance data, and autofluorescence data from plurality of body parts, sleep activity data, physical activity data and mental activity data.
claims 4 and 9, wherein the one or more real time spatiotemporal information data associated with the patient comprises health parameters of the patient with respect to a location and a period of time.
claims 5 and 10, wherein the recommendation message comprises of medical diagnosis of the disease, health parameters, one or more medical remedies, and treatment plan.
Since these claims are directed to an abstract idea, the Office must determine whether the remaining limitations “do significantly more” than describe the abstract idea.
Part II. Determine whether any Element, or Combination, Amounts to“Significantly More” than the Abstract Idea itself
Under Part II, the steps of the claims, when considered individually and as an ordered combination, do not improve another technology or technical field, do not improve the functioning of the computer itself, and are not enough to qualify as "significantly more". For example, the steps require no more than a conventional computer to perform generic computer functions. As stated above in Prong Two, these limitations, along with the processor, memory, medical devices, and display, are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Therefore, based on the two-part Mayo analysis, there are no meaningful limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself. Claims 1-10, when considered individually and as an ordered combination, are rejected as ineligible subject matter under 35 U.S.C. 101.
Dependent claims 2-5 and 7-10 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional claims do no recite significantly more than an abstract idea.
Conclusion
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/CHINYERE MPAMUGO/Primary Examiner, Art Unit 3685