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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference signs mentioned in the description: in FIG. 1, reference sign “100” is not included for the Computer System (See specification [0172]), and “Bus 140” is missing as well (See specification [0277]). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities: In each paragraph between paragraphs [0267]-[0270], it states “As illustrated in FIG. 2, such data may be received by an application server 230 or an application server 220.” However, based on Fig. 2, the application server is reference number 220 and the web server is reference number 230. Therefore, the sentence should read “As illustrated in FIG. 2, such data may be received by a web server 230 or an application server 220.” Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claims
Step 1 analysis:
Claims 1 and 15 are drawn to a method (i.e., process), and Claim 10 is drawn to a system, which are all within the four statutory categories. (Step 1 – Yes, the claims fall into one of the statutory categories).
Step 2A analysis – Prong One:
Claim 1 recites:
A method for generating a recommendation, comprising:
receiving a first data of a first wellness type comprising a physical type, a social type, or a mental type;
receiving a second data of the first wellness type;
receiving a wellness state of a second wellness type comprising the physical type, the social type, or the mental type, wherein the second wellness type is different than the first wellness type;
determining a change in the wellness state of the second wellness type based on a change between the first data of the first wellness type and the second data of the first wellness type; and
generating, based on the determining, the recommendation.
The series of steps as recited above describes managing personal behavior or relationships or interactions between people including following rules or instructions, and therefore fall within the scope of certain methods of organizing human activity. Fundamentally, the method is that of a person gathering health data of a patient and determining plan for treatment based on the health data through a series of instructions, which encompasses a person interacting with another individual including following rules or instructions. The method encompasses functions that are typically performed by a healthcare provider, in which a provider gathers physical data, social data, and mental health information while performing a routine checkup, and recommends actions the patient can take to improve any of the data types. Accordingly, the claim recites an abstract idea of managing interactions between people.
Additionally, the series of steps as recited above also falls within the “mental processes” grouping of abstract ideas, and describes concepts that can be performed in the human mind through observation, evaluation, judgement, and opinion. Receiving wellness data including physical, mental, and social data, determining a change in the wellness data, and generating a recommendation based on the change can all be performed in the human mind with or without the use of a physical aid. For example, a healthcare provider can analyze a patient in their mind to gather physical, social, or mental data, and can determine a change in the wellness through observation, evaluation, and judgement. The provider can then generate a recommendation through use of their observations, evaluation of the patient, judgement, and opinion. Therefore, the claim recites an abstract idea of a mental process.
Claim 10 recites/describes nearly identical steps as claim 1 (and therefore also recites limitations that fall within this subject matter grouping of abstract ideas), and this claim is therefore determined to recite an abstract idea under the same analysis.
Claim 15 recites:A method for determining a wellness score for a wellness type of a subject comprising:
obtaining clinical data associated with the subject;
processing the clinical data to extract a plurality of clinical features;
determining, by at least in part on an algorithm, the wellness score based at least in part on the plurality of clinical features and a plurality of weights, wherein each of the weights in the plurality of weights is indicative of a level of a contribution of a corresponding clinical feature to the wellness score.
The series of steps as recited above describes managing personal behavior or relationships or interactions between people including following rules or instructions, and therefore fall within the scope of certain methods of organizing human activity. Fundamentally, the method is that of a person gathering health data of a patient and determining a level of health through a series of instructions, which encompasses a person interacting with another individual including following rules or instructions. The method encompasses functions that are typically performed by a healthcare provider, in which a provider gathers clinical data pertaining to a patient while performing a routine checkup, and determines a level of their health based on various clinical features of the patient. Accordingly, the claim recites an abstract idea of managing interactions between people.
Additionally, the series of steps as recited above also falls within the “mental processes” grouping of abstract ideas, and describes concepts that can be performed in the human mind through observation, evaluation, judgement, and opinion. Extracting clinical features, and determining a wellness score based on the features and weights can all be performed in the human mind with or without the use of a physical aid. Therefore, the claim recites an abstract idea of a mental process.
