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 .
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 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.
Claim Rejections - 35 USC § 102
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.
Claims 1-18 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Kraus (US 2022/0138402 A1, hereinafter referenced “Kraus”).
In regards to claim 1. Kraus discloses a computer implemented method for automatically animating a set of design elements (Kraus, Abstract), the method including:
-generating a set of categoriser pre-inputs based on the set of design elements, wherein generating the set of categoriser pre-inputs includes processing a first design element in the set of design elements to generate a first categoriser pre-input (Kraus, Fig. 1 and para [0045]; Reference discloses in FIG. 1, text style suggestion tool 110 includes metadata collector 112, style discriminator 114, style wheel 116, and style preview component 118. At a high-level, when invoked, text style suggestion tool 110 generates context-specific text style suggestions by collecting contextual metadata (i.e. generated set of categorizer pre-inputs) about a selected text element (e.g., metadata collector 112) (i.e. set of design elements) and providing a representation of the collected metadata with a call to text style suggestion service 180 (i.e. generating the set of categoriser pre-inputs includes processing a first design element in the set of design elements to generate a first categoriser pre-input);
-generating a categoriser input based on the set of categoriser pre-inputs (Kraus, Fig. 1 and para [0045]; Reference discloses at a high-level, when invoked, text style suggestion tool 110 generates context-specific text style suggestions by collecting contextual metadata about a selected text element (e.g., metadata collector 112) and providing a representation of the collected metadata with a call to text style suggestion service 180 (i.e. generated categoriser input based on the set of categoriser pre-inputs). Upon receiving the call, text style suggestion service 180 (e.g., style selector 182 of text style suggestion service 180) identifies text style suggestions (e.g., from representative styles 194) based on the collected metadata and returns the identified text style suggestions to text style suggestion tool 110);
-determining a category by processing the categoriser input (Kraus, para [0045]; Reference discloses style discriminator 114 performs a contextual analysis of the selected text element to identify text style suggestions that are compatible with the context of the user's design (i..e contextual analysis interpreted as the determining category processing by processing categorizer input regarding the text element);
-and automatically applying one or more animations to the set of design elements (Kraus, para [0025]; Reference discloses In some cases, the present techniques may be used to automatically generate designs or design elements without user intervention. In these cases, the user no longer needs to figure out a font, shape, arrangement, emphasis, or the like, as these decisions may be made for them by automatically mapping context to good design) , wherein automatically applying the one or more animations to the set of design elements includes: determining, based on the category, a first animation for the first design element (Kraus, para [0042] and [0045]; Reference at [0042] discloses turning now to design application 104 on client device, a user may use any known type of design tool(s) 106 to select or generate a design with any number and arrangement of text and/or graphic elements, and to apply desired styles (e.g., color, effects, animation, etc.) and other design features. Para [0045] discloses style discriminator 114 performs a contextual analysis of the selected text element to identify text style suggestions that are compatible with the context of the user's design. The identified compatible text styles are mapped to different positions of style wheel 116 (e.g., which may correspond to style wheel 250 of FIG. 2), and the user may scroll through the positions of style wheel 116 to iterate through the compatible style suggestions (i.e. iterating through text style suggestions interpreted as selecting a first animation for the first design element));
-and applying the first animation to the first design element (Kraus, para [0045]; Reference discloses the identified compatible text styles are mapped to different positions of style wheel 116 (e.g., which may correspond to style wheel 250 of FIG. 2), and the user may scroll through the positions of style wheel 116 to iterate through the compatible style suggestions. Upon selection of a particular text style suggestion (e.g., corresponding to the position of indicator 255 of style wheel 250 of FIG. 2), style preview component previews and/or applies the text suggestion to the user's design (i.e. applying the first animation to the first design element)).
In regards to claim 2. Kraus discloses the computer implemented method according to claim 1.
Kraus further discloses
-wherein generating the set of categoriser pre-inputs includes processing a plurality of design elements in the set of design elements to generate a plurality of categoriser pre-inputs (Kraus, Fig. 1 and para [0045]; Reference discloses in FIG. 1, text style suggestion tool 110 includes metadata collector 112, style discriminator 114, style wheel 116, and style preview component 118. At a high-level, when invoked, text style suggestion tool 110 generates context-specific text style suggestions by collecting contextual metadata (i.e. generated set of categorizer pre-inputs) about a selected text element (e.g., metadata collector 112) (i.e. set of design elements) and providing a representation of the collected metadata with a call to text style suggestion service 180 (i.e. generating the set of categoriser pre-inputs includes processing a design elements in the set of design elements to generate a plurality of pre-inputs).
