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 .
Status of Claims
This action is in reply to the claims filed on 30 October 2024. Claims 1-33 are currently pending and have been examined.
Claim Objections
Claim 12 is objected to because of the following informalities: “physical activity associated with at least one of user movement” in line 3 is misleading because usually there would be more than one listed option. Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6, 15-19, 29, and 30 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 15 recites the limitation “a plurality of training data sets” in lines 2-3. It is unclear whether this is the same “plurality of training data sets” already recited in line 21 of claim 1. For the purposes of examination, this claim will be considered to state “a second plurality of training data sets.” Appropriate correction is required. Claims 16-19 inherit this deficiency.
Regarding claims 6, 29, and 30, the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d). 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 1-33 are rejected under 35 USC § 101
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Claims 1-33 fall within one or more statutory categories. Claims 1-33 fall within the category of a machine.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claims 1-33 recite an abstract idea. Representative claim 1 recites:
receive raw lifestyle data collected from different data sources associated with the user including at least a user’s mobile device and/or one or more wearable devices associated with the user, the raw lifestyle data comprising data associated with sleep and physical activity of the user;
process the lifestyle data, including processing daily aggregates … to thereby standardize lifestyle data received from the different data sources and generate a structured timeline by merging the standardized lifestyle data from the different data sources;
process the structured timeline data … to generate a structured, standardized set of daily aggregates comprising sleep data and physical activity time data associated with a specific level of user activity including: 1) sedentary-activity minutes; 2) light-intensity activity minutes; and 3) medium- to vigorous-activity minutes;
analyze … at least the structured, standardized set of daily aggregates of the user … ; and
calculate, based on the analysis, a digital biomarker that is indicative of the user’s risk in developing a health condition and/or predictive of the user’s morbidity and mortality risk of a health condition.
Therefore, the claim as a whole is directed to “predicting patient health,” which is an abstract idea because it is a method of organizing human activity. “Predicting patient health” is considered to be a method of organizing human activity because it is an example of managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The broadest reasonable interpretation of the claims include the interaction between a healthcare provider and a patient.
Alternatively, the claims are considered to recite a mental process because the above concepts are capable of being performed in the human mind (including an observation, evaluation, judgment, opinion).
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the following additional element(s):
a cloud-based digital health platform configured to communicate with one or more user-associated computing devices or software-agent-associated computing devices over a network, the digital health platform comprising a hardware processor coupled to non-transitory, computer-readable memory containing instructions executable by the processor to cause the platform to: [perform the abstract idea listed above];
running a harmonization algorithm;
running a stacking algorithm;
a risk analytics engine running one or more machine learning (ML) models;
wherein the one or more ML models have been trained using a plurality of training data sets, each training data set comprises reference sociodemographic data, reference lifestyle data, and reference health data associated with known health conditions.
The additional elements individually or in combination do not integrate the exception into a practical application. These additional elements, including the elements that broadly recite machine learning principles, amount to merely reciting the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claim 1 is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Claim 1 does not include additional elements, considered individually or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s), individually and in combination, amount to merely reciting the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Accordingly, claim 1 is ineligible.
Dependent claim 2 recites the method of claim 1, wherein:
the platform is further configured to identify user behavior and match the user to closest peers by running a clustering-based segmentation algorithm.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 2 is ineligible.
Dependent claim 3 recites the method of claim 2, wherein:
the platform is further configured to predict user behavior and determine recommended interventions identified as reducing risk in longitudinal timeframe.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 3 is considered to be ineligible.
Dependent claim 4 recites the method of claim 1, wherein:
the known health conditions comprise mental and physical health conditions.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 4 is considered to be ineligible.
Dependent claim 5 recites the method of claim 4, wherein:
the known health conditions comprise non-communicable diseases.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 5 is considered to be ineligible.