Step 2A analysis – Prong 2:
This judicial exception is not integrated into a practical application. Specifically, independent claims 1 and 15 do not recite any additional elements beyond the abstract idea. Claim 10 recites the following additional elements beyond the abstract idea: a computer system, a processor, and a non-transitory memory. These limitations are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. The limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Specifically, computer system 100 may have any suitable physical form, including but not limited to one or more integrated circuits (ICs), printed circuit boards (PCBs), mobile handheld devices (such as mobile telephones or PDAs), laptop or notebook computers, distributed computer systems, computing grids, or servers (see Specification para. [0277]). Computer system 100 includes one or more processor(s) 101 (e.g., central processing units (CPUs), general purpose graphics processing units (GPGPUs), or quantum processing units (QPUs)) that carry out functions (See Spec. para. [0278]). The memory 103 may include various components (e.g., machine readable media) including, but not limited to, a random access memory component (e.g., RAM 104) (e.g., static RAM (SRAM), dynamic RAM (DRAM), ferroelectric random access memory (FRAM), phase-change random access memory (PRAM), etc.), a read-only memory component (e.g., ROM 105), and any combinations thereof (See Spec. para. [0279]).
The additional elements do not show an improvement to the functioning of a computer or to any other technology, rather the additional elements perform general computing functions and do not indicate how the particular combination improves any technology or provides a technical solution to a technical problem. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, Claims 1, 10, and 15 are directed to an abstract idea without practical application. (Step 2A – Prong 2: No, the additional elements are not integrated into a practical application).
Step 2B analysis:
As discussed above in “Step 2A analysis – Prong 2”, there are no additional elements recited in independent claims 1 and 15, and the identified additional elements in Independent Claim 10 are equivalent to adding the words “apply it” on a generic computer. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
For the role of a computer in a computer implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of “well- understood, routine, [and] conventional activities previously known to the industry.” Further, “the mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention.”
The applicant’s specification discloses: computer system 100 may have any suitable physical form, including but not limited to one or more integrated circuits (ICs), printed circuit boards (PCBs), mobile handheld devices (such as mobile telephones or PDAs), laptop or notebook computers, distributed computer systems, computing grids, or servers (See Specification para. [0277]). Computer system 100 includes one or more processor(s) 101 (e.g., central processing units (CPUs), general purpose graphics processing units (GPGPUs), or quantum processing units (QPUs)) that carry out functions (See Spec. para. [0278]). The memory 103 may include various components (e.g., machine readable media) including, but not limited to, a random access memory component (e.g., RAM 104) (e.g., static RAM (SRAM), dynamic RAM (DRAM), ferroelectric random access memory (FRAM), phase-change random access memory (PRAM), etc.), a read-only memory component (e.g., ROM 105), and any combinations thereof (See Spec. para. [0279]).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the steps for generating a recommendation amount to no more than using computer related devices to implement the abstract idea.
The use of a computer or processor to merely automate or implement the abstract idea cannot provide significantly more than the abstract idea itself. (See MPEP 2106.05(f) where mere instructions to apply an exception does not render an abstract idea patent eligible). There is no indication that the additional limitations alone or in combination improves the functioning of a computer or any other technology, improves another technology or technical field, or effects a transformation or reduction of a particular article to a different state or thing. Therefore, the claims are not patent eligible. The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claims amount to significantly more than the abstract idea identified above (Step 2B: Independent claims - NO).
Dependent Claims
Dependent Claims 2-9, 11-14, and 16- 20 are directed towards elements used to describe the wellness score, recommendations, and correlations between wellness states. These elements include: (claim 2 and 11) recommendation is directed to an improvement of the wellness state of the second wellness type; (claim 3 and 12) determining that an action based on the recommendation has been taken; (claim 4 and 13) determining an improvement of the wellness state of the second wellness type based on the taken action; (claim 5 and 14) determining an improvement of a wellness state of the first wellness type based on the improvement of the wellness state of the second wellness type; (claim 6) determining a correlation between or among the taken action, the improvement of the wellness state of the second wellness type, and the improvement of the wellness state of the first wellness type; (claim 7) concluding that the improvement to the wellness state of the first wellness type is caused by the improvement to the wellness state of the second wellness type, or not; (claim 8) suggesting a diagnosis based on the concluding; (claim 9) suggesting a change to a treatment; (claim 16) the clinical data comprises one or more of biometrics measurements, clinical notes, subject input, or medical codes; (claim 17) the clinical data comprises text, and extracting the plurality of clinical features comprises processing the clinical data with a large language model (LLM); (claim 18) the LLM is pre-trained, trained, or fine-tuned using clinical notes; (claim 19) determining one or more additional wellness scores for one or more additional wellness types; (claim 20) aggregating the wellness score and the one or more additional wellness scores to generate an aggregate wellness score.
