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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05MAR2026 has been entered.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 21NOV2025 & 24MAR2026 were filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of Claims
The amendments and remarks filed on 05MAR2026 have been entered and considered.
Claims 1-11, 13-15, & 21-25 are currently pending.
Claims 1, 4, 9, & 15 have been amended.
Claims 12, & 16-20 have been canceled.
Claims 2-4, 9, & 10 have been withdrawn
Claims 23-25 have been added.
New matter has been added.
Claims 1, 5-8, 11, 13-15, & 21-25 are under examination.
Response to Arguments
Applicant's amendments filed 05MAR2026 regarding the 112(f) interpretations have been fully considered and obviate the interpretation. Therefore, the 112(f) interpretation has been withdrawn.
Applicant's amendments filed 05MAR2026 regarding the 112(b) rejections have been fully considered and obviate the rejection. Therefore, the 112(b) rejection has been withdrawn.
Applicant's arguments filed 05MAR2026 regarding the rejection under 35 U.S.C 101 have been fully considered but are not persuasive. Parts deemed not persuasive discussed below:
Applicant argues (Page 11 of the Remarks):
Here, Applicant respectfully submits that independent claim 1 is patent eligible for at least similar reasons that the claims in CardioNet were found to be patent eligible. In particular, the instant claims are directed to a technological improvement for illness detection based on physiological data. Indeed, the written description of the instant application describes a technical improvement provided by amended independent claim 1: "by inputting the pattern adjustment model into a classifier, the system 200 may be able to more effectively differentiate between changes in sleeping patterns/activity patterns which are indicative of oncoming illness, and changes which are attributable to normal, cycle changes in behavior." Specification 1 [0259].
For example, "the system 200 may determine that the user goes to bed later, gets up later, and is generally more active on weekends as compared to weekdays" such that "the system 200 may generate a weekly pattern adjustment model which captures this information." Id., 1 [0261]. Thus, "by generating the weekly pattern adjustment model for the user, the system 200 may be able to more effectively differentiated between changes in the user's behavior and/or physiological data which are attributed to illness, and which are simply attributable to the user's normal weekly routine." Id. In other words, the system may be tailored to each specific user in a manner such that the system is capable of determining when variations in activity or sleep are normal for a user based on cyclic changes in the user's behavior or are indicative of illness, thus improving illness detection for each specific user by reducing the likelihood of "false positive" detections of illness that are merely due to cyclic changes in the user's behavior.
The court in CardioNet indicated that "the district court erred by disregarding the written description's recitation of the advantages of the claimed invention." CardioNet [0017]. Similarly, in the instant case, Applicant submits that the oversimplification of the claimed features into an abstract idea ignores the technological benefits and advantages provided by the claimed method, apparatus, and computer readable medium, namely, improvements for illness detection that consider cyclic patterns of behavior of a user to determine what is normal for the user and what is indicative of illness. For at least these reasons, Applicant submits that the instant claims are patent eligible for at least similar reasons that the claims in CardioNet were found to be patent eligible.
The examiner is not persuaded because CardioNet provides a more specific and detailed invention than that of the instant application’s disclosure and claims. CardioNet points to a specific improvement to a specific issue (i.e. using the heart rate variability to detect specifically AFIB in a better way than had been previously done before, and that could better differentiate between AFIB and other heart arrythmias that would otherwise cause false positives). Comparatively, the instant application doesn't specify in the claims what type of illness they are detecting If this is an improvement to the detection of illnesses. As cited by by the applicant on the bottom of Page. 10 the Remarks, CardioNet claims two specific medical conditions out of a host of other possible heart conditions, but specify right after on Page 11 that the instant application is just "a technological improvement for illness detection based on physiological data". Therefore, the instant application has not provided sufficient details to be comparable to CardioNet or patentable over 35 U.S.C 101. MPEP 2106.05(a)(II) states the following:
However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.
In this case, the stated improvement is in the illness detection based on physiological data and it appears that the improvement is in the abstract idea, which would not be considered an improvement in technology.
Applicant's arguments filed 05MAR2026 regarding the rejection under 35 U.S.C 103 have been fully considered and are not persuasive. However, in light of the submitted amendments, a new ground for rejection using the previously cited references with amended limitation being addressed with updated prior art citation has been provided below. Therefore, the rejection has been maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 5-8, 11, 13-15, & 21-25 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
The claim(s) 1 contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The claims recite term “pattern adjustment model” which is only found in the specification ¶0025 stating: “In some aspects, techniques described herein may utilize models (e.g., menstrual cycle models, weekly pattern adjustment models, annual pattern adjustment models, seasonal pattern adjustment models)”. The specification does not appear to disclose how this pattern adjustment model is constructed, or how the adjustment model is used by the machine learning classifier to make predictions Though there is details regarding using the adjustment models to produce results, the specification lacks details regarding data analysis in the model that will produce the results. “The algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed” Mpep 2161.01. ”It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015).
Claims 5-8, 11, 13-15, & 21-25 are additionally rejected for depending upon the rejected independent claim 1.
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, 5-8, 11, 13-15, and 22-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. MPEP 2106(III) outlines steps for determining whether a claim is directed to statutory subject matter. The stepwise analysis for the instant claim is provided here.
Step 1 – Statutory categories
Claim 1 is directed to a system (i.e. machine)
Thus, claim 1 meets the step 1 requirements.