Dependent claim 6 recites the method of claim 4, wherein:
the known health conditions are selected from the group consisting of: mental illnesses such as depression; metabolic diseases such as diabetes; cardiovascular diseases such as hypertension, heart failure, transient ischemic attack (TIA) and coronary heath disease; stroke; and cerebrovascular diseases.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 6 is considered to be ineligible.
Dependent claim 7 recites the method of claim 1, wherein:
the platform is configured to transmit, to a user-associated computing device, the digital biomarker to be presented to the user via a display on the user-associated computing device.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 7 is ineligible.
Dependent claim 8 recites the method of claim 7, wherein:
the digital biomarker is a numerical value ranging from 1 to 99.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 8 is considered to be ineligible.
Dependent claim 9 recites the method of claim 8, wherein:
when the value of the digital biomarker is below a predefined threshold, the user’s risk in developing a health condition increases; and
when the value of the digital biomarker is above a predefined threshold, the user’s risk in developing a health condition decreases.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 9 is considered to be ineligible.
Dependent claim 10 recites the method of claim 9, wherein:
when the value of the digital biomarker is below the predefined threshold, the platform is configured to generate and provide scientific feedback to motivate the user to increase the value of the digital biomarker.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 10 is considered to be ineligible.
Dependent claim 11 recites the method of claim 10, wherein:
said scientific feedback comprises recommendations for increasing sleep and/or physical activity of the user.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 11 is considered to be ineligible.
Dependent claim 12 recites the method of claim 1, wherein:
the mobile device comprises a smartphone and the one or more wearable devices comprise a watch or fitness tracker device, and
wherein the raw lifestyle data comprises physical activity associated with at least one of user movement.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 12 is ineligible.
Dependent claim 13 recites the method of claim 12, wherein:
the user activity is captured by a motion sensor, wherein the motion sensor comprises at least one of an accelerometer, one or more gyroscopes, and a magnetometer for capturing motion of the smartphone and/or the one or more wearable devices to thereby provide corresponding cadence of the user.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 13 is ineligible.
Dependent claim 14 recites the method of claim 12, wherein:
the raw lifestyle data comprises one or more sets of step counts logged in minute intervals and a corresponding physical activity for each of the one or more step counts, the physical actively being selected from the group consisting of sleep, sedentary activity, low-intensity activity, and high-intensity activity.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 14 is considered to be ineligible.
Dependent claim 15 recites the method of claim 14, wherein:
running a harmonization algorithm comprises running a neural network, wherein the neural network has been trained using a plurality of training data sets, each training data set comprises reference step count data associated with known physical activity from corresponding smartphones and wearable devices running different operating systems and/or platforms.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 15 is ineligible.
Dependent claim 16 recites the method of claim 15, wherein:
the neural network comprises a deep learning neural network that includes an input layer, a plurality of hidden layers, and an output layer.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 16 is ineligible.
Dependent claim 17 recites the method of claim 16, wherein:
each training data set is represented using a plurality of features, wherein each feature comprises a feature vector.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 17 is ineligible.
Dependent claim 18 recites the method of claim 17, wherein:
the neural network comprises a recurrent neural network (RNN) with a combination of aggregating model using model stacking.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 18 is ineligible.
Dependent claim 19 recites the method of claim 15, wherein:
processing the lifestyle data by running a harmonization algorithm and a stacking algorithm to generate a structured, standardized set of daily aggregates ensures concordance across different types of devices.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 19 is considered to be ineligible.
Dependent claim 20 recites the method of claim 1, wherein:
the one or more ML models is selected from the group consisting of a linear-based model, a tree-based model, and a neural network-based model.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 20 is ineligible.
Dependent claim 21 recites the method of claim 1, wherein:
the risk analytics engine is configured to analyze the structured, standardized set of daily aggregates of the user and other user-related information.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 21 is considered to be ineligible.
Dependent claim 22 recites the method of claim 21, wherein:
said other user-related information comprises at least one of user gender, user age, user body mass index (BMI), user medical record data, user self-assessment data associated with user sleep, user self-assessment data associated with user mental health, and user diet data.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 22 is considered to be ineligible.