The elements of claims 2-9, 11-14, and 16 describe managing personal behavior or relationships or interactions between people including following rules or instructions, and therefore fall within the scope of certain methods of organizing human activity as described with regards to the independent claims. The limitations are functions that are typically performed by a healthcare provider, in which the provider will recommend a treatment to a patient, determine if the treatment improves or doesn’t improve the wellbeing of the patient, and suggests a change in the treatment. Therefore, claims 2-9, 11-14, and 16 fall within the same abstract idea of methods of organizing human activity as described in the independent claims.
The elements as recited above in claims 3-7, 12-14, 19-20, and claim 17 stating the clinical data comprises text, and extracting the plurality of clinical features, also falls within the “mental processes” grouping of abstract ideas as described with respect to the independent claims, and describes concepts that can be performed in the human mind through observation, evaluation, judgement, and opinion. Determining an action has been taken, determining improvements to wellness states, determining correlations between actions and improvements to wellness states, concluding whether or not improvements to the first wellness state are caused by improvements to the second wellness state, determining wellness scores, and aggregating wellness scores are all tasks that can be performed in the human mind. Therefore, the dependent claims recite an abstract idea of a mental process.
This judicial exception is not integrated into a practical application. Specifically, the dependent claims 2-9 and 11-14 do not recite any additional elements, and dependent claims 16-20 recite the following additional elements beyond the abstract idea: a large language model (LLM), wherein the LLM is pre-trained, trained, or fine-tuned using clinical notes. These limitations are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. The limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
The additional elements do not show an improvement to the functioning of a computer or to
any other technology, rather the additional elements perform general computing functions and do not
indicate how the particular combination improves any technology or provides a technical solution to a
technical problem. Accordingly, these additional elements, when considered separately and as an
ordered combination, 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 dependent claims are
directed to an abstract idea without practical application. (Step 2A – Prong 2: No, the additional
elements are not integrated into a practical application).
The use of a computer or processor to merely automate or implement the abstract idea cannot
provide significantly more than the abstract idea itself. (See MPEP 2106.05(f) where mere instructions to apply an exception does not render an abstract idea patent eligible). There is no indication that the
additional limitations alone or in combination improves the functioning of a computer or any other
technology, improves another technology or technical field, or effects a transformation or reduction of a
particular article to a different state or thing. Therefore, the claims are not patent eligible.
The Examiner has therefore determined that no additional element, or combination of
additional claims elements is/are sufficient to ensure the claims amount to significantly more than the
abstract idea identified above (Step 2B: Dependent claims - NO).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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)(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 1-2 and 10-11 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Madnani (US 2023/0309830 A1).
Regarding Claim 1, Madnani teaches the following:
A method for generating a recommendation ([0117] generating a notification for providing a remedial procedure to a user), comprising:
receiving a first data of a first wellness type comprising a physical type, a social type, or a mental type ([0081]-[0082] the strain gauge and the accelerometer senses breathing of the user. The sensors may sense health parameters of the user periodically and send a set of health parameters to the processors in every minute, 2 minutes, 5 minutes, 10 minutes, 30 minutes, and so forth. The breathing data of the user may include a number of breaths per minute performed by the user. The breathing data is received by the processors. Similarly, posture data (See [0091]-[0102]) and sleep data (see [0104]-[0108]) can be used);
receiving a second data of the first wellness type ([0081]-[0082] the strain gauge and the accelerometer senses breathing of the user. The sensors may sense health parameters of the user periodically and send a set of health parameters to the processors in every minute, 2 minutes, 5 minutes, 10 minutes, 30 minutes, and so forth. The breathing data of the user may include a number of breaths per minute performed by the user. The breathing data is received by the processors. The examiner interprets the processor receiving data every minute, 2 minutes, or any other time interval as receiving a first and second data.);
receiving a wellness state of a second wellness type comprising the physical type, the social type, or the mental type, wherein the second wellness type is different than the first wellness type ([0078] the sensors of the wearable device may sense other health parameters such as state of mind data);
determining a change in the wellness state of the second wellness type based on a change between the first data of the first wellness type and the second data of the first wellness type ([0073], [0081], [0083], [0087], [0089] The breathing data may be sensed by measuring a change in values of the strain gauge and the accelerometer. The sensors may sense health parameters of the user periodically and send a set of health parameters to the processors in every minute, 2 minutes, 5 minutes, 10 minutes, 30 minutes, and so forth. The breathing data of the user may include a number of breaths per minute performed by the user. The sensed breathing data may be used to generate a mental wellness value of the user and may be correlated to a state of mind of the user. In an example, if breathing data of the user indicates that a number of breaths of the user may be abnormal, or the physical wellness value of the user is low, then the physical wellness and the mental wellness of the user may be poor. Subsequently, the output may include an indicator depicting decrease in the mental wellness of the user.); and
generating, based on the determining, the recommendation ([0119] the processors may output the notification indicating the remedial procedure for improving the physical wellness value and/or the mental wellness value of the user. The notification may be displayed on the user device associated with the user. The notification may assist the user in performing the resonant breathing activity, reduce stress, improve cardiac health, and other personalized wellness recommendations.).