Step 2A – Prong 1
Regarding claim 1, the following step is an abstract idea: “determine, one or more predictive weights associated with the physical activity data , the sleep data, or both, based at least in part on adjusting the sleep expectation, the one or more predictive weights associated with a relative predictive accuracy for detecting illness”, “identify, using the machine learning classifier and based at least in part on applying the first predictive weight to the first subset of the sleep data, a satisfaction of one or more deviation criteria between a first subset of the physical activity data, the first subset of the sleep data, or both, collected throughout the first time interval and a second subset of the physical activity data, a second subset of the sleep data, or both, collected throughout the second time interval”, “adjust a sleep expectation for the user using a pattern adjustment model that is based at least in part on a cyclical pattern of activity or behavior associated with the user” which is a mental process when given its broadest reasonable interpretation. As discussed in MPEP 2106.04(a)(2)(II), the mental process grouping includes observations, evaluations, judgements, and opinions. In this case, a human could baseline data and physiological data to determine an illness risk metric. Additionally, the claims recite “determine, one or more predictive weights associated with the physical activity data , the sleep data, or both, based at least in part on adjusting the sleep expectation, the one or more predictive weights associated with a relative predictive accuracy for detecting illness;”, and “to apply a first predictive weight of the one or more predictive weights to a first subset of the sleep data collected throughout the first time interval based at least on part on a difference between the first subset of the sleep data relative to the sleep expectation for the user” which is also a mathematical concept when given its broadest reasonable interpretation. As discussed in MPEP 2106.04(a)(2)(I), The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. In this case, the calculation of weights is a mathematical calculation.
Step 2A – Prong 2
Regarding claim 1, the abstract idea is not integrated into a practical application.
The following claim elements do not add any meaningful limitation to the abstract idea:
“processor”, “GUI”, “wearable ring device”, and “light-emitting components”, and “light-receiving components” which are recited at a high level of generality and are generic computer components amounting to insignificant extra-solution activity in that they are merely objects on which the functional limitations operate [MPEP 2106.05(b)]. The light emitting and receiving components are known to the art as generic components that form a PPG system, such as seen in (Moon ¶0006 “Conventional pulse oximeters thus typically feature light sources (most typically light-emitting diodes, or LEDs) that radiate in the red (near 660 nm) and infrared (typically between 900-950 nm) spectral regions. A photodetector measures a portion of radiation at each wavelength that transmits through the patient's pulsating blood, but is not absorbed. At 660 nm, for example, Hb absorbs about ten times as much radiation as HbO2, whereas at 905 nm HbO2 absorbs about two times as much radiation as Hb. Detection of transmitted radiation at these wavelengths yields two time-dependent waveforms, each called a plethysmogram (PPG)”). The “machine learning classifier” is merely performing generic computer implementation of the abstract idea [MPEP 2106.05(d)]; The illness risk metric”, and “physiological data” are data that is necessary to implement the abstract idea on a computer [MPEP 2106.05(g)]; The “… measure physiological data from a user …”, “receive physiological data measured from a user…the physiological data collected…throughout a first time interval and a second time interval subsequent to the first time interval”, “light-emitting components, light-receiving components”, and “input the physical activity data, the sleep data, or both, into a machine learning classifier, wherein the machine learning classifier is trained to identify illness onset for the user based on changes in an activity pattern of the user, a sleeping pattern of the user, or both” are merely extra-solution activity of data gathering and input [MPEP 2106.05(g)]; and “generate a signal to indicate to a graphical user interface (GUI) of the user device to cause the GUI to display an illness risk metric associated with the user and one or more recommendations for the user to prepare for illness based at least in part on the satisfaction of the one or more deviation criteria, the illness risk metric associated with a probability that the user will transition from a healthy state to an unhealthy state” amounts to no more than data reporting & display. The references as cited below can also be seen to use such elements in their inventions as to accomplish processing tasks in a manner that is useable by the subject. The physiological data and illness risk metric are additionally items that can be found in the referenced artwork, along with being found in the medical field as generic daily items that are collected, reviewed, and evaluated for by practitioners for diagnostic purposes.
Step 2B
The additional elements of claim 1, when considered separately and in combination, do not add significantly more (i.e. an inventive concept) to the abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application. The “a wearable ring device”, “one or more light-emitting components and one or more light-receiving components”, “a user device communicatively coupled with the wearable ring device”, “one or more processors communicatively coupled with the wearable ring device and the user device”, “machine learning classifier”, “graphical user interface (GUI)”, and “physiological data, physical activity data, sleep data, “generate a signal to indicate to a graphical user interface (GUI) of the user device to cause the GUI to display an illness risk metric associated with the user and one or more recommendations for the user to prepare for illness based at least in part on the satisfaction of the one or more deviation criteria, the illness risk metric associated with a probability that the user will transition from a healthy state to an unhealthy state”, “… measure physiological data from a user …”, “receive physiological data measured from a user…the physiological data collected…throughout a first time interval and a second time interval subsequent to the first time interval”, and “input the physical activity data, the sleep data, or both, into a machine learning classifier, wherein the machine learning classifier is trained to identify illness onset for the user based on changes in an activity pattern of the user, a sleeping pattern of the user, or both”, along with their associated functions, are recited at a high level of generality and simply amount to implementing the abstract idea on a computer using generic physical parts. The additional elements are insignificant extra-solution activity and do not amount to more than what is well- understood, routine, and conventional.
Dependent claims 5-8, 11, 13-15, and 21-25 do not integrate the abstract idea into a practical application and do not add significantly more to the abstract idea of Claim 1. The dependent claim limitations are directed to: data gathering (Claim 7); abstract ideas of generating a model and further defining the abstract idea (Claims 5-6 & 21-25); the abstract idea of identifying (Claim 11), language containing abstract ideas, mental processes, and mathematical concepts (Claim 8); physiological data inputs (claims 5, 15) and to linking the abstract idea to another field (Claim 14), which are insignificant extra-solution activity and do not amount to more than what is well-understood, routine, and conventional. The limitation of data collection can be commonly seen for applications such as applications menstrual cycle tracking for fertility, and glucose tracking for diabetics.
