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 .
Priority
This application claims priority of the Chinese Patent Application No. 202310813786.8, filed on July 4, 2023, the disclosure of which is incorporated herein by reference in the present application.
Response to Amendment
Claims 1, 3-5, 7-8, 10-12, 14-15, and 17-19 are amended. Claims 6, 13 and 20 are cancelled. Claims 1-5, 7-12 and 14-19 are presented for examination.
Response to Arguments
Applicant’s arguments filed on 4/24/2026 have been reviewed. Following are the response:
Claim Rejections - 35 U.S.C. § 101
In light of amendments, rejection under 35 U.S.C. § 101 is withdrawn.
Claim Rejections - 35 U.S.C. § 102
Applicant argues “First, Wang does not disclose first prompt information that is used to instruct the question answering model to simulate watching of the video to execute the dialog question answering generation task of creating and answering a question. None of the three parts of Wang's prompt instruct the question answering model to simulate watching of the video to execute the dialog question answering generation task of creating and answering a question. In particular, the "instruction" part of Wang's prompt is simply a concise description of the generation task. Nowhere does Wang disclose that the generation task includes an instruction to simulate watching of the video to execute the dialog question answering generation task of creating and answering a question. Further, the "few-shot context" part of Wang's prompt contains selected in-context examples as well as the test video instance. But Wang does not disclose that the "selected in-context examples" or the "test video instance" is in any way related to an instruction to simulate watching of the video to execute the dialog question answering generation task of creating and answering a question. Finally, the "task query" part of Wang's prompt simply indicates the target text format. Nowhere does Wang disclose that the "task query" part of the prompt is in any way related to an instruction to simulate watching of the video to execute the dialog question answering generation task of creating and answering a question” However, the examiner relied on the combination of Wang modified by Muhammad to teach this concept. Specifically, Muhammad teaches that the first prompt information is used to instruct the target question answering model to simulate watching of the target video to execute the dialog question answering generation task of creating and answering a question ( prompts based on the following template: USER: <instruction> <vid-tokens> Assistant: Using the notations, we can represent it as, USER: <Q1><Qv>Assistant:, Under 3.2 Video Instruction Training )
Applicant further argues “Second, Wang does not disclose third prompt information that is used to instruct the question answering model to give a definite answer when the question answering model executes the dialog question answering generation task. None of the three parts of Wang's prompt instruct the question answering model to simulate watching of the video to execute the dialog question answering generation task of creating and answering a question. In particular, the "instruction" part of Wang's prompt is simply a concise description of the generation task. Nowhere does Wang disclose that the generation task includes an instruction to give a definite answer when the question answering model executes the dialog question answering generation task. Further, the "few-shot context" part of Wang's prompt contains selected in-context examples as well as the test video instance. But Wang does not disclose that the "selected in- context examples" or the "test video instance" is in any way related to an instruction to give a definite answer when the question answering model executes the dialog question answering generation task. Finally, the "task query" part of Wang's prompt simply indicates the target text format. Nowhere does Wang disclose that the "task query" part of the prompt is in any way related to an instruction to give a definite answer when the question answering model executes the dialog question answering generation task.” However, as shown in Fig. 2 and Fig. 10, the model is instructed to generate a definitive answer, as a task specific instruction, proving the prompt contains an explicit directive. Additionally, Wang's prompt includes a specific task instruction, as also demonstrated in the prompt layout of Fig. 5. In contrast to applicant assertion, the examiner has not relied on task query for to read on this part of the limitation.
Applicant further argues “Third, Wang does not disclose fourth prompt that information is used to instruct the question answering model to ask a question of a preset type when the question answering model executes the dialog question answering generation task. None of the three parts of Wang's prompt instruct the question answering model to simulate watching of the video to execute the dialog question answering generation task of creating and answering a question. In particular, the "instruction" part of Wang's prompt is simply a concise description of the generation task. Nowhere does Wang disclose that the generation task includes an instruction to ask a question of a preset type when the question answering model executes the dialog question answering generation task. Further, the "few-shot context" part of Wang's prompt contains selected in- context examples as well as the test video instance. But Wang does not disclose that the "selected in-context examples" or the "test video instance" is in any way related to an instruction to ask a question of a preset type when the question answering model executes the dialog question answering generation task. Finally, the "task query" part of Wang's prompt simply indicates the target text format. Nowhere does Wang disclose that the "task query" part of prompt is in any way related to an instruction to ask a question of a preset type when the question answering model executes the dialog question answering generation task.” However, refer to Fig. 2 and Fig. 10, which show that the question is generated by a model based on the instruction. Additionally, refer to Fig. 5, which clarifies that the question is generated as a “Vlep task query”. In contrast to applicant assertion, the examiner has not relied on task query for to read on this part of the limitation.
