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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 09/17/2025 has been entered.
Status of Amendments
Claims 1-20 are currently pending in this case and have been examined and
addressed below. This communication is a Non-Final Rejection in response to the
Amendment to the Claims and Remarks filed on 09/17/2025.
Claims 1 and 17 are amended claims.
Claims 2-16 and 18-20 are previously presented.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1-20 are drawn to a system and method, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a system for process the physiological data associated with the user, the physiological data comprising the skin temperature data and heart rate data; receive historical restorative moment data associated with the user and physiological data from one or more second users that share one or more physiological characteristics with the user; determine a plurality of weights associated with a plurality of time intervals in accordance with the historical restorative moment data associated with the user and the physiological data from the one or more second users; receive the physiological data, the physiological data comprising at least the heart rate data and the skin temperature data; identify that the heart rate data is less than or equal to a heart rate threshold for at least a portion of a time interval, wherein the heart rate threshold is based at least in part on a relative position of the time interval relative to a circadian rhythm associated with the user; identify that the skin temperature data is within a temperature range of a baseline temperature associated with the user for at least the portion of the time interval, wherein the baseline temperature is based at least in part on the relative position of the time interval relative to the circadian rhythm associated with the user; identify a restorative moment for the time interval that the user is in a relaxed state based at least in part on a relationship between the heart rate data and the skin temperature data, wherein the relationship is based at least in part on the heart rate data being less than or equal to the heart rate threshold and the skin temperature data being within the temperature range of the baseline temperature, wherein the restorative moment is identified.
Independent Claim 17 recites a method for determining a plurality of weights associated with a plurality of time intervals in accordance with historical restorative moment data associated with the user and physiological data from one or more second users; identifying that the heart rate data is less than or equal to a heart rate threshold for at least a portion of a time interval, wherein the heart rate threshold is based at least in part on a relative position of the time interval relative to a circadian rhythm associated with the user; identifying that the temperature data is within a temperature range of a baseline temperature associated with the user for at least the portion of the time interval, wherein the baseline temperature is based at least in part on the relative position of the time interval relative to the circadian rhythm associated with the user; identifying a restorative moment for the time interval that the user is in a relaxed state based at least in part on a relationship between the heart rate data and the skin temperature data measured, wherein the relationship is based at least in part on the heart rate data being less than or equal to the heart rate threshold and the skin temperature data being within the temperature range of the baseline temperature, wherein the restorative moment is identified.
These steps amount to certain methods of organizing human activity which includes functions relating to managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (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 – also note MPEP § 2106.04(a)(2)(II) stating certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping).
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)].
The claims recite the additional elements of one or more processors, wireless
communications module, user device, health-related application, one or more additional
processors, one or more servers, and a graphical user interface of a user device.
These elements are recited at a high-level of generality such that it amounts to mere instructions to apply the exception because this is an example of applying the abstract idea by use of general-purpose computer which does not integrate the abstract idea into a practical application.
Claims 1 recites a wearable ring device configured to acquire physiological data from a finger of a user; one or more temperature sensors disposed at least partially within or beneath an inner curved surface of the wearable ring device and configured to acquire skin temperature data from the user through the inner curved surface; one or more light-emitting components configured to emit light into a tissue of the user through the inner curved surface of the wearable ring device; one or more light-receiving components configured to receive, through the inner curved surface, the light emitted by the one or more light-emitting components through the tissue of the user; the physiological data comprising the skin temperature data and heart rate data that is based at least in part on the light received by the one or more light- receiving components. Claim 17 recites measuring, using a wearable ring device configured to be worn on a finger of a user, physiological data associated with the user via blood flow at one or more periphery parts of a body of the user, the one or more periphery parts comprising at least the finger of the user, the physiological data comprising at least heart rate data acquired through an inner curved surface of the wearable ring device via one or more light-emitting components and one or more light- receiving components, and skin temperature data acquired through the inner curved surface of the wearable ring device via one or more temperature sensors. These limitations recite devices and sensors being used in its ordinary capacity which amounts to merely being a tool to execute the abstract idea, and thus does not integrate a judicial exception into a practical application or provide significantly more (MPEP § 2106.05(f)(2) see TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)).
Claims 1 and 17 recite a machine learning classifier; inputting the plurality of weights into a machine learning classifier; inputting the physiological data into the machine learning classifier; train the machine learning classifier to identify restorative moments associated with the user based at least in part on inputting the plurality of weights; and he machine learning classifier based at least in part on training the machine learning classifier using the plurality of weights. These limitations are recited as tools 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 1 and 17 recites transmit the physiological data and transmit a signal configured to cause a graphical user interface (GUI) of the user device to display an indication of the restorative moment. The above claims, as a whole, are therefore directed to an abstract idea. Transmitting a signal is recited as a tool which only serves as extra solution activities incidental to the primary process that is merely a nominal or tangential addition to the claim (MPEP § 2106.05(g) - insignificant pre/post-solution activity) and is therefore not a practical application of the recited judicial exception.
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.
As discussed above with respect to integration of the abstract idea into a
practical application, the claims recite the additional elements of one or more processors, wireless communications module, user device, health-related application, one or more additional processors, one or more servers, and a graphical user interface of a user device.
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 1 recites a wearable ring device configured to acquire physiological data from a finger of a user; one or more temperature sensors disposed at least partially within or beneath an inner curved surface of the wearable ring device and configured to acquire skin temperature data from the user through the inner curved surface; one or more light-emitting components configured to emit light into a tissue of the user through the inner curved surface of the wearable ring device; one or more light-receiving components configured to receive, through the inner curved surface, the light emitted by the one or more light-emitting components through the tissue of the user; the physiological data comprising the skin temperature data and heart rate data that is based at least in part on the light received by the one or more light- receiving components. Claim 17 recites measuring, using a wearable ring device configured to be worn on a finger of a user, physiological data associated with the user via blood flow at one or more periphery parts of a body of the user, the one or more periphery parts comprising at least the finger of the user, the physiological data comprising at least heart rate data acquired through an inner curved surface of the wearable ring device via one or more light-emitting components and one or more light- receiving components, and skin temperature data acquired through the inner curved surface of the wearable ring device via one or more temperature sensors. The specification recites: “System 200 illustrates an example of a ring 104 (e.g., wearable device 104), a user device 106, and a server 110, as described with reference to FIG. 1. The ring 104 may be configured to be worn around a user's finger, and may determine one or more user physiological parameters when worn around the user's finger. Example measurements and determinations may include, but are not limited to, user skin temperature, pulse waveforms, respiratory rate, heart rate, HRV, blood oxygen levels, and the like,” (Para. 0033), “The temperature sensor 240 may be configured to generate a temperature signal (e.g., temperature data) that indicates a temperature read or sensed by the temperature sensor 240. In the ring 104, temperature data generated by the temperature sensor 240 may indicate a temperature of a user at the user's finger (e.g., skin temperature). In some implementations, the temperature sensor 240 may contact the user's skin,” (Para. 0052), “The ring 104 may include a PPG system 235. The PPG system 235 may include one or more optical transmitters that transmit light. The PPG system 235 may also include one or more optical receivers that receive light transmitted by the one or more optical transmitters. An optical receiver may generate a signal (hereinafter "PPG" signal) that indicates an amount of light received by the optical receiver. The optical transmitters may illuminate a region of the user's finger. The PPG signal generated by the PPG system 235 may indicate the perfusion of blood in the illuminated region. For example, the PPG signal may indicate blood volume changes in the illuminated region caused by a user's pulse pressure,” (Para. 0061). The specification and the instant claims do not provide any indication that a wearable ring device, one or more temperature sensors, one or more light-emitting components, and one or more light-receiving components is being utilized beyond its ordinary capacity. Therefore, this step is directed towards the medical sensor being used in its ordinary capacity which amounts to merely being a tool to execute the abstract idea, and thus does not integrate a judicial exception into a practical application or provide significantly more (MPEP § 2106.05(f)(2) see TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)).
Claims 1 and 17 recite a machine learning classifier; inputting the plurality of weights into a machine learning classifier; inputting the physiological data into the machine learning classifier; train the machine learning classifier to identify restorative moments associated with the user based at least in part on inputting the plurality of weights; and he machine learning classifier based at least in part on training the machine learning classifier using the plurality of weights. These limitations are recited as tools 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 1 and 17 recites transmit the physiological data and transmit a signal configured to cause a graphical user interface (GUI) of the user device to display an indication of the restorative moment. The courts have decided that presenting generated data as well-understood, routine, conventional activity when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II) other types of activities example iv. presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93).
For the reasons stated, these claims fail the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. § 101.
Analysis of Dependent Claims
Dependent claim 2 and 18 recites identify the baseline temperature associated with the user based at least in part on receiving the physiological data, wherein the baseline temperature is based at least in part on comprises a nighttime temperature baseline.
Dependent Claim 3 and 19 recites identify that the movement data is within a range of a baseline movement associated with the user for at least the portion of the time interval, wherein identifying the restorative moment for the time interval is based at least in part on the movement data being within the range of the baseline movement.
Dependent Claim 4 recites cause the graphical user interface of the user device to display an indication of the heart rate data.
Dependent Claim 5 and 20 recites identify that the heart rate variability data is within a range of a baseline heart rate variability associated with the user for at least the portion of the time interval, wherein identifying the restorative moment for the time interval is based at least in part on the heart rate variability data being within the range of the baseline heart rate variability.
Dependent Claim 6 recites identify that the galvanic skin response data is within a range of a baseline galvanic skin response associated with the user for at least the portion of the time interval, wherein identifying the restorative moment for the time interval is based at least in part on the galvanic skin response data being within the range of the baseline galvanic skin response.
Dependent Claim 7 recites update a readiness score associated with the user based at least in part on identifying the restorative moment.
Dependent Claim 8 recites wherein the restorative moment is identified based at least in part on an increase in the skin temperature data and a decrease in the heart rate data
Dependent claim 13 recites identify a training stress score associated with a workout performed by the user during a second time interval that precedes the time interval, wherein the heart rate threshold, the baseline temperature, or both, are based at least in part on the training stress score.
