Office Action 3: Notice of Allowance (2024-09-20)

CLAIMS:
IN THE CLAIMS:

Please amend the claims, as set forth below:

1\. (Currently amended) An image analysis method, comprising:
receiving a target image for processing by an image analysis model composed of at least one expert generated by learning a training image;
analyzing the target image based on an output value from the at least one expert to generate an analysis result;
determining validity of the analysis result for the target image based on a probability of an input target image belonging to an input category of the at least one expert by at least:obtaining a likelihood of an input and a class with respect to each of the at least one expert;
obtaining a prior probability of each of the at least one expert; and
obtaining a probability of the input target image belonging to the input category of the at least one expert based on the obtained likelihood and the obtained prior probability; and
responsive to determining that the analysis result for the target image is not valid, classifying the target image into a new category so that a new expert for the new category is trained using the target image.

2\. (Previously Presented) The method of claim 1, wherein the determining of the validity of the analysis result for the target image comprises randomly determining the validity of the analysis result for the target image based on a probability of the input target image belonging to the input category of the at least one expert.

3\. (Canceled)

4\. (Currently amended) The method of claim \[\[3\]\] 1, wherein the obtaining the likelihood of an input and a class with respect to each of the at least one expert comprises:
obtaining a first probability of a class with respect to each of the input target image and the at least one expert by using a discriminative model of each of the at least one expert;
obtaining a second probability of an input with respect to each of the at least one expert by using a generative model of each of the at least one expert; and
obtaining the likelihood of an input and a class with respect to each of the at least one expert based on the first probability and the second probability.

5\. (Original) The method of claim 4, wherein the obtaining the second probability comprises estimating a lower bound of the second probability as the second probability.

6\. (Previously Presented) The method of claim 1, further comprising:
storing the target image classified into the new category in a memory; and
when an accumulated capacity of images including the target image and stored in the memory is greater than or equal to a predetermined reference capacity, generating the new expert for the new category by performing training using the images stored in the memory.

7\. (Previously Presented) The method of claim 6, wherein the generating of the expert for the new category comprises:
labeling the images stored in the memory; and
generating the new expert for the new category by training the new expert using the labeled images.

8\. (Currently amended) An image analysis apparatus, comprising:
one or more processors;
a memory storing instructions thereon, the instructions when executed by the one or more processors cause the one or more processors to:analyze a target image based on an image analysis model composed of at least one expert generated by learning training images to generate an analysis result;
determine validity of the analysis result for the target image based on a probability of the target image belonging to an input category of the at least one expertby at least:obtaining a likelihood of an input and a class with respect to each of the at least one expert;
obtaining a prior probability of each of the at least one expert; and obtaining a probability of the input target image belonging to the input category of the at least one expert based on the obtained likelihood and the obtained prior probability; and
responsive to determining that the analysis result for the target image is invalid,
classify the target image into a new category so that a new expert for the new category is trained using the target image.

9\. (Previously Presented) The apparatus of claim 8, further comprising: wherein the memory is further configured to store the target image classified into the new category.

10\. (Previously Presented) The apparatus of claim 9, wherein the instructions further cause the one or more processors to:
when an accumulated capacity of images including the target image stored in the memory is greater than or equal to a predetermined reference capacity, generate the new expert for the new category by performing training using the images stored in the memory.

11\. (Previously Presented) The apparatus of claim 10, wherein the instructions further cause the one or more processors to:
label the images stored in the memory, and
generate the new expert for the new category by training the new expert using the labeled images.

12\. (Previously Presented) The apparatus of claim 8, wherein the instructions further cause the one or more processors to randomly determine the validity of the analysis result for the target image based on a probability of the input target image belonging to the input category of the at least one expert.

13\. (Canceled)

14\. (Currently amended) The apparatus of claim \[\[13\]\]8, wherein the instructions further cause the one or more processors to:
obtain a first probability of a class with respect to each of the input target image and the at least one expert by using a discriminative model of each of the at least one expert,
obtain a second probability of an input with respect to each of the at least one expert by using a generative model of each of the at least one expert, and
obtain the likelihood of an input and a class with respect to each of the at least one expert based on the first probability and the second probability.

15\. (Previously Presented) The apparatus of claim 14, wherein the instructions further cause the one or more processors to estimate a lower bound of the second probability as the second probability.

16\. (Original) A method for generating an image analysis model, the method comprising:
continually receiving first training images during a first receiving period;
generating a first expert composed of a discriminative model and a generative model for a first category by learning the first training images during a first learning period right after the first receiving period;
continually receiving second training images during a second receiving period right after the first learning period;
determining whether to learn each of the second training images for the first expert based on a probability of each of the second training images to belong to the first category; and
generating a second expert for a second category by learning, during a second learning period right after the second receiving period, a training image determined not to be learned for the first expert among the second training images.

OFFICE ACTION:
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 Amendment
The amendment and written response filed 09/11/2024 have been entered and considered.
Claims 3 and 13 have been cancelled.
Claims 1, 4, 8 and 14 have been amended.
Claims 1-2, 4-12 and 14-16 are pending.
Upon entry of the claim amendment, the rejection and objection of claims 1-2, 4-12 and 14-15 have been withdrawn.
Allowable Subject Matter
Claims 1-2, 4-12 and 14-16 are allowed.
The following is an examiner’s statement of reasons for allowance:
Independent claims 1 and 8 have been amended to incorporate allowable subject matter as previously indicated in office action dated 07/11/2024.
Claims 2 and 4-7 are dependent upon claim 1.
Claims 9-12 and 14 are dependent upon claim 8.
Claim 16 defines features of “generating a first expert composed of a discriminative model and a generative model for a first category by learning the first training images during a first learning period right after the first receiving period; continually receiving second training images during a second receiving period right after the first learning period; determining whether to learn each of the second training images for the first expert based on a probability of each of the second training images to belong to the first category; and generating a second expert for a second category by learning, during a second learning period right after the second receiving period, a training image determined not to be learned for the first expert among the second training images”, which are not taught or suggested by the art of record.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee.  Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion

APPLICANT REBUTTAL: REMARKS
Claims 1-16 were pending in this application. In the final Office Action dated June 11, 2024, claims 1-2 and 6-12 were rejected, claims 3-5 and 13-15 were objected to, and claim 16 was allowed.
Claims 1, 4, 8, and 14 are hereby amended. Claims 3 and 13 are canceled herein. No claim is added herein.
Based on the above Amendment and the following Remarks, withdrawal of all outstanding objections and rejections is respectfully requested.
Res
p
onse to Re
j
ections under 35 U.S.C.
&
In the Office Action, claims 1-3, 8-9 and 12-13 were rejected under 35 U.S.C. § 102(a)(2) as being anticipated by U.S. Patent Application Publication No. 2019/0220967 ("Bhatt").
Independent claim 1 is hereby amended to incorporate the features of claim 3 (now canceled). Claim 3 was indicated in the Office Action as being allowable over Bhatt. Accordingly, claim 1, as amended, is patentable over Bhatt.
Independent claims 8 is also amended to incorporate the feature of claim 13, indicated as being allowable over Bhatt. Hence, claim 8, as amended, is also patentably distinguishable over Bhatt.
Hence, withdrawal of the rejection of claims 1, 8 and their dependent claims is respectfully requested.
Conclusion