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 .
DETAILED ACTION
1. This is in response to application filed on 10/03/2024 in which claims 1-20 are presented for examination.
Status of Claims
2. Claims 1-20 are pending, of which claim 1, 8 and 15 are in independent form.
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.
3. Claims 1-20 are rejected under 35 U.S.C 103 as being unpatentable over GU et al. (US PG Pub 2024/0427995) published on December 26, 2024in view of Fadeev (US PG Pub 2018/0314880) published on November 1, 2018.
As per Claim 1 and 15, Gu teaches A computing device comprising: a processor; and non-transitory memory storing instructions that, when executed by the processor, cause the computing device to:
train, based on a training data set, an engagement classifier(Para[0030] training the visual text identifier to classify text, as taught by GU);
iteratively generate an image-text pair based on a similarity score, wherein the similarity score corresponds to a calculated similarity between original text and the one or more images(Para[0062] fig 6 a first sentence result in a visual score of 0.87. As shown at 606, a second sentence results in a visual score of 0.03. The visual score of the first sentence 602 satisfies a visual text threshold. As a result, the image manager 106 of the text visualization system 100 obtains an image 604 associated with the first sentence. In some implementations, a user interacts with button 610 to obtain the image 604 associated with the first sentence, as taught by GU); and
present, based on a final similarity score meeting a threshold, a final image-text pair(Para[0062] fig 6 responsive to a visual score satisfying a visual score threshold, an image associated with the sentence is obtained, as taught by GU).
Gu does not explicitly teach generate, based on gradient information from the engagement classifier, one or more images;
generate, based on gradient information from the engagement classifier, one or more images(fig 4 Para[006][0036[0050-0056] image is generated based on comparison with other images by predicting similarity score, as taught by Fadeev);
It would have been obvious to one of ordinary skill in the art before the filing date of the
invention to modify GU invention with the teaching of Fadeev because doing so would
result in similarity of image data and the similarity of textual information associated with the user are used
by the online system as indicators of matched user accounts belonging to the same user across both
systems.
As per Claim 8, Gu teaches A method comprising:
training an engagement classifier(Para[0030] training the visual text identifier to classify text, as taught by GU);
iteratively generating an image-text pair based on a similarity score, wherein the similarity score corresponds to a calculated similarity between original text and the one or more images(Para[0062] fig 6 a first sentence result in a visual score of 0.87. As shown at 606, a second sentence results in a visual score of 0.03. The visual score of the first sentence 602 satisfies a visual text threshold. As a result, the image manager 106 of the text visualization system 100 obtains an image 604 associated with the first sentence. In some implementations, a user interacts with button 610 to obtain the image 604 associated with the first sentence, as taught by GU); and
presenting, based on a final similarity score meeting a threshold, a final image-text pair determined from the iteratively generating step meeting a threshold(Para[0062] fig 6 responsive to a visual score satisfying a visual score threshold, an image associated with the sentence is obtained, as taught by GU).
Gu does not explicitly teach generating, based on gradient information from the engagement classifier, one or more images;
generating, based on gradient information from the engagement classifier, one or more images(fig 4 Para[006][0036[0050-0056] image is generated based on comparison with other images by predicting similarity score, as taught by Fadeev);
It would have been obvious to one of ordinary skill in the art before the filing date of the
invention to modify GU invention with the teaching of Fadeev because doing so would
result in similarity of image data and the similarity of textual information associated with the user are used
by the online system as indicators of matched user accounts belonging to the same user across both
systems.
As per claim 2, 9 and 16, the combination of GU and Fadeev teaches wherein the instructions
further cause the computing device to train the engagement classifier on a collection of published
combinations of images and text(fig 6 shows image and text data, as taught by Gu).
As per claim 3, 10 and 17, the combination of GU and Fadeev teaches wherein iteratively
generating the image-text pair is limited to a maximum number of iterations(Para[0062], as taught by
Gu).
As per claim 4, 11 and 18, the combination of GU and Fadeev teaches wherein a pre-trained
contrastive language-image pretraining (CLIP) model measures a similarity between a generated
image and the original text(Para[0066][0075], as taught by GU).
As per claim 5, 12 and 19, the combination of GU and Fadeev teaches wherein the instructions further cause the computing device to: compare a first similarity score associated with a first text-image pair with a second similarity score associated with a second text-image pair(Para[0045-0048][0052], as taught by Fadeev); and
identify whether a difference between the first similarity score and the second similarity score meets a threshold condition(Para[0025-0026][0062], as taught by Gu)
As per claim 6, 13 and 20, the combination of GU and Fadeev teaches wherein the instructions further cause the computing device to return, when the difference meets the threshold condition, one of the first text-image pair and the second text-image pair(fig 6-9Para[0060-0065], as taught by Gu).
As per claim 7 and 14, the combination of GU and Fadeev teaches wherein the instructions further cause the computing device to generate, when the difference fails to meet the threshold condition, a third text-image pair(Para[0063-0065], as taught by Gu).
Conclusion
4. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hinz(US 2024/0320789) discloses diffusion model is trained to generate a low-resolution image. In some cases, diffusion model generates the low-resolution image based on a text prompt using a reverse diffusion process. In some aspects, diffusion model takes the text embedding as input. In some aspects, diffusion model takes the image embedding as input. In some aspects, the low-resolution image is generated using multiple iterations of diffusion model.
5. The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
6. When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Point of Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAYEEZ R CHOWDHURY whose telephone number is (571)270-3069. The examiner can normally be reached Monday-Friday 9AM-6:30PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William L Bashore can be reached at 571-272-4088. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RAYEEZ R CHOWDHURY/Primary Examiner, Art Unit 2174 Friday, September 25, 2026