Dependent claim 14 recites identify a nighttime temperature baseline for a plurality of users; and identify the baseline temperature associated with the user based at least in part on identifying the nighttime temperature baseline.
Dependent claim 15 recites identify one or more historical restorative moments associated with the user based at least in part on baseline physiological data acquired from the user via the wearable ring device; and determine a plurality of weights associated with a plurality of time intervals based at least in part on the one or more historical restorative moments, the plurality of weights associated with a relative probability that the plurality of time intervals include restorative moments, wherein the restorative moment is identified based at least in part on the plurality of weights.
Each of these steps of the preceding dependent claims 1-3, 5-8, 10-15, and 18-20 only serve to further limit or specify the features of independent claims 1 or 17 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim.
Dependent Claim 9 recites input the physiological data into a machine learning classifier, wherein identifying the restorative moment is based at least in part on inputting the physiological data into the machine learning classifier. The use of a machine learning classifier to carry out the steps of the abstract idea is mere instructions to apply the exception because a mathematical algorithm applied on a general-purpose computer has been found by the courts to be mere instructions to apply as in MPEP 2106.05(f)(2).
Dependent Claim 10 recites receive, via the user device and in response to identifying the restorative moment, a confirmation of the restorative moment. The user device is an additional element, which do not add meaningful limitations to the abstract idea beyond mere instructions to apply an exception, therefore do not provide a practical application or significantly more.
Dependent Claim 11 recites receive, via the user device, an indication of a relaxed moment, an indication of an emotional state associated with the user, or both, wherein identifying the restorative moment is based at least in part on receiving the indication of the relaxed moment, the indication of the emotional state, or both. The user device is an additional element, which do not add meaningful limitations to the abstract idea beyond mere instructions to apply an exception, therefore do not provide a practical application or significantly more.
Dependent claim 12 recites cause the graphical user interface of the user device to display a message associated with the identified restorative moment, wherein the message comprises a time of day that the restorative moment was identified, a duration of the restorative moment, a success metric associated with the restorative moment, a recommended duration for future restorative moments, a quantity of restorative moments identified, or a combination thereof. The graphical user interface of the user device is an additional elements, which do not add meaningful limitations to the abstract idea beyond mere instructions to apply an exception, therefore do not provide a practical application or significantly more.
Dependent claim 16 recites input the plurality of weights into a machine learning classifier; and train the machine learning classifier to identify restorative moments associated with the user based at least in part on inputting the plurality of weights, wherein the restorative moment is identified via the machine learning classifier based at least in part on training the machine learning classifier using the plurality of weights. The use of a machine learning classifier and train(ing) the machine learning classifier to carry out the steps of the abstract idea is mere instructions to apply the exception because a mathematical algorithm applied on a general-purpose computer has been found by the courts to be mere instructions to apply as in MPEP 2106.05(f)(2).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3, 5-6, 9-11, 13, 15-17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Derchak (US 9833184 B2) in view of Jain (US 20220188671 A1) in view of Orbach (US 20080214903 A1) in view of Heneghan (US 20220265208 A1).
As per Claim 1, Derchak teaches a system method for automatically detecting restorative moments, comprising:
one or more processors configured to process the physiological data associated with the user, the physiological data comprising the skin temperature data and heart rate data that is based at least in part on the light received by the one or more light-receiving components; ([Col. 2 Lines 39-53] a physiological monitoring device operatively coupled to the processor for measuring the person's physiological response. Preferably, the device is configured and arranged for monitoring an ambulatory subject. More preferably, the processor correlates the time of exposure to the stimulus and the type of stimulus with the measured physiological response. [Col. 7, Lines 14-17] the person is wearing an ambulatory monitoring system (e.g., the LIFESHIRT® ambulatory monitoring garment (i.e. wearable device)) that allows for the continuous monitoring and observation of physiological changes associated with emotional responses. [Col. 4, Lines 28-33] ambulatory physiological monitoring system configuration includes a wearable item, for example, a garment, band, patch, and the like, or associations with partial-shirts or shirts, on partial body suits, or in full body suits that are unobtrusive, comfortable, and preferably made of non-restricting fabric into which sensors are incorporated. [Col. 5, Lines 18-22] the garment 1 can include sensors for monitoring cardiac information, such as heart rate, ECG, or thoracocardiaogram. Other sensors can include thermistors that can sense skin or body core temperature.)
receive historical restorative moment data associated with the user and physiological data from one or more second users that share one or more physiological characteristics with the user; ([Col. 9, Lines 11-12 and 13-16] The known baseline physiological responses observed when the person was in selected emotional states (i.e. historical restorative moment data).The known baseline physiological responses and their variances are previously recorded, and stored in an electronic database in association with the particular emotion states. [Col. 10, Lines 12-21] The known baseline physiological response can be based on a distribution of physiological responses (i.e. physiological data) previously measured from a group of a population of similar people (i.e. one or more second users). Preferably, it is selected from empirical data based on histograms or other distributions of known physiological responses that were collected during exposure to a stimulus or a series of stimuli. Such data is preferably representative of a large population of persons and can be stratified by different individual characteristics, such as age, height, weight, and gender.)
inputting the physiological data into the machine learning classifier; ([Col. 2, Lines 55-61] identifying an emotional state of a person in real-time includes exposing a person to a stimulus, measuring the person's physiological responses to the stimulus, classifying the measured responses into one or more classes by means of a classifier trained to recognize patterns of physiological responses occurring in a plurality of emotional states.)
receive, from the wireless communications module of the wearable ring device, the physiological data associated with acquired from the finger of the user through the inner curved surface of the wearable ring device from a wearable device, the physiological data comprising at least the heart rate data and the skin temperature data; ([Col. 2 Lines 39-53] a physiological monitoring device operatively coupled to the processor for measuring the person's physiological response. Preferably, the device is configured and arranged for monitoring an ambulatory subject. More preferably, the processor correlates the time of exposure to the stimulus and the type of stimulus with the measured physiological response. [Col. 7, Lines 14-17] the person is wearing an ambulatory monitoring system (e.g., the LIFESHIRT® ambulatory monitoring garment (i.e. wearable device)) that allows for the continuous monitoring and observation of physiological changes associated with emotional responses. [Col. 4, Lines 28-33] ambulatory physiological monitoring system configuration includes a wearable item, for example, a garment, band, patch, and the like, or associations with partial-shirts or shirts, on partial body suits, or in full body suits that are unobtrusive, comfortable, and preferably made of non-restricting fabric into which sensors are incorporated. [Col. 5, Lines 18-22] the garment 1 can include sensors for monitoring cardiac information, such as heart rate, ECG, or thoracocardiaogram. Other sensors can include thermistors that can sense skin or body core temperature.)
inputting the physiological data into the machine learning classifier; ([Col. 2, Lines 55-61] identifying an emotional state of a person in real-time includes exposing a person to a stimulus, measuring the person's physiological responses to the stimulus, classifying the measured responses into one or more classes by means of a classifier trained to recognize patterns of physiological responses occurring in a plurality of emotional states.)
identify, using a machine learning classifier, that the heart rate data is less than or equal to a heart rate threshold for at least a portion of a time interval, wherein the heart rate threshold is based at least in part on a relative position of the time interval relative to a circadian rhythm associated with the user; ([Col. 1 Lines 62-67 and Col. 2 Line 1] measuring the person's physiological response to the stimulus, and comparing the measured physiological response (i.e. physiological data) to a known physiological response patterns associated with various emotional states. The pattern of deviations of the measured physiological response from a baseline physiological response is determinative of an emotional state. [Col. 7 Lines 22-23] the person's physiological responses are monitored in real-time. [Col. 2 Lines 14-21 and Lines 31-38] the measured physiological responses also include a cardiac response, which includes a heart rate response. The emotional state of the person is subsequently identified as likely to be the selected emotional state when the measured physiological responses significantly similar to the baseline physiological responses. More preferably, two sets of responses are significantly similar when the likelihood that the similarity is due to chance is less than a selected threshold. [Col. 10 Lines 22-35] After comparing the measured physiological response to the known baseline physiological response, a deviation between the two data sets can be determined, as shown in step 240. The known baseline physiological responses are preferably correlated with particular emotional states. In the case where a neutral stimulus is used to obtain a known baseline physiological response, particular emotional states are preferably correlated with changes in the physiological responses from the baseline. Based on whether the determined deviation falls within an acceptable error or deviation range (i.e. heart rate data is within a temperature range of a baseline heart rate), as shown in step 250, the method identifies that the person is experiencing the particular emotion state (i.e. calmness). Examiner interprets the deviation range to be indicative of heart rate threshold.)
identify, using a machine learning classifier, that the skin temperature data is within a temperature range of a baseline temperature associated with the user for at least the portion of the time interval, wherein the baseline temperature is based at least in part on the relative position of the time interval relative to the circadian rhythm associated with the user; ([Col. 1 Lines 62-67 and Col. 2 Line 1] measuring the person's physiological response to the stimulus, and comparing the measured physiological response (i.e. physiological data) to a known physiological response patterns associated with various emotional states. The pattern of deviations of the measured physiological response from a baseline physiological response is determinative of an emotional state. [Col. 7 Lines 22-23] the person's physiological responses are monitored in real-time. [Col. 2 Lines 14-16 and Lines 31-38] The measured physiological responses also include a temperature response. The emotional state of the person is subsequently identified as likely to be the selected emotional state when the measured physiological responses significantly similar to the baseline physiological responses. More preferably, two sets of responses are significantly similar when the likelihood that the similarity is due to chance is less than a selected threshold. [Col. 10 Lines 22-35] After comparing the measured physiological response to the known baseline physiological response, a deviation between the two data sets can be determined, as shown in step 240. The known baseline physiological responses are preferably correlated with particular emotional states. In the case where a neutral stimulus is used to obtain a known baseline physiological response, particular emotional states are preferably correlated with changes in the physiological responses from the baseline. Based on whether the determined deviation falls within an acceptable error or deviation range (i.e. temperature data is within a temperature range of a baseline temperature), as shown in step 250, the method identifies that the person is experiencing the particular emotion state (i.e. calmness). Examiner interprets the deviation range to be indicative of temperature range of baseline temperature.)