Regarding Claim 2, Madnani teaches the method of claim 1, and further teaches the following:
The method of claim 1, wherein the recommendation is directed to an improvement of the wellness state of the second wellness type ([0119] the processors may output the notification indicating the remedial procedure for improving the physical wellness value and/or the mental wellness value of the user. The notification may be displayed on the user device associated with the user. The notification may assist the user in performing the resonant breathing activity, reduce stress, improve cardiac health, and other personalized wellness recommendations. The notification may include suggestions to improve the mental wellness and the physical wellness and/or suggestions to improve the user's lifestyle.).
Regarding Claim 10, Madnani teaches the following:
A computer system for generating a recommendation ([0117] generating a notification for providing a remedial procedure to a user), comprising:
a non-transitory memory ([0020] a non-transitory computer readable medium); and
a processor in communication with the non-transitory memory, the processor configured to execute the following operations in order to effectuate a method comprising the operations ([0020] The computer programmable product comprises a non-transitory computer readable medium having stored thereon computer executable instructions, which when executed by one or more processors, cause the one or more processors to carry out operations) of:
receiving a first data of a first wellness type comprising a physical type, a social type, or a mental type ([0081]-[0082] the strain gauge and the accelerometer senses breathing of the user. The sensors may sense health parameters of the user periodically and send a set of health parameters to the processors in every minute, 2 minutes, 5 minutes, 10 minutes, 30 minutes, and so forth. The breathing data of the user may include a number of breaths per minute performed by the user. The breathing data is received by the processors. Similarly, posture data (See [0091]-[0102]) and sleep data (see [0104]-[0108]) can be used);
receiving a second data of the first wellness type ([0081]-[0082] the strain gauge and the accelerometer senses breathing of the user. The sensors may sense health parameters of the user periodically and send a set of health parameters to the processors in every minute, 2 minutes, 5 minutes, 10 minutes, 30 minutes, and so forth. The breathing data of the user may include a number of breaths per minute performed by the user. The breathing data is received by the processors. The examiner interprets the processor receiving data every minute, 2 minutes, or any other time interval as receiving a first and second data.);
receiving a wellness state of a second wellness type comprising the physical type, the social type, or the mental type, wherein the second wellness type is different than the first wellness type ([0078] the sensors of the wearable device may sense other health parameters such as state of mind data);
determining a change in the wellness state of the second wellness type based on a change between the first data of the first wellness type and the second data of the first wellness type ([0073], [0081], [0083], [0087], [0089] The breathing data may be sensed by measuring a change in values of the strain gauge and the accelerometer. The sensors may sense health parameters of the user periodically and send a set of health parameters to the processors in every minute, 2 minutes, 5 minutes, 10 minutes, 30 minutes, and so forth. The breathing data of the user may include a number of breaths per minute performed by the user. The sensed breathing data may be used to generate a mental wellness value of the user and may be correlated to a state of mind of the user. In an example, if breathing data of the user indicates that a number of breaths of the user may be abnormal, or the physical wellness value of the user is low, then the physical wellness and the mental wellness of the user may be poor. Subsequently, the output may include an indicator depicting decrease in the mental wellness of the user.); and
generating, based on the determining, the recommendation ([0119] the processors may output the notification indicating the remedial procedure for improving the physical wellness value and/or the mental wellness value of the user. The notification may be displayed on the user device associated with the user. The notification may assist the user in performing the resonant breathing activity, reduce stress, improve cardiac health, and other personalized wellness recommendations.).
Regarding Claim 11, Madnani teaches the system of claim 10, and further teaches the following:
The system of claim 10, wherein the recommendation is directed to an improvement of the wellness state of the second wellness type ([0119] the processors may output the notification indicating the remedial procedure for improving the physical wellness value and/or the mental wellness value of the user. The notification may be displayed on the user device associated with the user. The notification may assist the user in performing the resonant breathing activity, reduce stress, improve cardiac health, and other personalized wellness recommendations. The notification may include suggestions to improve the mental wellness and the physical wellness and/or suggestions to improve the user's lifestyle.).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3-8, 12-16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Madnani (US 2023/0309830 A1) in view of Fernandez-Luque et al. (WO 2023/172761 A1) (Hereinafter Fernandez-Luque).
