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 .
Notice to Applicant
In the amendment filed on 7/13/2026, claims 21, 30, 39, 40, and 46 have been amended. Claims 38 and 45 have been canceled. Now, claims 21-37, 39-44, and 46 are pending and examiner herein.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/13/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 21-37, 39-44, and 46 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under step 1 of the Alice/Mayo framework, it must be considered whether the claims are directed to one of the four statutory classes of invention. In the instant case Claims 21-37 and 39 are drawn to a process (method), claims 40-44 are drawn to a machine (system) and claim 46 is drawn to an apparatus (computer-readable media), which is one of the statutory categories of invention.
Under step 2A of the Alice/Mayo framework, it must be considered whether the claims are “directed to” an abstract idea. That is, whether the claims recite an abstract idea and fail to integrate the abstract idea into a practical application.
Regarding, Independent claims 21, 40 and 46 recites a method, system and non-transitory computer-readable media comprising the following:
obtaining health data of a user, wherein the health data comprises a time series stream of health events of the user including health data over a historical time period;
determining a position of the user in a latent health space from the health data of the user, wherein the latent health space comprises a plurality of latent behavioral or health dimensions;
determining a trajectory of the user in a latent health space based at least in part on the time series stream of the health events of the user, wherein the trajectory comprises an evolution of the position of the user in the latent health space over the historical time period, thereby indicating a change in a health condition of the user;
selecting a health intervention for the user based at least in part on the trajectory of the user in the latent health space, wherein the health intervention is selected based on the evolution of the position of the user in the latent space; and
automatically causing initiation of the health intervention on behalf of the user, wherein the initiating the health intervention comprises transmitting a notification in real-time, wherein the notification comprises the indication of the change in the health condition of the user, thereby providing the user with up-to-date health condition information.
These steps are directed to predicting a user’s health and determining a health intervention on behalf of the user, which amounts to certain methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships between people and human behavior; (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people).
The claims do recite additional elements: Independent Claim 21 recites a user device. Independent Claim 40 recites, in part, one or more processors; and one or more memories storing computer-executable instructions, and a user device. Independent Claim 46 recites, in part, one or more non-transitory computer-readable media comprising computer- executable instructions that, when executed by at least one processor, and a user device. The above claims, as a whole, are therefore directed to an abstract idea.
These additional elements merely amount to the general application of the abstract idea to a technological environment (“a user device”, “one or more processors”, “one or more memories storing computer-executable instructions”, “one or more non-transitory computer-readable media comprising computer- executable instructions that, when executed by at least one processor, cause the at least one processor”) and insignificant pre-and-post solution activity (obtaining, determining, selecting, initiating). The specification makes clear the general-purpose nature of the technological environment. Paragraphs 100-101, 105-114, indicate that while exemplary general purpose systems may be specific for descriptive purposes, any elements or combinations of elements capable of implementing the claimed invention are acceptable. That is, the technology used to implement the invention is not specific or integral to the claim.
Therefore, considered both individually and as an ordered combination, the additional elements do no more than generally link the use of the abstract idea to a particular technological environment or field of use. That is, given the generality with which the additional limitations are recited, the limitations do not implement the abstract idea with, or use the abstract idea in conjunction with, a particular machine or manufacture that is integral to the claim. Additionally, the claims do not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition, do not effect a transformation or reduction of a particular article to a different state or thing; and do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea. Accordingly, the Examiner concludes that the claim fails to integrate the abstract idea into a practical application, and is therefore “directed to” the abstract idea.
Under step 2B of the Alice/Mayo framework, it must finally be considered whether the claim includes any additional element or combination of elements that provide an inventive concept (i.e., whether the additional element or elements are sufficient to amount to significantly more than the abstract idea). As indicated above, considered both individually and as an ordered combination, the additional elements do not implement the abstract idea with, or use the abstract idea in conjunction with, a particular machine or manufacture that is integral to the claim, do not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition, do not effect a transformation or reduction of a particular article to a different state or thing, and do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea
Further, the additional elements (recited above) simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. Communicating information (i.e., receiving or transmitting data over a network) has been repeatedly considered well-understood, routine, and conventional activity by the Courts (See MPEP 2106.05(d)). Accordingly, the Examiner asserts that the additional elements, considered both individually, and as an ordered combination, do not provide an inventive concept, and the claim is ineligible for patent.