identify, using a machine learning classifier, a restorative moment for the time interval that the user is in a relaxed state based at least in part on a relationship between the heart rate data and the skin temperature data measured via blood flow at one or more periphery parts of a body of the user, the one or more periphery parts comprising at least the finger of the user, wherein the relationship is based at least in part on the heart rate data being less than or equal to the heart rate threshold and the skin temperature data being within the temperature range of the baseline temperature, ([Col. 2, Lines 55-67] automatically identifying an emotional state of a person in real-time includes exposing a person to a stimulus, measuring the person's physiological responses to the stimulus, classifying the measured responses into one or more classes by means of a classifier trained to recognize patterns of physiological responses occurring in a plurality of emotional states, and identifying a likely emotional state in dependence on the classes into which the measured responses have been classified. Preferably, the pattern of measured physiological responses includes deviations of the measured responses from baseline physiological responses, the baseline physiological responses being characteristic of an emotional state. [Col. 2, Lines 14-20] The measured physiological responses also include a cardiac response, which includes heart rate response and temperature response. [Col. 2 Lines 23-26] The identified emotional states include calmness (i.e. restorative moment).) [Col. 6 Line 67 and Col. 7 Lines 1-6] A deviation between the measured physiological response and the baseline physiological response can be determined, and if the deviation is within acceptable ranges of deviation (heart rate threshold and temperature data threshold) from the baseline physiological response, the person is identified as experiencing a particular emotional state that is associated therewith. [Col. 10 Lines 32-35] Based on whether the determined deviation falls within an acceptable error or deviation range (i.e. physiological data being less than or equal to the physiological data thresholds), as shown in step 250, the method identifies that the person is experiencing the particular emotion state.)
Derchak does not explicitly teach, however Jain teaches
a wearable ring device configured to acquire physiological data from a finger of a user, the wearable ring device comprising: ([Para. 0135] the sensing device 104a is a finger sensor and collects data. Examiner interprets finger sensor to be indicative of a wearable ring device.)j
one or more temperature sensors disposed at least partially within or beneath an inner curved surface of the wearable ring device and configured to acquire skin temperature data from the user through the inner curved surface;([Para. 0328] The feature scores can be based on sensor data that is acquired by one or more sensors during one or more activities of the subject or that indicates one or more attributes of the subject. Examples of sensors that can provide data include temperature sensors.) j
a user device communicatively coupled with the wearable ring device and configured to execute a health-related application associated with the wearable ring device; and one or more additional processors associated with the user device, one or more servers, or both, the one or more processors configured to: ([Para. 0130] One or more of the sensing devices 104a-104c may communicate with the computer system 110 through an intermediary device, e.g., a smart phone, a laptop computer, a desktop computer, or the like.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method for automatically identifying emotional states of a person in real-time based on physiological responses as taught by Derchak, and incorporate using a finger sensor to monitor physiological parameters of a patient as taught by Jain, with the motivation of evaluate and recommend actions for enhancing the readiness of a subject (Jain Para. 0006).
Derchak/ Jain do not explicitly teach, however Orbach teaches
one or more light-emitting components configured to emit light into a tissue of the user through the inner curved surface of the wearable ring device;([Para. 0148] IG. 2 depicts a sensor module 210 that may be used in the system 10 instead of the sensor module 110. The sensor module 210 is in contact with the user's finger 200. The sensor module 210 may be attached to the finger by a strap 212 as shown in FIG. 1, or the sensor module 210 may be shaped to fit over the finger. [Para. 0069-0070] The sensor module may comprise a blood flow sensor comprising: (a) a light source adapted to emit light towards a skin surface.)
one or more light-receiving components configured to receive, through the inner curved surface, the light emitted by the one or more light-emitting components through the tissue of the user; ([Para. 0069 and 0071] The sensor module may comprise a blood flow sensor comprising: (b) a light detector adapted to detecting light reflected from the skin surface.)
and a wireless communications module configured to transmit the physiological data generated by the one or more processors; ([Abstract] One or more transmitters wirelessly transmit signals indicative of values of the one or more physiological parameters to a mobile monitor.)
and transmit a signal configured to cause a graphical user interface (GUI) of the user device to display an indication of the restorative moment. ([Para. 0037-0038] The mobile monitor may be selected, for example, from the group comprising: a cellular phone. Examiner interprets the mobile monitor to be indicative of a GUI. [Para. 0270] FIGS. 7a to 7d may be used by a user to assess his physiological state [Para. 0271] The display screens may be flexibly designed to fit the size and type of display of the mobile monitor. Different combinations of signals and parameters may be displayed in various ways such as graphs, colors, pie charts, numerical values, bars, clock-like indicators, alert signals, alphanumerical messages, etc. Static or moving animations may also be displayed according to the interpretation of the physiological data. For example, a happy "smiley face" may be displayed when the state of the user is relaxed (i.e. restorative moment).[Abstract] Signals indicative of values of the one or more physiological parameters are wirelessly transmitted to a mobile monitor.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method for automatically identifying emotional states of a person in real-time based on physiological responses as taught by Derchak, using a finger sensor to monitor physiological parameters of a patient as taught by Jain, and incorporate a system and method for monitoring one or more physiological parameters of a user as taught by Orbach, with the motivation of monitoring the physiological parameters of the user (Orbach Abstract).
Derchak/ Jain/ Orbach does not explicitly teach, however Heneghan teaches
determining, using the one or more processors, a plurality of weights associated with a plurality of time intervals in accordance with historical restorative moment data associated with the user and physiological data from one or more second users; ([Para. 0018] Receiving motion data obtained from a set of one or more motion sensors in the wearable electronic device over the first time window, the motion data including at least one of a quantification of movement experienced by the wearable electronic device or data derived therefrom; and labeling a time period associated with the first time window with an indicator indicating a first sleep stage selected from a plurality of sleep stages using the motion data and the cardiopulmonary pulse-related data. [Para. 0120] The classifier may be trained using the training dataset, e.g., the features extracted from the data collected in association with known classifications. As part of the classifier training, each feature involved in the training may become associated with a weighting factor or other quantifier of the feature's importance in the classification determination. In block 106, the weighting factors (or other quantifier of importance) for each feature in the global set of features may be obtained, and the features may then be ranked by their weighting factors (or other quantifiers of importance) in block 108. )
input the plurality of weights into a machine learning classifier; ([Para. 0012] the instructions that cause the classifier to label the time period may include instructions that, when executed, cause the set of one or more processors to execute a classifier such as, for example, a nearest neighbor classifier, a random forest classifier, or a linear discriminant classifier. [Para. 0013] The classifier may be trained using movement features and pulse data features extracted from benchmark motion data and benchmark cardiopulmonary pulse-related data collected for a population of sleep study subjects. [Para. 0120] As part of the classifier training, each feature involved in the training may become associated with a weighting factor or other quantifier of the feature's importance in the classification determination. In block 106, the weighting factors (or other quantifier of importance) for each feature in the global set of features may be obtained, and the features may then be ranked by their weighting factors (or other quantifiers of importance) in block 108. [Para. 0154] classifiers may be trained, as discussed earlier, using features extracted from motion and pulse-related data collected during sleep sessions that are conducted under the oversight of professional or trained sleep scorers using specialized equipment and in-depth analysis of each time interval being evaluated—such data may be referred to herein as “benchmark” motion data and/or pulse-related data. Thus, for example, the features extracted from motion and pulse-related data collected during time periods or time windows that have been assigned, by a sleep scorer, a sleep stage of “deep sleep” may be used to train the classifier to assign a “deep sleep” classification to time windows or time periods having features that match the criteria of the classifier developed during such training.)
train the machine learning classifier to identify restorative moments associated with the user based at least in part on inputting the plurality of weights; ([Para. 0012] the instructions that cause the classifier to label the time period may include instructions that, when executed, cause the set of one or more processors to execute a classifier such as, for example, a nearest neighbor classifier, a random forest classifier, or a linear discriminant classifier. [Para. 0013] The classifier may be trained using movement features and pulse data features extracted from benchmark motion data and benchmark cardiopulmonary pulse-related data collected for a population of sleep study subjects. [Para. 0120] As part of the classifier training, each feature involved in the training may become associated with a weighting factor or other quantifier of the feature's importance in the classification determination. In block 106, the weighting factors (or other quantifier of importance) for each feature in the global set of features may be obtained, and the features may then be ranked by their weighting factors (or other quantifiers of importance) in block 108. [Para. 0154] Classifiers may be trained, as discussed earlier, using features extracted from motion and pulse-related data collected during sleep sessions that are conducted under the oversight of professional or trained sleep scorers using specialized equipment and in-depth analysis of each time interval being evaluated—such data may be referred to herein as “benchmark” motion data and/or pulse-related data. Thus, for example, the features extracted from motion and pulse-related data collected during time periods or time windows that have been assigned, by a sleep scorer, a sleep stage (i.e. restorative moment) of “deep sleep” may be used to train the classifier to assign a “deep sleep” classification to time windows or time periods having features that match the criteria of the classifier developed during such training.)