In summary, claims 1, 5-8, 11, 13-15, and 21-23 are directed to an abstract idea without significantly more and, therefore, are patent ineligible.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 11, 13-15, and 21-25 are rejected under 35 U.S.C. 103 as being unpatentable over Shariff et al. (US Publication No. 20160302671; Previously Cited), in view of Zhang et al. (US Publication No. 20080114219; Previously Cited).
Regarding claim 1, Shariff discloses a system for automatically detecting illness (Shariff Abstract “The algorithm may evaluate a value of the person's physiological information to generate probabilities that the person is healthy or that the person likely to become sick and/or develop a fever in the next few days.”), comprising: a wearable ring device (Shariff ¶0018 “Wearable electronic devices implemented as jewelry include wearable electronic devices that do not substantially cover a portion of the body and have aesthetic value but may have limited functionality other than the functionality of the wearable electronic device. Jewelry includes watches, bracelets, rings, earrings, pendants, necklaces, and the like.”)configured to measure physiological data from a user using one or more light-emitting components (Shariff ¶0023 “To obtain a PPG the physiological sensor(s) 110 may be implemented as an optical sensor which includes a light source (e.g. a light emitting diode) configured to illuminate a wearer's skin and a matched optical sensor (e.g., a photodiode) configured to detect light at frequencies that are based on the frequencies of light output by the light source.”; ¶0025; Figure 1 showing the wearable device with PPG sensor 110 on the inner curved surface) and one or more light-receiving components (Shariff ¶0023 “To obtain a PPG the physiological sensor(s) 110 may be implemented as an optical sensor which includes a light source (e.g. a light emitting diode) configured to illuminate a wearer's skin and a matched optical sensor (e.g., a photodiode) configured to detect light at frequencies that are based on the frequencies of light output by the light source.”; ¶0025);a user device communicatively coupled with the wearable ring device (Shariff ¶0130 “a display coupled to the one or more processing units”); and one or more processors communicatively coupled with the wearable ring device and the user device (Shariff ¶0029 “The processing unit(s) 202 may include any combination of central processing units (CPUs), graphical processing units (GPUs), single core processors, multi-core processors, application-specific integrated circuits (ASICs), programmable circuits such as Field Programmable Gate Arrays (FPGA), and the like. One or more of the processing unit(s) 202 may be implemented in software and/or firmware in addition to hardware implementations.”), the one or more processors configured to: receive physiological data measured from a user via the wearable ring device (Shariff Abstract “In an implementation a device such as a wearable band collects physiological information from its wearer.”), the physiological data collected via the wearable ring device throughout a first time interval and a second time interval subsequent to the first time interval (Shariff ¶0102 “wherein the baseline values for individual ones of the physiological data descriptors are derived from a representative value of the physiological data over a previous period of time [first subset].” [0015] “these devices are wearable they may provide continuous…monitoring of physiological data”);, wherein the physiological data comprises physical activity data, sleep data, or a combination thereof (Shariff ¶0037 “Measurement of physiological features such as skin temperature, heart rate, and or respiration rate may also be used to infer a state of rest. It is known that skin temperature, heart rate, and respiration rate all decrease during normal sleep. Thus, measurement of any of these metrics over time may be used to identify the individuals sleep and wake cycle. A wearable electronic device that detects brain waves such as an electroencephalogram (EEG) may detect that the individual is sleeping. The status of the individual as resting may also be inferred based on time. For example, the individual may be assumed to be resting or asleep between the times of 1 AM and 5 AM.”; ¶0017; ¶0114) input the physical activity data, the sleep data, or both, into a machine learning classifier, wherein the machine learning classifier is trained to identify illness onset for the user based on changes in an activity pattern of the user, a sleeping pattern of the user, or both (Shariff ¶0040 “A classification module 212 may receive physiological data and classify the physiological data as likely representing a given health status such as healthy or sick. The classification module 212 may receive [inputs into classifier] physiological data [second subset] and also receive associated physiological data descriptors [which includes the baseline values] from the memory 204.”; Abstract “The person's physiological information classified by an algorithm derived through machine learning techniques.”; ¶0016-¶0017; ¶0027); identify, using the machine learning classifier and based at least in part on applying the first predictive weight to the first subset of the sleep data, a satisfaction of one or more deviation criteria between a first subset of the physical activity data, the first subset of the sleep data, or both, collected throughout the first time interval and a second subset of the physical activity data, a second subset of the sleep data, or both, collected throughout the second time interval (Shariff ¶0121 “receiving, from the probabilistic classification model, a classification of the health state of the patient as healthy, ambiguous, or sick.”; ¶0042; ¶0049; ¶0052; ¶0056).
