DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1–3 and 14-16 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by Liu et al. (made of reference in ids: US 2024/0104275 A1).
Regarding claim 1, Liu teaches a design deliverable generation system comprising:at least one processor, internal storage configured to store at least one image, and memory configured to store a computer program executed by the processor (para 15–25: teaches a computer-implemented design-generation system having one or more processors, memory, storage, computer-readable instructions, and stored image or design data);receiving a first prompt from a user terminal and transmitting the first prompt to a generative artificial intelligence system (para 34, 54–57, 84, and 88–90: teaches receiving a user’s design intent or text prompt through a user interface and providing the prompt to a generative model for image generation);transmitting at least some of a plurality of images associated with the first prompt to the user terminal (para 58–59, 77–78, and 89–90: teaches generating a set of images relevant to the user’s prompt and displaying the relevant image set to the user);receiving a user’s selection input for at least one of the plurality of images associated with the first prompt (para 59, 78–79, and 90–91: teaches receiving the user’s selection of one of the displayed relevant images);transmitting a first image corresponding to the user’s selection input to the generative artificial intelligence system (para 31, 79, and 84: teaches using the user-selected image as an input or reference image for a subsequent image-generation operation); and receiving a second image generated by the generative artificial intelligence system based on the first image and the first prompt (para 31, 55–58, 77, 81, and 89–90: teaches generating a new or modified design image using the user’s prompt and a selected reference image).
Regarding claim 2, Liu teaches that the plurality of images associated with the first prompt includes: images generated by the generative artificial intelligence system based on the first prompt (para 24, 55–59, 77–78, and 89–90: teaches generating a relevant set of candidate images based on the user’s design intent or text prompt and displaying the generated images to the user).Liu additionally teaches that the image or design data may be maintained in local, remote, or externally accessible storage (para 18–19: teaches that memory and storage may be local to or remotely accessible by the processor; para 60, 79–80, and 91: teaches maintaining and retrieving previously generated, relevant, or selected image data).
Regarding claim 3, Liu teaches transmitting the second image to the user terminal (para 58–60 and 80–81: teaches providing the generated or refined image to the user interface for presentation to the user).Liu further teaches transmitting the second image and a description associated with the second image to the user terminal (para 29: teaches associating information or metadata with images included in a relevant image set; para 60, 80, and 91: teaches providing the relevant image and its associated information or data to the user).
Regarding claim 14, Liu teaches a method for automatically generating design deliverables, performed by a computing system, comprising: receiving a first prompt from a user terminal and transmitting the first prompt to a generative artificial intelligence system (para 34, 54–57, 84, and 88–90: teaches receiving a user’s design intent or text prompt through a user interface and supplying the prompt to a generative model);transmitting at least some of a plurality of images associated with the first prompt to the user terminal (para 58–59, 77–78, and 89–90: teaches generating and displaying a set of candidate images relevant to the user’s prompt);receiving a user’s selection input for at least one of the plurality of images associated with the first prompt (para 59, 78–79, and 90–91: teaches receiving the user’s selection of an image from the displayed relevant image set);transmitting a first image corresponding to the user’s selection input to the generative artificial intelligence system (para 31, 79, and 84: teaches using the selected image as an input or reference image for a subsequent generative operation); and receiving a second image generated by the generative artificial intelligence system based on the first image and the first prompt (para 31, 55–58, 77, 81, and 89–90: teaches generating a new or modified design image based on the user’s prompt and the selected reference image).
Regarding claim 15, it falls under the same rejection as claim 2 it is similar in scope dependent upon same references.
Regarding claim 16, it falls under the same rejection as claim 3 it is similar in scope dependent upon same references.
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.
Claims 4 , 6, 17, and 19 are rejected under 35 U.S.C. § 103 as being unpatentable over Liu in view of Strauss et al. (US 2018/0253661 A1) .
Regarding claim 4, Liu teaches the design deliverable generation system of claim 1, including generating a second image based on a prompt and a user-selected image (para 31, 55–59, 77–81, and 89–91: teaches generating and presenting a refined design image based on the user’s prompt and selected reference image).