wherein the restorative moment is identified via the machine learning classifier based at least in part on training the machine learning classifier using the plurality of weights ([Para. 0029] generate a first graphical user interface component that indicates a relative percentile breakdown of total time spent in each of the sleep stages for the first sleep session. [Para. 0085] Classify each time period may include instructions that, when executed, cause the one or more processors to transmit the one or more movement features and the one or more pulse data features to a server system that executes a classifier that generates the sleep stage classification (i.e. restorative moments) for each time period and provides that sleep stage classification to the one or more processors. [Para. 0012] the instructions that cause the classifier to label the time period may include instructions that, when executed, cause the set of one or more processors to execute a classifier such as, for example, a nearest neighbor classifier, a random forest classifier, or a linear discriminant classifier. [Para. 0013] The classifier may be trained using movement features and pulse data features extracted from benchmark motion data and benchmark cardiopulmonary pulse-related data collected for a population of sleep study subjects. [Para. 0120] As part of the classifier training, each feature involved in the training may become associated with a weighting factor or other quantifier of the feature's importance in the classification determination. In block 106, the weighting factors (or other quantifier of importance) for each feature in the global set of features may be obtained, and the features may then be ranked by their weighting factors (or other quantifiers of importance) in block 108. [Para. 0154] classifiers may be trained, as discussed earlier, using features extracted from motion and pulse-related data collected during sleep sessions that are conducted under the oversight of professional or trained sleep scorers using specialized equipment and in-depth analysis of each time interval being evaluated—such data may be referred to herein as “benchmark” motion data and/or pulse-related data. Thus, for example, the features extracted from motion and pulse-related data collected during time periods or time windows that have been assigned, by a sleep scorer, a sleep stage of “deep sleep” may be used to train the classifier to assign a “deep sleep” classification to time windows or time periods having features that match the criteria of the classifier developed during such training.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method for automatically identifying emotional states of a person in real-time based on physiological responses as taught by Derchak, using a finger sensor to monitor physiological parameters of a patient as taught by Jain, a system and method for monitoring one or more physiological parameters of a user as taught by Orbach, and incorporate estimating sleep state based on sensor data as taught by Heneghan, with the motivation of providing classifier to label a time period associated with the first time window with an identifier indicating a first sleep stage selected from a plurality of sleep stages (Heneghan Para. 0005).
As per Claim 3, Derchak/ Jain/ Orbach/ Heneghan teach the system method of claim 1, Derchak further teaches wherein the physiological data further comprises movement data, the method further comprising wherein the one or more processors are further configured to: identify that the movement data is within a range of a baseline movement associated with the user for at least the portion of the time interval, wherein identifying the restorative moment for the time interval is based at least in part on the movement data being within the range of the baseline movement. ([Col. 1 Lines 62-67 and Col. 2 Line 1] measuring the person's physiological response to the stimulus, and comparing the measured physiological response (i.e. physiological data) to a known physiological response patterns associated with various emotional states. The pattern of deviations (i.e. range of baseline) of the measured physiological response from a baseline physiological response is determinative of an emotional state. [Col. 7 Lines 22-23] the person's physiological responses are monitored in real-time. [Col. 2 Lines 14-16] the measured physiological responses also include a posture/activity response (i.e. movement data). [Col. 10 Lines 22-35] After comparing the measured physiological response to the known baseline physiological response, a deviation between the two data sets can be determined, as shown in step 240. The known baseline physiological responses are preferably correlated with particular emotional states. In the case where a neutral stimulus is used to obtain a known baseline physiological response, particular emotional states are preferably correlated with changes in the physiological responses from the baseline. Based on whether the determined deviation falls within an acceptable error or deviation range, as shown in step 250, the method identifies that the person is experiencing the particular emotion state (i.e. calmness). [Col. 2 Lines 23-26] The identified emotional states include calmness (i.e. restorative moment).)
As per Claim 5, Derchak/ Jain/ Orbach/ Heneghan teach system of claim 1, Derchak further teach wherein the physiological data further comprises heart rate variability data, the method further comprising wherein the one or more processors are further configured to: identify that the heart rate variability data is within a range of a baseline heart rate variability associated with the user for at least the portion of the time interval, wherein identifying the restorative moment for the time interval is based at least in part on the heart rate variability data being within the range of the baseline heart rate variability. ([Col. 1 Lines 62-67 and Col. 2 Line 1] measuring the person's physiological response to the stimulus, and comparing the measured physiological response (i.e. physiological data) to a known physiological response patterns associated with various emotional states. The pattern of deviations (i.e. range of baseline) of the measured physiological response from a baseline physiological response is determinative of an emotional state. [Col. 7 Lines 22-23] the person's physiological responses are monitored in real-time. [Col. 2 Lines 14-22] the measured physiological responses also include a cardiac response. The cardiac responses include a heart rate variability response. [Col. 10 Lines 22-35] After comparing the measured physiological response to the known baseline physiological response, a deviation between the two data sets can be determined, as shown in step 240. The known baseline physiological responses are preferably correlated with particular emotional states. In the case where a neutral stimulus is used to obtain a known baseline physiological response, particular emotional states are preferably correlated with changes in the physiological responses from the baseline. Based on whether the determined deviation falls within an acceptable error or deviation range, as shown in step 250, the method identifies that the person is experiencing the particular emotion state (i.e. calmness). [Col. 1 Lines 52-56] Specific emotional states include, for example, neutrality or indifference, fear, anxiety, excitement, happiness-laughter, sadness-crying, anticipation, boredom, anger-aggression, pleasure, love, tenderness, or calmness (i.e. restorative moment).)
As per Claim 6, Derchak/ Jain/ Orbach/ Heneghan teach the system of claim 1, Derchak further teach wherein the physiological data further comprises galvanic skin response data, the method further comprising wherein the one or more processors are further configured to: identify that the galvanic skin response data is within a range of a baseline galvanic skin response associated with the user for at least the portion of the time interval, wherein identifying the restorative moment for the time interval is based at least in part on the galvanic skin response data being within the range of the baseline galvanic skin response. ([Col. 1 Lines 62-67 and Col. 2 Line 1] measuring the person's physiological response to the stimulus, and comparing the measured physiological response (i.e. physiological data) to a known physiological response patterns associated with various emotional states. The pattern of deviations (i.e. range of baseline) of the measured physiological response from a baseline physiological response is determinative of an emotional state. [Col. 7 Lines 22-23] the person's physiological responses are monitored in real-time. [Col. 2 Lines 14-16] the measured physiological responses also include a galvanic skin response. [Col. 10 Lines 22-35] After comparing the measured physiological response to the known baseline physiological response, a deviation between the two data sets can be determined, as shown in step 240. The known baseline physiological responses are preferably correlated with particular emotional states. In the case where a neutral stimulus is used to obtain a known baseline physiological response, particular emotional states are preferably correlated with changes in the physiological responses from the baseline. Based on whether the determined deviation falls within an acceptable error or deviation range, as shown in step 250, the method identifies that the person is experiencing the particular emotion state (i.e. calmness). [Col. 1 Lines 52-56] Specific emotional states include, for example, neutrality or indifference, fear, anxiety, excitement, happiness-laughter, sadness-crying, anticipation, boredom, anger-aggression, pleasure, love, tenderness, or calmness (i.e. restorative moment).)
As per Claim 9, Derchak/ Jain/ Orbach/ Heneghan teach the system of claim 1, Derchak teaches further comprising wherein the one or more processors are further configured to: input the physiological data into a machine learning classifier, wherein identifying the restorative moment is based at least in part on inputting the physiological data into the machine learning classifier. ([Col. 2, Lines 55-63] identifying an emotional state of a person in real-time includes exposing a person to a stimulus, measuring the person's physiological responses to the stimulus, classifying the measured responses into one or more classes by means of a classifier trained to recognize patterns of physiological responses occurring in a plurality of emotional states, and identifying a likely emotional state in dependence on the classes into which the measured responses have been classified.)
As per Claim 10, Derchak/ Jain/ Orbach/ Heneghan teach the system of claim 1, Orbach teaches further comprising wherein the one or more processors are further configured to: receive receiving, via the user device and in response to identifying the restorative moment, a confirmation of the restorative moment. ([Para. 0037-0038] The mobile monitor may be selected, for example, from the group comprising: a cellular phone. Examiner interprets the mobile monitor to be indicative of a user device. [Para. 0271] The display screens may be flexibly designed to fit the size and type of display of the mobile monitor. Different combinations of signals and parameters may be displayed in various ways such as graphs, colors, pie charts, numerical values, bars, clock-like indicators, alert signals, alphanumerical messages, etc. Static or moving animations may also be displayed according to the interpretation of the physiological data. For example, a happy "smiley face" may be displayed when the state of the user is relaxed).
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method for automatically identifying emotional states of a person in real-time based on physiological responses as taught by Derchak, using a finger sensor to monitor physiological parameters of a patient as taught by Jain, and incorporate a system and method for monitoring one or more physiological parameters of a user as taught by Orbach, with the motivation of monitoring the physiological parameters of the user (Orbach Abstract).
As per Claim 11, Derchak/ Jain/ Orbach/ Heneghan teach the system method of claim 1, Derchak teaches further comprising wherein the one or more processors are further configured to: wherein identifying the restorative moment is based at least in part on receiving the indication of the relaxed moment, the indication of the emotional state, or both. ([Col. 6, Lines 45-62] determining the emotional state that a person is experiencing by comparing real-time measured physiological responses to given stimuli with baseline physiological responses that were previously measured after exposure to the same or other neutral stimuli. The physiological responses are measured by of the physiological monitoring systems and methods. Specific emotional states include, for example, neutrality or indifference, fear, anxiety, excitement, happiness-laughter, sadness-crying, anticipation, boredom, anger-aggression, pleasure, love, tenderness, or calmness (i.e. restorative moment). Additionally, the method can be used to determine a person's attentiveness or attentive state as an emotional state, i.e., the degree to which a person is engaged with, concentrated on, or focused on a particular stimulus, and thus more susceptible to experiencing an emotional state in response to that stimulus.)
Derchak does not explicitly teach, however Orbach teaches
receive, via the user device, an indication of a relaxed moment, an indication of an emotional state associated with the user, or both, ([Para. 0037-0038] The mobile monitor may be selected, for example, from the group comprising: a cellular phone. Examiner interprets the mobile monitor to be indicative of a user device. [Para. 0271] The display screens may be flexibly designed to fit the size and type of display of the mobile monitor. Different combinations of signals and parameters may be displayed in various ways such as graphs, colors, pie charts, numerical values, bars, clock-like indicators, alert signals, alphanumerical messages, etc. Static or moving animations may also be displayed according to the interpretation of the physiological data. For example, a happy "smiley face" may be displayed when the state of the user is relaxed).
