DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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
2. 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.
3. 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.
4. Claims 1-2, 5-6, 16-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Agarwal et al. (US Patent Application Publication No. 2025/0166243 A1) in view of Benedetto et al. (US Patent Application Publication No. 2024/0226734 A1).5. Regarding Claim 1, Agarwal discloses A method (paragraph [0002] reciting “A method, apparatus, and non-transitory computer readable medium for image processing are described. …”) for generating a digital component, (Abstract reciting “… A synthetic image is generated based on the text prompt, the first attribute token, and the second attribute token by providing the first attribute token to the first set layers of the image generation model during the first set of time-steps and providing the second attribute token to the second set of layers of the image generation model during the second set of time-steps.”) comprising:
obtaining digital content data for the digital component, the digital content data comprising at least a base image of a subject of the digital component; (see FIG. 1 wherein reference image corresponds to digital content data comprising at least a based image, such as a cat (as shown in FIG. 1) excluding background visuals, or some other animal etc.)
obtaining a prompt comprising a description of the subject; (see FIG. 1; paragraph [0048] reciting “In some examples, the generated synthetic images follow one of the attributes of the reference image, which are color, style, layout, and object. The generated synthetic images may also follow a combination of the attributes. …” Therefore, the combination text in prompt can include a cat object with color and layout. The combination text in the prompt can be a description of the subject.)
generating the digital component by processing the digital content data based at least on the one or more determined style features; (paragraph [0048] reciting “In some examples, the generated synthetic images follow one of the attributes of the reference image, which are color, style, layout, and object. The generated synthetic images may also follow a combination of the attributes. …” Generated synthetic image corresponds to the digital component by processing the reference image with one or more color/style/layout etc.) and distributing the generated digital component to one or more client devices. (paragraph [0045] reciting “At operation 220, the system displays the synthetic image to the user. In some cases, the operations of this step refer to, or may be performed by, an image processing apparatus as described with reference to FIGS. 5-8.”)
While not explicitly disclosed by Agarwal, Benedette discloses processing the prompt using a language model to generate one or more keywords related to the subject; (paragraph [0062] reciting “In some implementations, a theme can be defined for a given user or a given profile. The theme can be configured to define a particular style, and accordingly include certain words/phrases or other types of acceptable input that when applied to the image generation AI, will cause the image generation AI to generate an image in the particular style. In some implementations, the theme is editable so that the user may specify particular words/phrases or other particular input to be part of the theme's definition. Then, when user input is entered to generate an image, the theme is applied by appending the words/phrases/input which are stored to the theme.”
Words that are stored with the theme are keywords which can be extracted from the input prompt.) determining, based on the one or more keywords, one or more style features for the digital component; (paragraph [0062] reciting “In some implementations, a theme can be defined for a given user or a given profile. The theme can be configured to define a particular style, and accordingly include certain words/phrases or other types of acceptable input that when applied to the image generation AI, will cause the image generation AI to generate an image in the particular style. In some implementations, the theme is editable so that the user may specify particular words/phrases or other particular input to be part of the theme's definition. Then, when user input is entered to generate an image, the theme is applied by appending the words/phrases/input which are stored to the theme.” Based on the words/phrases stored and matched to the input prompt text, the theme can be applied to change style of image.)
It would have been obvious to a person of ordinary skills in the art before the effective filing date of the claimed invention to modify Agarwal with Benedetto so that words from Agrawal’s prompt are recognized as words/phrases associated with one or more attributes which can be color/style/object etc. Once the words from the prompt are matched with words associated with one or more attributes, the attributes can be used to modify the style of the reference image. Agarwal modified by Benedetto is beneficial since Agarwal discloses using text to modify image and Benedetto allows a way for words to be keywords that are associated with one or more attributes.