Independent Claims 10 and 46 are parallel in scope to claim 21 and ineligible for similar reasons.
Dependent claims
The steps of the dependent claims 22-37, 39, and 41-44 only serve to further limit or specify the features of independent claim 21 and 40 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claims and utilize the additional elements already analyzed in the expected manner.
Dependent claim 24 recites, in part, wherein at least a portion of the health data of the user is collected.
Such a recitation merely embellishes the abstract idea of predicting a user’s health and determining a health intervention on behalf of the user, which amounts to certain methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships between people and human behavior; (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people). While the claim does set forth the additional limitation of “by a wearable device”, this recitation is similar to the additional limitations in claim 21, as it does no more than generally link the use of the abstract idea to a particular technological environment. As such, it does not integrate the abstract idea into a practical application, and does not provide an inventive concept. Accordingly, the claim does not confer eligibility on the claimed invention and is ineligible for similar reasons to claim 21.
Dependent claims 31 and 42, recite in part, the additional limitation of a machine learning prediction system. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the machine learning prediction system to output data. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Dependent claims 33 and 34 recite in part, the additional limitations of a machine learning prediction system and a machine learning model. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data and the computer and data processing devices to apply the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Claims 37 and 44 recite in part, the additional limitations of a Markov Jump Process, a hidden Markov model, or a particle filter. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data and the computer and data processing devices to apply the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Dependent claims 38 and 45, recite in part, the additional element of a user device. This element is only recited as a tool for performing steps of the abstract idea, such as the use of the user device to display data. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Dependent claim 39, recites in part, the additional elements of an interface of the user device to display the notification. This element is only recited as a tool for performing steps of the abstract idea, such as the use of the user device to display a notification to the user. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
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 21-27, 29-37, 39-44, and 46 is rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Application Publication Number 2008/0146334, Kil, et al., hereinafter Kil in view of United States Patent Application Publication Number 2007/0208263, John, et al., hereinafter John.
Regarding claim 21, Kil discloses a method, comprising:
(A) obtaining health data of a user, wherein the health data comprises a time series stream of health events of the user including health data over a historical time period, (para. 23, obtaining input data indicative of health behavior of a participant is obtained and para. 216, Sensor signal processing module 805: The activity data collected by a wireless pedometer or a 2-axis accelerometer are processed through digital signal processing (DSP) and projection algorithms from which a set of features is extracted to determine the type of activities (running, walking, stationary, etc.) as a function of time. Both time-series and speech features can be used to improve the overall classification accuracy);
(B) determining a position of the user in a latent health space from the health data of the user, wherein the latent health space comprises a plurality of latent behavioral or health dimensions, (para. 33, multimode health-trajectory predictors, para. 139, Multimode health-trajectory predictors 407: Predictors 407 predict future states of one's health around disease progression, engagement, and impact and para. 140, Past-future dynamic clustering 409: Clustering is performed on the vector space spanned by the current set of features and predicted attributes. In one embodiment of such a system, the current set of features encompasses the parameterization of current disease conditions, utilization of medical resources, and lifestyle/health behavior); and
(E) automatically causing initiation of the health intervention on behalf of the user, wherein the initiating the health intervention comprises transmitting a notification to a user device in real-time, wherein the notification comprises the indication of the change in the health condition of the user, thereby providing the user with up-to-date health condition information, (Fig. 10, para. 53, multimode health-trajectory predictors attempt to understand current and predict transitions in Bayesian relationships among the many semi-orthogonal outcomes attributes so that one can maximize positive impact through delivering the right intervention touch points to the right consumers at the right time before adverse transitions occur, para. 172, it is constantly monitoring the relationship between intervention and outcomes to ensure that every member gets the best possible touch points to maximize population health using both high-tech and human interventions, para. 195, Progress and outcomes measured in real time, para. 257, Create a tailored feedback with action items using metaphors that have a high probability of appealing to the user and para. 258 Work with the user on interactive goal setting).
Kil does not explicitly disclose C) determining a trajectory of the user in a latent health space based at least in part on the time series stream of the health events of the user, wherein the trajectory comprises an evolution of the position of the user in the latent health space over the historical time period, thereby indicating a change in a health condition of the user;
(D) selecting a health intervention for the user based at least in part on the trajectory of the user in the latent health space, wherein the health intervention is selected based on the evolution of the position of the user in the latent space.
