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 .
Claim Objections
Claim 6 objected to because of the following informalities: Claim 6 recites “distal skin temperature data acquired via the finger” which should be amended to “distal skin temperature data acquired from the finger”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 6 and 15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. Claim 6 recites “vasoconstriction at one or more periphery parts of a body of the user that is identified based at least in part on the distal skin temperature data, the one or more periphery parts comprising at least the finger of the user”; it is unclear how other vasoconstriction at other periphery parts of a body of the user are identified based on the distal skin temperature data captured from the finger. In other words, “one or more periphery parts of a body of the user” includes any periphery part, not just the finger/hand of the user. Neither the claim nor the specification disclose, teach or provide any working examples how the temperature acquired from the finger could be used to determine vasoconstriction at other parts of a body, i.e., feet.
Claim 15 recites similar limitations and is being rejected for the same reasons cited above.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5, 7-12, 16-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 5 recites “wherein the satisfaction of one or more criteria is based at least in part on a relative timing between the increase in the duration of the REM sleep stages and the decrease in the RMSSD data”; it is unclear whether this limitation is intended to be in furtherance, additional, or in lieu of, replacing the limitation “satisfaction of one or more criteria for identifying illness associated with the user based at least in part on a relative timing between a decrease in the RMSSD data and a decrease in the resting heart rate data” as required by claim 2.
Claim 7 recites “identifying the satisfaction of the one or more criteria is based at least in part on the respiratory rate data”; it is unclear whether this limitation is intended to be in furtherance, additional, or in lieu of, replacing the limitation “satisfaction of one or more criteria for identifying illness associated with the user based at least in part on a relative timing between a decrease in the RMSSD data and a decrease in the resting heart rate data” as required by claim 2.
Claim 8 recites “identifying the satisfaction of the one or more criteria is based at least in part on the frequency content”; it is unclear whether this limitation is intended to be in furtherance, additional, or in lieu of, replacing the limitation “satisfaction of one or more criteria for identifying illness associated with the user based at least in part on a relative timing between a decrease in the RMSSD data and a decrease in the resting heart rate data” as required by claim 2.
Claim 9 is rejected for depending on claim 8, inheriting the same deficiency.
Claim 10 recites “identify the satisfaction of the one or more criteria based at least in part on the change in the heart rate variability data corresponding to the at least one sleep stage”; it is unclear whether this limitation is intended to be in furtherance, additional, or in lieu of, replacing the limitation “satisfaction of one or more criteria for identifying illness associated with the user based at least in part on a relative timing between a decrease in the RMSSD data and a decrease in the resting heart rate data” as required by claim 2.
Claim 11 recites “identifying the satisfaction of the one or more criteria is based at least in part on the circadian rhythm”; it is unclear whether this limitation is intended to be in furtherance, additional, or in lieu of, replacing the limitation “satisfaction of one or more criteria for identifying illness associated with the user based at least in part on a relative timing between a decrease in the RMSSD data and a decrease in the resting heart rate data” as required by claim 2.
Claim 12 recites “wherein the satisfaction of the one or more criteria is based at least in part on the one or more deviations”; it is unclear whether this limitation is intended to be in furtherance, additional, or in lieu of, replacing the limitation “satisfaction of one or more criteria for identifying illness associated with the user based at least in part on a relative timing between a decrease in the RMSSD data and a decrease in the resting heart rate data” as required by claim 2.
Claim 16 recites “identifying the satisfaction of the one or more criteria is based at least in part on the respiratory rate data”; it is unclear whether this limitation is intended to be in furtherance, additional, or in lieu of, replacing the limitation “satisfaction of one or more criteria for identifying illness associated with the user based at least in part on identifying a decrease in the RMSSD data and a decrease in the resting heart rate data occurring at approximately a same time” as required by claim 13.
Claim 17 recites “identifying the satisfaction of the one or more criteria is based at least in part on the frequency content”; it is unclear whether this limitation is intended to be in furtherance, additional, or in lieu of, replacing the limitation “satisfaction of one or more criteria for identifying illness associated with the user based at least in part on identifying a decrease in the RMSSD data and a decrease in the resting heart rate data occurring at approximately a same time” as required by claim 13.
Claim 18 recites “identifying the satisfaction of the one or more criteria is based at least in part on the low frequency content, the high frequency content, or both”; it is unclear whether this limitation is intended to be in furtherance, additional, or in lieu of, replacing the limitation “satisfaction of one or more criteria for identifying illness associated with the user based at least in part on identifying a decrease in the RMSSD data and a decrease in the resting heart rate data occurring at approximately a same time” as required by claim 13.