6. Regarding Claim 2, Benedetto further discloses he method of claim 1, further comprising: storing a set of structured data items with each respective structured data item linking a respective set of one or more keywords with a respective set of one or more style features, wherein determining, based on the one or more keywords, the one or more style features of the digital component comprises: identifying, in the set of structured data items, the one or more style features as a respective set of style features that are linked to the one or more keywords. (paragraph [0062] reciting “In some implementations, a theme can be defined for a given user or a given profile. The theme can be configured to define a particular style, and accordingly include certain words/phrases or other types of acceptable input that when applied to the image generation AI, will cause the image generation AI to generate an image in the particular style. In some implementations, the theme is editable so that the user may specify particular words/phrases or other particular input to be part of the theme's definition. Then, when user input is entered to generate an image, the theme is applied by appending the words/phrases/input which are stored to the theme.” The words/phrases/input stored to the theme corresponds to a structured data item that links keywords with one or more features. Text prompt input is used to identify which theme is used based on matching words/phrases etc.)
7. Regarding Claim 5, Agarwal further discloses The method of claim 1, wherein the one or more style features comprise one or more image effects, and generating the digital component comprises: applying the one or more image effects to the base image. (paragraph [0047] reciting “… Synthetic images are generated based on one or more attributes of the reference image, for example, color, style, layout, or object.”)
8. Regarding Claim 6, Agarwal further discloses The method of claim 5, wherein the one or more image effects comprise: a brightness adjustment effect, a contrast adjustment effect, a sharpen or blur effect, a color adjustment effect, a distortion effect, a 3D image effect, or a torn edge effect. (paragraph [0031] reciting “…
For example, if the reference cat is portrayed in the style of Vincent van Gogh with a specific layout and color scheme, …” Color scheme corresponds to color adjustment effect.)
9. Regarding Claim 16, Benedetto further discloses The method of claim 1, further comprising: performing contextual learning of the language model using a set of examples, each example comprising a respective input description and a respective output set of keywords. (paragraph [0062] reciting “In some implementations, a given profile can define a learning model that is trained to predict or infer the user's preferred words/phrases based on a given supplied word or phrase. The learning model is trained using any of the presently described techniques and data describing the user's understanding of words, terminology, phrases, etc. In some implementations, the learning model is configured to associate or map or cluster various words or phrases, and these associations are strengthened or weakened as a result of training over time. The trained learning model is used by the interpreter 408 to generate predicted words based on the user input 412, which can be appended to the user input 412 or otherwise included to generate the translated input 410 that is fed to the image generation AI 102.” Keywords are generated based on input text processed with machine learning.)
10. Regarding Claim 17, Agarwal discloses A system comprising: one or more computers; (see FIG. 1 were the system shows at least one computer.) and one or more storage devices storing instructions that when executed by the one or more computers, cause the one or more computers to perform operations for generating a digital component, the operations comprising: (paragraph [0068] reciting “Memory unit 520 includes one or more memory devices. Examples of a memory device include random access memory (RAM), read-only memory (ROM), or a hard disk. Examples of memory devices include solid state memory and a hard disk drive. In some examples, memory is used to store computer-readable, computer-executable software including instructions that, when executed, cause at least one processor of processor unit 505 to perform various functions described herein.”
obtaining digital content data for the digital component, the digital content data comprising at least a base image of a subject of the digital component; (see FIG. 1 wherein reference image corresponds to digital content data comprising at least a based image, such as a cat (as shown in FIG. 1) excluding background visuals, or some other animal etc.)
obtaining a prompt comprising a description of the subject; (see FIG. 1; paragraph [0048] reciting “In some examples, the generated synthetic images follow one of the attributes of the reference image, which are color, style, layout, and object. The generated synthetic images may also follow a combination of the attributes. …” Therefore, the combination text in prompt can include a cat object with color and layout. The combination text in the prompt can be a description of the subject.)
generating the digital component by processing the digital content data based at least on the one or more determined style features; (paragraph [0048] reciting “In some examples, the generated synthetic images follow one of the attributes of the reference image, which are color, style, layout, and object. The generated synthetic images may also follow a combination of the attributes. …” Generated synthetic image corresponds to the digital component by processing the reference image with one or more color/style/layout etc.)
and distributing the generated digital component to one or more client devices. (paragraph [0045] reciting “At operation 220, the system displays the synthetic image to the user. In some cases, the operations of this step refer to, or may be performed by, an image processing apparatus as described with reference to FIGS. 5-8.”)
