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 .
This Office action is responsive to amendment filed on 11/03/25.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zakharov (Zakharov et al. US 2024/0295953) in view of Pathak et al. (US 2024/0394479).
Regarding claims 1 and 20, Zakharov discloses a method for determining an
image for display (e.g., 114, 116, 118, para. 0026), comprising:
obtaining a plurality of images associated with an object (paras. 0103-
0104);
generating a prompt for a language model based on the plurality of
images (paras. 0105-0106);
obtaining a plurality of attractiveness ranks corresponding to the plurality of images by feeding the prompt to the language model (paras. 0106-0107);
determining a plurality of probability distributions corresponding to the plurality of images based on the plurality of attractiveness ranks (paras. 0108-0109,
0167); and
determining a target image for display from the plurality of images based on the plurality of probability distributions (paras. 0110-0111).
Zakharov does not specifically disclose the plurality of images are candidate images.
In a similar field of endeavor of image generation system, Pathak discloses a plurality of candidate images (such as the prompt-managing component 122 uses the number of content units specified by the complexity-analyzing component 132 to govern an amount of the candidate content information that is selected for incorporation into the prompt information, para. 0079).
Therefore, it would have been obvious to one of ordinary skill in the art before effective filling date of the claimed invention to incorporate the candidate images as taught by Pathak in the system of Zakharov in order to reduce a number of content units in the prompt information (para. 0006).
Regarding claims 2 and 12, the combination of Zakharov and Pathak discloses generating the prompt for the
language model based on the plurality of candidate images comprises:
obtaining a description of task objective (paras. 0103-0104 of Zakharov);
obtaining an image list based on the plurality of candidate images (paras. 0103-
0104 of Zakharov); and
generating the prompt based on the description of task objective and the image
list (paras. 0105-0106 of Zakharov).
Regarding claims 3 and 13, the combination of Zakharov and Pathak discloses generating the prompt based on the description of task objective and the image list comprises:
obtaining a scoring criteria associated with the object (paras. 0106-0108 of Zakharov); and
generating the prompt based on the description of task objective, the image list,
and the scoring criteria (paras. 0106-0108 of Zakharov).
Regarding claims 4 and 14, the combination of Zakharov and Pathak discloses generating the prompt based on the description of task objective, the image list, and the scoring criteria comprises:
obtaining a template of output, the template of output comprising a field of image
identification and a field of attractiveness score (paras. 0105-0108 of Zakharov); and
generating the prompt based on the description of task objective, the image list,
the scoring criteria, and the template of output (paras. 0105-0108 of Zakharov).
Regarding claims 5 and 15, the combination of Zakharov and Pathak discloses determining the target image for display from the plurality of candidate images based on the plurality of probability distributions comprises:
generating a plurality of sample values corresponding to the plurality of candidate
images by performing random samplings on the plurality of probability distributions
respectively (paras. 0110-0111 of Zakharov); and
determining the target image from the plurality of candidate images based on the
plurality of sample values (paras. 0110-0111 of Zakharov).
Regarding claims 6 and 16, the combination of Zakharov and Pathak discloses determining the target image from the plurality of candidate images based on the plurality of random sample values comprises:
determining a candidate image with a greatest sample value as the target image
(paras. 0110-0111 of Zakharov).
Regarding claims 7 and 17, the combination of Zakharov and Pathak discloses generating the plurality of probability distributions corresponding to the plurality of candidate images based on the plurality of probability distributions comprises:
generating a plurality of Beta distributions corresponding to the plurality of candidate images based on the plurality of attractiveness ranks, a Beta distribution of
the plurality of Beta distributions comprising an alpha parameter and a beta parameter,
the alpha parameter indicating a number of times that users interact with a candidate
image, the beta parameter indicating a number of times that users have not interacted
with the candidate image (para. 0091 of Zakharov).
Regarding claims 8 and 18, the combination of Zakharov and Pathak discloses the plurality of candidate images comprises a first candidate image and a second candidate image, an attractiveness rank of the first candidate image is higher than an attractiveness rank of the second candidate image, and generating the plurality of Beta distributions corresponding to the plurality of candidate images based on the plurality of attractiveness ranks comprises:
initializing a plurality of alpha parameters of the plurality of Beta distributions
based on the plurality of attractiveness ranks, wherein a value of an alpha parameter
corresponding to the first candidate image is greater than a value of an alpha parameter
corresponding to the second candidate image (paras. 0124-0126 of Zakharov).
Regarding claims 9 and 19, the combination of Zakharov and Pathak discloses transmitting the target image to a user device for display;
receiving a feedback data, the feedback data indicating whether a user has
interacted with the target image (paras. 0124-0126 of Zakharov); and
updating a target Beta distribution corresponding to the target image based on
the feedback data (paras. 0124-0126 of Zakharov).
Regarding claim 10, the combination of Zakharov and Pathak discloses updating the target Beta distribution corresponding to the target image based on the feedback data comprises:
increasing a value of an alpha parameter of the target Beta distribution in
response to the feedback data indicating that the user has interacted with the target
image (paras. 0124-0126 of Zakharov); and
increasing a value of a beta parameter of the target Beta distribution in response
to the feedback data indicating that the user has not interacted with the target image
(paras. 0124-0126 of Zakharov).
Regarding claim 11, Zakharov discloses an electronic device (e.g., 114, 116,
118, para. 0026), comprising:
a memory and a processor (paras. 0034 and 0036);
wherein the memory is configured to store one or more computer instructions
which, when executed by the processor, cause the processor to:
obtain a plurality of candidate images associated with an object; generate a
prompt for a language model based on the plurality of images (paras. 0103-
0104);
obtain a plurality of attractiveness ranks corresponding to the plurality of images by feeding the prompt to the language model (paras. 0106-0107);
determine a plurality of probability distributions corresponding to the plurality of images based on the plurality of attractiveness ranks (paras. 0108-0109,
0167); and
determine a target image for display from the plurality of images based
on the plurality of probability distributions (paras. 0110-0111).
Zakharov does not specifically disclose the plurality of images are candidate images.
In a similar field of endeavor of image generation system, Pathak discloses a plurality of candidate images (such as the prompt-managing component 122 uses the number of content units specified by the complexity-analyzing component 132 to govern an amount of the candidate content information that is selected for incorporation into the prompt information, para. 0079).
Therefore, it would have been obvious to one of ordinary skill in the art before effective filling date of the claimed invention to incorporate the candidate images as taught by Pathak in the system of Zakharov in order to reduce a number of content units in the prompt information (para. 0006).
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on at least one reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JENNIFER T NGUYEN whose telephone number is (571)272-7696. The examiner can normally be reached Mon-Fri 7:00-5:00.
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/JENNIFER T NGUYEN/Primary Examiner, Art Unit 2629