Regarding Claim 3, Madnani teaches the method of claim 2, however, Madnani does not teach the following that is met by Fernandez-Luque:
The method of claim 2, further comprising determining that an action based on the recommendation has been taken ([007], [088], Fig. 1, item 170: the health recommender system can include an engagement matrix comprising a table that includes the logs of the interaction of the users with the different types of content, that may include both sensing and therapeutic content (e.g., CBT exercises, educational content, behavioral messages). The matrix describes which content has been accessed/interacted with, and the type of interaction (e.g., rated, opened, completed)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method of generating a recommendation, as taught by Madnani, with the determination that an action has been taken, as taught by Fernandez-Luque, because by determining an action has been taken, a user rating can be created that gives feedback for the recommendation. In turn, the ratings can be used in the future to identify similar patients with needs ([0103]-[0104]).
Regarding Claim 4, the combination of Madnani and Fernandez-Luque teaches the method of claim 3, and Madnani further teaches:
The method of claim 3, further comprising determining an improvement of the wellness state of the second wellness type based on the taken action ([0147] the processors may cause the user to perform the resonant breathing activity as heart beats optimally with consistency and high oscillations at resonant breathing of 6-8 bpm leading to high heart rate variability. Resonant breathing may also increase lung capacity, help in reduction of blood pressure, help in better control of autonomic nervous system (ANS) on cardiovascular function, bring calmness and clarity, reduce stress, and improve sleep).
Regarding Claim 5, the combination of Madnani and Fernandez-Luque teaches the method of claim 4, and Fernandez-Luque further teaches:
The method of claim 4, further comprising determining an improvement of a wellness state of the first wellness type based on the improvement of the wellness state of the second wellness type ([091]-[092], [0106]-[0107], FIG. 5, and FIG. 6: according to some embodiments, a recommendation of content aiming at promoting physical activity as a way to reduce stress may include visualizations of the status of the weekly goal or visualizations of the prediction on the improvement of sleep quality if the users do increase physical activity. A clinician or health personnel may get the suggestion to recommend the patient mental wellbeing exercises prior to going to sleep to reduce stress and promote healthier sleep habits. (i.e., the improvement of the sleep habits is based on an improvement to mental health). User feedback is collected throughout and after the recommendation is given and taken. Feedback can include ratings of the contents, whether the content has been seen or opened, and user’s health captured by psychometrics and sensors/wearables. The sensor and psychometrics data is used to create a visualization of an objective and subjective health-gram that is annotated with the data over time. As an example, the visualization can include contributing factors for fatigue that are objective (e.g., sleep quality from a sensor, physical activity, heart rate) with subjective data (e.g., mood, perceived tiredness, perceived sleep quality) that together with relevant clinical data can provide a more holistic visualization of the physical and mental health of the user).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the combination of Madnani and Fernandez-Luque to include the determination that an improvement of a wellness state of the first wellness type is based on the improvement of the second type, because by utilizing a multi-dimensional health recommendation system that considers the interconnection between physical, mental, and social wellness, specifically biology, psychology, and socioenvironmental factors, a more precise assessment and management of life-style related symptoms of chronic conditions can be achieved (See Fernandez-Luque [001] and [077]).
Regarding Claim 6, the combination of Madnani and Fernandez-Luque teaches the method of claim 4, and Fernandez-Luque further teaches:
The method of claim 5, further comprising determining a correlation between or among the taken action, the improvement of the wellness state of the second wellness type, and the improvement of the wellness state of the first wellness type ([091]-[092], [0106] according to some embodiments, a recommendation of content aiming at promoting physical activity as a way to reduce stress may include visualizations of the status of the weekly goal or visualizations of the prediction on the improvement of sleep quality if the users do increase physical activity. A clinician or health personnel may get the suggestion to recommend the patient mental wellbeing exercises prior to going to sleep to reduce stress and promote healthier sleep habits. The user may provide feedback that rates the therapeutic content, which shows the user took the action and rated it.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the combination of Madnani and Fernandez-Luque to include the determination of a correlation between the taken action, improvement of the first wellness state, and the second wellness state, because by utilizing a multi-dimensional health recommendation system that considers the interconnection between physical, mental, and social wellness, specifically biology, psychology, and socioenvironmental factors, a more precise assessment and management of life-style related symptoms of chronic conditions can be achieved (See Fernandez-Luque [001] and [077]).
