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 .
Response to Amendment
The Amendment, filed on 4/23/2026, has been entered and acknowledged by the Examiner. Claims 1-20 are pending. Rejection under 35USC 101 has been withdrawn in light of the amendment.
Response to Arguments
Applicant's arguments with respect to claims 1-20 have been considered but are moot in view of the new ground(s) of rejection.
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 1-10 and 15-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Skrypnyk (US Pub. 2024/0355064) in view of Mercs (US Pub. 2020/0004404), and further in view of Boyd (WO2024/233828).
Regarding claim 1, Skrypnyk discloses a method comprising:
receiving, by a processing device, a text-based input (¶ [0107], The generative machine learning models can be trained to generate a variety of different content. For example, the generative machine learning models are trained to receive a prompt as input (which can include any combination of text, images, audio, and/or videos);
generating, by the processing device, asset recommendation data based on the one or more embedding of the text-based input using a machine-learning model (¶ [0107], the generative machine learning models generate an artificial image/video and/or text that is responsive to the prompt. In some cases, the generative machine learning model generates content augmentations, such as filters that can overlay, modify, or augment a real-world camera feed with digital content items);
receiving, by the processing device, a selection of a plurality of assets from the asset recommendation data that is displayed in a user interface (¶ [0114], In response to receiving a selection from the user for directions, the personal AI agent 302 overlays directions to the dentist office location on the AR device);
receiving, by the processing device, a selection of at least one interaction from a plurality of interactions that is displayed in a user interface for the plurality of assets (¶ [0114]); and
generating, by the processing device, digital content as having the interaction between the selection of the plurality of assets that is displayed in a user interface (Mercs).
Mercs further discloses asset information (and other media content) (¶ [0116]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Mercs into Skrypnyk to implement a portion of the various consciousness affect determination techniques described herein, various aspects described herein may be implemented using machine readable media that include program instructions or state information as technology allows.
Mercs and Skrypnyk disclose embedding, but Boyd further discloses extracting one or more embeddings of the text-based input (¶ [0151], an embedding space can have P dimensions. Input sequence 8 can be configured to contain a plurality of elements that have P dimensions. In this manner, for instance, example implementations can facilitate information extraction). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Boyd into Mercs and Skrypnyk to facilitate information across diverse data modalities.
Regarding claim 15, Skrypnyk discloses a computing device comprising: a processing device; and a computer-readable storage medium storing instructions that, responsive to execution by the processing device, causes the processing device to perform operations including:
receiving a text-based input as a selection of text displayed in a user interface (¶ [0107], The generative machine learning models can be trained to generate a variety of different content. For example, the generative machine learning models are trained to receive a prompt as input (which can include any combination of text, images, audio, and/or videos);
responsive to the receiving, displaying representations of a plurality of visualizations selectable for inclusion in digital content, the plurality of visualizations displayed based on processing the one or mode embedding of the text-based input by a machine-learning model (¶ [0107], the generative machine learning models generate an artificial image/video and/or text that is responsive to the prompt. In some cases, the generative machine learning model generates content augmentations, such as filters that can overlay, modify, or augment a real-world camera feed with digital content items);
displaying representations of a plurality of interactions (¶ [0114], In response to receiving a selection from the user for directions, the personal AI agent 302 displays directions to the dentist office location on the AR device); and
generating the digital content based on a selection of one or more of the plurality of visualizations and a selection one or more of the plurality of interactions received via the user interface (¶ [0114], In response to receiving a selection from the user for directions, the personal AI agent 302 overlays directions to the dentist office location on the AR device).
Mercs further discloses visualizations information (and other media content) (¶ [0116]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Mercs into Skrypnyk to implement a portion of the various consciousness affect determination techniques described herein, various aspects described herein may be implemented using machine readable media that include program instructions or state information as technology allows.
Mercs and Skrypnyk disclose embedding, but Boyd further discloses extracting one or more embeddings of the text-based input (¶ [0151], an embedding space can have P dimensions. Input sequence 8 can be configured to contain a plurality of elements that have P dimensions. In this manner, for instance, example implementations can facilitate information extraction). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Boyd into Mercs and Skrypnyk to facilitate information across diverse data modalities.