Dependent claim 23 recites the method of claim 1, wherein:
at least some of the reference lifestyle data and the associated known health conditions for each training data set, of the plurality of training sets of the one or more ML models, are obtained from one or more third-party sources.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 23 is ineligible.
Dependent claim 24 recites the method of claim 23, wherein:
the one or more third-party sources comprise publicly available or subscription-based data sources.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 24 is considered to be ineligible.
Dependent claim 25 recites the method of claim 23, wherein:
at least some of the reference lifestyle data and the associated known health conditions are obtained from a large-scale reputable biomedical database and research resource.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 25 is considered to be ineligible.
Dependent claim 26 recites the method of claim 25, wherein:
the at least some of the reference lifestyle data and the associated known health conditions are obtained from biobanks.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 26 is considered to be ineligible.
Dependent claim 27 recites the method of claim 23, wherein:
association of the reference lifestyle data and the known health conditions are based, at least in part, on National Health and Nutrition Examination Survey (NHANES) guidance.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 27 is considered to be ineligible.
Dependent claim 28 recites the method of claim 1, wherein:
the platform is configured to provide real-time health recommendations and feedback to the user based, at least in part, on ongoing data collection and analysis.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 28 is considered to be ineligible.
Dependent claim 29 recites the method of claim 1, wherein:
the platform is configured to maintain end-to-end encryption of all user-related data collected and stored in compliance with data privacy regulations such as GDPR, HIPAA, or equivalent.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 29 is ineligible.
Dependent claim 30 recites the method of claim 1, wherein:
the system is configured to integrate and/or communicate with a plurality of third-party health and fitness applications to import additional user-related data such as dietary habits and blood pressure.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 30 is ineligible.
Dependent claim 31 recites the method of claim 1, wherein:
comprising a user interface configured to provide health scores, recommendations, and comparative analytics in a gamified format that incentivizes use by rewarding users for achieving personalized health milestones.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 31 is ineligible.
Dependent claim 32 recites the method of claim 31, wherein:
the gamified format comprises a reward-based format including challenges, in which users are rewarded for achieving consistent improved scores.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 32 is considered to be ineligible.
Dependent claim 33 recites the method of claim 1, wherein:
the platform provides multilingual support to ensure accessibility for users across different regions and languages.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 33 is considered to be ineligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-26, 28, and 30-32 are rejected under 35 U.S.C. 103 as being unpatentable over Pauley et al. (U.S. 2021/0104173), hereinafter “Pauley,” in view of Wexler et al. (U.S. 2021/0383925), hereinafter “Wexler.”
Regarding Claim 1, Pauley discloses a system for providing real-time personalized health risk assessments, the system comprising:
a cloud-based digital health platform configured to communicate with one or more user-associated computing devices or software-agent-associated computing devices over a network (See Pauley [0063] system may receive sensor data associated with one or more physiological parameters from at least one physiological sensor, such as a glucose sensor or monitor, smart watch, mobile device, etc. [0070] can include processes for collecting data from user devices, such as a mobile device, digital scale, smart watch, fitness tracker, glucose monitor, or other device capable of measuring physiological parameters associated with the user. See also Fig. 5 and [0101].), the digital health platform comprising a hardware processor coupled to non-transitory, computer-readable memory containing instructions executable by the processor (See Pauley [0081] system can include a processor for operating the various modules stored by the system.) to cause the platform to:
receive raw lifestyle data collected from different data sources associated with the user including at least a user’s mobile device and/or one or more wearable devices associated with the user (See Pauley [0064] system may determine a relationship between physiological factors, such as blood glucose and heart rate, and behavioral factors, such as sleep and activity information, to generate a uniquely personalized health recommendation and determine health risks specific to the user's lifestyle challenges and successes. [0072] system can log parameters that include carbohydrate intake, beverage intake, medicine intake (for example, insulin), activity, sleep, blood glucose values, weight, mood, notes, some combination thereof or the like.), the raw lifestyle data comprising data associated with sleep and physical activity of the user (See Pauley [0106] user input can include activity information and sleep information. [0117] he user's physical activity can include a number of steps taken per day, a type and quantity of cardiovascular exercise, a type and quantity of strength training, a number of waking or sleeping hours in a person's day. See also Fig. 2 “Event Logging” (216) includes sleep and activity sections.);