Applicant argues “Fourth, Wang does not disclose fifth prompt information that is used to instruct the
question answering model to give an answer comprising a detailed reasoning process when the question answering model executes the dialog question answering generation task. None of the three parts of Wang's prompt instruct the question answering model to simulate watching of the video to execute the dialog question answering generation task of creating and answering a question. In particular, the "instruction" part of Wang's prompt is simply a concise description of the generation task. Nowhere does Wang disclose that the generation task includes an instruction to give an answer comprising a detailed reasoning process when the question answering model executes the dialog question answering generation task. Further, the "few-shot context" part of Wang's prompt contains selected in-context examples as well as the test video instance. But Wang does not disclose that the "selected in-context examples" or the "test video instance" is in any way related to an instruction to give an answer comprising a detailed reasoning process when the question answering model executes the dialog question answering generation task.
Finally, the "task query" part of Wang's prompt simply indicates the target text format. Nowhere does Wang disclose that the "task query" part of the prompt is in any way related to an instruction to give an answer comprising a detailed reasoning process when the question answering model executes the dialog question answering generation task.” Because temporal prompting's main goal is to drive model reasoning, the fifth prompt is already built into the task instructions. Consider an example under Introduction part of the paper “In contrast, a correct video-level de scription would be "a woman makes realistic looking leaves and flowers for a cake", which involves reasoning over a collection of objects and events that occur at different timestamps in the video clip, such as "cake decorating" and “flowered design”. Hence, to inform video-level description and queries we need to represent all of this information and its temporal ordering.” Therefore, an answer comprising a detailed reasoning process is covered by the teachings that model reasons and provide an answer based on instructions. It appears the applicant is trying to cover the concept of outputting/displaying the reasoning with answering which is not a part of the claim. If applicant wants to cover that, it must be explicitly present in the claim. In contrast to applicant assertion, the examiner has not relied on task query for to read on this part of the limitation.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
And
KSR, 550 U.S. at 418, 82 USPQ2d at 1396. Exemplary rationales that may support a conclusion of obviousness include:
(A) Combining prior art elements according to known methods to yield predictable results;
(B) Simple substitution of one known element for another to obtain predictable results;
(C) Use of known technique to improve similar devices (methods, or products) in the same way;
(D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results;
(E) "Obvious to try" – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success;
(F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art;
(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
See MPEP § 2143 for a discussion of the rationales listed above along with examples illustrating how the cited rationales may be used to support a finding of obviousness. See also MPEP § 2144 - § 2144.09 for additional guidance regarding support for obviousness determination.