Claim 18 recites “identify the satisfaction of the one or more criteria based at least in part on a comparison between the first portion of the heart rate variability data and the second portion of the heart rate variability data”; it is unclear whether this limitation is intended to be in furtherance, additional, or in lieu of, replacing the limitation “satisfaction of one or more criteria for identifying illness associated with the user based at least in part on identifying a decrease in the RMSSD data and a decrease in the resting heart rate data occurring at approximately a same time” as required by claim 13.
Double Patenting
Claims 2-21 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,268,530 B2. Although the claims at issue are not identical, they are not patentably distinct from each other (see exemplary comparison listed below.
2. (New) A system, comprising: a wearable ring device configured to measure physiological data from a finger of a user, the physiological data comprising heart rate variability data; a user device communicatively coupled with the wearable ring device; and one or more processors communicatively coupled with the wearable ring device and the user device, the one or more processors configured to: receive the physiological data measured from the finger of the user via the wearable ring device, the physiological data comprising at least the heart rate variability data collected via the wearable ring device; input the heart rate variability data into a first machine learning classifier, wherein the first machine learning classifier is configured to extract a set of features from the heart rate variability data, the set of features comprising at least root mean square of successive differences (RMSSD) data and resting heart rate data associated with the user; input the set of features extracted by the first machine learning classifier into a second machine learning classifier; identify, using the second machine learning classifier, a satisfaction of one or more criteria for identifying illness associated with the user based at least in part on a relative timing between a decrease in the RMSSD data and a decrease in the resting heart rate data; and transmit instructions to a graphical user interface of the user device to cause the graphical user interface to display an illness risk metric associated with the user based at least in part on the satisfaction of the one or more criteria, the illness risk metric associated with a relative probability that the user will transition from a healthy state to an unhealthy state due to a bacterial infection, a viral infection, or both.
1. A system, comprising: a wearable device configured to measure physiological data from a user, the physiological data comprising heart rate variability data measured from the user throughout a first time interval and a second time interval subsequent to the first time interval; a user device communicatively coupled with the wearable device; and one or more processors communicatively coupled with the wearable device and the user device, the one or more processors configured to: receive heart rate variability data measured from the user via the wearable device, the heart rate variability data collected via the wearable device throughout the first time interval and the second time interval subsequent to the first time interval; input the heart rate variability data into a first machine learning classifier, wherein the first machine learning classifier is configured to extract a set of features from the heart rate variability data; identify, using the first machine learning classifier, root mean square of successive differences (RMSSD) data and resting heart rate data associated with the user based at least in part on the heart rate variability data, input the set of features extracted by the first machine learning classifier into a second machine learning classifier, wherein the set of features extracted by the first machine learning classifier comprise the RMSSD data and the resting heart rate data; identify, using the second machine learning classifier, a satisfaction of one or more deviation criteria between a first subset of the set of features associated with the first time interval and a second subset of the set of features associated with the second time interval, wherein the second machine learning classifier is configured to identify the satisfaction of the one or more deviation criteria based at least in part on identifying a decrease in the RMSSD data and a decrease in the resting heart rate data occurring at approximately a same time within the second time interval; and transmit instructions to a graphical user interface of the user device to cause the graphical user interface to display an illness risk metric associated with the user based at least in part on the satisfaction of the one or more deviation criteria, the illness risk metric associated with a relative probability that the user will transition from a healthy state to an unhealthy state due to a bacterial infection, a viral infection, or both.
3. (New) The system of claim 2, wherein the satisfaction of the one or more criteria are identified based at least in part on the decrease in the RMSSD data and the decrease in the resting heart rate data occurring at approximately the same time.
1 [] identifying a decrease in the RMSSD data and a decrease in the resting heart rate data occurring at approximately a same time within the second time interval [].
4. (New) The system of claim 3, wherein the decrease in the RMSSD data and the decrease in the resting heart rate data occur at approximately the same time based at least in part on the decrease in the RMSSD data and the decrease in the resting heart rate data both occurring within a 24 hour period.
1 [] identifying a decrease in the RMSSD data and a decrease in the resting heart rate data occurring at approximately a same time within the second time interval [].
5. (New) The system of claim 2, wherein the one or more processors are further configured to: classify the physiological data into a plurality of sleep stages comprising at least rapid eye movement (REM) sleep stages; and identify an increase in a duration of the REM sleep stages, wherein the satisfaction of one or more criteria is based at least in part on a relative timing between the increase in the duration of the REM sleep stages and the decrease in the RMSSD data.
claim 10 [] a rapid eye movement sleep stage []
6. (New) The system of claim 2, wherein the physiological data further comprises distal skin temperature data acquired via the finger of the user, wherein the illness risk metric is based at least in part on a vasoconstriction at one or more periphery parts of a body of the user that is identified based at least in part on the distal skin temperature data, the one or more periphery parts comprising at least the finger of the user.