While not explicitly disclosed by Agarwal, Benedette discloses processing the prompt using a language model to generate one or more keywords related to the subject; (paragraph [0062] reciting “In some implementations, a theme can be defined for a given user or a given profile. The theme can be configured to define a particular style, and accordingly include certain words/phrases or other types of acceptable input that when applied to the image generation AI, will cause the image generation AI to generate an image in the particular style. In some implementations, the theme is editable so that the user may specify particular words/phrases or other particular input to be part of the theme's definition. Then, when user input is entered to generate an image, the theme is applied by appending the words/phrases/input which are stored to the theme.”
Words that are stored with the theme are keywords which can be extracted from the input prompt.) determining, based on the one or more keywords, one or more style features for the digital component; (paragraph [0062] reciting “In some implementations, a theme can be defined for a given user or a given profile. The theme can be configured to define a particular style, and accordingly include certain words/phrases or other types of acceptable input that when applied to the image generation AI, will cause the image generation AI to generate an image in the particular style. In some implementations, the theme is editable so that the user may specify particular words/phrases or other particular input to be part of the theme's definition. Then, when user input is entered to generate an image, the theme is applied by appending the words/phrases/input which are stored to the theme.” Based on the words/phrases stored and matched to the input prompt text, the theme can be applied to change style of image.)
It would have been obvious to a person of ordinary skills in the art before the effective filing date of the claimed invention to modify Agarwal with Benedetto so that words from Agrawal’s prompt are recognized as words/phrases associated with one or more attributes which can be color/style/object etc. Once the words from the prompt are matched with words associated with one or more attributes, the attributes can be used to modify the style of the reference image. Agarwal modified by Benedetto is beneficial since Agarwal discloses using text to modify image and Benedetto allows a way for words to be keywords that are associated with one or more attributes.
11. Regarding Claim 18, Benedetto further discloses The system of claim 17, wherein the operations further comprise: storing a set of structured data items with each respective structured data item linking a respective set of one or more keywords with a respective set of one or more style features, wherein determining, based on the one or more keywords, the one or more style features of the digital component comprises: identifying, in the set of structured data items, the one or more style features as a respective set of style features that are linked to the one or more keywords. (paragraph [0062] reciting “In some implementations, a theme can be defined for a given user or a given profile. The theme can be configured to define a particular style, and accordingly include certain words/phrases or other types of acceptable input that when applied to the image generation AI, will cause the image generation AI to generate an image in the particular style. In some implementations, the theme is editable so that the user may specify particular words/phrases or other particular input to be part of the theme's definition. Then, when user input is entered to generate an image, the theme is applied by appending the words/phrases/input which are stored to the theme.” The words/phrases/input stored to the theme corresponds to a structured data item that links keywords with one or more features. Text prompt input is used to identify which theme is used based on matching words/phrases etc.)
12. Regarding Claim 20, Agarwal discloses One or more non-transitory computer-readable storage media storing instructions that, when executed by one or more computers, cause the one or more computers to perform operations for generating a digital component, the operations comprising: (paragraph [0178] reciting “Computer-readable media includes both non-transitory computer storage media and communication media including any medium that facilitates transfer of code or data. A non-transitory storage medium may be any available medium that can be accessed by a computer. For example, non-transitory computer-readable media can comprise random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), compact disk (CD) or other optical disk storage, magnetic disk storage, or any other non-transitory medium for carrying or storing data or code.”)
obtaining digital content data for the digital component, the digital content data comprising at least a base image of a subject of the digital component; (see FIG. 1 wherein reference image corresponds to digital content data comprising at least a based image, such as a cat (as shown in FIG. 1) excluding background visuals, or some other animal etc.)
