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 .
DETAIL ACTION
Notice to Applicant(s)
This application has been examined. Claims 1-17 are pending.
Receipt is acknowledged of papers submitted under 35 U.S.C. § 119, which have been placed of record in the file.
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.
Claims 1-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Banno et al. (2021/0101564).
As per claim 1, Banno et al. discloses a defrosting method for a vehicle which includes the steps of training a neural network model with collected image data (see at least the abstract; figure 4); determining whether frost is present on a glass of the vehicle based on the trained neural network model (see at least figure 7, item 70; paragraph 0051); and performing control of defrosting of the vehicle according to whether the frost is present (see at least figure 7, item 75; paragraphs 0050, 0051; claim 13).
As per claim 2, Banno et al. discloses that the determining whether the frost is present on the glass further includes capturing an image of the glass of the vehicle using a camera; and determining whether the frost is present using the captured image of the glass of the vehicle (see at least figure 7, items 10 and 50).
As per claim 3, Banno et al. discloses that the camera is mounted in the vehicle ; and the glass of the vehicle includes a windshield of the vehicle (see at least figure 2; paragraph 0020).
As per claim 4, Banno et al. discloses that the determining whether the frost is present on the glass includes determining whether the frost is present on the glass of the vehicle using an artificial intelligence or a neural network; wherein when the frost is present on the glass of the vehicle, it is determined that the glass is in an abnormal state; and wherein when the frost is not present on the glass of the vehicle, it is determined that the glass is in a normal state (see at least the abstract and figure 7).
As per claim 5, Banno et al. discloses that the collected image data include an image of the glass of the vehicle obtained through a camera mounted on the vehicle while the vehicle travels (see at least figure 2).
As per claim 6, Banno et al. discloses the limitation of this claim in at least figure 7, item 51, figure 4, paragraphs 0021, 0041, 0046 and 0055.
As per claim 7, Banno et al. further discloses the step of receiving a signal to request removing frost (see at least figure 7, item 75).
As per claim 15, Banno et al. discloses that the neural network model training module, the frost presence determination module, and the defrosting control module constitute individual controllers of the defrosting apparatus (see at least figures 2 and 7).
As per claim 16, it is inherently that the neural network model training module, the frost presence determination module, and the defrosting control module constitute a single controller of the defrosting apparatus.
With respect to claims 8-14 and 17, the limitations of these claims have been noted in the rejections above. They are therefore considered rejected as set forth above.
Conclusion
All claims are rejected.
The following references are cited as being of general interest: Reed (10,220,675), Neubauer (2004/0100217), Fox (2007/0182816), Almedia et al. (2013/0103257), Du et al. (2020/0198587) and Sandhu et al. (2021/0094386).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAN QUANG NGUYEN whose telephone number is (571) 272-6966. The examiner can normally be reached on Monday to Thursday from 7:00am to 5:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Nolan, can be reached at 570-270-7016.
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March 6, 2026
/TAN Q NGUYEN/Primary Examiner, Art Unit 3661
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