Liu does not expressly teach determining whether the second image complies with a deliverable review guide and determining whether the second image complies with design guidelines.
Strauss teaches reviewing user content, including image, text, and component content, to determine whether the content complies with established policies or guidelines (para 5: teaches an automated content-review system that determines whether submitted content complies with applicable content policies).Strauss further teaches extracting and evaluating features of the submitted content (para 26: teaches evaluating text and other content features, including word, size, and content characteristics); calculating compliance or risk scores and comparing the scores against applicable thresholds (para 31–33: teaches generating risk or compliance values and applying threshold-based tests to determine whether the content presents a policy concern); and determining whether the content should be accepted, rejected, or revised based on the applicable guidelines (para 34: teaches applying conditional policy tests to determine how the content should be handled).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Liu’s system to perform the compliance review taught by Strauss. The modification would have predictably allowed Liu’s system to determine whether an AI-generated design deliverable complies with applicable deliverable-review and design guidelines before providing the deliverable to the user.
Regarding claim 6, Liu and Strauss teach the design deliverable generation system of claim 4 as set forth above.
Strauss further teaches that determining whether content complies with applicable guidelines comprises determining whether a text component complies with predefined specifications of the guidelines (para 5: teaches determining whether submitted content complies with established content policies; para 26: teaches analyzing properties of text content, including text length, size, recognized or unrecognized words, and the presence of particular words; para 31–34: teaches applying predetermined tests and thresholds to the analyzed content properties to determine compliance).
It would have been obvious to apply Strauss’s text-property review to the text components of Liu’s generated image because doing so would have allowed the system to identify text that fails predefined design requirements before providing the generated design deliverable to the user.
Regarding claim 17, it falls under the same rejection as claim 4 it is similar in scope dependent upon same references.
Regarding claim 19, it falls under the same rejection as claim 6 it is similar in scope dependent upon same references.
Claim 5 and 18 is rejected under 35 U.S.C. § 103 as being unpatentable over Liu in view of Strauss, and further in view of Jessen et al. (US 2016/0350886 A1).
Regarding claim 5, Liu and Strauss teach the design deliverable generation system of claim 4, including determining whether a generated image complies with applicable security or content-review guidelines, as set forth above.
Liu and Strauss do not expressly teach determining whether the second image includes a component having a similarity exceeding a threshold with a component included in data of the security guidelines and determining whether a visual component of the second image includes an element that infringes copyrights.
Jessen teaches a computer-implemented intellectual-property surveillance system for identifying potentially infringing visual material (para 45: teaches identifying parties or products that may be using copyrighted or other protected material without authorization); using target images associated with copyrighted material and comparing candidate images or portions of candidate images with the target images (para 53–60: teaches establishing protected target images and comparing candidate product images or portions thereof with the protected images); calculating a similarity metric or composite similarity score for the compared images or image components (para 62: teaches calculating a composite score representing the degree of similarity between a target image and a candidate image); determining whether the calculated similarity exceeds a predetermined threshold (para 64: teaches comparing the calculated similarity score with a predetermined similarity threshold); generating an alert when the threshold is satisfied (para 65: teaches identifying or flagging a candidate image when its similarity score satisfies the predetermined threshold); and determining whether the identified visual material may constitute copyright infringement (para 67–68: teaches evaluating the detected visual material for infringement and initiating an infringement-related procedure, including a takedown procedure).
It would have been obvious to one of ordinary skill in the art to incorporate Jessen’s similarity-based copyright review into the compliance-review system produced by the combination of Liu and Strauss. The modification would have predictably protected against the inclusion of copyrighted or otherwise restricted visual components in an automatically generated design deliverable.
Regarding claim 18, it falls under the same rejection as claim 5 it is similar in scope dependent upon same references.
Claim 7 is rejected under 35 U.S.C. § 103 as being unpatentable over Liu in view of Strauss, and further in view of Bailes-Colinesins et al. (US 2021/0358114 A1).
Regarding claim 7, Liu and Strauss teach the design deliverable generation system of claim 4 as set forth above.