obtaining a prompt comprising a description of the subject; (see FIG. 1; paragraph [0048] reciting “In some examples, the generated synthetic images follow one of the attributes of the reference image, which are color, style, layout, and object. The generated synthetic images may also follow a combination of the attributes. …” Therefore, the combination text in prompt can include a cat object with color and layout. The combination text in the prompt can be a description of the subject.)
generating the digital component by processing the digital content data based at least on the one or more determined style features; (paragraph [0048] reciting “In some examples, the generated synthetic images follow one of the attributes of the reference image, which are color, style, layout, and object. The generated synthetic images may also follow a combination of the attributes. …” Generated synthetic image corresponds to the digital component by processing the reference image with one or more color/style/layout etc.) and distributing the generated digital component to one or more client devices. (paragraph [0045] reciting “At operation 220, the system displays the synthetic image to the user. In some cases, the operations of this step refer to, or may be performed by, an image processing apparatus as described with reference to FIGS. 5-8.”)
While not explicitly disclosed by Agarwal, Benedette discloses processing the prompt using a language model to generate one or more keywords related to the subject; (paragraph [0062] reciting “In some implementations, a theme can be defined for a given user or a given profile. The theme can be configured to define a particular style, and accordingly include certain words/phrases or other types of acceptable input that when applied to the image generation AI, will cause the image generation AI to generate an image in the particular style. In some implementations, the theme is editable so that the user may specify particular words/phrases or other particular input to be part of the theme's definition. Then, when user input is entered to generate an image, the theme is applied by appending the words/phrases/input which are stored to the theme.”
Words that are stored with the theme are keywords which can be extracted from the input prompt.) determining, based on the one or more keywords, one or more style features for the digital component; (paragraph [0062] reciting “In some implementations, a theme can be defined for a given user or a given profile. The theme can be configured to define a particular style, and accordingly include certain words/phrases or other types of acceptable input that when applied to the image generation AI, will cause the image generation AI to generate an image in the particular style. In some implementations, the theme is editable so that the user may specify particular words/phrases or other particular input to be part of the theme's definition. Then, when user input is entered to generate an image, the theme is applied by appending the words/phrases/input which are stored to the theme.” Based on the words/phrases stored and matched to the input prompt text, the theme can be applied to change style of image.)
It would have been obvious to a person of ordinary skills in the art before the effective filing date of the claimed invention to modify Agarwal with Benedetto so that words from Agrawal’s prompt are recognized as words/phrases associated with one or more attributes which can be color/style/object etc. Once the words from the prompt are matched with words associated with one or more attributes, the attributes can be used to modify the style of the reference image. Agarwal modified by Benedetto is beneficial since Agarwal discloses using text to modify image and Benedetto allows a way for words to be keywords that are associated with one or more attributes.
13. Claims 3-4, 7-9, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Agarwal in view of Benedetto and further in view of Reddy et al. (US Patent Application Publication No. 2025/0225609 A1).
14. Regarding Claim 3, while the combination of Agarwal and Benedetto does not explicitly disclose, Reddy discloses The method of claim 1, further comprising: determining salient features of the base image; and cropping the base image based on the determined salient features such that the cropped base image includes the determined salient features and an additional are for adding text to the image. (see FIG. 1; paragraph [0078] reciting “To determine a position for the foreground object 126 over the re-dimensioned background 130, the localization module 212 uses a machine learning regressor model trained to determine optimal positions and sizes of segmented foreground objects on background images. The machine learning regressor model is a type of model used in supervised learning tasks with a goal to predict a continuous numeric value by learning patterns and relationships in training data to make predictions on new, unseen data. In some examples, the machine learning regressor model determines an optimal position to place the foreground object 126 based on a focal point or center of the re-dimensioned background 130. In other examples, the machine learning regressor model determines an optimal position to place the foreground object 126 that avoids obscuring or covering up salient objects, text, logos, or other content incorporated into the re-dimensioned background 130.” The text and objects are placed by the machine learning regressor model so they do not overlap or obscure each other.)