However, John teaches C) determining a trajectory of the user in a latent health space based at least in part on the time series stream of the health events of the user, wherein the trajectory comprises an evolution of the position of the user in the latent health space over the historical time period, thereby indicating a change in a health condition of the user, (para. 78, cardiotrend 500 provides improved methods of choosing appropriate normative data records and utilizing multivariate and/or classification schemes. In one embodiment, the method entails: computing a clinical state vector as a combination of two or more features related to the heart status of a patient, where each feature is associated with a particular weighting factor; computing a difference score, which may be represented as a vector, between the clinical state vector and a reference state vector; plotting this difference score as trend data, wherein if the magnitude of the vector increases over time then this indicates a worsening of cardiac status; and modifying CTOs according to evaluation of this trend data, for example, modifying CTOs when the trend, or slope of the trend, exceeds a threshold criterion. Further, in the present invention cardiotrend diagnostic module 516 of FIG. 3, the state module 568 may cause the multivariate analysis module 562 to utilize equations such that the clinical state vector is compared to a reference state vector that is selected according to the current patient stat index. Accordingly, instead of using a single disease state vector to detect or quantify cardiac abnormalities, two or more vectors and reference data vector sets can be used so that evaluation of sensed data are adjusted according to the patient state. This can provide great advantage because decreases in the sensitivity/specificity performance of the vector does not result due to an attempt to use a single formula to assess different abnormal event signatures which may be inherent within different patient states. Rather each disease state vectors can serve to assess abnormality within, rather than across, each of several different patient states. For example, cardiac disease state vectors that are specifically designed to detect abnormalities when the patient is awake, asleep, or engaged in exercise can be invoked based upon a detection of a patient state by the state module 568);
(D) selecting a health intervention for the user based at least in part on the trajectory of the user in the latent health space, wherein the health intervention is selected based on the evolution of the position of the user in the latent space, (para. 9, The diagnostic module preferably employs methods that determine if, which, how, and when to provide one or more types of alarm warning, intervention (e.g. therapy), and/or storage of data, para.78, para. 78, cardiotrend 500 provides improved methods of choosing appropriate normative data records and utilizing multivariate and/or classification schemes. In one embodiment, the method entails: computing a clinical state vector as a combination of two or more features related to the heart status of a patient, where each feature is associated with a particular weighting factor; computing a difference score, which may be represented as a vector, between the clinical state vector and a reference state vector; plotting this difference score as trend data, wherein if the magnitude of the vector increases over time then this indicates a worsening of cardiac status; and modifying CTOs according to evaluation of this trend data, for example, modifying CTOs when the trend, or slope of the trend, exceeds a threshold criterion. Further, in the present invention cardiotrend diagnostic module 516 of FIG. 3, the state module 568 may cause the multivariate analysis module 562 to utilize equations such that the clinical state vector is compared to a reference state vector that is selected according to the current patient stat index. Accordingly, instead of using a single disease state vector to detect or quantify cardiac abnormalities, two or more vectors and reference data vector sets can be used so that evaluation of sensed data are adjusted according to the patient state, and para. 90, The conditional alarm module 602 and associated methods provide one or more types of alarm warnings (and may also induce changes in the intervention and/or data storage protocols) based upon conditional meta-criteria which are applied to the results derived by the diagnostic module).
One having ordinary skill in the art at the time the invention was filed would combine the techniques of Kil with the method of John with the motivation of providing early detection and/or prediction of unwanted medical conditions and alarming the patient in response thereto (John, para. 2).
Regarding claim 22, Kil discloses the method of claim 21 as described above. Kil further discloses wherein the health data of the user comprises one or both of behavior data or medical data, (para. 22, Input data indicative of the health behavior is obtained).
Regarding claim 23, Kil discloses the method of claim 21 as described above. Kil further discloses wherein the time series stream of the health events of the user comprises a plurality of physical statistics data of the user over a plurality of time periods, (paragraphs 138, From each time frame of each projection space, one extracts an appropriate number of summarization and dynamic features so that we can track their trajectories over time, para. 139 Multimode health-trajectory predictors 407: Predictors 407 predict future states of one's health around disease progression, engagement, and impact, para. 140 Past-future dynamic clustering 409: Clustering is performed on the vector space spanned by the current set of features and predicted attributes. In one embodiment of such a system, the current set of features encompasses the parameterization of current disease conditions, utilization of medical resources, and lifestyle/health behavior).
