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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/8/26 has been entered.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 12/10/25 has/have been acknowledged and is/are being considered by the Examiner.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-23 have been considered but are moot because the new ground of rejection necessitated by the Applicant’s amendment to the original claims. The newly amended claim 1 necessitated a new search that resulted in new art that is applied below. Further the newly added claim 24 is also rejected below.
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, 6-8, 10-11, 13-16, 20-21-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Young et al. (U.S. Pub. 2020/0110194 hereinafter “Young”) in view of Komine et al. (U.S. Pub. 2020/0315512 hereinafter “Komine”).
Regarding claims 1, 9, 20-21 and 23, Young discloses a system comprising: a bed (e.g. 102) having a mattress (e.g. 20); one or more sensors (e.g. 106) configured to: sense one or more physical phenomena of a sleeper on the bed (e.g. ¶36); generate data signals based on the sensed physical phenomena (e.g. ¶36); and send, to a computing system, the data signals (e.g. ¶36); the computing system (e.g. 200) comprising one or more processors and computer memory, the computing system configured to: receive the data signals (e.g. ¶37); generate, from data signals of a sleep-session of the sleeper, a feature vector of features, each feature having a feature value that represents one of the physical phenomena (e.g. ¶¶34, 43); and classify the sleeper into a classified physical state or illness for the sleep session based on the feature vector (e.g. ¶¶34, 38). Young discloses the claimed invention except for explicitly stating the features that are used in the feature vector and how the parameters are selected for each particular illness or diagnosis. However, Komine teaches a similar system that utilizes features including respiration rate, heart rate, gross-body motion, sleep quality, sleep duration, restful sleep duration and sleep latency (time to fall asleep) and selects the required features based on the particular illness as set forth in Paragraphs 249, 314, 318 and 332 to provide a known means for the feature space of the features selected according to the physiological condition (for example, each disease) to be assessed. It would have been obvious to one having ordinary skill in the art at the time the invention was made to modify the system as taught by Young, with utilizing features including respiration rate, heart rate, gross-body motion, sleep quality, sleep duration, restful sleep duration and sleep latency (time to fall asleep) as taught by Komine, since such a modification would provide the predictable results of using known features that combined can determine and predict a physiological condition and disease.
Regarding claim 3, meeting the limitations of claim 1 above, Young further discloses wherein, to classify the sleeper into a physical state for the sleep session, the computing system further configured to: provide the feature vector to a state-classifier that is configured to receive as input feature vectors and to return as output a classification of the sleeper associated with the feature vector relative to a pre-defined plurality of possible physical states (e.g. ¶34).
Regarding claim 6, meeting the limitations of claim 1 above, Young further discloses wherein to classify the sleeper into a classified physical state for the sleep session based on the feature vector, the computing system is further configured to analyze historical data for the sleeper (e.g. ¶26).
Regarding claim 7, meeting the limitations of claim 1 above, Young further discloses wherein the classified physical state is selected from the group consisting of healthy and not-healthy (e.g. ¶38; “illness or no illness”).
Regarding claim 8, meeting the limitations of claim 1 above, Young further discloses wherein each feature is a physical measure of the sleeper (e.g. ¶34).
Regarding claim 10, meeting the limitations of claim 1 above, Young further discloses wherein at least one of the features is an environmental measure of the environment around the sleeper (e.g. ¶¶27, 55).
Regarding claim 11, meeting the limitations of claims 1 and 10 above, Young further discloses wherein the environmental measure is a measure of one of the group consisting of ambient temperature, bed temperature, air-quality, and ambient illumination (e.g. ¶¶27, 55).
Regarding claim 12, meeting the limitations of claim 1 above, Young further discloses wherein the computing system is further configured to, responsive to classifying the sleeper into a classified physical state for the sleep session based on the feature vector (e.g. ¶38), perform at least one of the group consisting of storing the classified physical state to the computer memory (e.g. ¶¶37, 66-67), transmitting the classified physical state over a data network, and initiating an automated process based on the classified physical state without specific user input (e.g. ¶¶68, 111).
Regarding claim 13, meeting the limitations of claim 1 above, Young further discloses wherein the computing system is further configured to generate a report of the sleep session, the report comprising a record of the classified physical state (e.g. ¶28).
Regarding claim 14, meeting the limitations of claims 1 and 14 above, Young further discloses wherein the report further comprises a record of at least some of the feature values (e.g. ¶28).
Regarding claim 15, meeting the limitations of claims 1 and 13-14 above, Young further discloses wherein: the computing system is further configured to generate, based on the classified physical state, a recovery recommendation, the recovery recommendation including human-readable text; and the report further comprises the human-readable text of the recovery recommendation (e.g. ¶28).