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method for automatically identifying emotional states of a person in real-time based on physiological responses as taught by Derchak and incorporate a system and method for monitoring one or more physiological parameters of a user as taught by Orbach, with the motivation of monitoring the physiological parameters of the user (Orbach Abstract).
As per Claim 13, Derchak/ Jain/ Orbach/ Heneghan teach the system of claim 1, Orbach further teaches wherein the one or more processors are further configured to: identify a training stress score associated with a workout performed by the user during a second time interval that precedes the time interval, wherein the heart rate threshold, the baseline temperature, or both, are based at least in part on the training stress score. ([Para. 0232] A stress level can be inferred from the average heart rate (AHR) wherein a high level of stress is characterized by higher than normal AHR. This level may need to be updated from time to time, for example by measuring and averaging the AHR over an extended duration or by measuring it during a calibration session while the person is in a known state of mind. Similarly, the two ends of each axis may be calibrated during training and calibration sessions, for example: vigorous physical exercises vs. meditation rest or sleep.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method for automatically identifying emotional states of a person in real-time based on physiological responses as taught by Derchak, using a finger sensor to monitor physiological parameters of a patient as taught by Jain, and incorporate a system and method for monitoring one or more physiological parameters of a user as taught by Orbach, with the motivation of monitoring the physiological parameters of the user (Orbach Abstract).
As per Claim 15, Derchak/ Jain/ Orbach/ Heneghan teach the system method of claim 1, Derchak further teaches wherein the one or more processors are further configured to: identify one or more historical restorative moments associated with the user based at least in part on baseline physiological data acquired from the user via the wearable ring device; and determine a plurality of weights associated with a plurality of time intervals based at least in part on the one or more historical restorative moments, the plurality of weights associated with a relative probability that the plurality of time intervals include restorative moments, wherein the restorative moment is identified based at least in part on the plurality of weights. ([Col. 2, Lines 55-63] the method for automatically identifying an emotional state of a person in real-time includes exposing a person to a stimulus, measuring the person's physiological responses to the stimulus, classifying the measured responses into one or more classes by means of a classifier trained to recognize patterns of physiological responses occurring in a plurality of emotional states, and identifying a likely emotional state in dependence on the classes into which the measured responses have been classified. [Col. 3, Lines 1-21] the classifier training further includes measuring physiological responses one or more emotional states, determining feature vectors that characterize the emotional states, and grouping the determined feature vectors into groups that characterize the emotional states, the grouping including use of a learning algorithm. The method can also include scaling the determined feature vectors, or applying dimension-reduction analysis to the determined feature vectors, or both scaling and applying dimension-reduction analysis to the determined feature vectors. Preferably, the learning algorithm includes a kernel machine algorithm. The classifier training may also include extracting the determined feature vectors from the measured physiological responses, the determined feature vectors being those determined during classifier training, and applying the trained classifier to the extracted feature vectors. Preferably, scaling the extracted feature vectors, or applying dimension-reduction analysis to the extracted feature vectors, or both scaling and applying dimension-reduction analysis to the extracted feature vectors is also performed.)
As per Claim 16, Derchak/ Jain/ Orbach/ Heneghan teach the system method of claim 15, Derchak further teaches wherein the one or more processors are further configured to: input the plurality of weights into a machine learning classifier; and train the machine learning classifier to identify restorative moments associated with the user based at least in part on inputting the plurality of weights, wherein the restorative moment is identified via the machine learning classifier based at least in part on training the machine learning classifier using the plurality of weights.([Col. 3, Lines 1-21] the classifier training further includes measuring physiological responses one or more emotional states, determining feature vectors that characterize the emotional states, and grouping the determined feature vectors into groups that characterize the emotional states, the grouping including use of a learning algorithm. The method can also include scaling the determined feature vectors, or applying dimension-reduction analysis to the determined feature vectors, or both scaling and applying dimension-reduction analysis to the determined feature vectors. Preferably, the learning algorithm includes a kernel machine algorithm. The classifier training may also include extracting the determined feature vectors from the measured physiological responses, the determined feature vectors being those determined during classifier training, and applying the trained classifier to the extracted feature vectors. Preferably, scaling the extracted feature vectors, or applying dimension-reduction analysis to the extracted feature vectors, or both scaling and applying dimension-reduction analysis to the extracted feature vectors is also performed.)
As per Claim 17, Derchak teaches a method for automatically detecting restorative moments, comprising:
inputting the physiological data into the machine learning classifier; ([Col. 2, Lines 55-61] identifying an emotional state of a person in real-time includes exposing a person to a stimulus, measuring the person's physiological responses to the stimulus, classifying the measured responses into one or more classes by means of a classifier trained to recognize patterns of physiological responses occurring in a plurality of emotional states.)
identifying, using the one or more processors, that the heart rate data is less than or equal to a heart rate threshold for at least a portion of a time interval, wherein the heart rate threshold is based at least in part on a relative position of the time interval relative to a circadian rhythm associated with the user; ([Col. 1 Lines 62-67 and Col. 2 Line 1] measuring the person's physiological response to the stimulus, and comparing the measured physiological response (i.e. physiological data) to a known physiological response patterns associated with various emotional states. The pattern of deviations of the measured physiological response from a baseline physiological response is determinative of an emotional state. [Col. 7 Lines 22-23] the person's physiological responses are monitored in real-time. [Col. 2 Lines 14-21 and Lines 31-38] the measured physiological responses also include a cardiac response, which includes a heart rate response. The emotional state of the person is subsequently identified as likely to be the selected emotional state when the measured physiological responses significantly similar to the baseline physiological responses. More preferably, two sets of responses are significantly similar when the likelihood that the similarity is due to chance is less than a selected threshold. [Col. 10 Lines 22-35] After comparing the measured physiological response to the known baseline physiological response, a deviation between the two data sets can be determined, as shown in step 240. The known baseline physiological responses are preferably correlated with particular emotional states. In the case where a neutral stimulus is used to obtain a known baseline physiological response, particular emotional states are preferably correlated with changes in the physiological responses from the baseline. Based on whether the determined deviation falls within an acceptable error or deviation range (i.e. heart rate data is within a temperature range of a baseline heart rate), as shown in step 250, the method identifies that the person is experiencing the particular emotion state (i.e. calmness). [Col. 2, Line 43] a processor. Examiner interprets the deviation range to be indicative of heart rate threshold.)
identifying, using the one or more processors, that the temperature data is within a temperature range of a baseline temperature associated with the user for at least the portion of the time interval, wherein the baseline temperature is based at least in part on the relative position of the time interval relative to the circadian rhythm associated with the user; ([Col. 1 Lines 62-67 and Col. 2 Line 1] measuring the person's physiological response to the stimulus, and comparing the measured physiological response (i.e. physiological data) to a known physiological response patterns associated with various emotional states. The pattern of deviations of the measured physiological response from a baseline physiological response is determinative of an emotional state. [Col. 7 Lines 22-23] the person's physiological responses are monitored in real-time. [Col. 2 Lines 14-16 and Lines 31-38] The measured physiological responses also include a temperature response. The emotional state of the person is subsequently identified as likely to be the selected emotional state when the measured physiological responses significantly similar to the baseline physiological responses. More preferably, two sets of responses are significantly similar when the likelihood that the similarity is due to chance is less than a selected threshold. [Col. 10 Lines 22-35] After comparing the measured physiological response to the known baseline physiological response, a deviation between the two data sets can be determined, as shown in step 240. The known baseline physiological responses are preferably correlated with particular emotional states. In the case where a neutral stimulus is used to obtain a known baseline physiological response, particular emotional states are preferably correlated with changes in the physiological responses from the baseline. Based on whether the determined deviation falls within an acceptable error or deviation range (i.e. temperature data is within a temperature range of a baseline temperature), as shown in step 250, the method identifies that the person is experiencing the particular emotion state (i.e. calmness). Examiner interprets the deviation range to be indicative of temperature range of baseline temperature. [Col. 2, Line 43] a processor)
identifying, using the one or more processors, a restorative moment for the time interval that the user is in a relaxed state based at least in part on a relationship between the heart rate data and the skin temperature data measured via the blood flow at the one or more periphery parts of the body of the user, wherein the relationship is based at least in part on the heart rate data being less than or equal to the heart rate threshold and the skin temperature data being within the temperature range of the baseline temperature; ([Col. 2, Lines 55-67] automatically identifying an emotional state of a person in real-time includes exposing a person to a stimulus, measuring the person's physiological responses to the stimulus, classifying the measured responses into one or more classes by means of a classifier trained to recognize patterns of physiological responses occurring in a plurality of emotional states, and identifying a likely emotional state in dependence on the classes into which the measured responses have been classified. Preferably, the pattern of measured physiological responses includes deviations of the measured responses from baseline physiological responses, the baseline physiological responses being characteristic of an emotional state. [Col. 2, Lines 14-20] The measured physiological responses also include a cardiac response, which includes heart rate response and temperature response. [Col. 2 Lines 23-26] The identified emotional states include calmness (i.e. restorative moment).) [Col. 6 Line 67 and Col. 7 Lines 1-6] A deviation between the measured physiological response and the baseline physiological response can be determined, and if the deviation is within acceptable ranges of deviation (heart rate threshold and temperature data threshold) from the baseline physiological response, the person is identified as experiencing a particular emotional state that is associated therewith. [Col. 10 Lines 32-35] Based on whether the determined deviation falls within an acceptable error or deviation range (i.e. physiological data being less than or equal to the physiological data thresholds), as shown in step 250, the method identifies that the person is experiencing the particular emotion state. [Col. 2, Line 43] a processor)
Derchak does not explicitly teach, however Jain teaches
measuring, using a wearable ring device configured to be worn on a finger of a user, ([Para. 0135] the sensing device 104a is a finger sensor and collects data. Examiner interprets finger sensor to be indicative of a wearable ring device.)