In regards to claim 3. Kraus discloses the computer implemented method according to claim 1.
Kraus further discloses
-wherein processing the first design element to generate the first categoriser pre-input includes generating a text content based pre-input that is based on text content of the first design element (Kraus, Fig. 1 and para [0045]; Reference discloses in FIG. 1, text style suggestion tool 110 includes metadata collector 112, style discriminator 114, style wheel 116, and style preview component 118. At a high-level, when invoked, text style suggestion tool 110 generates context-specific text style suggestions by collecting contextual metadata about a selected text element (e.g., metadata collector 112) (i.e. text content based pre-input) and providing a representation of the collected metadata with a call to text style suggestion service 180).
In regards to claim 4. Kraus discloses the computer implemented method according to claim 1.
Kraus further discloses
-wherein processing the first design element to generate the first categoriser pre-input includes generating a text format based pre-input that is based on a format of text content of the first design element (Kraus, para [0033]; Reference discloses more specifically, style collection 192 may be represented by any number of data structures, files, or other ways of organizing information. In an example embodiment, each style (e.g., lockup, logo, other stylized text element) in style collection 192 is represented by its own design document (which stores a stylized text element) and/or a corresponding manifest that represents the design document and specifies the design elements in the document and their corresponding features (e.g., element size, element location, element content, font, color, effects, backing shapes, etc.) (i.e. format of text content of first design element)).
In regards to claim 5. Kraus discloses the computer implemented method according to claim 1.
Kraus further discloses
-wherein processing the first design element to generate the first categoriser pre-input includes generating a metadata based pre-input that is based on metadata associated with the first design element (Kraus, para [0045]; Reference discloses at a high-level, when invoked, text style suggestion tool 110 generates context-specific text style suggestions by collecting contextual metadata about a selected text element (e.g., metadata collector 112) and providing a representation of the collected metadata with a call to text style suggestion service 180).
In regards to claim 6. Kraus discloses the computer implemented method according to claim 1.
Kraus further discloses
-wherein: generating the set of categoriser pre-inputs includes processing a second design element in the set of design elements to generate a second categoriser pre-input (Kraus, Fig. 1 and para [0045]; Reference discloses in FIG. 1, text style suggestion tool 110 includes metadata collector 112, style discriminator 114, style wheel 116, and style preview component 118. At a high-level, when invoked, text style suggestion tool 110 generates context-specific text style suggestions by collecting contextual metadata (i.e. generated set of categorizer pre-inputs) about a selected text element (e.g., metadata collector 112) (i.e. set of design elements) and providing a representation of the collected metadata with a call to text style suggestion service 180 (i.e. generating the set of categoriser pre-inputs includes processing a first design element in the set of design elements to generate a first categoriser pre-input);
-and processing the second design element to generate the second categoriser pre-input includes generating a media content based pre-input that is based on media content of the second design element (Kraus, para [0033]; Reference discloses more specifically, style collection 192 may be represented by any number of data structures, files, or other ways of organizing information. In an example embodiment, each style (e.g., lockup, logo, other stylized text element) in style collection 192 is represented by its own design document (which stores a stylized text element) and/or a corresponding manifest that represents the design document and specifies the design elements in the document and their corresponding features (e.g., element size, element location, element content, font, color, effects, backing shapes, etc.)).
In regards to claim 7. Kraus discloses the computer implemented method according to claim 6.
Kraus further discloses
-wherein the media content based pre-input includes text describing the media content of the second design element (Kraus, Fig. 1 and para [0045]; Reference discloses in FIG. 1, text style suggestion tool 110 includes metadata collector 112, style discriminator 114, style wheel 116, and style preview component 118. At a high-level, when invoked, text style suggestion tool 110 generates context-specific text style suggestions by collecting contextual metadata about a selected text element (e.g., metadata collector 112) and providing a representation of the collected metadata with a call to text style suggestion service 180).
In regards to claim 8. Kraus discloses the computer implemented method according to claim 1.
Kraus further discloses
-wherein: the first design element is associated with a design page (Kraus, para [0033]; Reference discloses more specifically, style collection 192 may be represented by any number of data structures, files, or other ways of organizing information. In an example embodiment, each style (e.g., lockup, logo, other stylized text element) in style collection 192 is represented by its own design document (which stores a stylized text element) and/or a corresponding manifest that represents the design document and specifies the design elements in the document and their corresponding features…Generally, the method of parsing and/or extraction may depend on the structure of the manifest (e.g., field lookups, string matching or other type of extraction rule, etc.));
-and generating the set of categoriser pre-inputs includes generating a page-based pre-input string, the page-based pre-input string being based on page data associated with the design page (Kraus, para [0033]; Reference discloses more specifically, style collection 192 may be represented by any number of data structures, files, or other ways of organizing information. In an example embodiment, each style (e.g., lockup, logo, other stylized text element) in style collection 192 is represented by its own design document (which stores a stylized text element) and/or a corresponding manifest that represents the design document and specifies the design elements in the document and their corresponding features…Generally, the method of parsing and/or extraction may depend on the structure of the manifest (e.g., field lookups, string matching or other type of extraction rule, etc.)).