Regarding claim 24, Kil discloses the method of claim 21 as described above. Kil further discloses wherein at least a portion of the health data of the user is collected by a wearable device, (para. 65, This encompasses data from wearable sensors (Bodymedia's BodyBugg.TM., Nike+ shoe sensor, polar band) and attachable sensors (glucometer, blood-pressure cuff, spirometer, etc.) transmitted through wired or wireless networks).
Regarding claim 25, Kil discloses the method of claim 21 as described above. Kil further discloses wherein the latent health space comprises a plurality of dimensions including a behavior dimension that comprises one or more of a user adherence, receptivity, responsiveness, fidelity, shareability, or consistency of interaction with a device configured to track user health data, (Fig. 1, 123: Treatment Adherence Behavior, and para. 46, treatment adherence 123).
Regarding claim 26, Kil discloses the method of claim 21 as described above. Kil further discloses wherein the latent health space comprises a plurality of dimensions including a medical dimension, (para. 22, Input data indicative of the health behavior is obtained and para. 140, Clustering is performed on the vector space spanned by the current set of features and predicted attributes. In one embodiment of such a system, the current set of features encompasses the parameterization of current disease conditions, utilization of medical resources, and lifestyle/health behavior).
Regarding claim 27, Kil discloses the method of claims 21 and 26 as described above. Kil further discloses wherein the medical dimension comprises a future medical cost dimension that indicates a projected future medical cost for the target user, (para. 89, Cost scores for multiple future time periods in chronic vs. acute categories, and para. 140, Predicted attributes may include disease progression, the level of impactability, and future cost).
Regarding claim 29, Kil discloses the method of claims 21 and 26 as described above. Kil further discloses wherein the medical dimension comprises a risk of illness dimension that indicates a risk of contracting an illness within a threshold period of time, (para. 5, A prior art health-simulation model estimates one's probability of developing a chronic disease in the future based on multiple parameters encompassing clinical data, disease markers, and lifestyle, para. 7, Web-based questionnaire that calculates one's risk of developing several chronic diseases in the future based on participant-provided information on previous diagnoses, lifestyle, behavior, family disease history, and physiological data, para. 51, Health Risk Assessment (HRA) database, para. 91, This module generates a tailored report of 1-2 pages succinctly summarizing current health conditions, likely future states, targets of opportunities, action plan, and benefits with drilldown menu).
Regarding claim 30, Kil discloses the method of claim 21 as described above. Kil further discloses wherein the medical dimension comprises a disease progression dimension that indicates a stage of a disease, (para. 139, Predictors 407 predict future states of one's health around disease progression, engagement, and impact).
Regarding claim 31, Kil discloses the method of claim 21 as described above. Kil further discloses wherein the trajectory of the user in the latent health space is based at least in part on an output of a machine learning prediction system, wherein the machine learning prediction system is configured to output a score indicating a latent state of the user in the latent health space, (para. 44, Multimode health-trajectory predictors module, para. 45, A modular predictive model is developed for each consumer cluster so that a collection of locally optimized predictive models can provide a globally optimal performance 117. Finally, a set of health scores encompassing health scores, behavior/lifestyle scores, engagement scores, impact scores, data-conflict scores, cost scores, and clinical scores is output, and para. 49, A key idea here is maximizing synergy among business process primitives, data models, and algorithm models so that one can reduce latency between the generation of actionable knowledge and its production implementation).
Regarding claim 32, Kil discloses the method of claims 21 and 31 as described above. Kil further discloses wherein the trajectory is based at least in part on a change of the score indicating the latent state of the user, (para. 44, Multimode health-trajectory predictors module and para. 46, A modular predictive model is developed for each consumer cluster so that a collection of locally optimized predictive models can provide a globally optimal performance 117. Finally, a set of health scores encompassing health scores, behavior/lifestyle scores, engagement scores, impact scores, data-conflict scores, cost scores, and clinical scores is output 119. [0046] 2. Targets-of-opportunity finder 103: Leveraging consumer-understanding technologies, an evidence-based-medicine (EBM) supercharger (shown as EBM supercharger 300 in FIG. 3), and an autonomous insight crawler, one can identify targets of opportunities in various consumer touch points).