7. (New) The system of claim 2, wherein the one or more processors are further configured to: identify respiratory rate data associated with the user based at least in part on the heart rate variability data; and input the respiratory rate data into the second machine learning classifier, wherein identifying the satisfaction of the one or more criteria is based at least in part on the respiratory rate data.
14. The system of claim 1, wherein the one or more processors are further configured to: identify respiratory rate data associated with the user based at least in part on the heart rate variability data; and input the respiratory rate data into the second machine learning classifier, wherein identifying the satisfaction of the one or more deviation criteria is based at least in part on the respiratory rate data.
8. (New) The system of claim 2, wherein the one or more processors are further configured to: determine, using the first machine learning classifier, frequency content of the heart rate variability data, wherein the set of features comprise the frequency content, and wherein identifying the satisfaction of the one or more criteria is based at least in part on the frequency content.
2. The system of claim 1, wherein the one or more processors are further configured to: determine, using the first machine learning classifier, frequency content of the heart rate variability data throughout at least a portion of the first time interval and at least a portion of the second time interval, wherein the set of features comprise the frequency content, and wherein identifying the satisfaction of the one or more deviation criteria is based at least in part on the frequency content.
9. (New) The system of claim 8, wherein, to determine the frequency content of the heart rate variability data, the one or more processors are further configured to: determine, using the first machine learning classifier, a low frequency content of the heart rate variability data; determine, using the first machine learning classifier, a high frequency content of the heart rate variability data, wherein the set of features comprise the low frequency content and the high frequency content; and identify, using the second machine learning classifier, a divergence between the low frequency content of the heart rate variability data and the high frequency content of the heart rate variability data, wherein identifying the satisfaction of the one or more criteria is based at least in part on identifying that the divergence occurs at approximately the same time as the decrease in the RMSSD data and the decrease in the resting heart rate data.
3. The system of claim 2, wherein to determine the frequency content of the heart rate variability data, the one or more processors are further configured to: determine, using the first machine learning classifier, a low frequency content of the heart rate variability data; and determine, using the first machine learning classifier, a high frequency content of the heart rate variability data, wherein the set of features comprise the low frequency content and the high frequency content, wherein identifying the satisfaction of the one or more deviation criteria is based at least in part on the low frequency content, the high frequency content, or both.
10. (New) The system of claim 2, wherein the one or more processors are further configured to: identify a plurality of sleep stages associated with the user based at least in part on the physiological data collected throughout a first time interval and a second time interval; and identify, using the first machine learning classifier, a change in the heart rate variability data corresponding to at least one sleep stage of the plurality of sleep stages, wherein the second machine learning classifier is configured to identify the satisfaction of the one or more criteria based at least in part on the change in the heart rate variability data corresponding to the at least one sleep stage.
5. The system of claim 1, wherein the one or more processors are further configured to: receive physiological data associated with the user from the wearable device, the physiological data collected via the wearable device throughout the first time interval and the second time interval; identify a plurality of sleep stages associated with the user based at least in part on the physiological data; and identify, using the first machine learning classifier, a first portion of the heart rate variability data which corresponds to a first sleep stage of the plurality of sleep stages within the first time interval, and a second portion of the heart rate variability data which corresponds to a second sleep stage of the plurality of sleep stages within the second time interval, the first sleep stage and the second sleep stage comprising the same type of sleep stage, wherein the second machine learning classifier is configured to identify the satisfaction of the one or more deviation criteria based at least in part on a comparison between the first portion of the heart rate variability data and the second portion of the heart rate variability data.
11. (New) The system of claim 2, wherein the one or more processors are further configured to: identify a circadian rhythm associated with the user, wherein identifying the satisfaction of the one or more criteria is based at least in part on the circadian rhythm.
6. The system of claim 1, wherein the one or more processors are further configured to: identify at least a first portion of the first time interval and a second portion of the second time interval based at least in part on a circadian rhythm associated with the user; and identify a first portion of the heart rate variability data which corresponds to the first portion of the first time interval and a second portion of the heart rate variability data which corresponds to the second portion of the second time interval, wherein inputting the heart rate variability data into the machine learning classifier comprises inputting the first portion of the heart rate variability data and the second portion of the heart rate variability data.