In regards to claim 9. Kraus discloses the computer implemented method according to claim 1.
Kraus further discloses
-wherein determining the category by processing the categoriser input includes inputting the categoriser input into a category identifier system and receiving the determined category as an output of the category identifier system (Kraus, Fig. 1 and para [0045]; Reference discloses in FIG. 1, text style suggestion tool 110 includes metadata collector 112, style discriminator 114, style wheel 116, and style preview component 118. At a high-level, when invoked, text style suggestion tool 110 generates context-specific text style suggestions by collecting contextual metadata about a selected text element (e.g., metadata collector 112) and providing a representation of the collected metadata with a call to text style suggestion service 180 (i.e. style suggestion service interpreted as tool for performing categorization)).
In regards to claim 10. Kraus discloses the computer implemented method according to claim 9.
Kraus further discloses
-wherein the category identifier system includes a machine learning model (Kraus, para [0048]; Reference discloses style selector 182 includes one or more neural networks or some other machine-learning model configured to predict a compatibility score for each candidate style based on encoded contextual metadata from the request. In an example architecture, a neural network is configured and trained to predict a compatibility score for each candidate text style (e.g., one score per output neuron) from an encoded representation of contextual metadata. In this example, text style suggestion service 180 may receive the contextual metadata as part of a request).
In regards to claim 11. Kraus discloses the computer implemented method according to claim 9.
Kraus further discloses
-wherein the machine learning model is a large language model, and wherein generating the categoriser input includes generating a prompt for the large language model (Kraus, para [0057]; Reference discloses upon receipt of a request for text emphasis suggestions, text emphasis suggestion service 184 generates suggestions for which words in the received text phrase can be emphasized in a semantically meaningful way. To accomplish this, in some embodiment, natural language processing and a rules engine are applied to assign suggested emphasis categories from a taxonomy that represents different types of semantic nuance, and the emphasis categories are associated with corresponding words from the phrase that are subject to emphasis).
In regards to claim 12. Kraus discloses the computer implemented method according to claim 1.
Kraus further discloses
-wherein the set of design elements include all elements of a current design (Kraus, para [0031]; Reference discloses generally, a user may use various design tools 106 of design application 104 to generate a design with any number and arrangement of text and/or graphic elements, and to apply desired styles (e.g., color, effects, animation, etc.) and other design elements. The user selects a text element (e.g., a text field that includes a text phrase) using a selection tool of design application 104, and activates text style tool 108 (e.g., a text style menu). Upon invocation of text style tool 108 with a selected text element, text style suggestion tool 110 makes a call to text style suggestion service 180 to retrieve context-specific text style suggestions, and/or text emphasis suggestion tool 120 makes a call to text emphasis suggestion service 184 to retrieve text emphasis suggestions).
In regards to claim 13. Kraus discloses the computer implemented method according to claim 1.
Kraus further discloses
-wherein the set of design elements includes a plurality of design elements and applying one or more animations to the set of design elements further includes: determining, based on the category, a respective animation for each of the plurality of design elements; and applying the respective animation to each of the plurality of design elements (Kraus, para [0031]; Reference discloses Generally, a user may use various design tools 106 of design application 104 to generate a design with any number and arrangement of text and/or graphic elements, and to apply desired styles (e.g., color, effects, animation, etc.) and other design elements…Upon invocation of text style tool 108 with a selected text element, text style suggestion tool 110 makes a call to text style suggestion service 180 to retrieve context-specific text style suggestions, and/or text emphasis suggestion tool 120 makes a call to text emphasis suggestion service 184 to retrieve text emphasis suggestions (e.g., a list of emphasis categories to which the text phrase can belong, and which word(s) to emphasize for each emphasis category)).
In regards to claim 14. Kraus discloses the computer implemented method according to claim 1.
Kraus further discloses
-wherein the first animation for the first design element is determined based on a set of predefined element animation rules (Kraus, para [0057]; Reference discloses upon receipt of a request for text emphasis suggestions, text emphasis suggestion service 184 generates suggestions for which words in the received text phrase can be emphasized in a semantically meaningful way. To accomplish this, in some embodiment, natural language processing and a rules engine are applied to assign suggested emphasis categories from a taxonomy that represents different types of semantic nuance, and the emphasis categories are associated with corresponding words from the phrase that are subject to emphasis).