Regarding claim 33, Kil discloses the method of claims 21 and 31 as described above. Kil further discloses wherein the machine learning prediction system comprises a machine learning model trained to output the score indicating the latent state of the user, wherein the machine learning model was trained using a training dataset comprising a plurality of time series streams of health events for a plurality of users, (Fig. 5, para. 45, A modular predictive model is developed for each consumer cluster so that a collection of locally optimized predictive models can provide a globally optimal performance 117. Finally, a set of health scores encompassing health scores, behavior/lifestyle scores, engagement scores, impact scores, data-conflict scores, cost scores, and clinical scores is output and para. 83, Multiple-model scoring 219: Once process 200 has been trained, multiple-model scoring 219 is performed for input data).
Regarding claim 34, Kil discloses the method of claims 21 and 31 as described above. Kil further discloses wherein the machine learning prediction system comprises a plurality of machine learning models configured to output a plurality of scores indicating the latent state of the user, (Fig. 5, para. 45, A modular predictive model is developed for each consumer cluster so that a collection of locally optimized predictive models can provide a globally optimal performance 117. Finally, a set of health scores encompassing health scores, behavior/lifestyle scores, engagement scores, impact scores, data-conflict scores, cost scores, and clinical scores is output and para. 83, Multiple-model scoring 219: Once process 200 has been trained, multiple-model scoring 219 is performed for input data).
Regarding claim 35, Kil discloses the method of claim 21 as described above. Kil further discloses wherein the selecting the health intervention is based at least in part on a similarity between the trajectory of the user in the latent health space and a trajectory of another user in the latent health space, (para. 48, After selecting candidate population for analysis 141, one performs thorough matching in the two-dimensional space of propensity and predictive scores 143 to create control and intervention groups 145 for an "apple-to-apple comparison.").
Regarding claim 36, Kil discloses the method of claim 21 as described above. Kil further discloses wherein the latent health space comprises a plurality of segments, wherein a segment corresponds to a population of users comprising similar latent health states, (para. 56, Some predictive models divide the population into sub-groups using inputs from clinicians with the goal of designing a model tailored to each sub-group (MedAI).).
Regarding claim 37, Kil discloses the method of claim 21 as described above. Kil further discloses wherein the trajectory of the user in the latent health space is determined by one or more of a Markov Jump Process, a hidden Markov model,or a particle filter, (Fig. 6 and para. 162, Markov model 600 shows the probability of transitioning from one disease state to another disease state based on whether the consumer obtains a prescribed treatment.).
Regarding claim 38, Kil discloses the method of claim 21 as described above. Kil further discloses wherein the initiating the health intervention comprises transmitting a notification to a user device, wherein the notification comprises an indication of a change in a health condition of a user, thereby providing the user with up- to-date health condition information, (para. 44, Consumer-engagement channels may encompass secure e-mails, Interactive Voice Recording (IVR) calls, cellphone text messages, and nurse calls. Data Merge & Cleaning 109 performs extract-transform-load (ETL) of disparate data assets to form a consumer-centric view while cleaning data prior to weak-signal transformation through digital signal processing (DSP) and feature extraction, para. 266, messages flashing on his wellness device……Consumer feedback may be asynchronous (adheres to predefined or user-customizable feedback criteria), regular (once a day, for example), or on demand through the preferred or available communication channel.).
Regarding claim 39, Kil discloses the method of claim 21 as described above. Kil further discloses further comprising modifying an interface of the user device to display the notification, wherein the notification is configured to change a behavior of the user, (para. 44, Consumer-engagement channels may encompass secure e-mails, Interactive Voice Recording (IVR) calls, cellphone text messages, and nurse calls. Data Merge & Cleaning 109 performs extract-transform-load (ETL) of disparate data assets to form a consumer-centric view while cleaning data prior to weak-signal transformation through digital signal processing (DSP) and feature extraction, para. 266, messages flashing on his wellness device……Consumer feedback may be asynchronous (adheres to predefined or user-customizable feedback criteria), regular (once a day, for example), or on demand through the preferred or available communication channel).
Regarding claims 40-44 and 46, these claims are rejected for the same reasons as set forth above with regard to claims 21, 22, 26, 31, 35, and 37. Kil discloses a processor (para. 174), a memory (para. 174), and computer-readable media (para. 175).