Regarding claim 16, meeting the limitations of claims 1 and 13-15 above, Young further discloses wherein to generate, based on the classified physical state, a recovery recommendation, the computing system is further configured to compare the classified physical state against a rule-set of recovery recommendations generated by medically-expert users (e.g. ¶28).
Regarding claim 23, Young discloses a system comprising: a bed (e.g. 102) having a mattress (e.g. 20); one or more sensors (e.g. 106) configured to: sense one or more physical phenomena of a sleeper on the bed (e.g. ¶36); generate data signals based on the sensed physical phenomena (e.g. ¶36); and send, to a computing system, the data signals (e.g. ¶36); the computing system (e.g. 200) comprising one or more processors and computer memory, the computing system configured to: receive the data signals (e.g. ¶37); generate, from data signals of a sleep-session of the sleeper, a feature vector of features, each feature having a feature value that represents one of the physical phenomena (e.g. ¶¶34, 43); and classify the sleeper into a classified physical state or illness for the sleep session based on the feature vector (e.g. ¶¶34, 38). Young discloses the claimed invention except for the system classifying and treating Covid-19. However, Young teaches that it is known to use the system to classify and treat illnesses. It is noted that Covid-19 was identified as an illness at the time of filing of the Young reference, but utilizes the same sensors and learning algorithms as claimed. It would have been obvious to one having ordinary skill in the art at the time the invention was made to modify the system as taught by Young, with identifying Covid-19, since such a modification would provide the predictable results of analyzing the sensors information to predict illnesses including Covid-19. Young discloses the claimed invention except for explicitly stating the features that are used in the feature vector and how the parameters are selected for each particular illness or diagnosis. However, Komine teaches a similar system that utilizes features including respiration rate, heart rate, gross-body motion, sleep quality, sleep duration, restful sleep duration and sleep latency (time to fall asleep) and selects the required features based on the particular illness as set forth in Paragraphs 249, 314, 318 and 332 to provide a known means for the feature space of the features selected according to the physiological condition (for example, each disease) to be assessed. It would have been obvious to one having ordinary skill in the art at the time the invention was made to modify the system as taught by Young, with utilizing features including respiration rate, heart rate, gross-body motion, sleep quality, sleep duration, restful sleep duration and sleep latency (time to fall asleep) as taught by Komine, since such a modification would provide the predictable results of using known features that combined can determine and predict a physiological condition and disease.
Regarding claim 24, meeting the limitations of claims 1 and 13-15 above, Komine further discloses wherein the one or more sensors are configured to classify the sleeper into a classified physical state for the sleep session by executing a state-classifier, wherein the state-classifier uses a prediction model that uses the feature vector to output the classified physical state based on a pre-defined plurality of possible physical states (e.g. ¶¶ 249, 256 and 319).
Claim(s) 4-5 and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Young in view of Komine as applied to claims 1-3, 6-8, 10-11, 13-16 and 20 above, and further in view of Halperin et al. (U.S. Pat. 9,131,902 hereinafter “Halperin”).
Regarding claims 4-5, Young in view of Komine discloses the claimed invention except for the system determining the probability that a sleeper is a in a particular state based on a threshold. However, Halperin teaches a similar sleep system and further that it is known to use probabilities and thresholds as set forth in Column 33, line 62 to Column 34, line 15 and Column 53, lines 4-20 to provide a means for determining the sleep state of the user. It would have been obvious to one having ordinary skill in the art at the time the invention was made to modify the system as taught by Young in view of Komine, with probabilities and thresholds as taught by Halperin, since such a modification would provide the predictable results of analyzing the sensed data with probabilities and thresholds to correctly classify each part of the sleep session into the various sleep states for enhanced analysis.
Regarding claims 17-19, Young in view of Komine discloses the claimed invention except for the system determining future milestones and scheduling future tests to confirm the state. However, Halperin teaches a similar sleep system and further that it is known to use predicted analysis as set forth in Columns 29-30 to provide a means for determining future events along with scheduling future tests to confirm the analysis. It would have been obvious to one having ordinary skill in the art at the time the invention was made to modify the system as taught by Young in view of Komine, with predicted analysis and tests as taught by Halperin, since such a modification would provide the predictable results of predicting future events and scheduling tests to confirm the analysis.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REX R HOLMES whose telephone number is (571)272-8827. The examiner can normally be reached Monday-Thursday 7:00AM-5:30PM.
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/REX R HOLMES/ Primary Examiner, Art Unit 3796