RCE DETAILED ACTION
Introduction
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . A request for continued examination (RCE) under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application on 08/10/2026 after the final rejection of 04/09/2026, the Applicant initiated interview of 07/09/2026 and the advisory action of 07/27/2026. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. The Applicants’ RCE submission is therefore entered. No claims have been added or cancelled, but claims 1, 6, 8, 13, 15, 20 have been amended in this submission. Thus claims 1-20 are currently pending for reconsideration by the Examiner and are examined below.
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 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.
2. Claims 1-3, 6-10, 13-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Finegan (U.S. Patent # 211797780 B1) in view of Mika (U.S. Patent Application Publication # 2016/0179835 A1).
Regarding claim 1, Finegan teaches a system comprising a processor and a memory storing machine-readable instructions that, when executed by the processor, cause the processor to train a generative language model based on a machine learning model using a set of training data (Finegan teaches a computer implemented system including a processor, memory, and a variety of other connected devices on col. 10, line 57 to col. 11, line 41. Finegan teaches generative language models are trained on more than a first threshold amount of data. i.e., Finegan's generative model is trained, and therefore Finegan explicitly discloses training a generative machine learning model on col. 2, line 45 to col. 3, line 32);
train a generative visual model based on a machine learning model using a second set of training data (Finegan teaches the text-to-image model i.e., generative visual model, is trained to generate digital images using a corpus comprising a large collection of potentially noisy text-image pairs i.e., the generative text-to-image model is trained using a second set of training data, namely the potentially noisy text-image pairs on col. 3, line 57 to col. 4, line 6);
generate a plurality of texts that are related and semantically diverse based on one or more prompts using the generative language model (Finegan teaches generating a summary of a plurality of text documents i.e., related text summaries generated from prompts or input text documents, using a large language machine model i.e., a generative language model on col. 5, line 41 to col. 8, line 47);
generate a plurality of images based on at least the selected subset of the plurality of texts using the generative visual model (Finegan teaches generating a plurality of images from a collection of keywords distilled from the summaries generated by the language model i.e., generating a plurality of images based on a portion of a plurality of texts on col. 5, line 41 to col. 8, line 47);
and output at least a portion of the plurality of images to a data storage location (Finegan teaches generating a video clip i.e., a plurality of images and both outputting the video clip and storing the video clip i.e., at least a portion of the plurality of images is output by storing the video clip on col 8, line 36 to col. 8, line 47);
However, Finegan may not explicitly detail the limitation of determining a measure of semantic diversity among the plurality of texts. This is taught by Mika (Paragraphs 81-83, teach various different measures of semantic diversity);
Mika also teaches selecting a subset of the plurality of texts based on the measure of semantic diversity (Para 84, teaches selecting a subset of initial recommendations);
Finegan and Mika can be considered as analogous art as they belong to a similar field of endeavor in text processing. It would thus have been obvious to one having ordinary skill in the art to advantageously combine the teachings of Mika (Use of a diversity measure and selection of data based on this measure) with those of Finegan (Use of LLMs to summarize a set of text documents) so as to increase semantic diversity in the results in line with user intent (Mika, para 84).
Regarding claim 2, Finegan and Mika teach all the limitations of claim 1 as laid out above. Further, Finegan teaches the system of claim 1, wherein the one or more prompts include at least one of a text (Finegan teaches using text as input to generate refined prompts for generation of images from a generative language model on col. 5, line 41 to col. 8, line 47);
Regarding claim 3, Finegan teaches the system of claim 1, wherein the one or more prompts are based on at least one of machine-generated data or human-generated data (Finegan teaches that summary sentences are generated from input documents i.e., human-generated data on col. 5, line 41 to col. 8, line 47. Further, Finegan teaches using the generated summaries to generate keywords which are used to generate images i.e., machine-generated data on col. 5, line 41 to col. 8, line 47. As such, a person of ordinary skill in the art would have understood that the prompts are based on both machine generated and human generated data because the keywords are extracted from machine-generated summaries based on user input documents which, in essence, are based on both machine-generated and human-generated content).
Regarding claim 6, Finegan teaches the system of claim 1, wherein a portion of the plurality of texts is based on a diverse selection of the plurality of texts. (Finegan teaches generating a plurality of images from a collection of keywords distilled from the summaries generated by the language model i.e., generating a plurality of images based on a portion of a plurality of texts col. 5, line 41 to col. 8, line 47. As such, a collection of keywords amounts to a diverse selection of the plurality of texts as the collection of keywords can be extracted from all the texts resulting in a spread as diverse as the collection of input documents).