Shariff does not disclose a system for automatically detecting illness which is configured to adjust a sleep expectation for the user using a pattern adjustment model that is based at least in part on a cyclical pattern of activity or behavior associated with the user; determine one or more predictive weights associated with the physical activity data , the sleep data, or both, based at least in part on adjusting the sleep expectation, the one or more predictive weights associated with a relative predictive accuracy for detecting illness; and generate a signal to indicate to a graphical user interface (GUI) of the user device to cause the GUI to display an illness risk metric associated with the user and one or more recommendations for the user to prepare for illness based at least in part on the satisfaction of the one or more deviation criteria, the illness risk metric associated with a probability that the user will transition from a healthy state to an unhealthy state and to apply a first predictive weight of the one or more predictive weights to a first subset of the physiological data collected throughout the first time interval based at least on part on a difference between the first subset of the physiological data relative to the expected physiological data for the user. Zhang in a similar field of endeavors of Disease Monitoring teaches a system for automatically detecting illness (Zhang ¶0017 “a method comprises sensing or receiving at an implantable device, information about at least one physiological process having a chronobiological rhythm whose presence, absence, or change is statistically associated with a disease; comparing the chronobiological rhythm of the at least one physiological process to one or more chronobiological rhythm prediction criteria; and at least one of predicting, detecting, or identifying an occurrence of disease using the comparison.”; Abstract) configured to adjust a sleep expectation for the user using a pattern adjustment model that is based at least in part on a cyclical pattern of activity or behavior associated with the user (Zhang ¶0099 “n another example, the pattern of the subject's wake/sleep cycle 618 is used as a physiological process having a certain circadian rhythm, which when lost or changed from a baseline, may be associated with impending heart failure.”; ¶0072 “A subject's 110 autonomic balance may vary in accordance with circadian rhythms. To this end, the neural stimulation circuit 257 (via the therapy control module 282) may be programmed to schedule delivery of neurostimulation in accordance with the subject's circadian rhythms for increased beneficial effect. The neural stimulation circuit 257 (via the therapy control module 282) may be programmed to titrate the delivery of neurostimulation by scheduling such delivery or adjusting the level of the neurostimulation in an open- or closed-loop manner that takes into consideration the effects of the circadian rhythm representative signals sensed or received.”; ¶0058; ); determine one or more predictive weights associated with the physical activity data , the sleep data, or both, based at least in part on adjusting the sleep expectation, the one or more predictive weights associated with a relative predictive accuracy for detecting illness (Zhang ¶0082 “Each weight may be computed using not only information about which physiological process the circadian rhythm is associated with, but may be computed using information about which other or how many other physiological process(es)' circadian rhythms also being used to predict the occurrence of impending heart failure or other disease state. As an illustrative example, suppose sensed or received circadian rhythms A and B each have weights of 0.1, leading to a combined prediction weight of 0.2. In another example, however, the circadian rhythms A and B each have weights of 0.1 when these rhythms are individually used to predict the occurrence of impending disease, but have a different (e.g., greater or lesser) weight when both are present (e.g., stronger weights of 0.5 when both A and B are sufficiently present and used to predict the occurrence of impending disease). That is, the weight values may depend on cross-correlation between two or more circadian rhythms. In a further example, a weight value depends on how many circadian rhythms are being used to compute the predicted occurrence of impending disease.”); and generate a signal to indicate to a graphical user interface (GUI) of the user device to cause the GUI to display an illness risk metric associated with the user and one or more recommendations for the user to prepare for illness based at least in part on the satisfaction of the one or more deviation criteria, the illness risk metric associated with a probability that the user will transition from a healthy state to an unhealthy state (Zhang ¶0050 “In certain examples, the remote portions of the physiological information collection device 104 include a visual or other display 124, such as a LCD or LED display, for textually or graphically relaying information to the subject 110 or a caregiver regarding operation, findings (e.g., loss or baseline change of one or more circadian rhythms; recovery of the one or more circadian rhythms), or predictions of the system 100.”) and to apply a first predictive weight of the one or more predictive weights to a first subset of the physiological data collected throughout the first time interval based at least on part on a difference between the first subset of the physiological data relative to the expected physiological data for the user (Zhang “0079 “For instance, data analysis and comparison techniques that may be used in the prediction of an occurrence of impending disease include, among others, spectral analysis such as a strength or width of the circadian peak of the rhythm spectrum, 24-hour synchronous averaging, day/night differences, daily minimum/maximum differences, order statistics such as upper-quartile vs. lower quartile differences, phase lag/drift/stability with respect to a 24-hour clock, or wake/sleep differences.”). Before the effective filing date, it would have been obvious to a person of skill in the art to modify the system of Shariff in combination with Zhang by integrating a system for automatically detecting illness which is configured to adjust a sleep expectation for the user using using a pattern adjustment model that is based at least in part on a cyclical pattern of activity or behavior associated with the user; determine one or more predictive weights associated with the physical activity data , the sleep data, or both, based at least in part on adjusting the sleep expectation, the one or more predictive weights associated with a relative predictive accuracy for detecting illness; and generate a signal to indicate to a graphical user interface (GUI) of the user device to cause the GUI to display an illness risk metric associated with the user and one or more recommendations for the user to prepare for illness based at least in part on the satisfaction of the one or more deviation criteria, the illness risk metric associated with a probability that the user will transition from a healthy state to an unhealthy state and to apply a first predictive weight of the one or more predictive weights to a first subset of the physiological data collected throughout the first time interval based at least on part on a difference between the first subset of the physiological data relative to the expected physiological data for the user, as taught by Zhang, into the processing circuitry of Shariff for the purposes of allowing the device to adjust parameter as needed to remain effective in detecting illness onset based on specific users, since one’s routines will commonly change over time, depending on the holidays, seasons, and personal commitments such as school or work.
Regarding claim 11, Shariff combined with Zhang teaches the information of claim 1, and additionally teaches wherein the one or more processors are further configured to: identify adherence data associated with the user, the adherence data associated with a frequency that the user wears the wearable ring device; and identify a change in the adherence data, (Shariff ¶0068 “The physiological data may be smoothed using a median filter or other technique to remove or reduce the influence of outlying data. The median filter keeps a media value for each set of data of a given type of physiological data over a given time period. Values other than the median are not used in subsequent analysis. The given period of time may be, for example, a 6-hour period, a 12-hour period, an 18-hour period, a 24-hour period, or another length of time. In an implementation the physiological data may be smoothed by taking the average value for a given type of physiological data over the given time period.” ¶0023. The device has the capability to identify outlier in the data set, which can be directly caused by the user not wearing the device, damaging it, or incorrect sensor readings. This relates to the adherence data as the values of outliers need to be smoothed over for correct diagnosis) wherein identifying the satisfaction of the one or more deviation criteria, causing the graphical user interface of the user device to display the illness risk metric, or both, is based at least in part on identifying the change in the adherence data. (Shariff ¶0049 “In an implementation the notification module 216 may cause a display on the mobile electronic device 104 [administrator user device] and/or the display 112 on the wearable electronic device 102 to display a color and or a symbol associated with the health state having a highest probability as determined by the one or more probabilistic classification model(s) 214.” See the textual descriptions that describe the illness risk metric on the graphical user interface (since different outputs can be obtained from the mobile electronic device). The comparison is between the possible states of illnesses.; Wherein the classification models are based upon the data as collected and filtered as described in ¶0068; ¶0023 which is accounting for adherence information).