Regarding Claim 7, the combination of Madnani and Fernandez-Luque teaches the method of claim 4, and Fernandez-Luque further teaches:
The method of claim 6, further comprising concluding, based on the correlation, that the improvement to the wellness state of the first wellness type is caused by the improvement to the wellness state of the second wellness type, or that the change to the wellness state of the second wellness type is not caused by the change between the first data and the second data ([002] interventions aiming at improving mental wellbeing (i.e., mindfulness, Cognitive Behavioral Therapy, etc.) and promoting physical activity have been found to be effective for reducing insomnia symptoms and chronic-condition-related fatigue).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the combination of Madnani and Fernandez-Luque to include the conclusion that the improvement to the wellness state of the first wellness type is caused by an improvement to the second wellness state, as taught by Fernandez-Luque, because by utilizing a multi-dimensional health recommendation system that considers the interconnection between physical, mental, and social wellness, specifically biology, psychology, and socioenvironmental factors, a more precise assessment and management of life-style related symptoms of chronic conditions can be achieved (See Fernandez-Luque [001] and [077]).
Regarding Claim 8, the combination of Madnani and Fernandez-Luque teaches the method of claim 3, and Madnani further teaches:
The method of claim 7, further comprising suggesting a diagnosis with respect to the wellness state of the second wellness type based on the concluding ([0147] after or during the resonant breathing activity, the HRV of the user may be used to gauge physical wellness and/or mental wellness of the user. Based on such data, the processors may be able to accurately determine a disease or state of physical and mental health of the user.)
Regarding Claim 12, Madnani teaches the system of claim 11, however, Madnani does not teach the following that is met by Fernandez-Luque:
The system of claim 11, wherein the method further comprises determining that an action based on the recommendation has been taken ([007], [088], Fig. 1, item 170: the health recommender system can include an engagement matrix comprising a table that includes the logs of the interaction of the users with the different types of content, that may include both sensing and therapeutic content (e.g., CBT exercises, educational content, behavioral messages). The matrix describes which content has been accessed/interacted with, and the type of interaction (e.g., rated, opened, completed)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method of generating a recommendation, as taught by Madnani, with the determination that an action has been taken, as taught by Fernandez-Luque, because by determining an action has been taken, a user rating can be created that gives feedback for the recommendation. In turn, the ratings can be used in the future to identify similar patients with needs ([0103]-[0104]).
Regarding Claim 13, the combination of Madnani and Fernandez-Luque teaches the system of claim 12, and Madnani further teaches:
The system of claim 12, wherein the method further comprises determining an improvement of the wellness state of the second wellness type based on the taken action ([0147] the processors may cause the user to perform the resonant breathing activity as heart beats optimally with consistency and high oscillations at resonant breathing of 6-8 bpm leading to high heart rate variability. Resonant breathing may also increase lung capacity, help in reduction of blood pressure, help in better control of autonomic nervous system (ANS) on cardiovascular function, bring calmness and clarity, reduce stress, and improve sleep).
Regarding Claim 14, the combination of Madnani and Fernandez-Luque teaches the system of claim 13, and Fernandez-Luque further teaches:
The system of claim 13, wherein the method further comprises determining an improvement of a wellness state of the first wellness type based on the improvement of the wellness state of the second wellness type ([091]-[092] according to some embodiments, a recommendation of content aiming at promoting physical activity as a way to reduce stress may include visualizations of the status of the weekly goal or visualizations of the prediction on the improvement of sleep quality if the users do increase physical activity. A clinician or health personnel may get the suggestion to recommend the patient mental wellbeing exercises prior to going to sleep to reduce stress and promote healthier sleep habits. (i.e., the improvement of the sleep habits is based on an improvement to mental health)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the combination of Madnani and Fernandez-Luque to include the determination that an improvement of a wellness state of the first wellness type is based on the improvement of the second type, because by utilizing a multi-dimensional health recommendation system that considers the interconnection between physical, mental, and social wellness, specifically biology, psychology, and socioenvironmental factors, a more precise assessment and management of life-style related symptoms of chronic conditions can be achieved (See Fernandez-Luque [001] and [077]).