Regarding claim 7, Finegan teaches the system of claim 1, wherein the plurality of images includes at least one of new images created by the generative visual model (Finegan teaches generating a plurality of images from a collection of keywords distilled from the summaries generated by the language model i.e., generating a plurality of images based on a portion of a plurality of texts on col. 5, line 41 to col. 8, line 47)
With regards to claims 8-10 and 13-14, these are method claims for the corresponding apparatus claims 1-3 and 6-7. These two sets of claims are related as method and apparatus of using the same, with each claimed system element's function corresponding to the claimed method step. Accordingly, claims 8-10 and 13 are similarly rejected under the same rationale as applied above with respect to apparatus claims 1-3 and 6-7.
With regards to claims 15-17 and 20, these are computer readable medium (CRM) claims for the corresponding apparatus claims 1-3 and 6. These two sets of claims are related as CRM and apparatus of using the same, with each claimed system element's function corresponding to the claimed CRM step. Accordingly, claims 15-17 and 20 are similarly rejected under the same rationale as applied above with respect to apparatus claims 1-3 and 6.
3. Claims 4-5, 11-12 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Finegan in view of Mika and further in view of Choi (U.S. Patent Application Publication # 2019/0251355 A1).
Regarding claim 4, Finegan and Mika teach all the limitations of claim 1 as laid out above. However, Finegan and Mika may not explicitly detail that the one or more prompts are based on at least one of the plurality of images. In a similar field of endeavor of generating text using machine learning and artificial intelligence based on input text or prompts, Choi teaches this limitation (Para 88, teaches generating a text based on user input wherein the user input is retrieved by an A/V interface which uses an audio signal, video signal, or photography mode to collect/input data for generation of a text comment. i.e., the prompts include an image, sound, or video. As such, a person of ordinary skill in the art would have understood that images generated by Finegan could also be used as input to the Finegan's system based on Choi's teaching of using images, sound, and video as prompts for text generation);
It would have been prima facie obvious to one of ordinary skill in the art to combine the teachings of Finegan and Mika with the teachings of Choi to provide prompts based on at least one of the plurality of images. A person of ordinary skill in the art would have recognized the significant overlapping material between Finegan, Mika and Choi. As such, because of their significant overlapping fields of endeavor, a person of ordinary skill in the art would have been motivated to combine the concepts of Choi with the concepts of Finegan and Mika as Choi's alternate input interfaces would have expanded Finegan's capabilities to receive input. Further, various forms of input are commonly understood in the art such that a person of ordinary skill in the art would have found it obvious to provide additional modes of input for the generative model.
Regarding claim 5, Finegan and Mika teach all the limitations of claim 1 as laid out above. However, Finegan and Mika may not explicitly detail generating the plurality of images based on at least an image. In a similar field of endeavor of generating text using machine learning and artificial intelligence based on input text or prompts, Choi teaches this limitation (Para 88, teaches generating a text based on user input wherein the user input is retrieved by an A/V interface which uses an audio signal, video signal, or photography mode to collect/input data for generation of a text comment i.e., the prompts include an image, sound, or video. As such, Choi's use of image, audio, and video-based inputs for text generation in combination with Finegan's generation of text based off of user input amounts to generating a plurality of images based off of at least an image);
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to combine the teachings of Finegan and Mika with the teachings of Choi to provide generating the plurality of images based on at least an image. A person of ordinary skill in the art would have recognized the significant overlapping material between Finegan, Mika and Choi. As such, because of their significant overlapping fields of endeavor, a person of ordinary skill in the art would have been motivated to combine the concepts of Choi with the concepts of Finegan and Mika as Choi's alternate input interfaces would have expanded Finegan's capabilities to receive input. Further, various forms of input are commonly understood in the art, as such, a person of ordinary skill in the art would have found it obvious to provide additional modes of input for the generative model.
With regards to claims 11-12, these are method claims for the corresponding apparatus claims 4-5. These two sets of claims are related as method and apparatus of using the same, with each claimed system element's function corresponding to the claimed method step. Accordingly, claims 11-12 are similarly rejected under the same rationale as applied above with respect to apparatus claims 4-5.
With regards to claims 18-19, these are computer readable medium (CRM) claims for the corresponding apparatus claims 4-5. These two sets of claims are related as CRM and apparatus of using the same, with each claimed system element's function corresponding to the claimed CRM step. Accordingly, claims 18-19 are similarly rejected under the same rationale as applied above with respect to apparatus claims 4-5.
Conclusion
4. The following prior art, made of record but not relied upon, is considered pertinent to applicant's disclosure: Peleg (U.S. Patent Application Publication # 2022/0198136 A1), Tran (U.S. Patent # 11748555 B2). These references are also included in the PTO-892 form attached with this office action.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to NEERAJ SHARMA whose contact information is given below. The examiner can normally be reached on Monday to Friday 8 am to 5 pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Louis-Desir can be reached on 571-272-7799 (Direct Phone). The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
/NEERAJ SHARMA/
Primary Examiner, Art Unit 2659
571-270-5487 (Direct Phone)
571-270-6487 (Direct Fax)
neeraj.sharma@uspto.gov (Direct Email)
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