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 Rejections - 35 USC § 112
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.
Claim 20 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.
In claim 20, it is unclear as to what is the difference between “the general model” and “the model” since “the general model” is just a refining of the model; in other words, in claim 20 “the model” appears to be the same “as the general mode” as set forth in claim 19.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-5, 10, 11, 13-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Youngblood (US 2020/00077942).
With respect to claim 1, Youngblood discloses a system (Fig. 1-4) comprising: a bed having a mattress (see para. 0033);
one or more sensors configured to: sense at least one physiological phenomenon of a user of the bed (see para. 0109-0115, 0118-0124);
generate one or more data streams based on the sensing of the physiological phenomenon of the user; a computing system comprising at least one processor and computer memory, the computing system configured to: receive the one or more data streams; (see para. 0135, 0155)
provide, as input, sleep-data from the data stream for the user to a sleep classifier and receive, as output, a sleep metric, (see para. 0156) wherein the sleep classifier comprises a model defining relationships between sleep-data and disorder risk or relevant sleep metric; (para. 0156)
use the sleep metric for the user reflective of a phenomena of sleep; and intermittently updating, after generating the sleep metric, the sleep metric using the data streams by changing the model (see para. 0156-0158).
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With respect to claim 2, Youngblood discloses wherein intermittently updating, after generating the sleep metric, the sleep metric using the data streams comprises using stored records of previous sleep sessions (see para. 0154).
With respect to claim 3, Youngblood discloses wherein the sleep classifier is an insomnia-risk classifier (see para. 0137)
With respect to claim 4, Youngblood discloses the computing-system further configured to initiate a home automation device responsive to updating the sleep metric (see para. 0155, 0157).
With respect to claim 5, Youngblood discloses wherein the model is created by machine-learning analysis of a training set of training-sleep-data and training-sleep-data (see para. 0154).
With respect to claim 10, Youngblood discloses wherein the mattress compresses at least one air bladder and wherein the one or more sensors include a pressure sensor in fluid communication with the air bladder (see para. 0153) .
With respect to claim 11, Youngblood discloses wherein the computing-system comprises a single housing that is mechanically connected to a bedframe that supports the mattress (see 0032).
With respect to claim 13, Youngblood discloses a controller for a bed, the controller comprising a memory and one or more processors (fig. 2-4), the controller configured to: receive one or more data streams;
provide, as input, sleep-data from the data stream for the user to a sleep classifier and receive, as output, a sleep metric, (see para. 0156) wherein the sleep classifier comprises a model defining relationships between sleep-data and disorder risk or relevant sleep metric; (para. 0156)
use the sleep metric for the user reflective of a phenomena of sleep; and intermittently updating, after generating the sleep metric, the sleep metric using the data streams by changing the model (see para. 0156-0158).
With respect to claim 14, Youngblood wherein the controller is further configured to initiate a home automation device responsive to updating the disorder-risk metric (see para. 0152).
With respect to claim 15, Youngblood wherein the model is generated by refining a general model that is trained on a general population according to sensed data of the user gathered while the user sleeps (para. 0154).
With respect to claim 16, Youngblood a bed configured to: receive one or more data streams; provide, as input, sleep-data for a user to a disorder-risk classifier and receive, as output, a disorder-risk metric, wherein the disorder-risk classifier comprises a model defining relationships between sleep-data and disorder risk; use the disorder-risk metric for the user reflective of risk that the user will or is experiencing symptoms consistent with a disorder; and intermittently updating, after generating the disorder-risk metric, the disorder-risk metric using the data streams by changing the model (0152, 0153, 0154, 0156-0171).
With respect to claim 17, Youngblood wherein the bed is further configured further configured to initiate a home automation device responsive to updating the disorder-risk metric (para. 0152).
With respect to claim 18, Youngblood, wherein the data streams includes sensed data of the user collected while the user is sleeping on or proximate to the bed (see para. 0155).
With respect to claim 19, Youngblood, wherein the model is generated by refining a general model that is trained on a general population based upon the sensed data of the user collected while the user is sleeping on or proximate to the bed (0153-0157).
With respect to claim 20, Youngblood, wherein the general model is configured to predict the disorder risk more accurately for the general population than is the model and wherein the model is configured to predict the disorder risk more accurately for the user of the bed than is the general model (0153-0157).
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) 6-9, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Youngblood (US 2020/00077942) in view of Vakulin et al (US 2021/0327584)
With respect to claim 6, Youngblood disclose the system, as set forth above but fails to explicitly teach wherein the sleep classifier is implemented as at least one of the group consisting of a random forest and a passive-aggressive classifier.
Vakulin in the same field of endeavor in the subject of sleep disorder identification, discloses wherein the sleep classifier is implemented as at least one of the group consisting of a random forest and a passive-aggressive classifier (see para. 0149).
It would have been obvious to one skilled in the art before the effective filing date for the the sleep classifier is implemented as at least one of the group consisting of a random forest and a passive-aggressive classifier in order to better identify sleep disorders (Vakulin, para. 0012).
With respect to claim 7, Vakunin further discloses wherein the sleep-data is a feature vector created from sleep-data for the user across a plurality of sleep sessions (see para. 0140, 0159).
With respect to claim 8, Vakunin further discloses wherein the feature vector comprises features selected from the group i) age, ii) gender, iii) respiration rate, iv) heart rate, v) motion, vi) sleep quality, vii) sleep duration, viii) restful sleep duration, viii) time to fall asleep, ix) demographic data, and x) number of individuals in a bed (see the sensor data, para. 0023).
With respect to claim 9, Vakunin further discloses wherein the feature vector comprises features selected from the group i) average heart rate, ii) percent of quality values for heart rate estimation, iii) percent motion, iv) restful time, v) respiration rate, vi) sleep debt, vii) sleep duration, and viii) sleep quality (para. 0023).
With respect to claim 12, Vakunin further discloses wherein the sleep classifier is one of the group consisting of i) a machine-learning classifier, and ii) a regression machine (see para. 0172).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH M SANTOS RODRIGUEZ whose telephone number is (571)270-7782. The examiner can normally be reached Monday-Friday 8:30am to 5:30pm.
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/JOSEPH M SANTOS RODRIGUEZ/Primary Examiner, Art Unit 3797