Regarding claim 13, Shariff combined with Zhang teaches the information of claim 1, and additionally teaches wherein the wearable ring device collects the physiological data from the user based on arterial blood flow. (Shariff ¶0023 “The same optical sensor can also detect respiration rate because blood flow is effected by movement of the lungs as well as the heart.” This relates to both the arterial blood flow and that are the veins, as the cardiac system is as a whole, a closed loop.).
Regarding claim 14, Shariff combined with Zhang teaches the information of claim 1, and additionally teaches wherein the user device comprises a user device associated with the user, a user device associated with an administrator associated with a group of users including the user, or both. (Shariff ¶0050“the notification module 216 may securely send the classification to an electronic device associated with another individual or organization such as a caregiver like a nurse, physician, hospital, or the like.”).
Regarding claim 15, Shariff combined with Zhang teaches the information of claim 1, and additionally teaches wherein the physiological data is associated with a plurality of users including the user, the physiological data collected via a plurality of wearable ring devices associated with the plurality of users (Shariff ¶0020 “Distributed systems generally include multiple pieces of hardware distributed across a plurality of locations. For example, a server farm containing many different servers interacting together is a distributed system.”; ¶0054 “The physiological data used as training data may be population data from a plurality of individuals classified as healthy and a plurality of individuals classified as sick.”; ¶0063) wherein the one or more processors are further configured to: identify, based at least in part on the physiological data, second physical activity data (Shariff ¶0014 “This disclosure describes a correlation between elevated heart rate, elevated respiration rate, and ill health. People who are sick, or people who are becoming sick, often have higher heart rates and higher respiration rates than healthy individuals. Heart rate and respiration rate can vary with activities such as exercise, so gradual changes over a course of multiple days may be tracked by analysis of resting heart rate and resting respiration rate.”), second sleep data associated with each user of the plurality of users (Shariff ¶0037 “Measurement of physiological features such as skin temperature, heart rate, and or respiration rate may also be used to infer a state of rest. It is known that skin temperature, heart rate, and respiration rate all decrease during normal sleep. Thus, measurement of any of these metrics over time may be used to identify the individuals sleep and wake cycle. A wearable electronic device that detects brain waves such as an electroencephalogram (EEG) may detect that the individual is sleeping. The status of the individual as resting may also be inferred based on time. For example, the individual may be assumed to be resting or asleep between the times of 1 AM and 5 AM.”; ¶0017; ¶0114); input the second physical activity data, the second sleep data, or both, for each user of the plurality of users into the machine learning classifier (Shariff ¶0040 “A classification module 212 may receive physiological data and classify the physiological data as likely representing a given health status such as healthy or sick. The classification module 212 may receive [inputs into classifier] physiological data [second subset] and also receive associated physiological data descriptors [which includes the baseline values] from the memory 204.”; Abstract “The person's physiological information classified by an algorithm derived through machine learning techniques.”; ¶0016-¶0017; ¶0027); identify, using the machine learning classifier, an illness risk metric associated with each user of the plurality of users based at least in part on the second physical activity data, the second sleep data, or both, for each user of the plurality of users (Shariff ¶0121 “receiving, from the probabilistic classification model, a classification of the health state of the patient as healthy, ambiguous, or sick.”; ¶0042; ¶0049; ¶0052; ¶0056); and cause a GUI of an administrator user device to display at least one illness risk metric associated with at least one user of the plurality of users. (Shariff ¶0049 “In an implementation the notification module 216 may cause a display on the mobile electronic device 104 and/or the display 112 on the wearable electronic device 102 to display a color and or a symbol associated with the health state having a highest probability as determined by the one or more probabilistic classification model(s) 214.” See the textual descriptions that describe the illness risk metric on the graphical user interface (since different outputs can be obtained from the mobile electronic device).).
Regarding claim 21, Shariff in combination with Zhang teaches the information of claim 1, and further teaches wherein the cyclical pattern of user activity or behavior is longer than a circadian rhythm associated with the user. (Shariff ¶0061 “At 406, is determined if the plurality of time points of the physiological data stored at 404 stands at least a first threshold length of time. If the physical data has been collected over a sufficiently long (i.e., first threshold length) period of time, the physiological data may be useful for determining trends. The first threshold length of time may be any length of time sufficient for collecting data to support further analysis.”; ¶0065 “Upon providing the plurality of time points of physiological data to the probabilistic classification model, the probabilistic classification model returns probabilities that the patient belongs to one of a number of different groups. In an implementation, the groups comprise two groups: a group that will develop a fever within a second threshold length of time and a group that will not develop a fever within the second threshold length of time.”).
