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
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-5, 9-13, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over WEN (US 2021/0209739)in view of LEE (KR 10-2021-0038143 English Translation).
As to claim 1, WEN discloses a battery defect detection apparatus, comprising:
a communication module (Fig. 6, para. 0058);
a processor (Fig. 6, para. 0058); and
a memory configured to store a first artificial intelligence model, a second artificial intelligence model (para. 0024, 0058), and instructions,
wherein the processor is configured to execute the instructions by the battery defect detection apparatus (para. 0041), to perform operations comprising:
obtaining an image of a subject product by using the communication module (para. 0042);
inputting the image of the subject product to the first artificial intelligence model to classify the subject product (para. 0045, 0050).
WEN is silent regarding inputting the image of the subject product to the second artificial intelligence model to determine whether the image of the subject product corresponds to first data for classifying the subject product as normal among learning data of the first artificial intelligence model, when the subject product is classified as normal.
LEE teaches inputting the image of the subject product to the second artificial intelligence model to determine whether the image of the subject product corresponds to first data for classifying the subject product as normal among learning data of the first artificial intelligence model, when the subject product is classified as normal (para. 27, “According to an embodiment, the classification unit 102 systematically classifies the defect data and the normal data based on a first classification model provided by the data learning unit 104, and systematically classifies the defect data and the normal data based on the second classification model when the first classification model is changed to the second classification model due to discovery of a new defect or the like. In this case, in the first classification model, an image determined as a normal image or an image that has not been determined as a new defect may be determined as a defect image in the second classification model”).
It would have been obvious to one of ordinary skill in the art to incorporate LEE’s teachings into WEN since doing so would merely combine prior art elements according to known methods to yield predictable results, and improve performance.
As to claim 2, the combination of WEN and LEE discloses the battery defect detection apparatus of claim 1, wherein the operations further comprise: re-classifying the subject product as defective when the image does not correspond to the first data (KR, para. 27, “According to an embodiment, the classification unit 102 systematically classifies the defect data and the normal data based on a first classification model provided by the data learning unit 104, and systematically classifies the defect data and the normal data based on the second classification model when the first classification model is changed to the second classification model due to discovery of a new defect or the like. In this case, in the first classification model, an image determined as a normal image or an image that has not been determined as a new defect may be determined as a defect image in the second classification model”).
As to claim 3, the combination of WEN and LEE discloses the battery defect detection apparatus of claim 1, wherein the operations further comprise: determining the image as second data for classifying the subject product as defective, when the image does not correspond to the first data (WEN, para. 0030, 0050).
As to claim 4, the combination of WEN and LEE discloses the battery defect detection apparatus of claim 3, wherein the operations further comprise: training the first artificial intelligence model based on the second data (WEN, para. 0030, 0045, 0050).
As to claim 5, the combination of WEN and LEE discloses the battery defect detection apparatus of claim 1, wherein the image of the subject product is obtained by an image obtaining device through multiple channels (WEN, para. 0022, 0024).
As to claims 9-13, 17, these claims recite features similar to those discussed above. Therefore, they are rejected for reasons similar to those discussed above.
Allowable Subject Matter
Claims 6-8, 14-16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: The prior art discloses the claim limitations discussed above, but fails to disclose the combined features required by each of dependent claims 6, 14.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ge et al. disclose a defect detection system that can automatically select an AI model (e.g., a deep learning model) from a plurality of AI models based on the type of inspection to be performed (e.g., determination of the presence of a defect, identification of a defect type, determination of a defect characteristic, etc.). The plurality of AI models can be pre-trained and can be included in an AI model library. The various AI models can be trained to detect defect in one or more batteries e.g., batteries of given battery types, battery models, etc.), various scanning environments, defect modes and original equipment manufacturers (OEMs) of batteries.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHUOC TRAN whose telephone number is (571)272-7399. The examiner can normally be reached 9am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached at 571-272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PHUOC TRAN/Primary Examiner, Art Unit 2668
Prosecution Insights