Claims 1, 5, 8 , 12, 15 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang( Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners Audio Visual Scene-Aware Dialog) and further in view of Muhammad ( Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models)
Regarding claim 1, Wang teaches a method for generating video dialog question answering data ( video-to-text generation tasks, such as video captioning and video question answering, Under 3.2 Video Level: Temporal-Aware Few-shot Prompting) , the method comprising: determining video description information corresponding to a video (Each video instance is represented by the aggregated visual tokens3 , e.g., "Objects: First, bath toy. Then,...", the frame captions, such as "Frame Captions: First, a toddler playing in a bathtub filled with toys. Then,...", and the ASR inputs if available, e.g., "Subtitle:"., Fig 2; 3 Method ) ; determining a prompt used for a question answering model, wherein the question answering model is pre-configured based on a large language model (The few-shot prompt consists of three parts: instruction, few-shot context, and task query. The instruction is a concise description of the generation task, e.g., "Generate a video caption based on the objects, events, attributes and frame captions. Example:", which is proved to be effective in zero-shot and few-shot settings [6, 59]. The few-shot context contains the selected in-context examples as well as the test video instance. Each video instance is represented by the aggregated visual tokens3 , e.g., "Objects: First, bath toy. Then,...", the frame captions, such as "Frame Captions: First, a toddler playing in a bathtub filled with toys. Then,...", and the ASR inputs if available, e.g., "Subtitle:". Finally, the task query is a task-specific suffix indicating the text format, e.g. "Video Caption:". For in-context examples (omitted here for simplicity), the task query is followed by ground truth annotation, while for the test instance, the generation starts at the end of the task query. Under 3.3) , and the prompt is capable of guiding the question answering model to a desired dialog question answering effect based on the video description information when the question answering model executes a dialog question answering generation task ( prompt template ( answer) in response to a question is an input to a language model, Fig 2-3);
wherein the prompt is configured with first prompt information, second prompt information, third prompt information, fourth prompt information, and fifth prompt information, instruct the question answering model to simulate watching of the video to execute the dialog question answering generation task of creating and answering a question (feed each frame to a language model which generates frame caption and object attribute etc., Fig 2) , the second prompt information is used to instruct the question answering model to use details comprised in the video description information when the question answering model executes the dialog question answering generation task so that an answer fits the video description information ( details for e.g. object/attributed etc., Fig 10; since the model is a large language style model these instructions are called prompts), the third prompt information is used to instruct the question answering model to give a definite answer when the question answering model executes the dialog question answering generation task (as shown in Fig. 2 and Fig. 10, the model is instructed to generate a definitive answer, as a task specific instruction including the temporal clips and attribute, proving the prompt contains an explicit directive. Additionally, Wang's prompt includes a specific task instruction, as also demonstrated in the prompt layout of Fig. 5) , the fourth prompt information is used to instruct the question answering model to ask a question of a preset type when the question answering model executes the dialog question answering generation task (prompt question, Fig 2, Fig 10; Additionally, refer to Fig. 5, which clarifies that the question is generated as a “Vlep task query”) , and the fifth prompt information is used to instruct the question answering model to give an answer comprising a detailed reasoning process when the question answering model executes the dialog question answering generation task ( answering, Fig 10-12; Because temporal prompting's main goal is to drive model reasoning, the fifth prompt is already built into the task instructions. Consider an example under Introduction part of the paper “In contrast, a correct video-level de scription would be "a woman makes realistic looking leaves and flowers for a cake", which involves reasoning over a collection of objects and events that occur at different timestamps in the video clip, such as "cake decorating" and “flowered design”. Hence, to inform video-level description and queries we need to represent all of this information and its temporal ordering.” Therefore, an answer comprising a detailed reasoning process); and outputting, using the question answering model and based on the video description information and the prompt, dialog question answering data associated with the video ( for e.g. bathtub is the dialog answer data, Under 3. Method, Fig 2; or Fig 3 using temporal prompting -- Temporal-aware prompt successfully distinguishes the Sunset and Sunrise scenarios based on the temporal ordering change of objects and frame captions, while the static prompt fails.)
While Wang design takes video frames as an input and passes through an Image language model, it does not explicitly teach the first prompt information is used to instruct the question answering model to simulate watching of the video to execute the dialog question answering generation task of creating and answering a question
In the same field of endeavor Muhammad teaches the first prompt information is used to instruct the question answering model to simulate watching of the video to execute the dialog question answering generation task of creating and answering a question ( prompts based on the following template: USER: <instruction> <vid-tokens> Assistant: Using the notations, we can represent it as, USER: <Q1><Qv>Assistant:, Under 3.2 Video Instruction Training )
It would have been obvious for Wang to include the concept of Muhammad before effective filing date since the large language model takes instruction as prompt and Wang image language model substituted with large language model and it would be obvious for to try for Wang as large model are increasingly become well known.
Regarding claim 5, Wang as above in claim 1, teaches wherein the outputting, using the question answering model and based on the video description information and the prompt, video dialog question answering data associated with the video comprises: adding the video description information to a preset position indicated by the prompt, to obtain input information for the question answering model ( Fig 10 – prompts) ; and controlling, based on the input information, the question answering model to execute the dialog question answering generation task, and outputting the video dialog question answering data of the video based on the execution of the dialog question answering generation task ( answering based on the prompt, Fig 10)
Regarding claim 8, arguments analogous to claim 1, are applicable. In addition, Wang teaches An electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor, wherein the memory stores a computer program executable by the at least one processor, and the computer program, when executed by the at least one processor, causes the at least one processor to perform a method of claim 1 ( fig 1-3 )
Regarding claim 12, arguments analogous to claim 5, are applicable.