Regarding claim 22, Shariff combined with Zhang teaches the information of claim 1, and further teaches wherein the one or more processors are further configured to: adjust an activity expectation for the user using the cyclical pattern of activity or behavior associated with the user, wherein determining the one or more predictive weights associated with the physical activity data, the sleep data , or both, is based at least in part on adjusting the activity expectation (Zhang ¶0097 “In another example, the subject's activity level 612 is used as a physiological process having a certain circadian rhythm, which when lost or changed from a baseline, may be associated with impending heart failure. In healthy subjects, activity level follows a circadian rhythm. This circadian rhythm, however, may begin to become less pronounced or otherwise change several hours to several days before the onset of a disease state, such as heart failure. Indications of a loss or baseline change of circadian rhythm may include a decrease in the subject's activity level. Monitoring the circadian rhythm associated with activity level in such instances and comparing the results to one or more baseline prediction criteria derived from one or more subjects in a non-disease state, provides a tool to predict, monitor, or treat an occurrence of impending heart failure.”). Before the effective filing date, it would have been obvious to a person of skill in the art to modify the system of Shariff in combination with Zhang by integrating one or more processors that are further configured to adjust an activity expectation for the user using the cyclical pattern of activity or behavior associated with the user wherein determining the one or more predictive weights associated with the physical activity data, the sleep data , or both, is based at least in part on adjusting the activity expectation, as taught by Zhang, into the processing circuitry of Shariff in combination with Zhang for the purposes of allowing the device to adjust parameter as needed to remain effective in detecting illness onset based on specific users, since one’s routines will commonly change over time, depending on the holidays, seasons, and personal commitments such as school or work.
Regarding Claim 23, Shariff combined with Zhang teaches the information of claim 1. Neither Shariff or Akutagawa further disclose wherein the first subset of the sleep data comprises a first sleep duration, wherein the sleep expectation comprises an expected sleep duration and wherein application of the first predictive weight to the first subset of the sleep data is based at least in part on a difference between the first sleep duration and the expected sleep duration. Zhang further teaches wherein the first subset of the sleep data comprises a first sleep duration (Zhang ¶0067 “The rhythm collection module 276 may include the memory 274 to store signals representative of such circadian rhythm(s) and may further classify such rhythm(s) as being associated with one or more of body temperature (core or peripheral), heart rate, heart rate variability, respiration rate, respiration rate variability, minute ventilation, activity, blood pressure, posture, tidal volume, sleep quality or duration”), wherein the sleep expectation comprises an expected sleep duration (Zhang ¶0099 “Monitoring the circadian rhythm associated with sleep patterns 618 in such instances and comparing the results to one or more baseline prediction criteria derived from one or more subjects in a non-disease state, provides a tool to predict, monitor, or treat an occurrence of impending heart failure.” Where the examiner maintains that the sleep patterns equate to any expected variable regarding sleeping due to it’s reoccurring nature) and wherein application of the first predictive weight to the first subset of the sleep data is based at least in part on a difference between the first sleep duration and the expected sleep duration. (Zhang “0079 “For instance, data analysis and comparison techniques that may be used in the prediction of an occurrence of impending disease include, among others, spectral analysis such as a strength or width of the circadian peak of the rhythm spectrum, 24-hour synchronous averaging, day/night differences, daily minimum/maximum differences, order statistics such as upper-quartile vs. lower quartile differences, phase lag/drift/stability with respect to a 24-hour clock, or wake/sleep differences.”).
Before the effective filing date, it would have been obvious to a person of skill in the art to modify the system of Shariff with Zhang by integrating instructions wherein the first subset of the sleep data comprises a first sleep duration, wherein the sleep expectation comprises an expected sleep duration and wherein application of the first predictive weight to the first subset of the sleep data is based at least in part on a difference between the first sleep duration and the expected sleep duration, as taught by Zhang, into the system of Shariff for the purposes of allowing the device to adjust parameter as needed to remain effective in detecting illness onset based on specific users, since one’s routines will commonly change over time, depending on the holidays, seasons, and personal commitments such as school or work.
Regarding Claim 24, Shariff combined with Zhang teaches the information of claim 1. Shariff does not further disclose wherein the first subset of the sleep data comprises a first wake time wherein the sleep expectation comprises an expected wake time and wherein application of the first predictive weight to the first subset of the sleep data is based at least in part on a difference between the first wake time and the expected wake time. Zhang further teaches wherein the first subset of the sleep data comprises a first wake time, wherein the sleep expectation comprises an expected wake time (Zhang ¶0099 “Monitoring the circadian rhythm associated with sleep patterns 618 in such instances and comparing the results to one or more baseline prediction criteria derived from one or more subjects in a non-disease state, provides a tool to predict, monitor, or treat an occurrence of impending heart failure.” Where the examiner maintains that the sleep patterns equate to any expected variable regarding sleeping due to it’s reoccurring nature). and wherein application of the first predictive weight to the first subset of the sleep data is based at least in part on a difference between the first wake time and the expected wake time. (Zhang “¶0079 “For instance, data analysis and comparison techniques that may be used in the prediction of an occurrence of impending disease include, among others, spectral analysis such as a strength or width of the circadian peak of the rhythm spectrum, 24-hour synchronous averaging, day/night differences, daily minimum/maximum differences, order statistics such as upper-quartile vs. lower quartile differences, phase lag/drift/stability with respect to a 24-hour clock, or wake/sleep differences.”).Before the effective filing date, it would have been obvious to a person of skill in the art to modify the system of Shariff with Zhang by integrating instructions wherein the first subset of the sleep data comprises a first wake time wherein the sleep expectation comprises an expected wake time and wherein application of the first predictive weight to the first subset of the sleep data is based at least in part on a difference between the first wake time and the expected wake time, as taught by Zhang, into the system of Shariff for the purposes of allowing the device to adjust parameter as needed to remain effective in detecting illness onset based on specific users, since one’s routines will commonly change over time, depending on the holidays, seasons, and personal commitments such as school or work.