process the lifestyle data, including processing daily aggregates by running a harmonization algorithm to thereby standardize lifestyle data received from the different data sources (See Pauley [0099] the system can use input data that includes data from any number of sources. [0108] the system can normalize the input information. [0245] The parameters may be associated with a certain number of past days. See also [0214].) and generate a structured timeline by merging the standardized lifestyle data from the different data sources (See Pauley [0274] the information can be formatted into daily trends showing the parameter values as a function of time.);
process the structured timeline data by running a stacking algorithm to generate a structured, standardized set of daily aggregates (See Pauley [0274] the information can be formatted into daily trends showing the parameter values as a function of time. [0186] data can be aggregated by week.) … ;
analyze, via a risk analytics engine running one or more machine learning (ML) models, at least the structured, standardized set of daily aggregates of the user (See Pauley [0076] system can use models learned by a machine learning algorithm for predicting health related parameters. [0089] health risks identified through AI analysis of a database of users. See also [0165].), wherein the one or more ML models have been trained using a plurality of training data sets, each training data set comprises reference sociodemographic data, reference lifestyle data, and reference health data associated with known health conditions (See Pauley [0089] health risks identified through AI analysis of a database of users. [0165] system can analyze input data to learn and identify risk values. The algorithms are trained using genetic, behavioral, or physiological health data. [0064] system can determine a relationship by accessing relationship data from an external, cloud-based, or third party source. The system may download or access NIH, WHO, or other third party information relating to the relationship between a physiological factor, such as obesity, and a disease, such as a heart disease.); and
calculate, based on the analysis, a digital biomarker that is indicative of the user’s risk in developing a health condition and/or predictive of the user’s morbidity and mortality risk of a health condition (See Pauley [0079] can include a process to predict a future health condition of the user based on the user data from the one or more coaching modules. The prediction can include any number of statistics, risks, models, or other predictive data associated with the health of the user. [0085] if a health risk determined by analytics engine is that the user is at a 75% risk of type 2 diabetes. [0109] the system can predict one or more physiological parameter values associated with a user based on analyzing input data.).
Pauley does not disclose:
[the daily aggregates] comprising sleep data and physical activity time data associated with a specific level of user activity including: 1) sedentary-activity minutes; 2) light-intensity activity minutes; and 3) medium- to vigorous-activity minutes.
Wexler teaches:
[the daily aggregates] comprising sleep data and physical activity time data associated with a specific level of user activity including: 1) sedentary-activity minutes; 2) light-intensity activity minutes; and 3) medium- to vigorous-activity minutes (See Wexler [0020] input data for the system includes physical activity or exercise data (e.g., time and/or duration of activity; activity type such as walking, running, swimming; strenuousness of the activity such as low, moderate, high; etc.). The activity level categorizes of low, medium, and high meet the broadest reasonable interpretation of “1) sedentary-activity minutes; 2) light-intensity activity minutes; and 3) medium- to vigorous-activity minutes.”).
The system of Wexler is applicable to the disclosure of Pauley as they both share characteristics and capabilities, namely, they are directed to monitoring and providing personalized health recommendations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pauley to include the physical parameter and neural network elements as taught by Wexler. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Pauley because there is a need for improved systems and methods for biomonitoring and/or providing personalized healthcare recommendations or information for the treatment of diabetes and other chronic conditions (see Wexler [0004]).
Regarding claim 2, Pauley in view of Wexler discloses the system of claim 1 as discussed above. Pauley further discloses a system, wherein:
the platform is further configured to identify user behavior and match the user to closest peers (See Pauley [0327] leaderboard of related users. The leaderboard can include data associated with where a user lies in relation to a group of users.) … .
