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 .
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Krishna (Patent No. 12,182,149 filed October 13, 2023, priority to provisional application no. 63/584,539 filed on September 22, 2023); Upchurch (Patent No. 11,334,147 filed July 22, 2021); Shekhar et al. (Publication No. 2021/0224332 filed January 22, 2020, hereinafter Shekhar).
Regarding Claims 1, 12, and 13, Krishna teaches generating thumbnail data corresponding to content data (column 18, lines 13-20, an updated query (element 446) can include the text of the input and a thumbnail depicting the image segment); obtaining a question answering (QA) set generated based on text data extracted from the content data (the Q&A set is generated in the form of a query and result sets function; column 17, lines 7-21, The query suggestions provide a scrollable carousel and may be selectable to perform a search. A language model may be utilized to generate natural language query suggestions. The query input box 412 can be configured to receive text inputs, image inputs, audio inputs, video inputs; column 17, lines 35-40, Pixels descriptive of the object may be segmented and searched. The image segment can be processed with a generative model (e.g., a vision language model and/or a large language model) to generate a model-generated response 418 to the query. The model-generated response 418 can include a natural language response that summarizes one or more web resources determined to be associated with the segmented object. Additionally and/or alternatively, the segmented image can be processed to determine one or more visual search results 420.).
Krishna teaches generating visual question (e.g., a visual search) and result data comprising a pair of the thumbnail data (column 17, lines 30-35, visual search interface can process the region e.g., an object detection model and a segmentation model), column 18, lines 12-24, a text input (element 442) (e.g., “Are there other shapes available?”, the input can be obtained and provided as an updated query (element 446) which can include the text of the input and a thumbnail depicting the image segment).
However, Krishna does not expressly teach generating visual question answering (VQA) data comprising a pair of the thumbnail data.
Upchurch teaches generating visual question answering (VQA) data (column 8, lines 24-25, a visual-question-and-answer (VQA) question preprocessor (element 246)) comprising a pair of the thumbnail data (column 15, lines 20-67, column 16, lines 1-8, FIG. 4A, the annotation prompt window 425 is associated with a virtual action that corresponds to applying a flame applicator to the object mask 404 (e.g., the wood pile). As such, the annotation prompt window 425 includes VOA questions associated with the plurality of virtual effect instructions including a first radio button 410 indicating a binary VQA question as to whether or not the wood pile should exhibit one or more visual flames when the virtual action is applied thereto, a second radio button 420 indicating a binary VOA question as to whether or not the wood pile should exhibit a visual glow when the virtual action is applied FIG. 4A, the virtual effect system updates the reference image based on the values associated with the user manipulatable portions of the annotation prompt window 425 in order to provide a live preview of the application of the virtual action to the object mask 404. ).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the method of Krishna with the method of Upchurch because Krishna’s method includes a visual search of an object selection, where a circling gesture can be received that selects a particular object depicted in the display content but does not include visual question answering (VQA) data. Upchurch teaches a training data generator comprises a visual question-and-answer (VQA) engine. Incorporating the method of Upchurch into the method of Krishna would improve Krishna’s method to enable the generation of a VQA question to be associated with one or more potential responses, and a VQA answer may include one or more selections of the potential responses to the VQA question.
However, Upchurch does not expressly teach the QA set.
Shekhar teaches the QA set ([0028] FIG. 1 shows a system for chart question answering disclosure. The example shown includes user 100, network 105, question answering server 110, and database 115.).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the method of Upchurch with the method of Shekhar because Upchurch’s method includes the VQA question consisting of at least one drawing prompt and the VQA answer consisting of at least one selection of a portion of an image but does not include the QA set. Upchurch’s method includes visual question answering (VQA) sets to provide an answer to a text query based on answer probability vector. Incorporating the method of Upchurch into the method of Shekhar would improve Upchurch’s method to enable a reasoning component to generate an answer interpretation of information based on an identified chart element from the VQA chart question answering representation.
Regarding Claims 2 and 14, Krishna teaches training a first generative model (column 7, lines 20-23, generative model).
Upchurch teaches using the VQA data (column 15, lines 20-67, column 16, lines 1-8).
Regarding Claims 3 and 15, Shekhar teaches obtaining of the QA set ([0028] ).
Krishna teaches separating the content data into image-type data and text-type data; and obtaining a QA set generated based on the text-type data (column 16, lines 11-18, e.g., an image-to-text model and text-to-image model).
Regarding Claims 4 and 16, Shekhar teaches obtaining of the QA set ([0028]).
Krishna teaches the content data into image-type data and text-type data; and applying the text-type data to a model to generate the set (column 16, lines 11-18, e.g., an image-to-text model and text-to-image model).
Regarding Claims 5 and 17, Shekhar teaches obtaining of the QA set ([0028]).