12. (New) The system of claim 2, wherein the one or more processors are further configured to: identify an average bedtime of the user, an average wake time of the user, or both; and identify, using the second machine learning classifier, one or more deviations in the heart rate variability data collected during time periods immediately subsequent to the average bedtime, during time periods immediately preceding the average wake time, or both, wherein the satisfaction of the one or more criteria is based at least in part on the one or more deviations.
9. The system of claim 8, wherein the one or more processors are further configured to: identify an average bedtime of the user, an average wake time of the user, or both, based at least in part on the circadian rhythm of the user; and identify, using the second machine learning classifier, one or more deviations between the heart rate variability data collected during the second time interval from the rhythmic pattern of the heart rate variability data within a first time period immediately subsequent to the average bedtime, within a second time period immediately preceding the average wake time, or both, wherein the satisfaction of the one or more deviation criteria is based at least in part on the one or more deviations.
13. (New) A system, comprising: a wearable ring device configured to measure physiological data from a finger of a user via one or more light-emitting components and one or more light-receiving components, the physiological data comprising at least heart rate variability data; a user device communicatively coupled with the wearable ring device; anode or more processors communicatively coupled with the wearable ring device and the user device, the one or more processors configured to: receive the physiological data measured from the user via the wearable ring device, the physiological data comprising at least the heart rate variability data collected via the wearable ring device; identify root mean square of successive differences (RMSSD) data and resting heart rate data associated with the user based at least in part on the heart rate variability data; input the RMSSD data and the resting heart rate data into one or more machine learning classifiers; identify, using the one or more machine learning classifiers, a satisfaction of one or more criteria for identifying illness associated with the user based at least in part on identifying a decrease in the RMSSD data and a decrease in the resting heart rate data occurring at approximately a same time; and transmit instructions to a graphical user interface of the user device to cause the graphical user interface to display an illness risk metric associated with the user based at least in part on the satisfaction of the one or more criteria, the illness risk metric associated with a relative probability that the user will transition from a healthy state to an unhealthy state due to a bacterial infection, a viral infection, or both.
1. A system, comprising: a wearable device configured to measure physiological data from a user, the physiological data comprising heart rate variability data measured from the user throughout a first time interval and a second time interval subsequent to the first time interval; a user device communicatively coupled with the wearable device; and one or more processors communicatively coupled with the wearable device and the user device, the one or more processors configured to: receive heart rate variability data measured from the user via the wearable device, the heart rate variability data collected via the wearable device throughout the first time interval and the second time interval subsequent to the first time interval; input the heart rate variability data into a first machine learning classifier, wherein the first machine learning classifier is configured to extract a set of features from the heart rate variability data; identify, using the first machine learning classifier, root mean square of successive differences (RMSSD) data and resting heart rate data associated with the user based at least in part on the heart rate variability data, input the set of features extracted by the first machine learning classifier into a second machine learning classifier, wherein the set of features extracted by the first machine learning classifier comprise the RMSSD data and the resting heart rate data; identify, using the second machine learning classifier, a satisfaction of one or more deviation criteria between a first subset of the set of features associated with the first time interval and a second subset of the set of features associated with the second time interval, wherein the second machine learning classifier is configured to identify the satisfaction of the one or more deviation criteria based at least in part on identifying a decrease in the RMSSD data and a decrease in the resting heart rate data occurring at approximately a same time within the second time interval; and transmit instructions to a graphical user interface of the user device to cause the graphical user interface to display an illness risk metric associated with the user based at least in part on the satisfaction of the one or more deviation criteria, the illness risk metric associated with a relative probability that the user will transition from a healthy state to an unhealthy state due to a bacterial infection, a viral infection, or both.
14. (New) The system of claim 13, wherein the decrease in the RMSSD data and the decrease in the resting heart rate data occur at approximately the same time based at least in part on the decrease in the RMSSD data and the decrease in the resting heart rate data both occurring within a 24 hour period.
1 [] identifying a decrease in the RMSSD data and a decrease in the resting heart rate data occurring at approximately a same time within the second time interval [].
15. (New) The system of claim 13, wherein the physiological data further comprises distal skin temperature data acquired via the finger of the user, wherein the illness risk metric is based at least in part on a vasoconstriction at one or more periphery parts of a body of the user that is identified based at least in part on the distal skin temperature data, the one or more periphery parts comprising at least the finger of the user.
16. (New) The system of claim 13, wherein the one or more processors are further configured to: identify respiratory rate data associated with the user based at least in part on the heart rate variability data; and input the respiratory rate data into the one or more machine learning classifiers, wherein identifying the satisfaction of the one or more criteria is based at least in part on the respiratory rate data.
14. The system of claim 1, wherein the one or more processors are further configured to: identify respiratory rate data associated with the user based at least in part on the heart rate variability data; and input the respiratory rate data into the second machine learning classifier, wherein identifying the satisfaction of the one or more deviation criteria is based at least in part on the respiratory rate data.
