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 .
Information Disclosure Statement
The references listed in the Information Disclosure Statement filed on May 19, 2024 have been considered by the examiner (see attached PTO-1449 form).
Response to Election with Traverse
In response to Applicant’s Election with Traverse, Examiner respectfully disagrees. Claim 1 is distinct from claim 2. Claim 1 and claim 2 describe different methods for achieving content curation, each with unique components and processes.
Claim 1 describes a method that uses a combination of generative artificial intelligence models to create and provide curated multi-modal content to a user in real-time. The focus is on generating new content based on real-time multimedia inputs.
Claim 2 describes a method that analyzes video content to identify and apply augmentative content from a local filter library. The focus is on selecting and applying pre-existing or filtered content rather than generating new, multi-modal content. Therefore, the restriction is proper and claims 1, 3-10 are being examined.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, recites the limitation “the curated multi-model content" in line 7. There is insufficient antecedent basis for this limitation in the claim. Since claims 3-10 are dependent on claim 1, they inherit the same problem.
Regarding claim 4, recites the limitation “the user preferences" in line 3. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 7, recites the limitation “said first generative large language model" and “said second generative AI model" in line 3. There is insufficient antecedent basis for this limitation in the claim.
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, 3-10 are rejected under 35 U.S.C. 103 as being unpatentable over Luo (U.S. Pub. No. 2020/0359104) in view of Graham et al. (U.S. Pub. No. 2024/0346731).
Regarding claim 1, Luo discloses a method of real-time continuous content curation, comprising:
a user client configured to intake multimedia inputs based on a real-time event (see paragraphs 0047, 0049, 0052; a live recording terminal 260 such as smartphone etc. is used to record a live video stream);
an application interface configured to receive and process live stream multimedia content (see paragraphs 0051-0052; the server acts as an application interface for receiving and processing live stream multimedia content).
Although Luo discloses “real-time translation" includes "speech recognition and instant translation based on artificial intelligence" in paragraph 0035. However, Luo fails to explicitly disclose an Al processing module which further processes the data via a combination of generative artificial intelligence models, wherein the curated multi-model content is created and provided back to the user in real-time.
Graham et al. discloses an Al processing module which further processes the data via a combination of generative artificial intelligence models, wherein the curated multi-model content is created and provided back to the user in real-time (see abstract and paragraphs 0014-0015, 0024, 0028, 0068; using multiple different AI models to create synthetic content).
It would have been obvious to a skilled artisan before the effective filing date of the claimed invention to modify the system of Luo with the teachings of Graham et al., the motivation being to see the AI-modified character on screen in real-time and create a more immersive experience for users on the movie set to improve the quality of directing the movie and/or acting in the movie.
Regarding claim 3, Luo and Graham et al. discloses everything claimed as applied above (see claim 1). Graham et al. discloses wherein the said application interface accepts preferences and ongoing feedback from the user to create an interactive content curation session (see paragraphs 0002, 0016 – real-time, iterative prompting system that includes a learning feedback loop. Paragraphs 0016, 0041 – describes how the model learns from live, iterative input and/or output, and the output data can be fed back to the model to generate further output data).
Regarding claim 4, Luo and Graham et al. discloses everything claimed as applied above (see claim 1). Graham et al. discloses a method of real-time continuous content curation as in claim 1, wherein the curated content generated by said generative Al model is an image or series of images based on the user preferences selected in the application interface (see paragraphs 0015, 0018-0019, 0031; generating a video of an actor or a synthetic version of a person based on user prompts. A video is a series of images. This is based on user preferences provided via a prompt in para. 0015).
Regarding claim 5, Luo and Graham et al. discloses everything claimed as applied above (see claim 1). Graham et al. discloses wherein said application interface is configured to transmit fragments of the live stream multimedia content and curated text transcriptions to said Al processing module for further processing (see paragraphs 0028, 0063; using speech-to-text technology to convert audio data from a microphone into text data representing a voice prompt. This text data is then used to prompt the AI model. This is a clear example of transmitting fragments of multimedia (audio) and a transcription (text data) to the AI processing module).
Regarding claim 6, Luo and Graham et al. discloses everything claimed as applied above (see claim 1). Graham et al. discloses wherein said live stream multimedia content is pre-processed into structured capsules before being transmitted to said Al processing module (see paragraphs 0028, 0063, 0069; converting a voice prompt into text data via a speech-to-text component and filtering incoming prompts against a predefined set of prompts. This process transforms the raw multimedia input into a more structured, or “pre-processed” format (text data, predefined prompts) before the AI model uses it).
Regarding claim 7, Luo and Graham et al. discloses everything claimed as applied above (see claim 1). Graham et al. discloses wherein said first generative large language model analyzes transcriptions to generate text-based prompts, which are then used by said second generative Al model to create said curated content (see paragraphs 0063, 0043; a processor using an ASR component and/or NLU component to generate text data from a voice prompt. The text data is then used to identify a “predefined text prompt” which is then used to prompt a trained AI model(s) to generate output data. This clearly anticipates the use of a first model (ASR/NLU component) to generate a prompt for a second model to create content).
Regarding claim 8, Luo and Graham et al. discloses everything claimed as applied above (see claim 1). Graham et al. discloses wherein said Al processing module automatically creates content at natural pauses within the said multimedia inputs (see paragraphs 0014-0015; describes a live prompting system that provides real-time output. Paragraph 0069 discusses the latency between an input prompt and the generation of output data. While it doesn’t explicitly use the phrase “natural pauses”, the entire system is based on generating content in real-time in response to a prompt. It is an obvious design choice for such a system to process and generate content when there is a break in input, such as a complete sentence).
Regarding claim 9, Luo and Graham et al. discloses everything claimed as applied above (see claim 1). Graham et al. discloses wherein said Al processing module will re-process curated content through the Al, to combine multiple pieces of content into, a single newly curated piece of content post session (see paragraphs 0057, 0076, 0084; a user can continue to prompt the AI model(s) with additional prompts in order to iteratively build upon an original synthetic manipulation. An example is given of de-aging an actor and then adding synthetic snow to the scene. This is a direct description of combining multiple pieces of content into a newly curated piece of content. Paragraph 0043 mentions that the output data from one model can be combined with another to generate the final video).
Regarding claim 10, Luo and Graham et al. discloses everything claimed as applied above (see claim 1). Graham et al. discloses post-session options for interacting with said curated content (see paragraphs 0018-0019; the generated media content (e.g., image, video, audio) can be saved and interacted with after the real-time session has concluded. The content, such as a synthetic movie scene, is created for later use.
Conclusion
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NNENNA EKPO
Primary Examiner
Art Unit 2425
/NNENNA N EKPO/Primary Examiner, Art Unit 2425 October 23, 2025.