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 February 11, 2025, claims 1-20 have been cancelled. Claims 21-46 are new. Claims 21-26 are pending and examiner herein.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/18/2025; 5/29/2025; and 9/25/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
This application is a continuation of U.S. Patent Application No. 17/968,413, filed October 18, 2022, which is a continuation-in-part of U.S. Patent Application No. 16/953,256, filed November 19, 2020, which is a continuation of 14/977,194, filed December 21, 2015. Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
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 21-46 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.
Step 1 – Statutory Categories of Invention:
Claims 21-39 are drawn to a process (method), claims 40-46 are drawn to a machine (system) and apparatus (computer-readable media), which is one of the statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
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; 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; selecting a health intervention for the user based at least in part on the trajectory of the user in the latent health space; and causing initiation of the health intervention on behalf of the user.
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 or transactions between people, social activities, 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 steps of the dependent claims 22-39 and 41-45 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.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Independent Claim 21 recites, no additional elements. Independent Claim 40 recites, in part, one or more processors; and one or more memories storing computer-executable instructions. 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. The specification defines one or more processors as the processor 1202 can process instructions for execution within the computing device 1200, including instructions stored in the memory 1204 or on the storage device 1206 to display graphical information for a GUI on an external input/output device, such as display 1216 coupled to high speed interface 1208. In other implementations, multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices 1200 may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system), (Specification in ¶ 0100); and one or more memories storing computer-executable instructions as The memory 1204 stores information within the computing device 1200. In one implementation, the memory 1204 is a volatile memory unit or units. In another implementation, the memory 1204 is a non-volatile memory unit or units. The memory 1204 may also be another form of computer-readable medium, such as a magnetic or optical disk, (Specification in ¶ 0101), and one or more non-transitory computer-readable media comprising computer- executable instructions The memory 1264 can be implemented as one or more of a computer- readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units. Expansion memory 1274 may also be provided and connected to device 1250 through expansion interface 1272, which may include, for example, a SIMM (Single In Line Memory Module) card interface. Such expansion memory 1274 may provide extra storage space for device 1250, or may also store applications or other information for device (Specification in ¶ 0108). The one or more processors; and 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 limitation does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
Dependent claim 24 recites, in part, wherein at least a portion of the health data of the user is collected by a wearable device. The ‘wherein at least a portion of the health data of the user is collected by a wearable device’ limitation is only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception.
Dependent claims 31 and 42, recite in part, a machine learning prediction system. The limitations are only recited as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Dependent claims 33 and 34 recite in part, a machine learning prediction system and a machine learning model. The limitations are only recited as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Claims 37 and 44 recite in part, a Markov Jump Process, a hidden Markov model, or a particle filter. The limitations are only recited as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Dependent claims 38 and 45, recite in part, a user device. Transmitting a notification to a user device does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
Dependent claim 39, recites in part, an interface of the user device to display the notification. An interface of the user device to display the notification does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Independent claim 21 recites, no additional elements. Independent claim 40 recites, in part, one or more processors; and one or more memories storing computer-executable instructions. 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. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as use of a processor to process data, use of a memory to store data, use of a computer-readable media to store 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 24 recites, in part, wherein at least a portion of the health data of the user is collected by a wearable device which is well-understood, routine, and conventional activity. This position is supported by Applicant’s Specification at least at paragraph [0039], which states, “Effective targeting of interventions across a population of diverse individuals in varied health/behavioral states can achieved by using health/behavioral states and segments to better inform the interventions that should be used for each individual”.
Dependent claims 31 and 42, recite in part, 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, 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, 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, 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, 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”).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 21-46 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 21-27 and 29-46 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over United States Patent Application Publication Number 2008/0146334, Kil, et al., hereinafter Kil.
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, (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 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, (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);
(C) selecting a health intervention for the user based at least in part on the trajectory of the user in the latent health space, (para. 33, multimode health-trajectory predictors, 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 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); and
(D) causing initiation of the health intervention on behalf of the user, (Fig. 10, 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. 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).
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 or 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 claims 21 and 38 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 40-46, these claims are rejected for the same reasons as set forth above with regard to claims 21, 22, 26, 31, 35, 37, and 38. Kil discloses a processor (para. 174), a memory (para. 174), and computer-readable media (para. 175).
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.
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 United States Patent Application Publication Number 2018/0344215, Ohnemus, et al., hereinafter Ohnemus.
Regarding claim 28, Kil teaches the method of claims 21 and 26 as described above. Kil 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 with the method of Ohnemus with the motivation providing improved integration of information received from a plurality of tracking devices (Ohnemus, para. 48).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
SYSTEM AND METHOD FOR PROVIDING BIOMETRIC AND CONTEXT BASED MESSAGING (US 20160117937 A1) teaches The behavior is analyzed with messaging analytics to generate personalized messages related to the behavior. The message preferably is personalized at at least one level, such as time, place and format of message delivery, content of the message, and tone of the message. The message personalization can further adapt to changes in the user's behavior and/or preferences.
METHOD FOR MODELING BEHAVIOR AND HEALTH CHANGES (US 20140052474 A1) teaches detecting a change in health status of a patient includes: accessing a log of use of a native communication application executing on a mobile computing device by the patient; selecting a subgroup of a patient population based on the log of use of the native communication application and a communication behavior common to the subgroup; retrieving a health risk model associated with the subgroup, the health risk model defining a correlation between risk of change in a medical symptom and communication behavior for patients within the subgroup; predicting a risk of change in a medical symptom for the patient based on the log of use of the native communication application and the health risk model; and transmitting a notification to a care provider associated with the patient in response to the risk of change in the medical symptom for the patient that exceeds a threshold risk.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Amber Misiaszek whose telephone number is 571-270-1362. The examiner can normally be reached M-F 8:00-5:30, First Friday Off.
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/AMBER A MISIASZEK/Primary Examiner, Art Unit 3682