Regarding Claim 25, Shariff combined with Zhang teaches the information of claim 1. Shariff does not further disclose wherein the first subset of the sleep data comprises a first bedtime, wherein the sleep expectation comprises an expected bedtime and wherein application of the first predictive weight to the first subset of the sleep data is based at least in part on a difference between the first bedtime and the expected bedtime. Zhang further teaches wherein the first subset of the sleep data comprises a first bedtime, wherein the sleep expectation comprises an expected bedtime (Zhang ¶0099 “Monitoring the circadian rhythm associated with sleep patterns 618 in such instances and comparing the results to one or more baseline prediction criteria derived from one or more subjects in a non-disease state, provides a tool to predict, monitor, or treat an occurrence of impending heart failure.” Where the examiner maintains that the sleep patterns equate to any expected variable regarding sleeping due to it’s reoccurring nature). and wherein application of the first predictive weight to the first subset of the sleep data is based at least in part on a difference between the first bedtime and the expected bedtime. (Zhang “0079 “For instance, data analysis and comparison techniques that may be used in the prediction of an occurrence of impending disease include, among others, spectral analysis such as a strength or width of the circadian peak of the rhythm spectrum, 24-hour synchronous averaging, day/night differences, daily minimum/maximum differences, order statistics such as upper-quartile vs. lower quartile differences, phase lag/drift/stability with respect to a 24-hour clock, or wake/sleep differences.”). Before the effective filing date, it would have been obvious to a person of skill in the art to modify the system of Shariff with Zhang by integrating instructions wherein the first subset of the sleep data comprises a first bedtime, wherein the sleep expectation comprises an expected bedtime and wherein application of the first predictive weight to the first subset of the sleep data is based at least in part on a difference between the first bedtime and the expected bedtime, as taught by Zhang, into the system of Shariff for the purposes of allowing the device to adjust parameter as needed to remain effective in detecting illness onset based on specific users, since one’s routines will commonly change over time, depending on the holidays, seasons, and personal commitments such as school or work.
Claims 5-8 are rejected under 35 U.S.C. 103 as being unpatentable over Shariff et al. (US Publication No. 20160302671; Previously Cited), in view of Zhang et al. (US Publication No. 20080114219; Previously Cited), and Akutagawa et al. (US Publication No. 20200279339; Previously Cited).
Regarding claim 5, Shariff combined with Zhang discloses the system of claim 1. Neither Shariff or Zhang disclose wherein the cyclical pattern of activity or behavior comprises a menstrual cycle associated with the user. Akutagawa teaches wherein the cyclical pattern of activity or behavior comprises a menstrual cycle associated with the user (Akutagawa ¶0086 “In some embodiments, patterns such as activity patterns or food patterns that the user is aware of or unaware of are used to generate a recommendation. For example, every month, a week before the user's menstrual cycle, the user craves chocolate, so for an event during this time period, this information is factored in.”). Before the effective filing date, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify Shariff in combination with Zhang with a cyclical pattern of activity or behavior comprising a menstrual cycle associated with the user as taught by Akutagawa, for the purpose of allowing the device to track a user’s menstrual cycle over time in relation to changes in routine, since one’s routines will commonly change over time and this can directly affect ones’ menstrual cycle.
Regarding claim 6, Shariff combined with Zhang discloses the system of claim 1. Neither Shariff or Zhang disclose wherein the cyclical pattern of activity or behavior comprises the cyclical pattern of user activity or behavior that repeats on approximately a weekly basis, a seasonal basis, a yearly basis, or any combination thereof. Akutagawa in a similar field of illness tracking teaches wherein the cyclical pattern of activity or behavior comprises the cyclical pattern of user activity or behavior that repeats on approximately a weekly basis, a seasonal basis, a yearly basis, or any combination thereof (Akutagawa ¶0155 “The users are able to select different levels of interest (e.g., likely going, maybe going, not going). The events for selection are able to be based on daily, weekly, monthly, or other schedules.”; ¶0048 “The physical status is able to be determined based on recent trip information (e.g., jet lagged from cross-country trip), based on date and occupation (e.g., early April is a busy/stressful time for CPAs due to tax season) and/or based on medical information (e.g., contact has a broken leg)”; ¶0078 “In some embodiments, the reactions and/or other acquired data are used to provide suggestions, recommendations, alternatives, store results as favorites depending on the time of day, month, season, search parameters, and/or any other analysis.”; ¶0086). Before the effective filing date, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify Shariff in combination with Zhang with cyclical pattern of user activity or behavior that repeats on approximately a weekly basis, a seasonal basis, a yearly basis, or any combination thereof as taught by Akutagawa, for the purpose of allowing the device to adjust parameter as needed to remain effective in detecting illness onset based on specific users, since one’s routines will commonly change over time, depending on the holidays, seasons, and personal commitments such as school or work.