Claim 28 is rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Application Publication Number 2008/0146334, Kil, et al., hereinafter Kil in view of in view of United States Patent Application Publication Number 2007/0208263, John, et al., hereinafter John and further in view of United States Patent Application Publication Number 2018/0344215, Ohnemus, et al., hereinafter Ohnemus.
Regarding claim 28, Kil in view of John teaches the method of claims 21 and 26 as described above. Kil in view of John does not explicitly disclose wherein the medical dimension comprises a sleep-related fatigue dimension that indicates levels of fatigue resulting from a lack of sleep.
However, Ohnemus teaches wherein the medical dimension comprises a sleep-
related fatigue dimension that indicates levels of fatigue resulting from a lack of sleep, (para. 64, a health device 105 can be configured for sleep monitoring of the wearer as well and para. 187, A user's sleep duration/quality/Bed Exit).
One having ordinary skill in the art at the time the invention was filed would combine the techniques of Kil in view of John with the method of Ohnemus with the motivation of providing improved integration of information received from a plurality of tracking devices (Ohnemus, para. 48).
Response to Arguments
Applicant's arguments filed July 13, 2026 have been fully considered but they are not persuasive.
Applicant argues that Applicant submits amended claim 21 is subject-matter eligible for at least the same reasons provided in the U.S. Patent and Trademark Office's guidance on Example 42, that the claims do not recite a judicial exception because they do not recite: (i) any mathematical concepts, (ii) any methods of organizing human activity, or (iii) any mental processes, and that the claims as a whole amount to significantly more than any alleged judicial exception.
In response, Examiner respectfully disagrees. The concept of “real-time user health monitoring by providing a technological solution for "identifying appropriate interventions" by, per amended claim 21, "transmitting a notification to a user device" based on "evolution of the position of the user in the latent health space over [a] historical time period," thereby providing users "up-to-date health condition information", does not relate to an improvement to technology. In particular, the claims only recite additional elements of using one or more processors; and one or more memories storing computer-executable instructions, a user device, and one or more non-transitory computer-readable media comprising computer- executable instructions that, when executed by at least one processor. The elements in each of these independent claims are recited at a high-level of generality (i.e., one or more processors; and one or more memories storing computer-executable instructions, a user device, and one or more non-transitory computer-readable media comprising computer- executable instructions that, when executed by at least one processor), such that the claims are directed to utilizing general purpose computer components. As such, the limitations amount to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere 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). Thus, the claims are not patent eligible.
These steps of the claim limitations are directed to predict an individual's health risks and to determine more appropriate and effective interventions to prevent or mitigate health risks, which amounts to certain methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships between people and human behavior; (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people).
With regard to Example 42, this example is not analogous to the claims in the present application. The present claims do not follow the same fact pattern as Example 42. The claimed invention of Example 42, the claim recites a combination of additional elements including storing information, providing remote access over a network, converting updated information that was input by a user in a non-standardized form to a standardized format, automatically generating a message whenever updated information is stored, and transmitting the message in real time to all of the users. The claim as a whole integrates the method of organizing human activity into a practical application. Specifically, the additional elements recite a specific improvement over prior art systems by allowing remote users to share information in real time in a standardized format regardless of the format in which the information was input by the user. The present application does not convert the data and transmit an update message to all of the users. The present application is directed to an arrangement for predicting an individual's health risks and to determine more appropriate and effective interventions to prevent or mitigate health risks and notifying the user of the up-to-date health condition information, it does not transmit a message to all of the users automatically in real time when the information is updated. Therefore, the present claims are not analogous to those in example 42.
As a result, there are no meaningful limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, and the claims are properly rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. Thus, the claims do not recite additional limitations that integrate the exception into a Practical Application.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
SYSTEMS AND METHODS FOR TRANSITIONING PATIENT CARE FROM SIGNAL-BASED MONITORING TO RISK-BASED MONITORING (US 20130231949 A1) teaches a risk-based patient monitoring system for critical care patients combines data from multiple sources to assess the current and the future risks to the patient, thereby enabling providers to review a current patient risk profile and to continuously track a clinical trajectory.
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/AMBER A MISIASZEK/Primary Examiner, Art Unit 3682