Pauley does not disclose:
by running a clustering-based segmentation algorithm.
Wexler teaches:
by running a clustering-based segmentation algorithm (Wexler [0030] system can use clustering algorithms for analyzing the input information.).
The system of Wexler is applicable to the disclosure of Pauley as they both share characteristics and capabilities, namely, they are directed to monitoring and providing personalized health recommendations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pauley to include the physical parameter and neural network elements as taught by Wexler. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Pauley because there is a need for improved systems and methods for biomonitoring and/or providing personalized healthcare recommendations or information for the treatment of diabetes and other chronic conditions (see Wexler [0004]).
Regarding claim 3, Pauley in view of Wexler discloses the system of claim 2 as discussed above. Pauley further discloses a system, wherein:
the platform is further configured to predict user behavior and determine recommended interventions identified as reducing risk in longitudinal timeframe (See Pauley [0078] can include processes for analyzing user data, determining recommendations based on the user data, or other processes related to recommending user or healthcare provider actions to improve a user's health.).
Regarding claim 4, Pauley in view of Wexler discloses the system of claim 1 as discussed above. Pauley further discloses a system, wherein:
the known health conditions comprise mental and physical health conditions (See Pauley [0003] system used for reducing their health risk for so-called lifestyle diseases, such as heart disease, stroke, and type 2 diabetes. [0072] collected data includes data related to the physical health, mental health. Therefore, any conditions determined by the system that include mental health parameters can be considered mental health conditions, under its broadest reasonable interpretation.).
Regarding claim 5, Pauley in view of Wexler discloses the system of claim 4 as discussed above. Pauley further discloses a system, wherein:
the known health conditions comprise non-communicable diseases (See Pauley [0003] system used for reducing their health risk for so-called lifestyle diseases, such as heart disease, stroke, and type 2 diabetes.).
Regarding claim 6, Pauley in view of Wexler discloses the system of claim 4 as discussed above. Pauley further discloses a system, wherein:
the known health conditions are selected from the group consisting of: mental illnesses such as depression; metabolic diseases such as diabetes; cardiovascular diseases such as hypertension, heart failure, transient ischemic attack (TIA) and coronary heath disease; stroke; and cerebrovascular diseases (See Pauley [0003] system used for reducing their health risk for so-called lifestyle diseases, such as heart disease, stroke, and type 2 diabetes.).
Regarding claim 7, Pauley in view of Wexler discloses the system of claim 1 as discussed above. Pauley further discloses a system, wherein:
the platform is configured to transmit, to a user-associated computing device, the digital biomarker to be presented to the user via a display on the user-associated computing device (See Pauley [0065] an output can include an output device such as a user's mobile device, which a user may be likely to interact with on a consistent basis.).
Regarding claim 8, Pauley in view of Wexler discloses the system of claim 7 as discussed above. Pauley further discloses a system, wherein:
the digital biomarker is a numerical value ranging from 1 to 99 (See Pauley [0024] The scaled score can include a value between 1 and 10. The scaled score can include a percentage value between 0 and 100 percent. The first health condition can be associated with a user health goal. This meets the broadest reasonable interpretation of a numerical value ranging from 1 to 99. See also [0085].).
Regarding claim 9, Pauley in view of Wexler discloses the system of claim 8 as discussed above. Pauley further discloses a system, wherein:
when the value of the digital biomarker is below a predefined threshold, the user’s risk in developing a health condition increases (See Pauley [0156] system can determine that a user's GluScore or glucose score is associated with a threshold risk of certain health conditions, such as diabetes or heart disease.); and
when the value of the digital biomarker is above a predefined threshold, the user’s risk in developing a health condition decreases (See Pauley [0156] system can determine that a user's GluScore or glucose score is associated with a threshold risk of certain health conditions, such as diabetes or heart disease.).