Liu and Strauss do not expressly teach determining whether, when modified, a first property of a first component included in the second image complies with the deliverable review guide.
Bailes-Colinesins teaches checking a design against an applicable brand or design guideline (para 2: teaches reviewing package artwork or design content to determine compliance with established brand guidelines); identifying individual design objects and checking properties of those objects against corresponding rules (para 13: teaches identifying design objects and evaluating characteristics such as rotation, position, size, and color against applicable rules; para 20: teaches evaluating text and image properties, including font, size, style, and content); reporting whether the evaluated property complies with or violates the applicable guideline (para 15: teaches identifying adherence to or an infraction of a guideline); and modifying or automatically correcting a property to repair an identified violation (para 15: teaches automatically correcting the design to remedy a detected guideline violation).
It would have been obvious to incorporate Bailes-Colinesins’s property-modification and compliance-checking functionality into the system of Liu and Strauss. The modification would have allowed the system to determine whether modifying a particular component property brings the generated image into compliance with the applicable deliverable review guide.
Claim 8 is rejected under 35 U.S.C. § 103 as being unpatentable over Liu in view of Sjölander et al. (US 2024/0427565 A1).
Regarding claim 8, Liu teaches the design deliverable generation system of claim 1, including generating and displaying a second design image to a user (para 31, 55–59, 77–81, and 89–91: teaches generating a refined image based on the user’s prompt and selected reference image and providing the resulting image to the user).
Liu does not expressly teach providing a code-editing interface corresponding to properties of each component included in the second image to the user terminal.
Sjölander teaches representing a design using nodes or layers corresponding to individual design elements, including images, text, objects, groups, and components, with the respective design elements having corresponding properties and values (para 24, 28, and 33: teaches representing individual components of a visual design as separately identifiable design elements having corresponding attributes or properties);generating code corresponding to individual design elements and mapping each design element or layer to a corresponding code segment (para 32–33: teaches creating code resources corresponding to individual elements or layers of the design);providing a code interface displaying code, attributes, and values corresponding to the design elements (para 23 and 34–35: teaches displaying HTML, CSS, attributes, and property values associated with the visual design to a user); and allowing the user to modify an attribute or value through the code interface and updating the corresponding visual component based on the modification (para 40: teaches editing code or a property value and reflecting the edit in the corresponding component of the visual design).
It would have been obvious to provide Sjölander’s code-editing interface for Liu’s generated second image because doing so would have allowed the user to inspect and modify code corresponding to the properties of the individual components included in the generated design deliverable.
Claims 9, 11, 12, and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Liu in view of Krynski et al. (US 8,370,319 B1).
Regarding claim 9, Liu teaches a design deliverable generation system comprising at least one processor and memory configured to store a computer program executed by the processor (para 15 and 17–25: teaches a computer-implemented design-generation system having one or more processors, memory, storage, and executable instructions).receiving a second prompt from a user terminal (para 26–27 and 33–34: teaches receiving a design intent or text prompt entered by a user through a user interface);producing sub-concept data related to the second prompt (para 39 and 42–44: teaches identifying or generating design, style, part, and keyword concepts related to the user’s design intent);transmitting a list of the sub-concept data to the user terminal (para 48–50 and 70–73: teaches displaying lists of suggested design, style, part, or keyword concepts associated with the user’s design intent);receiving the user’s selection input for first sub-concept data from the list of sub-concept data (para 45, 48, 51–52, and 73–75: teaches receiving the user’s selection of one or more displayed design or concept suggestions); and generating a third prompt by modifying the second prompt using the selected sub-concept data and transmitting the third prompt to the user terminal (para 53–54, 74–76, 88, and 95: teaches generating a refined or final text prompt using the concepts selected by the user and displaying or otherwise providing the refined prompt).
Liu does not expressly teach evaluating the specificity of a first phrase included in the second prompt and transmitting the list of sub-concept data when the specificity of the first phrase falls below a threshold.