Regarding claim 2, Skrypnyk in view of Mercs and Boyd disclose the method as described in claim 1, wherein the generating the asset recommendation data includes generating a static visualization by:
generating extracted data by extracting column names from asset data describing the plurality of assets based on the text-based input using a machine-learning model (Mercs, ¶ [0211], filtered list 820 is substantially similar to concatenating list 810 as it includes identification of each of the extracted categories in column 812) ;
converting the extracted data into intent grammar data using a machine-learning model (¶ [0209]); and
selecting the static visualization from a plurality of static visualizations based on a ranking of the intent grammar (Skrypnyk, ¶ [0029], providing static; [0090], based on contextual data).
Regarding claim 3, Skrypnyk in view of Mercs and Boyd disclose the as described in claim 1, wherein the generating the asset recommendation data includes generating an animated visualization by:
generating extracted data by extracting a time-oriented column name from asset data based on the text-based input using a machine-learning model (Mercs, ¶ [0209], each of the extracted categories, a timestamp in column 816 that relates to the time of origin of each submission, and an aging index in column);
converting values of time-oriented column name into a set of ordered keys that correspond to respective frames of the animated visualization (Mercs, ¶ [0223], then the intensity of the dominant category is determined to be “less,” and a corresponding visual representation indicates an object of a small size); and
generating the animated visualization based on the set of ordered keys (¶ [0209], FIGS. 6A-6E, is attributed to a particular category and preferably varies, depending on a user's indication of the intensity associated with that particular category. In one embodiment of the present teachings, a submission's aging index is assigned a value of 100%, when the age of the submission is in a range of between about 0 days and about 31 days, is assigned a value of 75%, when the age of the submission is in a range of between about 31 days and about 63 days, is assigned a value of 50%).
Regarding claim 4, Skrypnyk in view of Mercs and Boyd disclose the method as described in claim 1, wherein the generating the asset recommendation data includes generating a data filter by: converting the text-based input into a structured query language (SQL) query (Skrypnyk, ¶ [0273]); generating filtered data by searching asset data based on the structured query language (SQL) query (Skrypnyk, ¶ [0273], (e.g., SQLite to provide various relational database functions); and generating the data filter as a data visualization based on the filtered data (Skrypnyk, ¶ [0273]).
Regarding claim 5, Skrypnyk in view of Mercs and Boyd disclose the method as described in claim 1, wherein the generating the asset recommendation data includes generating a static or animated graphic by: generating captions based on static graphics from asset data (¶ [0179], discrete share component is analyzed for consciousness state information that resides therein. One example of a preprocessing step includes identifying, as discrete items, one or more of share components from the share that they are embedded in. By way of example, the user's selection of consciousness state icons and user's text, audio and/or video embedded in the share are identified as discrete share components); extracting embeddings based on the captions using a machine-learning model ( Mercs, ¶ [0179]); ranking the embeddings by comparing the embedding extracted based on the captions and an embedding formed from the text-based input (¶ [0216]); and selecting the static or animated graphic based on the ranking (¶ [0216]).
Regarding claim 6, Skrypnyk in view of Mercs and Boyd disclose the method as described in claim 1, wherein the generating the asset recommendation data includes generating a color palette by: generating one or more digital images using a machine-learning model based on the text-based input (Skrypnyk, (¶ [0166], dataset includes images with various characteristics, such as colors, styles, and poses, to ensure that the model can generate a wide range of outputs); and extracting the color palette by computing color histograms based on the one or more digital images (¶ [0166]).
Regarding claim 7, Skrypnyk in view of Mercs and Boyd disclose the method as described in claim 1, wherein the generating the digital content as having the interaction includes generating a recolor interaction between a color palette and a visualization included in the plurality of assets (Skrypnyk, ¶ [0157], generating different color scheme).
Regarding claim 8, Skrypnyk in view of Mercs and Boyd disclose the method as described in claim 1, wherein the generating the digital content as having the interaction includes generating a data-oriented drawing (DOD) as a stylized visualization between a graphic and a visualization included in the plurality of assets (Skrypnyk, ¶ [0268, Dataglyph™ - implementing DoD).
Regarding claim 9, Skrypnyk in view of Mercs and Boyd disclose the method as described in claim 1, wherein the generating the digital content as having the interaction includes generating a highlight between a data filter and a visualization included in the plurality of assets (¶ [0306], highlight).