Regarding claim 10, Pauley in view of Wexler discloses the system of claim 9 as discussed above. Pauley further discloses a system, wherein:
when the value of the digital biomarker is below the predefined threshold, the platform is configured to generate and provide scientific feedback (See Pauley [0178] the system can make recommendations based on the health score exceeding a threshold.) to motivate the user to increase the value of the digital biomarker (non-limiting intended result which merely describes a desired result from use of the recited structure, and is given no patentable weight.).
Regarding claim 11, Pauley in view of Wexler discloses the system of claim 10 as discussed above. Pauley further discloses a system, wherein:
said scientific feedback comprises recommendations for increasing sleep and/or physical activity of the user (See Pauley [0078] can include processes for analyzing user data, determining recommendations based on the user data, or other processes related to recommending user or healthcare provider actions to improve a user's health. See also [0179] and [0192].).
Regarding claim 12, Pauley in view of Wexler discloses the system of claim 1 as discussed above. Pauley further discloses a system, wherein:
the mobile device comprises a smartphone and the one or more wearable devices comprise a watch or fitness tracker device (See Pauley [0063] system may receive sensor data associated with one or more physiological parameters from at least one physiological sensor, such as a glucose sensor or monitor, smart watch, mobile device, etc.), and
wherein the raw lifestyle data comprises physical activity associated with at least one of user movement (See Pauley [0099] monitor or track user activity. For example, the device application data can include step counters, location trackers.).
Regarding claim 13, Pauley in view of Wexler discloses the system of claim 12 as discussed above. Pauley further discloses a system, wherein:
the user activity is captured by a motion sensor (See Pauley [0099] monitor or track user activity. For example, the device application data can include step counters, location trackers.).
Pauley does not disclose:
wherein the motion sensor comprises at least one of an accelerometer, one or more gyroscopes, and a magnetometer for capturing motion of the smartphone and/or the one or more wearable devices to thereby provide corresponding cadence of the user.
Wexler teaches:
wherein the motion sensor comprises at least one of an accelerometer, one or more gyroscopes, and a magnetometer for capturing motion of the smartphone and/or the one or more wearable devices to thereby provide corresponding cadence of the user (See Wexler [0018] the system can collect data from the user device including location, acceleration, velocity, orientation, a change thereof over time. This is understood to be motion data collected with the use of at least an accelerometer.).
The system of Wexler is applicable to the disclosure of Pauley as they both share characteristics and capabilities, namely, they are directed to monitoring and providing personalized health recommendations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pauley to include the physical parameter and neural network elements as taught by Wexler. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Pauley because there is a need for improved systems and methods for biomonitoring and/or providing personalized healthcare recommendations or information for the treatment of diabetes and other chronic conditions (see Wexler [0004]).
Regarding claim 14, Pauley in view of Wexler discloses the system of claim 12 as discussed above. Pauley further discloses a system, wherein:
the raw lifestyle data comprises one or more sets of step counts logged in minute intervals and a corresponding physical activity for each of the one or more step counts, the physical actively being selected from the group consisting of sleep, sedentary activity, low-intensity activity, and high-intensity activity (See Pauley [0099] input data can include step counts. [0177] activity data can include the user's physical activity can include a number of steps taken per day, a type and quantity of cardiovascular exercise, a type and quantity of strength training, a number of waking or sleeping hours in a person's day, the like, or some combination thereof. [0192] a lower than minimum number of active minutes over a period of time, such as a week, a lower recorded intensity of physical activity over a period of time, a detected period of inactivity, or a detection that a goal is reached.).
Regarding claim 15, Pauley in view of Wexler discloses the system of claim 14 as discussed above. Pauley does not further disclose a system, wherein:
running a harmonization algorithm comprises running a neural network, wherein the neural network has been trained using a plurality of training data sets, each training data set comprises reference step count data associated with known physical activity from corresponding smartphones and wearable devices running different operating systems and/or platforms.