Regarding claim 15, arguments analogous to claim 1, are applicable. In addition, Wang teaches A non-transitory computer-readable medium, storing computer instructions that, when executed by a processor, cause a method for generating video dialog question answering data to be implemented, as in claim 1 ( Fig 1-3)
Regarding claim 19, arguments analogous to claim 5, are applicable.
Claims 2, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Wang( Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners Audio Visual Scene-Aware Dialog) and further in view of Muhammad ( Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models) and further in view of Rose ( US 20220139383)
Regarding claim 2, Wang as above in claim 1, teaches a description of a subject and a local detail event between subjects in a single frame of video picture ( object and event, Fig 2 ) , a description of a global detail event between subjects expressed sequentially in a plurality of consecutive frames of video pictures ( f As shown in the few-shot context in Figure 2, each visual token and frame caption is prefixed with a natural language phrase indicating its temporal ordering, e.g., "First,","Then,", and "Finally,".. Under 3.3) , a position of a subject in a video picture ( for e.g. toddler in the bathtub, Fig 2) , and content of dialog text of the subject in the video picture ( video level (for what the toddler is doing), Fig 2 and ASR for video captioning )
Wang modified by Muhammad does not explicitly teach wherein the video description information comprises a description of a video title
However, Rose teaches the video description information comprises a description of a video title
(the topic determiner 108 may determine the topic of discussion by determining one or more keywords associated with a topic from the transcript, applying data representative of the transcript to a neural network to compute data indicative of the topic, by employing computer vision to analyze frames of video included in the stream data 104 to determine the topic, and/or by analyzing metadata (e.g., title, description, host, creator, and/or author) associated with the stream data 104., Para 0026)
It would have been obvious having the teachings of Wang and Muhammad to further include the concept of Rose before effective filing date so to determine information related to video for e.g. topic etc. ( Para 0026, Rose)
Regarding claim 9, arguments analogous to claim 2, are applicable.
Regarding claim 16, arguments analogous to claim 2, are applicable.
Claims 3, 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Wang( Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners Audio Visual Scene-Aware Dialog) and further in view of Muhammad ( Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models) and further in view of Rose ( US 20220139383) and further in view of Pujari ( US 20240048839)
Regarding claim 3, Wang as above in claim 2, teaches wherein the determining video description information corresponding to a video comprises: detecting a subject appearing in at least two frames of video pictures extracted from the video ( multiple frame index) , and determining based on a frame, a position of the subject in the video picture (we consider the frame index from which they are extracted from as their temporal indicator. If a visual token occurs in multiple frames, we use the averaged frame index as its temporal indicator, under C Additional Experimental Details – for e.g. where and what a subject is doing , Fig 2, Fig 3) ; and determining, upon detecting that there is a subject appearing in the video picture and that a text subtitle appears in the video picture, the appearing text subtitle as content of dialog text of the subject in the video picture ( Generate a video caption based on the objects, events, attributes, frame captions and subtitle and further refer to Answering questions based on -- Answer the question based on the objects, events, attributes and frame captions. Example, Page 20-22); and upon detecting that there is a subject appearing in the video picture and that there is a matching audio in the video picture, converting the matching audio into text, and then determining the text as content of dialog text of the subject in the video picture ( Subtitle is an ASR transcript, Page 21-22)
Wang modified by Muhammad and Rose does not explicitly teach detecting whether there is a subject appearing in at least two frames of video pictures extracted from the video; and determining, upon detecting that there is a subject appearing, a position of the subject in the video picture
However Pujari teaches detecting whether there is a subject appearing in at least two frames of video pictures extracted from the video; and determining, upon detecting that there is a subject appearing, a position of the subject in the video picture ( Object detection systems generally include (i) an object detection component (e.g., a first machine learning model) that identifies the presence and location of one or more objects in the frames of the video and (ii) an object tracking component (e.g., a second machine learning model) that tracks the movement of each detected object over time across the frames of the video, Para 0047)
It would have been obvious having the teachings of Wang and Muhammad which mentions the concept of using atleast 4 frames to include the concept of Pujari to track object in atleast two or across frames to improve accuracy in detection of the object/subject
Regarding claim 10, arguments analogous to claim 3, are applicable.