Krishna teaches the applying of the text-type data to the model comprises: generating a prompt based on the text-type data and generating in response to the prompt from the model (column 16, lines 11-18, e.g., an image-to-text model and text-to-image model).
12. Regarding Claims 6 and 18, Krishna teaches the content data comprises document data including at least one piece of image data and associated text data (column 16, lines 11-18, e.g., an image-to-text model and text-to-image model).
13. Regarding Claims 7 and 19, Upchurch the content data comprises image data and associated description data (column 15, lines 20-67, column 16, lines 1-8, FIG. 4A, the annotation prompt window 425 is associated with a virtual action that corresponds to applying a flame applicator to the object mask 404 (e.g., the wood pile). As such, the annotation prompt window 425 includes VOA questions associated with the plurality of virtual effect instructions including a first radio button 410 indicating a binary VQA question as to whether or not the wood pile should exhibit one or more visual flames when the virtual action is applied thereto, a second radio button 420 indicating a binary VOA question as to whether or not the wood pile should exhibit a visual glow when the virtual action is applied FIG. 4A, the virtual effect system updates the reference image based on the values associated with the user manipulatable portions of the annotation prompt window 425 in order to provide a live preview of the application of the virtual action to the object mask 404. ).
14. Regarding Claims 8 and 20, Upchurch the generating of the thumbnail data comprises: dividing the content data into a plurality of sections; and generating thumbnail data for each section of the content data (column 15, lines 20-67, column 16, lines 1-8, FIG. 4A, the annotation prompt window 425 is associated with a virtual action that corresponds to applying a flame applicator to the object mask 404 (e.g., the wood pile). As such, the annotation prompt window 425 includes VOA questions associated with the plurality of virtual effect instructions including a first radio button 410 indicating a binary VQA question as to whether or not the wood pile should exhibit one or more visual flames when the virtual action is applied thereto, a second radio button 420 indicating a binary VOA question as to whether or not the wood pile should exhibit a visual glow when the virtual action is applied FIG. 4A, the virtual effect system updates the reference image based on the values associated with the user manipulatable portions of the annotation prompt window 425 in order to provide a live preview of the application of the virtual action to the object mask 404. ).
15. Regarding Claim 9, Upchurch teaches the obtaining of the QA set comprises: extracting text data from each section and obtaining a QA set for each section based on the extracted text data (column 15, lines 20-67, column 16, lines 1-8, FIG. 4A, the annotation prompt window 425 is associated with a virtual action that corresponds to applying a flame applicator to the object mask 404 (e.g., the wood pile). As such, the annotation prompt window 425 includes VOA questions associated with the plurality of virtual effect instructions including a first radio button 410 indicating a binary VQA question as to whether or not the wood pile should exhibit one or more visual flames when the virtual action is applied thereto, a second radio button 420 indicating a binary VOA question as to whether or not the wood pile should exhibit a visual glow when the virtual action is applied FIG. 4A, the virtual effect system updates the reference image based on the values associated with the user manipulatable portions of the annotation prompt window 425 in order to provide a live preview of the application of the virtual action to the object mask 404. ).
16. Regarding Claim 10, Upchurch teaches the generating of the VQA data comprises: generating one piece of VQA data for each section, the one piece of VOA data including a pair of thumbnail data and the QA set corresponding to that each section (column 15, lines 20-67, column 16, lines 1-8, FIG. 4A, the annotation prompt window 425 is associated with a virtual action that corresponds to applying a flame applicator to the object mask 404 (e.g., the wood pile). As such, the annotation prompt window 425 includes VOA questions associated with the plurality of virtual effect instructions including a first radio button 410 indicating a binary VQA question as to whether or not the wood pile should exhibit one or more visual flames when the virtual action is applied thereto, a second radio button 420 indicating a binary VOA question as to whether or not the wood pile should exhibit a visual glow when the virtual action is applied FIG. 4A, the virtual effect system updates the reference image based on the values associated with the user manipulatable portions of the annotation prompt window 425 in order to provide a live preview of the application of the virtual action to the object mask 404. ).
17. Regarding Claim 11, Krishna teaches generating of the thumbnail data comprises: arranging image data and corresponding text data in a predetermined template (column 17, lines 30-35, visual search interface can process the region e.g., an object detection model and a segmentation model), column 18, lines 12-24, a text input (element 442) (e.g., “Are there other shapes available?”, the input can be obtained and provided as an updated query (element 446) which can include the text of the input and a thumbnail depicting the image segment).
Conclusion
18. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHERYL R LEWIS whose telephone number is (571)272-4113. The examiner can normally be reached Monday-Thursday, 8am-5pm, EST.
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/CHERYL LEWIS/Primary Examiner, Art Unit 2166 September 4, 2026
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