17. (New) The system of claim 13, wherein the one or more processors are further configured to: determine, using the one or more machine learning classifiers, frequency content of the heart rate variability data, wherein identifying the satisfaction of the one or more criteria is based at least in part on the frequency content.
2. The system of claim 1, wherein the one or more processors are further configured to: determine, using the first machine learning classifier, frequency content of the heart rate variability data throughout at least a portion of the first time interval and at least a portion of the second time interval, wherein the set of features comprise the frequency content, and wherein identifying the satisfaction of the one or more deviation criteria is based at least in part on the frequency content.
18. (New) The system of claim 17, wherein, to determine the frequency content of the heart rate variability data, the one or more processors are further configured to: determine, using the one or more machine learning classifiers, a low frequency content of the heart rate variability data; and determine, using the one or more machine learning classifiers, a high frequency content of the heart rate variability data, wherein identifying the satisfaction of the one or more criteria is based at least in part on the low frequency content, the high frequency content, or both.
3. The system of claim 2, wherein to determine the frequency content of the heart rate variability data, the one or more processors are further configured to: determine, using the first machine learning classifier, a low frequency content of the heart rate variability data; and determine, using the first machine learning classifier, a high frequency content of the heart rate variability data, wherein the set of features comprise the low frequency content and the high frequency content, wherein identifying the satisfaction of the one or more deviation criteria is based at least in part on the low frequency content, the high frequency content, or both.
19. (New) The system of claim 13, wherein the one or more processors are further configured to: identify a plurality of sleep stages associated with the user based at least in part on the physiological data, the physiological data collected throughout a first time interval and a second time interval subsequent to the first time interval; and identify, using the one or more machine learning classifiers, a first portion of the heart rate variability data which corresponds to a first sleep stage of the plurality of sleep stages within the first time interval, and a second portion of the heart rate variability data which corresponds to a second sleep stage of the plurality of sleep stages within the second time interval, the first sleep stage and the second sleep stage comprising the same type of sleep stage, wherein the one or more machine learning classifiers are configured to identify the satisfaction of the one or more criteria based at least in part on a comparison between the first portion of the heart rate variability data and the second portion of the heart rate variability data.
5. The system of claim 1, wherein the one or more processors are further configured to: receive physiological data associated with the user from the wearable device, the physiological data collected via the wearable device throughout the first time interval and the second time interval; identify a plurality of sleep stages associated with the user based at least in part on the physiological data; and identify, using the first machine learning classifier, a first portion of the heart rate variability data which corresponds to a first sleep stage of the plurality of sleep stages within the first time interval, and a second portion of the heart rate variability data which corresponds to a second sleep stage of the plurality of sleep stages within the second time interval, the first sleep stage and the second sleep stage comprising the same type of sleep stage, wherein the second machine learning classifier is configured to identify the satisfaction of the one or more deviation criteria based at least in part on a comparison between the first portion of the heart rate variability data and the second portion of the heart rate variability data.
20. (New) The system of claim 13, wherein the illness risk metric is associated with a relative probability that the user is experiencing the viral infection, and wherein the physiological data is associated with an immunological response of the user during a pre- symptomatic period of the viral infection, and wherein the instructions are transmitted to the graphical user interface during the pre-symptomatic period prior to the user experiencing symptoms of the viral infection.
7. The system of claim 1, wherein the illness risk metric is associated with a relative probability that the user is experiencing a viral infection, and wherein the one or more features extracted by the first machine learning classifier are associated with an immunological response of the user during a pre-symptomatic period of the viral infection, and wherein the instructions are transmitted to the graphical user interface during the pre-symptomatic period prior to the user experiencing symptoms of the viral infection.
21. (New) The system of claim 13, wherein the one or more processors are further configured to: receive a user input via the user device and in response to causing the graphical user interface to display the illness risk metric, wherein the user input indicates a positive illness test, an onset of illness symptoms, or both; and train the one or more machine learning classifiers to predict illness for the user based at least in part on the user input.
12. The system of claim 1, wherein the one or more processors are further configured to: receive a user input via the user device and in response to causing the graphical user interface to display the illness risk metric, wherein the user input indicates a positive illness test, an onset of illness symptoms, or both; and train the second machine learning classifier to predict illness for the user based at least in part on the user input.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANA SAHAND whose telephone number is (571)272-6842. The examiner can normally be reached M-Th 8:30 am -5:30 pm; F 9 am-3 pm.
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/SANA SAHAND/Examiner, Art Unit 3796
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