It would have been obvious to a person of ordinary skills in the art before the effective filing date of the claimed invention to modify Agarwal and Benedetto with Reddy so text can be overlaid with the base image. This is an obviously beneficial modification since the synthetic image will reflect the text adding context for any viewer to comprehend the stylistic changes to the based digital image.
15. Regarding Claim 4, Reddy further discloses The method of claim 3, wherein determining the salient features of the base image comprises: processing the base image using a feature detection machine learning model to generate an output identifying the salient features of the base image. (paragraph [0057] reciting “… For example, the localization module 212 uses a machine learning model trained on optimal aesthetic placement of foreground objects on backgrounds to determine a placement for the foreground object 126 based on a focal point of the re-dimensioned background 130, salient content in the re-dimensioned background 130, readability of text over the re-dimensioned background 130, or other factors related to design of the re-dimensioned digital image 118.”)
16. Regarding Claim 7, while the combination of Agarwal and Benedetto does not explicitly disclose, Reddy discloses The method of claim 1, wherein the digital content data further comprises a text item, and generating the digital component comprises overlaying the text item on the base image. (see FIG. 1; paragraph [0078] reciting “To determine a position for the foreground object 126 over the re-dimensioned background 130, the localization module 212 uses a machine learning regressor model trained to determine optimal positions and sizes of segmented foreground objects on background images. The machine learning regressor model is a type of model used in supervised learning tasks with a goal to predict a continuous numeric value by learning patterns and relationships in training data to make predictions on new, unseen data. In some examples, the machine learning regressor model determines an optimal position to place the foreground object 126 based on a focal point or center of the re-dimensioned background 130. In other examples, the machine learning regressor model determines an optimal position to place the foreground object 126 that avoids obscuring or covering up salient objects, text, logos, or other content incorporated into the re-dimensioned background 130.” The text and objects are placed by the machine learning regressor model so they do not overlap or obscure each other.)
It would have been obvious to a person of ordinary skills in the art before the effective filing date of the claimed invention to modify Agarwal and Benedetto with Reddy so text can be overlaid with the base image. This is an obviously beneficial modification since the synthetic image will reflect the text adding context for any viewer to comprehend the stylistic changes to the based digital image.
17. Regarding Claim 8, Reddy further discloses The method of claim 7, wherein overlaying the text item on the base image comprises: determining a display position of the text item relative to the base image in the digital component. (see FIG. 1; paragraph [0078] reciting “To determine a position for the foreground object 126 over the re-dimensioned background 130, the localization module 212 uses a machine learning regressor model trained to determine optimal positions and sizes of segmented foreground objects on background images. The machine learning regressor model is a type of model used in supervised learning tasks with a goal to predict a continuous numeric value by learning patterns and relationships in training data to make predictions on new, unseen data. In some examples, the machine learning regressor model determines an optimal position to place the foreground object 126 based on a focal point or center of the re-dimensioned background 130. In other examples, the machine learning regressor model determines an optimal position to place the foreground object 126 that avoids obscuring or covering up salient objects, text, logos, or other content incorporated into the re-dimensioned background 130.” The text and objects are placed by the machine learning regressor model so they do not overlap or obscure each other.)
18. Regarding Claim 9, Reddy further discloses The method of claim 8, wherein determining the display position of the text item relative to the base image comprises: determining a set of one or more areas in the base image that are outside of salient features of the base image; and selecting the display position in a first area in the set of one or more areas. (see FIG. 1; paragraph [0078] reciting “To determine a position for the foreground object 126 over the re-dimensioned background 130, the localization module 212 uses a machine learning regressor model trained to determine optimal positions and sizes of segmented foreground objects on background images. The machine learning regressor model is a type of model used in supervised learning tasks with a goal to predict a continuous numeric value by learning patterns and relationships in training data to make predictions on new, unseen data. In some examples, the machine learning regressor model determines an optimal position to place the foreground object 126 based on a focal point or center of the re-dimensioned background 130. In other examples, the machine learning regressor model determines an optimal position to place the foreground object 126 that avoids obscuring or covering up salient objects, text, logos, or other content incorporated into the re-dimensioned background 130.” The text and objects are placed by the machine learning regressor model so they do not overlap or obscure each other.)