receive physiological data associated with the user from a wearable device via blood flow at one or more periphery parts of a body of the user, the one or more periphery parts comprising at least the finger of the user, ([Para. 0131] The sensing devices 104a-104c can include can include computing and/or wearable devices having one or more sensors. The sensing devices 104a-104c can include equipment having one or more sensors, such as workout equipment. The sensing devices 104a-104c may include, for example, a finger sensor (e.g., having one or more of an oxygen sensor, a heart rate sensor, etc.).)
the physiological data comprising at least heart rate data acquired through an inner curved surface of the wearable ring device via one or more light-emitting components and one or more light-receiving components, ([Para. 0131] a finger sensor (e.g., having one or more of an oxygen sensor, a heart rate sensor, etc. [Para. 0328] the sensor data can indicate measurements or detection of attributes and activities of the subject, and the feature scores can be the measured or detected values or other values derived from them (e.g., sensor measurements that have been normalized, quantized, rounded, combined, or otherwise adjusted, or classifications based on the measurements). Examples of sensors that can provide data include accelerometers, proximity sensors, temperature sensors.)
and skin temperature data acquired through the inner curved surface of the wearable ring device via one or more temperature sensors;([Para. 0131] a finger sensor (e.g., having one or more of an oxygen sensor, a heart rate sensor, etc.)
transmitting, using a wireless communications module of the wearable ring device, the physiological data to a user device, a server, or both, comprising one or more processors; ([Para. 0130] One or more of the sensing devices 104a-104c may communicate with the computer system 110 through an intermediary device, e.g., a smart phone, a laptop computer, a desktop computer, or the like.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method for automatically identifying emotional states of a person in real-time based on physiological responses as taught by Derchak, and incorporate using a finger sensor to monitor physiological parameters of a patient as taught by Jain, with the motivation of evaluate and recommend actions for enhancing the readiness of a subject (Jain Para. 0006).
Derchak/ Jain do not explicitly teach, however Orbach teaches
and transmitting cause a signal configured to cause a graphical user interface of user device to display an indication of the restorative moment. ([Para. 0037-0038] The mobile monitor may be selected, for example, from the group comprising: a cellular phone. Examiner interprets the mobile monitor to be indicative of a GUI. [Para. 0270] FIGS. 7a to 7d may be used by a user to assess his physiological state [Para. 0271] The display screens may be flexibly designed to fit the size and type of display of the mobile monitor. Different combinations of signals and parameters may be displayed in various ways such as graphs, colors, pie charts, numerical values, bars, clock-like indicators, alert signals, alphanumerical messages, etc. Static or moving animations may also be displayed according to the interpretation of the physiological data. For example, a happy "smiley face" may be displayed when the state of the user is relaxed (i.e. restorative moment).[Abstract] Signals indicative of values of the one or more physiological parameters are wirelessly transmitted to a mobile monitor.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method for automatically identifying emotional states of a person in real-time based on physiological responses as taught by Derchak, using a finger sensor to monitor physiological parameters of a patient as taught by Jain, and incorporate a system and method for monitoring one or more physiological parameters of a user as taught by Orbach, with the motivation of monitoring the physiological parameters of the user (Orbach Abstract).
Derchak/ Jain/ Orbach do not explicitly teach, however Heneghan teaches
determining, using the one or more processors, a plurality of weights associated with a plurality of time intervals in accordance with historical restorative moment data associated with the user and physiological data from one or more second users; ([Para. 0018] Receiving motion data obtained from a set of one or more motion sensors in the wearable electronic device over the first time window, the motion data including at least one of a quantification of movement experienced by the wearable electronic device or data derived therefrom; and labeling a time period associated with the first time window with an indicator indicating a first sleep stage selected from a plurality of sleep stages using the motion data and the cardiopulmonary pulse-related data. [Para. 0120] The classifier may be trained using the training dataset, e.g., the features extracted from the data collected in association with known classifications. As part of the classifier training, each feature involved in the training may become associated with a weighting factor or other quantifier of the feature's importance in the classification determination. In block 106, the weighting factors (or other quantifier of importance) for each feature in the global set of features may be obtained, and the features may then be ranked by their weighting factors (or other quantifiers of importance) in block 108. )
inputting the plurality of weights into a machine learning classifier; ([Para. 0012] the instructions that cause the classifier to label the time period may include instructions that, when executed, cause the set of one or more processors to execute a classifier such as, for example, a nearest neighbor classifier, a random forest classifier, or a linear discriminant classifier. [Para. 0013] The classifier may be trained using movement features and pulse data features extracted from benchmark motion data and benchmark cardiopulmonary pulse-related data collected for a population of sleep study subjects. [Para. 0120] As part of the classifier training, each feature involved in the training may become associated with a weighting factor or other quantifier of the feature's importance in the classification determination. In block 106, the weighting factors (or other quantifier of importance) for each feature in the global set of features may be obtained, and the features may then be ranked by their weighting factors (or other quantifiers of importance) in block 108. [Para. 0154] Classifiers may be trained, as discussed earlier, using features extracted from motion and pulse-related data collected during sleep sessions that are conducted under the oversight of professional or trained sleep scorers using specialized equipment and in-depth analysis of each time interval being evaluated—such data may be referred to herein as “benchmark” motion data and/or pulse-related data. Thus, for example, the features extracted from motion and pulse-related data collected during time periods or time windows that have been assigned, by a sleep scorer, a sleep stage (i.e. restorative moment) of “deep sleep” may be used to train the classifier to assign a “deep sleep” classification to time windows or time periods having features that match the criteria of the classifier developed during such training.)
training the machine learning classifier to identify restorative moments associated with the user based at least in part on inputting the plurality of weights; ([Para. 0012] the instructions that cause the classifier to label the time period may include instructions that, when executed, cause the set of one or more processors to execute a classifier such as, for example, a nearest neighbor classifier, a random forest classifier, or a linear discriminant classifier. [Para. 0013] The classifier may be trained using movement features and pulse data features extracted from benchmark motion data and benchmark cardiopulmonary pulse-related data collected for a population of sleep study subjects. [Para. 0120] As part of the classifier training, each feature involved in the training may become associated with a weighting factor or other quantifier of the feature's importance in the classification determination. In block 106, the weighting factors (or other quantifier of importance) for each feature in the global set of features may be obtained, and the features may then be ranked by their weighting factors (or other quantifiers of importance) in block 108. [Para. 0154] Classifiers may be trained, as discussed earlier, using features extracted from motion and pulse-related data collected during sleep sessions that are conducted under the oversight of professional or trained sleep scorers using specialized equipment and in-depth analysis of each time interval being evaluated—such data may be referred to herein as “benchmark” motion data and/or pulse-related data. Thus, for example, the features extracted from motion and pulse-related data collected during time periods or time windows that have been assigned, by a sleep scorer, a sleep stage (i.e. restorative moment) of “deep sleep” may be used to train the classifier to assign a “deep sleep” classification to time windows or time periods having features that match the criteria of the classifier developed during such training.)
wherein the restorative moment is identified via the machine learning classifier based at least in part on training the machine learning classifier using the plurality of weights ([Para. 0029] generate a first graphical user interface component that indicates a relative percentile breakdown of total time spent in each of the sleep stages for the first sleep session. [Para. 0085] Classify each time period may include instructions that, when executed, cause the one or more processors to transmit the one or more movement features and the one or more pulse data features to a server system that executes a classifier that generates the sleep stage classification (i.e. restorative moments) for each time period and provides that sleep stage classification to the one or more processors. [Para. 0012] the instructions that cause the classifier to label the time period may include instructions that, when executed, cause the set of one or more processors to execute a classifier such as, for example, a nearest neighbor classifier, a random forest classifier, or a linear discriminant classifier. [Para. 0013] The classifier may be trained using movement features and pulse data features extracted from benchmark motion data and benchmark cardiopulmonary pulse-related data collected for a population of sleep study subjects. [Para. 0120] As part of the classifier training, each feature involved in the training may become associated with a weighting factor or other quantifier of the feature's importance in the classification determination. In block 106, the weighting factors (or other quantifier of importance) for each feature in the global set of features may be obtained, and the features may then be ranked by their weighting factors (or other quantifiers of importance) in block 108. [Para. 0154] classifiers may be trained, as discussed earlier, using features extracted from motion and pulse-related data collected during sleep sessions that are conducted under the oversight of professional or trained sleep scorers using specialized equipment and in-depth analysis of each time interval being evaluated—such data may be referred to herein as “benchmark” motion data and/or pulse-related data. Thus, for example, the features extracted from motion and pulse-related data collected during time periods or time windows that have been assigned, by a sleep scorer, a sleep stage (i.e. restorative moment) of “deep sleep” may be used to train the classifier to assign a “deep sleep” classification to time windows or time periods having features that match the criteria of the classifier developed during such training.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method for automatically identifying emotional states of a person in real-time based on physiological responses as taught by Derchak, using a finger sensor to monitor physiological parameters of a patient as taught by Jain, a system and method for monitoring one or more physiological parameters of a user as taught by Orbach, and incorporate estimating sleep state based on sensor data as taught by Heneghan, with the motivation of providing classifier to label a time period associated with the first time window with an identifier indicating a first sleep stage selected from a plurality of sleep stages (Heneghan Para. 0005).