Regarding claim 10, Skrypnyk in view of Mercs and Boyd disclose the method as described in claim 1, wherein the generating the digital content as having the interaction includes generating a synchronization between an animated visualization and an animated graphic included in the plurality of assets (M, ¶ [0203], in synch).
Regarding claims 16-20, see discussion of claims 6 and 2-5 respectively for the same reason of rejection.
Claims 11-14 are rejected under 35 U.S.C. 103(a) as being unpatentable over Skrypnyk in view of Tobin (US Pub. 2025/0077590), and further in view of Boyd (WO2024/233828).
Regarding claim 11, Skrypnyk discloses a method comprising:
Receiving, by a processing device, a text-based input as a selection of text displayed in a user interface (¶ [0168]);
displaying, by the processing device, a user interface including an input panel configured for output of a plurality of visualizations for inclusion as part of an infographic (¶ [0168], The interaction system 100 trains the model using the prepared dataset. For each image in the dataset. The interaction system 100 provides the corresponding image template 612 and text embedding as inputs to the model);
receiving, by the processing device, a selection via the user interface, the selection specifying one or more visualizations of the plurality of visualizations from the input panel for inclusion in a canvas panel of the user interface (¶ [0114], In response to receiving a selection from the user for directions, the personal AI agent 302 overlays directions to the dentist office location on the AR device; ¶ [0121]);
arranging, by the processing device, the one or more visualizations in the canvas panel responsive to user inputs received via the user interface (¶ [0062], A media overlay may include text or image data that can be overlaid on top of a photograph taken by the user system 102 or a video stream produced by the user system 102. In some examples, the media overlay may be a location overlay (e.g., Venice beach), a name of a live event, or a name of a merchant overlay);
receiving, by the processing device, one or more inputs via the user interface specifying of at least one interaction between the one or more visualizations (¶ [0014]); and
generating, by the processing device, the infographic as having the interaction between the one or more visualizations using a machine-learning model (¶ [0183], The interaction system 100 updates the vertex positions, colors, or texture coordinates to match the new mesh. Depending on the specific requirements, the interaction system 100 blends the meshes, replaces parts of the original mesh, or applies other mesh editing techniques).
Tobin further discloses specifying selection of assets (¶¶ [0120]-[0123], with the prompt generator, calling the AI model with the generated prompt; receiving restructured content from the AI model; and providing the restructured content to a workstation submitting the user instruction, the restructured content presenting the content of the specified site in a form according to the user instruction).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Tobin into Skrypnyk to presenting the restructured content in a form according to the user instructions (¶ [0136]).
Tobin and Skrypnyk disclose embedding, but Boyd further discloses extracting one or more embeddings of the text-based input (¶ [0151], an embedding space can have P dimensions. Input sequence 8 can be configured to contain a plurality of elements that have P dimensions. In this manner, for instance, example implementations can facilitate information extraction). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Boyd into Tobin and Skrypnyk to facilitate information across diverse data modalities.
Regarding claim 12, Skrypnyk in view of Tobin and Boyd disclose the method as described in claim 11, wherein the representations of the plurality of assets include a static visualization, an animated visualization, a data filter (Skrypnyk, ¶ [0027]), a static or animated graphic, or a color palette.
Regarding claim 13, Skrypnyk in view of Tobin and Boyd disclose the method as described in claim 11, wherein the receiving the one or more inputs includes receiving a selection of a representation of a plurality of representations of interactions displayed in the user interface (Tobin, ¶ [0028]).
Regarding claim 14, Skrypnyk in view of Tobin and Boyd disclose the method as described in claim 11, further comprising displaying representations of a plurality of interactions, the plurality of interactions including: a recolor interaction between a color palette and a visualization (Skrypnyk, ¶ [0157], generating different color scheme); a data-oriented drawing (DOD) as a stylized visualization between a graphic and a visualization (Skrypnyk, ¶ [0268, Dataglyph™ - another form of DoD); a highlight between a data filter and a visualization (¶ [0306], highlight); or a synchronization between an animated visualization and an animated graphic.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TUANKHANH D PHAN whose telephone number is (571)270-3047. The examiner can normally be reached on Mon-Fri, 10:00am-18:00pm.
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/TUANKHANH D PHAN/ Examiner, Art Unit 2154
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