19. Regarding Claim 19, while the combination of Agarwal and Benedetto does not explicitly disclose, Reddy discloses The method of claim 17, wherein the operations further comprise: determining salient features of the base image; and cropping the base image based on the determined salient features such that the cropped base image includes the determined salient features and an additional are for adding text to the image. (see FIG. 1; paragraph [0078] reciting “To determine a position for the foreground object 126 over the re-dimensioned background 130, the localization module 212 uses a machine learning regressor model trained to determine optimal positions and sizes of segmented foreground objects on background images. The machine learning regressor model is a type of model used in supervised learning tasks with a goal to predict a continuous numeric value by learning patterns and relationships in training data to make predictions on new, unseen data. In some examples, the machine learning regressor model determines an optimal position to place the foreground object 126 based on a focal point or center of the re-dimensioned background 130. In other examples, the machine learning regressor model determines an optimal position to place the foreground object 126 that avoids obscuring or covering up salient objects, text, logos, or other content incorporated into the re-dimensioned background 130.” The text and objects are placed by the machine learning regressor model so they do not overlap or obscure each other.)
It would have been obvious to a person of ordinary skills in the art before the effective filing date of the claimed invention to modify Agarwal and Benedetto with Reddy so text can be overlaid with the base image. This is an obviously beneficial modification since the synthetic image will reflect the text adding context for any viewer to comprehend the stylistic changes to the based digital image.
20. Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Agarwal in view of Benedetto in view of Reddy and further in view of Chuanqi Jing (US Patent Application Publication No. 2025/0200849 A1).
21. Regarding Claim 10, while the combination of Agarwal, Benedetto, and Reddy does not explicitly disclose, Jing discloses The method claim 7, wherein the one or more style features comprise one or more text style features for the text item, and generating the digital component comprises applying the one or more text style features to the text item in the generated digital component. (paragraph [0123] reciting “In some alternative embodiments, in order to further improve the richness and diversity of the cover image for the target booklist, before adding the descriptive keywords onto the image content based on the presentation position for the descriptive keywords to generate a cover image in which the target object is not blocked, the present disclosure optionally first determines a type of descriptive keywords, wherein the types of descriptive keywords include title, intention and title intention. Then, according to the type of the descriptive keywords, a target text style for the descriptive keywords can be selected from a text style library. In turn, based on the presentation position for the descriptive keywords and the target text style, the descriptive keywords can be added onto the image content to generate a cover image for the target booklist.”)
It would have been obvious to a person of ordinary skills in the art before the effective filing date of the claimed invention to modify Agarwal, Benedetto, and Reddy with Jing so that the text is not only displayed but also stylistically changed based on its own text. This allows the entire image with text and image to look congruent in style.
22. Regarding Claim 11, Jing further discloses The method of claim 10, wherein the one or more text style features comprises one or more of: a font typeface, a font color, a font weight, a font style, a text alignment, a text spacing, or one or more text display effects. (paragraph [0123] reciting “In some alternative embodiments, in order to further improve the richness and diversity of the cover image for the target booklist, before adding the descriptive keywords onto the image content based on the presentation position for the descriptive keywords to generate a cover image in which the target object is not blocked, the present disclosure optionally first determines a type of descriptive keywords, wherein the types of descriptive keywords include title, intention and title intention. Then, according to the type of the descriptive keywords, a target text style for the descriptive keywords can be selected from a text style library. In turn, based on the presentation position for the descriptive keywords and the target text style, the descriptive keywords can be added onto the image content to generate a cover image for the target booklist.” Target text style corresponds to one or more text display effects.)
23. Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Agarwal in view of Benedetto in view Saraee et al. (US Patent Application Publication No. 2024/0378856 A1).
24. Regarding Claim 12, while the combination of Agarwal and Benedetto does not explicitly disclose, Saraee discloses The method of claim 1, wherein the digital content data further comprises an interactive element, and generating the digital component comprises combining the interactive element with the base image. (paragraph [0555] reciting “Move the location or position of an image feature, such an object, product, call-to-action, button, etc.”)