Regarding claim 7, Shariff combined with Zhang discloses the system of claim 1. Neither Shariff or Zhang disclose acquiring baseline physiological data from the user, throughout a reference third time interval which precedes at least a portion of the first time interval; and generate the cyclical pattern of activity or behavior based at least in part on the baseline physiological data, wherein the cyclical pattern of user activity or behavior repeats throughout at least a portion of the reference time interval. Akutagawa in a similar field of illness tracking teaches acquiring baseline physiological data from the user, throughout a reference third time interval which precedes at least a portion of the first time interval (Akutagawa ¶105 “In some embodiments, events with a recommendation score (e.g., score determined from tallying likes, dislikes, traffic, and other items) below a threshold are eliminated. In some embodiments, events with a recommendation score below a first threshold are eliminated, then the threshold is increased to a second threshold, and events with a recommendation score below the second threshold are eliminated, and the process repeats by increasing the threshold and eliminating events until a specified/desired number of events remain.” This shows the capability for the device to have iterative selection thresholds, as may be applied to many embodiments within the reference.); and generate the cyclical pattern of activity or behavior based at least in part on the baseline physiological data, wherein the cyclical pattern of user activity or behavior repeats throughout at least a portion of the reference time interval (Akutagawa ¶0086 “The habits are able to be seasonal. For example, in winter the user eats more comfort food, and in the spring, the user diets. In another example, the user travels to the mountains/snow only in winter, and recommendations are able to be provided for restaurants in the mountains. The habits are also able to be based on the day of the week. For example, it is determined based on previous purchases, that the user always purchases a coffee from Store X on Friday.”; ¶0048 “The physical status is able to be determined based on recent trip information (e.g., jet lagged from cross-country trip), based on date and occupation (e.g., early April is a busy/stressful time for CPAs due to tax season) and/or based on medical information (e.g., contact has a broken leg)”; ¶0064). Before the effective filing date, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify Shariff combined with Zhang with the methods of acquiring baseline physiological data from the user, throughout a reference third time interval which precedes at least a portion of the first time interval; and generate the cyclical pattern of activity or behavior based at least in part on the baseline physiological data, wherein the cyclical pattern of user activity or behavior repeats throughout at least a portion of the reference time interval, as taught by Akutagawa, for the purpose of allowing the device to adjust parameter as needed, and readjust iteratively over time to remain up to date on current trends to remain effective in detecting illness onset based on specific users, since one’s routines will commonly change over time.
Regarding claim 8, Shariff combined with Zhang discloses the system of claim 1. Neither Shariff or Zhang disclose applying a first predictive weight to the first subset of the physical activity data, based at least on part on a first timing of the first subset of the physical activity data, relative to the cyclical pattern of user activity or behavior and apply a second predictive weight to the second subset of the physical activity data, the second subset of the sleep data, or both, based at least on part on a second timing of the second subset of the physical activity data, the second subset of the sleep data, or both, relative to the cyclical pattern of user activity or behavior, wherein identifying the satisfaction of one or more deviation criteria is based at least in part on applying the first predictive weight, the second predictive weight, or both. Akutagawa teaches applying a first predictive weight to the first subset of the physical activity data, based at least on part on a first timing of the first subset of the physical activity data, relative to the cyclical pattern of user activity or behavior; and apply a second predictive weight to the second subset of the physical activity data, the second subset of the sleep data, or both, based at least on part on a second timing of the second subset of the physical activity data, the second subset of the sleep data, or both, relative to the cyclical pattern of user activity or behavior(Akutagawa ¶0086 “Patterns are able to be determined in any manner, for example, by storing historical data and locating matches of repetitive behavior. Furthering the example, GPS coordinates of the user are stored with timestamps for each day, and if the coordinates and timestamps match up for several days, a pattern is able to be determined. The habits are able to be seasonal.”; ¶0048 “In some embodiments, the current mood or physical status of the person/people meeting is determined. The mood is able to be determined based on facial analysis using a camera, user input/selections (e.g., selecting “sad”), analyzing user input (e.g., parsing text of a recent social networking post to find keywords), and/or any other way. In some embodiments, mood information and other information is determined using security cameras. The physical status is able to be determined based on recent trip information (e.g., jet lagged from cross-country trip), based on date and occupation (e.g., early April is a busy/stressful time for CPAs due to tax season) and/or based on medical information (e.g., contact has a broken leg).”; ¶0244; ¶0227), wherein identifying the satisfaction of one or more deviation criteria is based at least in part on applying the first predictive weight, the second predictive weight, or both (Akutagawa ¶221 “Then, based on the rate and count, a concentration is able to be generated. The concentration is able to be compared to a baseline or average white blood cell concentration particular to the person and/or a general white blood cell concentration. For example, each day for a month, the optical device 1500 is implemented for a specified time period (e.g., several seconds to several minutes) to determine a white blood cell concentration of a user using the device. The white blood cell concentration is averaged or another mathematical operation is implemented to determine a baseline. Then, the optical device 1500 periodically (e.g., 1 time per day, 3 times per day, 1 time per week, 1 time per month) checks the user's white blood cell concentration and compares the present value with the baseline. Based on the comparison, an alert or notification is able to be provided to the user via the device (e.g., an icon is displayed indicating a medical issue or the backlight color changes from green to red).” Since the device can measure items such as white blood cell counts and compare them to a baseline, it serves as a direct method of illness detection since the white blood cells are directly correlated to a user’s immune system being overactive to fight off an infection; ¶0280).
Before the effective filing date, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify Shariff in combination with Zhang with the method of applying a first predictive weight to the first subset of the physical activity data, based at least on part on a first timing of the first subset of the physical activity data, relative to the cyclical pattern of user activity or behavior and apply a second predictive weight to the second subset of the physical activity data, the second subset of the sleep data, or both, based at least on part on a second timing of the second subset of the physical activity data, the second subset of the sleep data, or both, relative to the cyclical pattern of user activity or behavior, wherein identifying the satisfaction of one or more deviation criteria is based at least in part on applying the first predictive weight, the second predictive weight, or both, as taught by Akutagawa, for the purpose of allowing the device to adjust parameter as needed to remain effective in detecting illness onset based on specific users, since one’s routines will commonly change over time, and some deviations may be more harmful or helpful to the user and their respective health and chosen biometrics depending on the severity of deviation. By using predictive weighting for the illness tracking and prediction, the device can maintain accuracy throughout the entire year as situations and baselines may shift.
Conclusion
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/MEGAN T FEDORKY/Examiner, Art Unit 3796
/UNSU JUNG/Supervisory Patent Examiner, Art Unit 3792
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