Wexler teaches:
running a harmonization algorithm comprises running a neural network (See Wexler [0030] the system can use deep learning algorithms such as recurrent neural networks.), wherein the neural network has been trained using a plurality of training data sets, each training data set comprises reference step count data associated with known physical activity from corresponding smartphones and wearable devices running different operating systems and/or platforms (See Wexler [0067] system can classify users into steps user classes [0070] in order for the system to train the model on users with realistic parameters, one or more databases can be accessed to obtain numbers of steps for users of a step-tracking software application.).
The system of Wexler is applicable to the disclosure of Pauley as they both share characteristics and capabilities, namely, they are directed to monitoring and providing personalized health recommendations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pauley to include the physical parameter and neural network elements as taught by Wexler. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Pauley because there is a need for improved systems and methods for biomonitoring and/or providing personalized healthcare recommendations or information for the treatment of diabetes and other chronic conditions (see Wexler [0004]).
Regarding claim 16, Pauley in view of Wexler discloses the system of claim 15 as discussed above. Pauley does not further disclose a system, wherein:
the neural network comprises a deep learning neural network that includes an input layer, a plurality of hidden layers, and an output layer.
Wexler teaches:
the neural network comprises a deep learning neural network that includes an input layer, a plurality of hidden layers, and an output layer (See Wexler [0030] the system can use deep learning algorithms such as recurrent neural networks.).
The system of Wexler is applicable to the disclosure of Pauley as they both share characteristics and capabilities, namely, they are directed to monitoring and providing personalized health recommendations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pauley to include the physical parameter and neural network elements as taught by Wexler. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Pauley because there is a need for improved systems and methods for biomonitoring and/or providing personalized healthcare recommendations or information for the treatment of diabetes and other chronic conditions (see Wexler [0004]).
Regarding claim 17, Pauley in view of Wexler discloses the system of claim 16 as discussed above. Pauley further discloses a system, wherein:
each training data set is represented using a plurality of features, wherein each feature comprises a feature vector (See Pauley [0165] the system can make the risk determinations using machine learning algorithms, such as a neural network, support vector machine, Bayesian network, genetic algorithm, or other data analysis algorithm. Training data sets for machine learning are understood to include feature vectors.).
Regarding claim 18, Pauley in view of Wexler discloses the system of claim 17 as discussed above. Pauley does not further disclose a system, wherein:
the neural network comprises a recurrent neural network (RNN) with a combination of aggregating model using model stacking.
Wexler teaches:
the neural network comprises a recurrent neural network (RNN) with a combination of aggregating model using model stacking (See Wexler [0030] the system can use deep learning algorithms such as recurrent neural networks.).
The system of Wexler is applicable to the disclosure of Pauley as they both share characteristics and capabilities, namely, they are directed to monitoring and providing personalized health recommendations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pauley to include the physical parameter and neural network elements as taught by Wexler. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Pauley because there is a need for improved systems and methods for biomonitoring and/or providing personalized healthcare recommendations or information for the treatment of diabetes and other chronic conditions (see Wexler [0004]).
Regarding claim 19, Pauley in view of Wexler discloses the system of claim 15 as discussed above. Pauley further discloses a system, wherein:
processing the lifestyle data by running a harmonization algorithm and a stacking algorithm to generate a structured, standardized set of daily aggregates ensures concordance across different types of devices (See Pauley [0063] system may receive sensor data associated with one or more physiological parameters from at least one physiological sensor, such as a glucose sensor or monitor, smart watch, mobile device, etc. [0070] can include processes for collecting data from user devices, such as a mobile device, digital scale, smart watch, fitness tracker, glucose monitor, or other device capable of measuring physiological parameters associated with the user. See also Fig. 5 and [0101].) (Examiner notes that this is a non-limiting intended result which merely describes a desired result from use of the recited structure, and is given no patentable weight.).
Regarding claim 20, Pauley in view of Wexler discloses the system of claim 1 as discussed above. Pauley further discloses a system, wherein:
the one or more ML models is selected from the group consisting of a linear-based model, a tree-based model, and a neural network-based model (See Pauley [0165] the system can make th