It would have been obvious to a person of ordinary skills in the art before the effective filing date of the claimed invention to modify Agarwal and Benedetto with Saraee so that buttons can be incorporated into the reference image of Agarwal. This is beneficial as buttons allows user to be interactive with the base photo.
25. Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Agarwal in view of Benedetto in view Saraee and further in view of Reddy.
26. Regarding Claim 13, while the combination of Agarwal, Benedetto, and Saraee does not explicitly disclose, Reddy discloses The method of claim 12, wherein combining the interactive element with the base image comprises: determining a display position of the interactive element relative to the base image in the digital component. (see FIG. 1; paragraph [0078] reciting “To determine a position for the foreground object 126 over the re-dimensioned background 130, the localization module 212 uses a machine learning regressor model trained to determine optimal positions and sizes of segmented foreground objects on background images. The machine learning regressor model is a type of model used in supervised learning tasks with a goal to predict a continuous numeric value by learning patterns and relationships in training data to make predictions on new, unseen data. In some examples, the machine learning regressor model determines an optimal position to place the foreground object 126 based on a focal point or center of the re-dimensioned background 130. In other examples, the machine learning regressor model determines an optimal position to place the foreground object 126 that avoids obscuring or covering up salient objects, text, logos, or other content incorporated into the re-dimensioned background 130.” The other content can be interactive buttons from Saraee.)
It would have been obvious to a person of ordinary skills in the art before the effective filing date of the claimed invention to modify Agarwal, Benedetto, and Saraee with Reddy so that buttons can be incorporated into the reference image of Agarwal without obscuring foreground objects This is beneficial as buttons allows user to be interactive with the base photo.
27. Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Agarwal in view of Benedetto in view of Saraee and further in view of Jing.
28. Regarding Claim 14, while the combination of Agarwal, Benedetto, and Saraee does not explicitly disclose, Jing discloses The method of claim 12, wherein the one or more style features comprise one or more element style features for the interactive element, and generating the digital component comprises applying the one or more element style features to the interactive element in the generated digital component. (paragraph [0123] reciting “In some alternative embodiments, in order to further improve the richness and diversity of the cover image for the target booklist, before adding the descriptive keywords onto the image content based on the presentation position for the descriptive keywords to generate a cover image in which the target object is not blocked, the present disclosure optionally first determines a type of descriptive keywords, wherein the types of descriptive keywords include title, intention and title intention. Then, according to the type of the descriptive keywords, a target text style for the descriptive keywords can be selected from a text style library. In turn, based on the presentation position for the descriptive keywords and the target text style, the descriptive keywords can be added onto the image content to generate a cover image for the target booklist.”)
It would have been obvious to a person of ordinary skills in the art before the effective filing date of the claimed invention to modify Agarwal, Benedetto, and Saraee with Jing so that the button can be displayed and stylized in a helpful manner that is congruent with the overall style of the image.
29. Regarding Claim 15, Agarwal further discloses The method of claim 14, wherein the one or more element style features comprises one or more of: a button shape, a button color, or a button pattern. (paragraph [0047] reciting “… Synthetic images are generated based on one or more attributes of the reference image, for example, color, style, layout, or object.”)
Unused References
30. Zhi et al. (US Patent Application publication 2023/0177250 A1) discloses image dataset and prompt input which uses stable diffusion to train a 3D generative adversarial network model.
31. Jacob Gorm Hansen (US Patent Application Publication No. 2023/0188731 A1) discloses pre-processed images with landmarks which are recognized by computer machine learning.
32. Lundin et al (US Patent Application Publication No. 2023/0177250 A1) discloses visual text summary generation which then generates keywords for style.
CONTACT
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANK S CHEN whose telephone number is (571)270-7993. The examiner can normally be reached Mon - Fri 8-11:30 and 1:30-6.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kee Tung can be reached at 5712727794. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/FRANK S CHEN/Primary Examiner, Art Unit 2611
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