Regarding claim 17, arguments analogous to claim 3, are applicable.
Claims 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Wang( Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners Audio Visual Scene-Aware Dialog) and further in view of Muhammad ( Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models) and further in view of Agarwal (US 11468675 )
Regarding claim 4, Wang as above in claim 1, teaches, wherein the determining a prompt used for a question answering model comprises: determining an application scenario of the dialog question answering data corresponding to the video ( object, attribute of the frames, Fig 2, Under 3.2. and 3.3) ; and determining, from candidate prompts associated with the question answering model, a prompt matching the application scenario of the dialog question answering data corresponding to the video ( At video level, we construct video representation by aggregating the visual tokens, frame captions and other text modalities such as ASR, using a few-shot temporal aware prompt. We then feed the prompt to a pre-trained language model together with task-specific instructions to generate text for a variety of video-language tasks. Examples of the full prompt for different tasks can be found in Appendix B., Fig 2)
Wang modified by Muhammad does not explicitly teach determining, in response to a select operation for the video, an application scenario of the dialog question answering data corresponding to the video
However, Agarwal teaches determining, in response to a select operation for the video, an application scenario of the dialog question answering data corresponding to the video ( the content selection module 708 may select a single instance of video content or the content selection module 708 may select more than one instance of video content. The selection of video content may be in response to received user input (e.g., the manual selection process 206 of FIG. 2) or the video content may be selected according to a predetermined rules set; and further once the video is selected determine the scene, Fig 8- At 804, a plurality of scenes within video content may be identified ,Col 19, line 1-15)
It would have been obvious for Wang and Muhammad to further include the concept of Agarwal that scene is detected after the selection of video so in a case of multiple video the scene detection happen when the selection takes place and it’s known in the art that user interface may have multiple video and processing takes place when the selection happens ( fig 8, Agarwal)
Regarding claim 11, arguments analogous to claim 4, are applicable.
Regarding claim 18, arguments analogous to claim 4, are applicable.
Claim 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Wang( Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners Audio Visual Scene-Aware Dialog) and further in view of Muhammad ( Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models) and further in view of Xie(US 20220394344)
Regarding claim 7, Wang modified by Muhammad as above in claim 1, does not explicitly teach wherein after the outputting, using the question answering model, dialog question answering data associated with the video, the method further comprises: determining, in response to a filter operation for the dialog question answering data associated with the video, target dialog question answering data from the dialog question answering data associated with the video; and adjusting or replacing, in response to an edit operation for the dialog question answering data, an answer corresponding to a question in the dialog question answering data, so that an answer obtained through the adjustment or replacement fits the video description
However Xie teaches determining, in response to a filter operation for the dialog question answering data associated with the video, dialog question answering data from the dialog question answering data associated with the video ( editing operation, Para 0007) ; and adjusting or replacing, in response to an edit operation for the dialog question answering data, an answer corresponding to a question in the dialog question answering data, so that an answer obtained through the adjustment or replacement fits the video description ( fig 21—editing operation, and the answer related to the video )
It would be obvious having the teachings of Wang and Muhammad to further include the concept of Xie before effective data to improve the efficiency of question answering ( Para 0005, Xie)
Regarding claim 14, arguments analogous to claim 7, are applicable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Vogel ( US 20230321548) teaches method for generating video dialog question answering data, the method comprising: determining description information (Para 0191) ; determining a prompt used for a question answering model, wherein the question answering model is pre-configured based on a large language model (A personalized prompt for the LLM may therefore be derived from text describing one or more of these objectives or some other objective provided by the account holder, Para 0191)and the prompt is capable of guiding the question answering model to a desired dialog question answering effect based on the video description information when the question answering model executes a dialog question answering generation task ( time ranges and fragments of the video, Fig 16) ; and outputting, using the question answering model and based on the video description information and the prompt, dialog question answering data associated with the video ( Fig 18)
Hang ( Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding)
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Richa Sonifrank/Primary Examiner, Art Unit 2654
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