Krynski teaches evaluating the specificity of a user query or phrase and determining a degree to which the query identifies a particular concept or subject (col. 12, lines. 1–30: teaches determining a specificity value or specificity level for a search query); determining that a query having low specificity may correspond to multiple categories or broader groups of information (col. 12, lines. 31–65: teaches that a general or insufficiently specific query may produce multiple categories or groups of potentially relevant results); and comparing the evaluated specificity with a threshold to determine whether the query is sufficiently specific or general (col. 13, lines. 38–49: teaches applying a threshold to the specificity value to classify the query according to its degree of specificity).
It would have been obvious to one of ordinary skill in the art to use Krynski’s specificity evaluation in Liu’s prompt-refinement system and to display Liu’s sub-concept suggestions when the original phrase has insufficient specificity. The modification would have predictably assisted the user in refining an ambiguous or insufficiently specific prompt and thereby improved the relevance of the generated design images.
Regarding claim 11, Liu and Krynski teach the design deliverable generation system of claim 9 as set forth above.
Liu further teaches that the sub-concept data includes a first image corresponding to the first phrase (para 24 and 28–31: teaches identifying a relevant image set based on a user’s design intent or text prompt; para 55 and 58–59: teaches generating and displaying images corresponding to the prompt; para 77–78 and 96: teaches providing images relevant to the user’s design concept for user selection).
Liu also teaches that the sub-concept data includes a second phrase corresponding to the first phrase (para 39 and 42–44: teaches generating design, style, part, or keyword concepts corresponding to the user’s original design intent; para 48 and 70–73: teaches displaying the corresponding concept or keyword phrases to the user as selectable suggestions).
Regarding claim 12, Liu and Krynski teach the design deliverable generation system of claim 9 as set forth above.
Liu further teaches: transmitting the third prompt to a generative artificial intelligence system (para 55–58, 77, and 88–90: teaches providing a refined or final user prompt to a generative model for image generation); transmitting at least some of a plurality of images associated with the third prompt to the user terminal (para 58–59, 78, and 90: teaches generating a relevant set of images based on the refined prompt and displaying the generated images to the user); receiving the user’s selection input for at least one image associated with the third prompt (para 59, 78–79, and 90–91: teaches receiving the user’s selection of an image from the displayed relevant image set);transmitting a third image corresponding to the user’s selection input to the generative artificial intelligence system (para 31, 79, and 84: teaches using the user-selected image as a reference input for a subsequent image-generation operation); and receiving a fourth image associated with the third image and the third prompt from the generative artificial intelligence system (para 31, 55–58, 77, 81, and 89–90: teaches generating a new or refined image based on the user-selected reference image and the corresponding text prompt).
Regarding claim 20, Liu teaches:receiving a second prompt from the user terminal and displaying the second prompt on a screen of the user terminal (para 26–27 and 33–34: teaches receiving the user’s design intent or text prompt through a user interface; para 54 and 88: teaches displaying or providing the resulting prompt through the user interface);transmitting a list of sub-concept data corresponding to the first phrase to the user terminal (para 39 and 42–44: teaches identifying design, style, part, or keyword concepts associated with the user’s design intent; para 48–50 and 70–73: teaches displaying a list of the corresponding concepts or suggestions to the user); receiving the user’s selection input for first sub-concept data from the list (para 45, 48, 51–52, and 73–75: teaches receiving the user’s selection of one or more design, style, part, or keyword suggestions); and generating a third prompt by modifying the second prompt using the selected sub-concept data and transmitting the third prompt to the user terminal (para 53–54, 74–76, 88, and 95: teaches generating a refined or final text prompt using the concept selected by the user and displaying or otherwise providing the refined prompt).
Liu does not expressly teach evaluating the specificity of the first phrase and transmitting the list when the specificity falls below a threshold.
Krynski teaches evaluating the specificity of a query or phrase (col. 12, lines. 1–30: teaches determining a specificity value representing how specifically a query identifies a particular subject); identifying a low-specificity query that corresponds to multiple categories or concepts (col. 12, lines. 31–65: teaches that a general query may correspond to multiple categories or groups of potentially relevant information); and comparing the evaluated specificity with a threshold (col. 13, lines. 38–49: teaches using a threshold to classify a query as sufficiently specific or general).
