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 § 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.
Claims 17-31 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al (US 7,956,755 -cited by applicant) in view of Hong et al (“0348 Sleep Staging Using End-to-End Deep Learning Model Based on Noctural Sound for Smartphones” -cited by applicant).
Re claims 17, 22, 27: Lee discloses a computing device for controlling a lighting device configured to adjust an environment of an object, the computing device comprising:
a sensor unit or network unit configured to acquire and receive sleep sound information, from a user terminal over a network, in the environment of the object, the sleep sound information including a sound related to breathing and a body movement of a user (col 3, lines 13-25; see the sensor to measure physiological signals); and
a processor or user terminal configured to: generate environment adjustment information for controlling the lighting device to adjust the environment of the object based on the sleep stage information (figure 2; col 4, lines 48-57; see the sleep environment adjustment unit 130),
wherein the environment adjustment information comprises at least one piece of control information, which when interpreted by the processor, causes the processor to perform at least one of: (a) causing the lighting device, within a predetermined period from a time point at which the sleep stage information indicating the user has fallen asleep is generated, to either: not emit light; or emit light having an illuminance value equal to or less than a first illuminance value and/or a color temperature value equal to or less than a first color temperature value; (b) causing the lighting device, when the sleep stage information indicating the user has awakened is generated within a predetermined period from a desired wake-up time set by the user, to emit light having an illuminance value equal to or greater than a second illuminance value and/or a color temperature value equal to or greater than a second color temperature value, and wherein the second illuminance value is greater than the first illuminance value, and the second color temperature value is greater than the first color temperature value (figure 6; col 7, lines 36-67; see the lighting conditions during the various sleep stages based on the sleep stage).
Lee discloses control of the lighting device based on sleep stage via physiological signal detection of sleep sound information, but does not disclose: convert the sleep sound information into a plurality of spectrograms, wherein each of the plurality of spectrograms corresponds to a predetermined epoch; generate sleep stage information related to a sleep depth of the user by processing the plurality of spectrograms as an input to a sleep analysis model including a feature extraction model and a feature classification model; wherein the feature extraction model includes one or more neural networks configured to extract a plurality of features, each of which is extracted from a corresponding one of the plurality of spectrograms, wherein the feature classification model includes one or more neural networks configured to estimate a plurality of sleep stages based on input of the plurality of features as the sleep stage information. However, Hong teaches of a deep learning model based on nocturnal sound including: convert the sleep sound information into a plurality of spectrograms, wherein each of the plurality of spectrograms corresponds to a predetermined epoch; generate sleep stage information related to a sleep depth of the user by processing the plurality of spectrograms as an input to a sleep analysis model including a feature extraction model and a feature classification model; wherein the feature extraction model includes one or more neural networks configured to extract a plurality of features, each of which is extracted from a corresponding one of the plurality of spectrograms, wherein the feature classification model includes one or more neural networks configured to estimate a plurality of sleep stages based on input of the plurality of features as the sleep stage information (see the 0348 intro, method, results, and conclusion wherein a first neural network extracts features from epochs of the converted spectrogram and a second neural network classifies the sleep stages). It would have been obvious to the skilled artisan to modify Lee, to apply the spectrogram-based feature extraction and sleep stage learning as taught by Hong, in order to provide reliable and convenient sleep tracking.
Re claims 18-20, 23-25, 28-30: Lee discloses all features except the extraction and classification models. However, Hong teaches the feature extraction model is based on one or more patterns related to at least one of breathing sounds, breathing patterns, and movement patterns in each of the plurality of spectrograms (see method section, where the neural network for extraction is based on breathing patterns). Hong also teaches the feature classification model estimates, as the sleep stage information, a change in the plurality of sleep stages over time by performing multi-epoch classification on at least some of the plurality of features, wherein at least some of the plurality of features, on which the multi-epoch classification is performed, correspond to a plurality of epochs in a time series (see the method section with the epoch analysis over time corresponding to a plurality of epochs over time). It would have been obvious to the skilled artisan to modify Lee, to apply the spectrogram-based feature extraction and sleep stage learning as taught by Hong, in order to provide reliable and convenient sleep tracking.
Re claims 21, 26, 31: Lee discloses the processor is further configured to determine a time point for acquiring the sleep sound information based on at least one of: a user terminal operation, a start time entered by the user, and a sleep pattern of the user stored in the computing device, and wherein the sleep sound information is acquired in the environment of the object from the determined time point (figures 2, 4; see selection unit and determination unit which determine a time point for acquiring sleep sound information).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 17-31 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,433,533. Although the claims at issue are not identical, they are not patentably distinct from each other because ‘533 features a computing device for analyzing sleep state including a unit configured to acquire sleep sound information in the environment of the object, the sleep sound information including a sound related to breathing and a body movement of a user; and a processor configured to: convert the sleep sound information into a plurality of spectrograms; generate sleep stage information related to a sleep depth of the user by processing the plurality of spectrograms as an input to a sleep analysis model including a feature extraction model and a feature classification model; and generate environment adjustment information for controlling the lighting device to adjust the environment of the object based on the sleep stage information,wherein the feature extraction model includes one or more neural networks configured to extract a plurality of features, each of which is extracted from a corresponding one of the plurality of spectrograms, wherein the feature classification model includes one or more neural networks configured to estimate a plurality of sleep stages based on input of the plurality of features as the sleep stage information, wherein the environment adjustment information comprises at least one piece of control information. Claims 5, 10, and 15 further recite that he adjusting is to illuminance of the sleep environment. While it is not recited that there is a first and second illuminance for the lighting device, it would have been obvious to the skilled artisan to control the device to have different illuminances as is well know and would enhance user feedback.
Conclusion
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/MICHAEL T ROZANSKI/Primary Examiner, Art Unit 3797
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