Regarding Claim 15, Madnani teaches the following:
A method for determining a wellness score for a wellness type of a subject ([0004] system and method to determine a physical wellness value for the user, and determine a mental wellness value for the user.) comprising:
obtaining clinical data associated with the subject ([0004], [0017]-[0019], [0057] The one or more processors are configured to receive the set of health parameters of the user from the one or more sensors, which may include a light sensor, noise sensor, temperature sensor, accelerometer, a gyroscope, a magnetometer, or a strain gauge. The health data may come from a wearable device that measures breathing data, state of mind data, activity data, posture data, sleep pattern data, and ambience data. Additionally breathing patterns, heart rate, heart rate variability, and blood pressure may be obtained.);
determining, by at least in part on an algorithm, the wellness score ([0084] the strain gauge and the accelerometer converts pressure and movement signal received from an actuator to an electrical signal. This electrical signal is filtered using combination of RC filters for noise reduction and then given to an analog to digital converter of the microprocessor. And further, the filtered data may be converted from analog to digital to determine the physical wellness value and the mental wellness value for the user. Internal signal processing is done in a microprocessor and transferred to an internal communication protocol for transferring data to an algorithm of the processors)
However, Madnani does not disclose the following which is met by Fernandez-Luque:
processing the clinical data to extract a plurality of clinical features ([019], [022], [036], and Claim 1: The term "health meta-feature", as used herein is intended to include but not be limited to any of the following: meta-data which describe characteristics of therapeutic and sensing content with regards to their design towards addressing aspects related to particular behavioral and mental health factors (e.g., content designed for users with low mood) and also physical health (e.g., content designed for users with a particular type of surgery). These meta-features also address related social health factors when relevant (e.g., relationships, family support, cultural factors, economic factors, education). These meta-features combined represent a semantic-space and a taxonomy of the contents of the Health Recommender System. Measurements that are not influenced by the opinion or perspective of people, such as age, lab value results, and sensor data. Objective data may also include information extracted from EMR (electronic medical records) as provided by medical systems. The first and second health parameters may be processed to generate a first user model, where the model describes the user characteristics);
based at least in part on the plurality of clinical features and a plurality of weights, wherein each of the weights in the plurality of weights is indicative of a level of a contribution of a corresponding clinical feature to the wellness score ([019], [036] the first and second health parameters may further include weighing factors, and wherein processing those parameters may take into consideration the weighing factors to generate the first user model. "health meta-feature", as used herein is intended to include but not be limited to any of the following: meta-data which describe characteristics of therapeutic and sensing content with regards to their design towards addressing aspects related to particular behavioral and mental health factors (e.g., content designed for users with low mood) and also physical health (e.g., content designed for users with a particular type of surgery). These meta-features also address related social health factors when relevant (e.g., relationships, family support, cultural factors, economic factors, education). These meta-features combined represent a semantic-space and a taxonomy of the contents of the Health Recommender System. Therefore, the examiner is interpreting the health meta-features as the plurality of clinical features, and the weighing factors as the plurality of weights for the features, which describes how the features characterize the health factors.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method for determining a wellness score including obtaining clinical data, as taught by Madnani, with the extraction of clinical features and using the clinical features and weights to describe the wellness score, as taught by Fernandez-Luque, because by utilizing a multi-dimensional health recommendation system that considers the interconnection between physical, mental, and social wellness, specifically biology, psychology, and socioenvironmental factors, a more precise assessment and management of life-style related symptoms of chronic conditions can be achieved (See Fernandez-Luque [001] and [077]).
Regarding Claim 16, the combination of Madnani and Fernandez-Luque teaches the method of claim 15, and Madnani further teaches:
The method of claim 15, wherein the clinical data comprises one or more of biometrics measurements, clinical notes, subject input, or medical codes ([0150] the sensors of the wearable device may measure certain parameters of the body of the user for collecting inputs therefrom).
Regarding Claim 19, the combination of Madnani and Fernandez-Luque teaches the method of claim 15, and Madnani further teaches:
The method of claim 15, further comprising determining one or more additional wellness scores for one or more additional wellness types of the subject ([0004] determine a physical wellness value for the user, and determine a mental wellness value for the user.).
Claims 9, 17-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Madnani (US 2023/0309830 A1) in view of Fernandez-Luque et al. (WO 2023/172761 A1) (Hereinafter Fernandez-Luque), in further view of Shriberg et al. (JP 2021/529382 A) (Hereinafter Shriberg).