As per Claim 19, Derchak/ Jain/ Orbach/ Heneghan teach the method of claim 17, Derchak further teaches wherein the physiological data further comprises movement data, and the instructions are further executable by the processor to cause the apparatus to the method further comprising: identifying that the movement data is within a range of a baseline movement associated with the user for at least the portion of the time interval, wherein identifying the restorative moment for the time interval is based at least in part on the movement data being within the range of the baseline movement. ([Col. 1 Lines 62-67 and Col. 2 Line 1] measuring the person's physiological response to the stimulus, and comparing the measured physiological response (i.e. physiological data) to a known physiological response patterns associated with various emotional states. The pattern of deviations (i.e. range of baseline) of the measured physiological response from a baseline physiological response is determinative of an emotional state. [Col. 7 Lines 22-23] the person's physiological responses are monitored in real-time. [Col. 2 Lines 14-16] the measured physiological responses also include a posture/activity response (i.e. movement data). [Col. 10 Lines 22-35] After comparing the measured physiological response to the known baseline physiological response, a deviation between the two data sets can be determined, as shown in step 240. The known baseline physiological responses are preferably correlated with particular emotional states. In the case where a neutral stimulus is used to obtain a known baseline physiological response, particular emotional states are preferably correlated with changes in the physiological responses from the baseline. Based on whether the determined deviation falls within an acceptable error or deviation range, as shown in step 250, the method identifies that the person is experiencing the particular emotion state (i.e. calmness). [Col. 2 Lines 23-26] The identified emotional states include calmness (i.e. restorative moment). [Col. 2, Line 43] a processor)
As per Claim 20, Derchak/ Jain/ Orbach/ Heneghan teach the method of claim 17, Derchak further teaches wherein the physiological data further comprises heart rate variability data, and the instructions are further executable by the processor to cause the apparatus to the method further comprising: identifying that the heart rate variability data is within a range of a baseline heart rate variability associated with the user for at least the portion of the time interval, wherein identifying the restorative moment for the time interval is based at least in part on the heart rate variability data being within the range of the baseline heart rate variability. ([Col. 1 Lines 62-67 and Col. 2 Line 1] measuring the person's physiological response to the stimulus, and comparing the measured physiological response (i.e. physiological data) to a known physiological response patterns associated with various emotional states. The pattern of deviations (i.e. range of baseline) of the measured physiological response from a baseline physiological response is determinative of an emotional state. [Col. 7 Lines 22-23] the person's physiological responses are monitored in real-time. [Col. 2 Lines 14-22] the measured physiological responses also include a cardiac response. The cardiac responses include a heart rate variability response. [Col. 10 Lines 22-35] After comparing the measured physiological response to the known baseline physiological response, a deviation between the two data sets can be determined, as shown in step 240. The known baseline physiological responses are preferably correlated with particular emotional states. In the case where a neutral stimulus is used to obtain a known baseline physiological response, particular emotional states are preferably correlated with changes in the physiological responses from the baseline. Based on whether the determined deviation falls within an acceptable error or deviation range, as shown in step 250, the method identifies that the person is experiencing the particular emotion state (i.e. calmness). [Col. 1 Lines 52-56] Specific emotional states include, for example, neutrality or indifference, fear, anxiety, excitement, happiness-laughter, sadness-crying, anticipation, boredom, anger-aggression, pleasure, love, tenderness, or calmness (i.e. restorative moment).)
Claim(s) 2, 7-8, 14, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Derchak (US 9833184 B2) in view of Jain (US 20220188671 A1) in view of Orbach (US 20080214903 A1) in view of Heneghan (US 20220265208 A1) in view of Pho (US 20210401378 A1).
As per Claim 2, Derchak/ Jain/ Orbach/ Heneghan teach the system method of claim 1,however Pho teaches further comprising wherein the one or more processors are further configured to: identify the baseline temperature associated with the user based at least in part on receiving the physiological data, wherein the baseline temperature is based at least in part on comprises a nighttime temperature baseline. ([Para. 0026] Determine physiological parameter values for a user over a first-time interval (e.g., reference window) while the user is in a healthy state to determine moving baseline parameters for the user (e.g., baseline temperature data). [Para. 0027] Determine personalized or group-derived “baseline” physiological parameter values (e.g., in a reference window) for the user in the healthy state. [Para. 0078] the processing module 230-a may sample the user's temperature continuously throughout the day and night. [Para. 0100] The physiological measurements may be taken continuously throughout the day and/or night. For example, the ring 104 may be configured to acquire physiological data (e.g., determine temperature readings) continuously in accordance to one or more measurement periodicities throughout the entirety of each day/sleep day. Examiner interprets that the night time temperature baseline would be included in the overall temperature baselines since the temperature data is taken for continuously.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method for automatically identifying emotional states of a person in real-time based on physiological responses as taught by Derchak, using a finger sensor to monitor physiological parameters of a patient as taught by Jain, a system and method for monitoring one or more physiological parameters of a user as taught by Orbach, estimating sleep state based on sensor data as taught by Heneghan, and incorporate methods, systems, and devices for illness detection as taught by Pho, with the motivation of collect physiological data from users to provide more insight regarding their physical health (Pho Para. 0002).
As per Claim 7, Derchak/ Jain/ Orbach/ Heneghan teach the system of claim 1, however Pho teaches further comprising wherein the one or more processors are further configured to: update a readiness score associated with the user based at least in part on identifying the restorative moment. ([Para. 0100] The physiological measurements may be taken continuously throughout the day and/or night. [Para. 101] the physiological measurements may be taken during 104 portions of the day and/or portions of the night. In some implementations, the physiological measurements may be taken in response to determining that the user is in a specific state, such as an active state, resting state (i.e. restorative moment), and/or a sleeping state. For example, the ring 104 can make physiological measurements in a resting/sleep state in order to acquire cleaner physiological signals. [Para. 0108] a user's overall readiness score may be calculated based on a set of contributors, including: sleep, sleep balance, heart rate, HRV balance, recovery index, temperature, activity, activity balance.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method for automatically identifying emotional states of a person in real-time based on physiological responses as taught by Derchak, using a finger sensor to monitor physiological parameters of a patient as taught by Jain, a system and method for monitoring one or more physiological parameters of a user as taught by Orbach, estimating sleep state based on sensor data as taught by Heneghan, and incorporate methods, systems, and devices for illness detection as taught by Pho, with the motivation of collect physiological data from users to provide more insight regarding their physical health (Pho Para. 0002).
As per Claim 8, Derchak/ Jain/ Orbach. Heneghan teach the system of claim 1, however Pho teaches wherein the restorative moment is identified based at least in part on an increase in the skin temperature data and a decrease in the heart rate data.
([Para. 0100] The physiological measurements may be taken continuously throughout the day and/or night. [Para. 0104] collect data from a user via the ring 104, and generate one or more scores (e.g., sleep score, readiness score) for the user based on the collected data. [Para. 0108] a user's overall readiness score may be calculated based on a set of contributors, including: sleep, sleep balance, heart rate, HRV balance, recovery index, temperature, activity, activity balance.)\
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method for automatically identifying emotional states of a person in real-time based on physiological responses as taught by Derchak, using a finger sensor to monitor physiological parameters of a patient as taught by Jain, a system and method for monitoring one or more physiological parameters of a user as taught by Orbach, estimating sleep state based on sensor data as taught by Heneghan, and incorporate methods, systems, and devices for illness detection as taught by Pho, with the motivation of collect physiological data from users to provide more insight regarding their physical health (Pho Para. 0002).
As per Claim 14, Derchak/ Jain/ Orbach/ Heneghan teach the system of claim 1, however Pho teaches further comprising wherein the one or more processors are further configured to: identify a nighttime temperature baseline for a plurality of users; and identify the baseline temperature associated with the user based at least in part on identifying the nighttime temperature baseline. ([Para. 0026] Determine physiological parameter values for a user over a first-time interval (e.g., reference window) while the user is in a healthy state to determine moving baseline parameters for the user (e.g., baseline temperature data). [Para. 0027] Determine personalized or group-derived “baseline” physiological parameter values (e.g., in a reference window) for the user in the healthy state. [Para. 0078] the processing module 230-a may sample the user's temperature continuously throughout the day and night. [Para. 0100] The physiological measurements may be taken continuously throughout the day and/or night. For example, the ring 104 may be configured to acquire physiological data (e.g., determine temperature readings) continuously in accordance to one or more measurement periodicities throughout the entirety of each day/sleep day. Examiner interprets that the night time temperature baseline would be included in the overall temperature baselines since the temperature data is taken for continuously.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method for automatically identifying emotional states of a person in real-time based on physiological responses as taught by Derchak, using a finger sensor to monitor physiological parameters of a patient as taught by Jain, a system and method for monitoring one or more physiological parameters of a user as taught by Orbach, estimating sleep state based on sensor data as taught by Heneghan, and incorporate methods, systems, and devices for illness detection as taught by Pho, with the motivation of collect physiological data from users to provide more insight regarding their physical health (Pho Para. 0002).
As per Claim 18, Derchak/ Jain/ Orbach/ Heneghan teach the method of claim 17, however Pho teaches wherein the instructions are further executable by the processor to cause the apparatus to further comprising: identifying the baseline temperature associated with the user based at least in part on receiving the physiological data, wherein the baseline temperature is based at least in part on a nighttime temperature baseline. ([Para. 0026] Determine physiological parameter values for a user over a first-time interval (e.g., reference window) while the user is in a healthy state to determine moving baseline parameters for the user (e.g., baseline temperature data). [Para. 0027] Determine personalized or group-derived “baseline” physiological parameter values (e.g., in a reference window) for the user in the healthy state. [Para. 0078] the processing module 230-a may sample the user's temperature continuously throughout the day and night. [Para. 0100] The physiological measurements may be taken continuously throughout the day and/or night. For example, the ring 104 may be configured to acquire physiological data (e.g., determine temperature readings) continuously in accordance to one or more measurement periodicities throughout the entirety of each day/sleep day. Examiner interprets that the night time temperature baseline would be included in the overall temperature baselines since the temperature data is taken for continuously.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method for automatically identifying emotional states of a person in real-time based on physiological responses as taught by Derchak, using a finger sensor to monitor physiological parameters of a patient as taught by Jain, a system and method for monitoring one or more physiological parameters of a user as taught by Orbach, estimating sleep state based on sensor data as taught by Heneghan, and incorporate methods, systems, and devices for illness detection as taught by Pho, with the motivation of collect physiological data from users to provide more insight regarding their physical health (Pho Para. 0002).
