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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-10 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 is/are rejected under 35 U.S.C. 103 as obvious over Wang et al. (CN110008879) in view of Mullaly et al. (US2017/0337485).
To claim 1, Wang teach a fatigue detection system (Figs. 2, 5-6), comprising:
an image sensor configured to capture at least one facial image of a driver (paragraph 0028);
a voice sensor configured to collect a voice of the driver (paragraph 0028);
a memory configured to store the at least one facial image and the voice (paragraph 0108, obviously stored locally); and
a processor coupled to the image sensor, the voice sensor, and the memory, the processor configured to extract at least one micro-expression feature from the at least one facial image (paragraph 0030), and establish a fatigue detection model based on the at least one micro-expression feature, and utilize the fatigue detection model to obtain a fatigue detection result of the driver (paragraphs 0031-0036),
wherein the processor is further configured to utilize a voice detection algorithm to identify the voice to obtain a voice recognition result of the driver (paragraph 0080, speech recognition), and
wherein the processor is further configured to determine a mental state of the driver based on the fatigue detection result and the voice recognition result (paragraph 0081).
But, Wang do not expressly disclose wherein the voice recognition result comprises a human voice category and a non-human voice category.
Mullaly teach an event condition detection system monitoring occupants, wherein voice recognition is applied to distinguish human and non-human occupants and/or to distinguish between different human occupants (paragraph 0036).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the system of Wang, in order to distinguish targeted human occupant.
To claim 6, Wang and Mullaly teach a fatigue detection method (as explained in response to claim 1 above).
To claims 2 and 7, Wang and Mullaly teach claims 1 and 6.
Wang and Mullaly teach wherein the processor is further configured to identify at least one facial region in the at least one facial image by utilizing a face detection algorithm (paragraph 0012).
To claims 3 and 8, Wang and Mullaly teach claims 2 and 7.
Wang and Mullaly teach wherein the processor is further configured to utilize a micro-expression feature extraction algorithm to extract the at least one micro-expression feature in the at least one facial region (paragraphs 0006, 0030).
Claim(s) 4-5, 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (CN110008879) in view of Mullaly et al. (US2017/0337485) and Chauhan et al. (US2024/0412379).
To claims 4 and 9, Wang and Mullaly teach claims 1 and 6.
Wang teach using machine learning (Fig. 3, paragraphs 0029-0030), but Wang do not expressly disclose wherein the processor is further configured to process the at least one facial image by utilizing a generative adversarial network model to generate at least one super-resolution facial image.
Chauhan teach processing the at least one facial image by utilizing a generative adversarial network model to generate at least one super-resolution facial image (paragraph 0042, Enhanced Super-Resolution Generative Adversarial Network), which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the system and method of Wang and Mullaly, in order to enhance image.
To claims 5 and 10, Wang, Mullaly and Chauhan teach claims 4 and 9.
Wang, Mullaly and Chauhan teach wherein the processor is further configured to extract the at least one micro-expression feature from the at least one facial image and the at least one super-resolution facial image (Chauhan, paragraph 0050, capture micro expression).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHIYU LU whose telephone number is (571)272-2837. The examiner can normally be reached Weekdays: 8:30AM - 5:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen R Koziol can be reached at (408) 918-7630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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ZHIYU . LU
Primary Examiner
Art Unit 2669
/ZHIYU LU/Primary Examiner, Art Unit 2665 August 19, 2026
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