Regarding Claim 9, the combination of Madnani and Fernandez-Luque teaches the method of claim 8, however, the combination does not disclose the following which is met by Shriberg:
The method of claim 8, further comprising suggesting a change to a treatment with respect to the wellness state of the first wellness type or the wellness state of the second wellness type based on the diagnosing (Pg. 3, para. 9, Pg. 4, para. 1 and 6, Pg. 52, para. 7, Pg. 57, para. 2: The method can further include processing subsequent responses to update the subject's assessment of their mental state. The mental states can be disorders such as depression, anxiety, post-traumatic stress disorder (PTSD), schizophrenia, suicidal tendencies, bipolar disorder, fatigue, loneliness, decreased motivation, or stress. The system may voluntarily recommend a change in treatment. In embodiments where the system continuously processes and analyzes the data, the system may detect a sudden increase in voice-based biomarkers indicating depression. Real-time results may include scores corresponding to mental states. In the fourth step, the case manager can update the care plan based on real-time results.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method of Madnani and Fernandez-Luque with the updating a treatment plan based on a diagnosis, as taught by Shriberg, because it allows the system to recommend changes in treatment that is more catered to the state of the patient and allows healthcare professionals to pursue treatment changes in ways that are more effective (See Shriberg Pg. 52, para. 6-7).
Regarding Claim 17, the combination of Madnani and Fernandez-Luque teaches the method of claim 15, and Fernandez-Luque further teaches:
The method of claim 15, wherein the clinical data comprises text ([087] The type of sensing content may include visual analog scale information, text items, multiple choice questions and other relevant information),
It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the combination of Madnani and Fernandez-Luque to include the clinical data comprising text, as taught by Fernandez-Luque, because incorporating different types of therapeutic data, the content can be personalized and tailored to the individual (See Fernandez-Luque [084]-[087]).
However, Madnani and Fernandez-Luque do not teach the following which is met by Shriberg:
and wherein extracting the plurality of clinical features comprises processing the clinical data with a large language model (LLM) (Pg. 5, para. 11-12 and Pg. 7, para. 1: process the data and generate output using one or more individual models, including a natural language processing (NLP) model. model may be configured to use the subject's demographic information and/or medical history to generate one or more assessments of the subject's associated mental state. The NLP model is one or more of an emotional model, a statistical language model, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method of Madnani and Fernandez-Luque with the large language modeling features, as taught by Shriberg, because utilizing linguistic analysis, visual cues, and acoustic analysis through natural language processing significantly improves the accuracy and effectiveness of health screening and monitoring (See Shriberg Pg. 11, para. 1).
Regarding Claim 18, the combination of Madnani, Fernandez-Luque, and Shriberg teaches the method of claim 17, and Shriberg further teaches:
The method of claim 17, wherein the LLM is pre-trained, trained, or fine-tuned using clinical notes (Pg. 11, para. 5: The health screening or monitoring server performs a health screening or monitoring test on the patient via the patient device and combines a language model, an acoustic model, and a visual model to produce a result. It may be a computer system and is collected from clinical data retrieved from the clinical data server to train the model of runtime model server, social data retrieved from the social data server, and past screening or monitoring).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method of Madnani and Fernandez-Luque with the large language modeling features, as taught by Shriberg, because utilizing linguistic analysis, visual cues, and acoustic analysis through natural language processing significantly improves the accuracy and effectiveness of health screening and monitoring (See Shriberg Pg. 11, para. 1).
Regarding Claim 20, the combination of Madnani and Fernandez-Luque teaches the method of claim 19, however, the combination does not disclose the following which is met by Shriberg:
The method of claim 19, further comprising aggregating the wellness score and the one or more additional wellness scores to generate an aggregate wellness score (Pg. 43, para. 1-4: various scores may be generated at different stages of evaluation. A scaled score can be used to represent the severity of the mental state, and is a combination of multiple scores.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method of Madnani and Fernandez-Luque with the aggregation of scores, as taught by Shriberg, because combining the scores to get an improved score allows the clinician to more accurately determine the severity of the state of the patient, and it helps to eliminate false negatives by adding redundancy and robustness (See Shriberg Pg. 43, para. 3).
Conclusion
The relevant art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Vella et al., Optimising the effects of physical activity on mental health and wellbeing: A joint consensus statement from Sports Medicine Australia and the Australian Psychological Society, discloses recommending physical activity treatments to improve the mental state of patients, and describes the correlation between mental and physical wellbeing.
Krishnamurthy (US 2025/0391571 A1) discloses a system that incorporates mental, physical, and social factors to provide a true health-wellness status of a user, and includes a composite score that is weighted based on relevancy to the wellness.
Balassanian (US 2015/0088542 A1) discloses a system for correlating emotional or mental states with quantitative data that incorporates influential data corresponding to the state of the individual. It provides a report with recommendations for the user to take, and shows correlations between social, mental, and physical data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXIS K VAN DUZER whose telephone number is (571)270-5832. The examiner can normally be reached Monday thru Thursday 8-5 CT.
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/A.K.V./Examiner, Art Unit 3682
/EVANGELINE BARR/Primary Examiner, Art Unit 3682
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