Claim(s) 4 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Derchak (US 9833184 B2) in view of Jain (US 20220188671 A1) in view of Orbach (US 20080214903 A1) in view of Heneghan (US 20220265208 A1) in view of Jain (US 20170071523 A1) [hereinafter referred to as Jain 2017].
As per Claim 4, Derchak/ Jain/ Orbach/ Heneghan teach the system method of claim 1, however Jain 2017 teaches further comprising wherein the one or more processors are further configured to: cause the graphical user interface of the user device to display an indication of the heart rate data. ([Para. 0058] Figure 4A illustrates a user interface 400 shows information on current user measurements (e.g., a heart rate).)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method for automatically identifying emotional states of a person in real-time based on physiological responses as taught by Derchak, using a finger sensor to monitor physiological parameters of a patient as taught by Jain, a system and method for monitoring one or more physiological parameters of a user as taught by Orbach, estimating sleep state based on sensor data as taught by Heneghan, and incorporate displaying patient data as taught by Jain 2017, with the motivation of monitoring the stress levels of the user (Jain 2017 Abstract).
As per Claim 12, Derchak/ Jain/ Orbach/ Heneghan teach the system method of claim 1, Orbach teaches further comprising wherein the one or more processors are further configured to: cause the graphical user interface of the user device to display a message associated with the identified restorative moment, ([Para. 0271] The display screens may be flexibly designed to fit the size and type of display of the mobile monitor. Different combinations of signals and parameters may be displayed in various ways such as graphs, colors, pie charts, numerical values, bars, clock-like indicators, alert signals, alphanumerical messages, etc. Static or moving animations may also be displayed according to the interpretation of the physiological data. For example, a happy "smiley face" may be displayed when the state of the user is relaxed).
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of detecting restorative moments as taught by Derchak and incorporate causing the graphical user interface of the user device to display a message associated with the identified restorative moment as taught by Orbach, with the motivation of monitoring the physiological parameters of the user (Orbach Abstract).
Orbach does not explicitly teach, however Jain 2017 teaches
wherein the message comprises a time of day that the restorative moment was identified, a duration of the restorative moment, a success metric associated with the restorative moment, a recommended duration for future restorative moments, a quantity of restorative moments identified, or a combination thereof. ([Para. 0185] the notification to the user may comprise one or more of user-interface notifications (e.g., message notifications, graphical notifications, etc. [Para. 0226] health monitoring system 200 may compute the time a user spends in a given HRV zone (e.g., showing high stress or high relaxation). Examiner interprets this to be indicative of duration of the restorative moment.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method for automatically identifying emotional states of a person in real-time based on physiological responses as taught by Derchak, using a finger sensor to monitor physiological parameters of a patient as taught by Jain, a system and method for monitoring one or more physiological parameters of a user as taught by Orbach, estimating sleep state based on sensor data as taught by Heneghan, and incorporate displaying message content as taught by Jain 2017, with the motivation of monitoring the stress levels of the user (Jain 2017 Abstract).
Response to Arguments
Applicant's arguments, pgs. 10- 13 “35 USC 101” filed 09/17/2025 have been fully considered but they are not persuasive.
Applicant argues that Independent claims 1 and 17 do not recite a judicial exception and the features of the independent claims do not recite a mental process. Examiner respectfully disagrees that the independent claims do not recite a judicial exception. The claims recite (Claim 1 being representative) process the physiological data associated with the user, the physiological data comprising the skin temperature data and heart rate data; receive historical restorative moment data associated with the user and physiological data from one or more second users that share one or more physiological characteristics with the user; determine a plurality of weights associated with a plurality of time intervals in accordance with the historical restorative moment data associated with the user and the physiological data from the one or more second users; receive the physiological data, the physiological data comprising at least the heart rate data and the skin temperature data; identify that the heart rate data is less than or equal to a heart rate threshold for at least a portion of a time interval, wherein the heart rate threshold is based at least in part on a relative position of the time interval relative to a circadian rhythm associated with the user; identify that the skin temperature data is within a temperature range of a baseline temperature associated with the user for at least the portion of the time interval, wherein the baseline temperature is based at least in part on the relative position of the time interval relative to the circadian rhythm associated with the user; identify a restorative moment for the time interval that the user is in a relaxed state based at least in part on a relationship between the heart rate data and the skin temperature data measured via blood flow at one or more periphery parts of a body of the user, the one or more periphery parts comprising at least the finger of the user, wherein the relationship is based at least in part on the heart rate data being less than or equal to the heart rate threshold and the skin temperature data being within the temperature range of the baseline temperature, wherein the restorative moment is identified, in which these behaviors are best categorized as human task. Because the claim elements fall under a series of rules or instructions that a person or persons would follow, the claimed invention is directed to an abstract idea.
The one or more processors, wireless communications module, user device, health-related application, one or more additional processors, one or more servers, and a graphical user interface of a user device are an additional element, which is mere instructions to apply the exception and does not provide a practical application or significantly more for the same reasons.
Claim 1 recites a wearable ring device configured to acquire physiological data from a finger of a user; one or more temperature sensors disposed at least partially within or beneath an inner curved surface of the wearable ring device and configured to acquire skin temperature data from the user through the inner curved surface; one or more light-emitting components configured to emit light into a tissue of the user through the inner curved surface of the wearable ring device; one or more light-receiving components configured to receive, through the inner curved surface, the light emitted by the one or more light-emitting components through the tissue of the user; the physiological data comprising the skin temperature data and heart rate data that is based at least in part on the light received by the one or more light- receiving components. Claim 17 recites measuring, using a wearable ring device configured to be worn on a finger of a user, physiological data associated with the user via blood flow at one or more periphery parts of a body of the user, the one or more periphery parts comprising at least the finger of the user, the physiological data comprising at least heart rate data acquired through an inner curved surface of the wearable ring device via one or more light-emitting components and one or more light- receiving components, and skin temperature data acquired through the inner curved surface of the wearable ring device via one or more temperature sensors. The limitation recite additional elements that are being used in its ordinary capacity which amounts to merely being a tool to execute the abstract idea, and thus does not integrate a judicial exception into a practical application or provide significantly more (MPEP § 2106.05(f)(2) see TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)).
The machine learning classifier; inputting the plurality of weights into a machine learning classifier; inputting the physiological data into the machine learning classifier; train the machine learning classifier to identify restorative moments associated with the user based at least in part on inputting the plurality of weights; and the machine learning classifier based at least in part on training the machine learning classifier using the plurality of weights are recited as tools 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).
The recitation of transmit the physiological data and transmit a signal configured to cause a graphical user interface (GUI) of the user device to display an indication of the restorative moment is recited as a tool which only serves as extra solution activities incidental to the primary process that is merely a nominal or tangential addition to the claim (MPEP § 2106.05(g) - insignificant pre/post-solution activity) and is therefore not a practical application of the recited judicial exception.
Applicant argues that the newly added limitations to the independent claim help to improve the functioning of a system that includes the wearable ring device by better-identifying periods of restfulness for a user, resulting in device and systems able to provide improved insights and guidance to a user to better-correlate their overall recovery with how they are feeling. Examiner respectfully disagrees. As noted above, the claim limitation of receive historical restorative moment data associated with the user and physiological data from one or more second users that share one or more physiological characteristics with the user and determine a plurality of weights associated with a plurality of time intervals in accordance with the historical restorative moment data associated with the user and the physiological data from the one or more second users are part of the abstract idea. The limitations of input the plurality of weights into a machine learning classifier and train the machine learning classifier to identify restorative moments associated with the user based at least in part on inputting the plurality of weights are recited as tools to apply data to an algorithm and report the results. An improvement to the abstract idea of providing improved insights and guidance to a user to better-correlate their overall recovery with how they are feeling does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “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. For example, in Trading Technologies Int’l v. IBG,921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”). There is no indication in the instant disclosure that the involvement of a computer assists in improving the technology for the outlined problem statement. Here, the improvement is to medical response efficiency. The instant application and claim language fail to detail how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Examiner notes that the use of wearable ring device is being used in its ordinary capacity which amounts to merely being a tool to execute the abstract idea, and thus does not integrate a judicial exception into a practical application or provide significantly more (MPEP § 2106.05(f)(2) see TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)).
Applicant's arguments, see pgs. 13-15 “35 USC 103 and Dependent Claims 2-16 and 18-20” filed 09/17/ 2025 have been fully considered but they are not persuasive.
Applicant argues that Derchak does not teach fail to teach or suggest at least the features of one or more processors configured to receive historical restorative moment data associated with the user and physiological data from one or more second users that share one or more physiological characteristics with the user. Examiner respectfully disagrees.
Derchak teaches at Col. 9, Lines 11-12 and 13-16 the known baseline physiological responses observed when the person was in selected emotional states (i.e. historical restorative moment data).The known baseline physiological responses and their variances are previously recorded, and stored in an electronic database in association with the particular emotion states. [Col. 10, Lines 12-21] The known baseline physiological response can be based on a distribution of physiological responses (i.e. physiological data) previously measured from a group of a population of similar people (i.e. one or more second users). Preferably, it is selected from empirical data based on histograms or other distributions of known physiological responses that were collected during exposure to a stimulus or a series of stimuli. Such data is preferably representative of a large population of persons and can be stratified by different individual characteristics, such as age, height, weight, and gender.). This is indicative of one or more processors configured to receive historical restorative moment data associated with the user and physiological data from one or more second users that share one or more physiological characteristics with the user.
However, Applicant’s argument that Derchak does not teach or suggest determine a plurality of weights associated with a plurality of time intervals in accordance with the historical restorative moment data associated with the user and the physiological data from the one or more second users, input the plurality of weights into a machine learning classifier, and train the machine learning classifier to identify restorative moments associated with the user based at least in part on inputting the plurality of weights is persuasive. However, upon further consideration, a new grounds of rejection is made in view of Heneghan, as per the rejection above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Patricia K Edouard whose telephone number is (571)272-6084. The examiner can normally be reached Monday - Friday 7:30 AM - 5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya M Long can be reached at 571-270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/P.K.E./Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682
Prosecution Insights