It would have been obvious to incorporate Krynski’s specificity-threshold evaluation into Liu’s method so that Liu’s sub-concept suggestions are displayed when the user’s initial prompt is insufficiently specific. The modification would have predictably assisted the user in refining the prompt before generating additional design images.
Claim 10 is rejected under 35 U.S.C. § 103 as being unpatentable over Liu in view of Krynski, and further in view of Lee et al. (US 10,282,377 B2).
Regarding claim 10, Liu and Krynski teach the design deliverable generation system of claim 9, including evaluating whether a phrase has sufficient specificity, as set forth above.
Liu and Krynski do not expressly teach performing semantic analysis on the second prompt and determining that the specificity of the first phrase falls below the threshold when the semantic analysis indicates that a plurality of pieces of data corresponding to the first phrase exists.
Lee teaches parsing a text query and identifying an ambiguous term that corresponds to multiple concept elements (col. 15, lines. 13–40: teaches processing a text query to identify terms that may correspond to multiple semantic concepts); dividing the query into words, phrases, or n-grams for semantic processing (col. 16, lines. 35–65: teaches segmenting the text query into units that can be semantically compared with concept data);scoring the semantic similarity or relevance between a phrase and corresponding concept elements (col. 17, lines. 1–40: teaches determining semantic matching scores between portions of the query and stored concept elements); determining that a phrase corresponds to multiple matching pieces of data when multiple matching edges satisfy an applicable threshold (col. 18, lines. 47–65 through col. 19, lines. 1–4: teaches identifying multiple semantic matches having scores that satisfy a matching threshold); and determining that the phrase is ambiguous when multiple concept nodes satisfy the applicable threshold (col. 19, lines. 16–42: teaches treating a query term as ambiguous when it maps to multiple qualifying concepts).
It would have been obvious to incorporate Lee’s semantic ambiguity analysis into the specificity evaluation taught by Krynski and used with Liu’s prompt-refinement system. The modification would have allowed the system to determine that a phrase lacks specificity when semantic analysis identifies multiple pieces of concept data corresponding to that phrase, thereby enabling more accurate selection of prompts requiring clarification.
Claim 13 is rejected under 35 U.S.C. § 103 as being unpatentable over Liu in view of Krynski, and further in view of Sjölander.
Regarding claim 13, Liu and Krynski teach the design deliverable generation system of claim 12 as set forth above.
Liu and Krynski do not expressly teach providing a code-editing interface corresponding to properties of each component included in the fourth image to the user terminal.
Sjölander teaches representing a design using nodes or layers corresponding to individual design elements, including images, text, objects, groups, and components, with the respective elements having corresponding properties and values (para 24, 28, and 33: teaches representing the individual components of a visual design as separately identifiable design elements having corresponding attributes or property values); generating code corresponding to the individual design elements and mapping each element or layer to a corresponding code segment (para 32–33: teaches creating code resources corresponding to individual elements or layers of the visual design); providing a code-editing interface displaying code, attributes, and property values associated with the design components (para 23 and 34–35: teaches displaying HTML, CSS, attributes, and values corresponding to the visual design to the user); and allowing the user to modify an attribute or value through the code interface and changing the corresponding visual component based on the modification (para 40: teaches editing a property or code value through the interface and reflecting the edit in the corresponding design component).
It would have been obvious to provide Sjölander’s code-editing interface for the fourth image generated by Liu’s system because doing so would have allowed the user to inspect and modify code corresponding to the properties of the individual components included in the generated image.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bean (US 20240320867 A1): discloses generating images from an initial text prompt, presenting multiple generated images receiving a user selection of one or more of those images, modifying /refining the prompt based on the selected images.
Benedetto et al (US 20240193351 A1): discloses analyzing user text prompt, identifying keywords, presenting keyword variations for user selection, receiving the user’s selection, modifying the original prompt based on selection and generating an ai image from the adjusted prompt.
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/LATRELL ANTHONY CREARY/Examiner, Art Unit 2613
/XIAO M WU/Supervisory Patent Examiner, Art Unit 2613