In regards to claim 15. Kraus discloses the computer implemented method according to claim 14.
Kraus further discloses
-wherein each predefined element animation rule is associated with: rule criteria data which is used to determine whether an animation rule applies to an element; and animation data which is used to determine a particular animation that is to be applied to an element that the animation rule applies to (Kraus, para [0057]; Reference discloses in the embodiment illustrated in FIG. 1, text emphasis suggestion service 184 initially triggers linguistic feature detector 186 to identify linguistic features from a selected text phrase, and text emphasis suggestion service 184 applies a rules engine that uses the linguistic features to assign emphasis categories and associate them with corresponding words).
In regards to claim 16. Kraus discloses the computer implemented method according to claim 15.
Kraus further discloses
-wherein the rule criteria data for a first animation rule in the set of predefined animation rules includes one or more of: a content type criterion; an element size criterion; a text size criterion; a text hierarchy level criterion (Kraus, para [0059] and Table 1; References discloses upon receipt of the linguistic features, text emphasis suggestion service 184 applies a rules engine that uses the linguistic features to assign emphasis categories and associate them with corresponding candidate words. Generally, any given phrase may have multiple ways to emphasize different words in a way that focuses the audience's attention on different semantic aspects (i.e. wherein the rule criteria data for a first animation rule in the set of predefined animation rules includes one or more of: a content type criterion; and text hierarchy level criterion)).
In regards to claim 17.Kraus discloses a system (Kraus, Abstract) including:
-one or more computer processing units (Kraus, para [0076]; Reference discloses with reference to FIG. 8, computing device 800 includes bus 810 that directly or indirectly couples the following devices: memory 812, one or more processors 814);
-and one or more non-transitory computer-readable media storing instructions which, when executed by the one or more computer processing units, cause the one or more computer processing units to perform a method for automatically animating a set of design elements (Kraus, Abstract), the method including:
-generating a set of categoriser pre-inputs based on the set of design elements, wherein generating the set of categoriser pre-inputs includes processing a first design element in the set of design elements to generate a first categoriser pre-input (Kraus, Fig. 1 and para [0045]; Reference discloses in FIG. 1, text style suggestion tool 110 includes metadata collector 112, style discriminator 114, style wheel 116, and style preview component 118. At a high-level, when invoked, text style suggestion tool 110 generates context-specific text style suggestions by collecting contextual metadata (i.e. generated set of categorizer pre-inputs) about a selected text element (e.g., metadata collector 112) (i.e. set of design elements) and providing a representation of the collected metadata with a call to text style suggestion service 180 (i.e. generating the set of categoriser pre-inputs includes processing a first design element in the set of design elements to generate a first categoriser pre-input);
-generating a categoriser input based on the set of categoriser pre-inputs (Kraus, Fig. 1 and para [0045]; Reference discloses at a high-level, when invoked, text style suggestion tool 110 generates context-specific text style suggestions by collecting contextual metadata about a selected text element (e.g., metadata collector 112) and providing a representation of the collected metadata with a call to text style suggestion service 180 (i.e. generated categoriser input based on the set of categoriser pre-inputs). Upon receiving the call, text style suggestion service 180 (e.g., style selector 182 of text style suggestion service 180) identifies text style suggestions (e.g., from representative styles 194) based on the collected metadata and returns the identified text style suggestions to text style suggestion tool 110);
-determining a category by processing the categoriser input (Kraus, para [0045]; Reference discloses style discriminator 114 performs a contextual analysis of the selected text element to identify text style suggestions that are compatible with the context of the user's design (i..e contextual analysis interpreted as the determining category processing by processing categorizer input regarding the text element);
-and automatically applying one or more animations to the set of design elements (Kraus, para [0025]; Reference discloses In some cases, the present techniques may be used to automatically generate designs or design elements without user intervention. In these cases, the user no longer needs to figure out a font, shape, arrangement, emphasis, or the like, as these decisions may be made for them by automatically mapping context to good design) , wherein automatically applying the one or more animations to the set of design elements includes: determining, based on the category, a first animation for the first design element (Kraus, para [0042] and [0045]; Reference at [0042] discloses turning now to design application 104 on client device, a user may use any known type of design tool(s) 106 to select or generate a design with any number and arrangement of text and/or graphic elements, and to apply desired styles (e.g., color, effects, animation, etc.) and other design features. Para [0045] discloses style discriminator 114 performs a contextual analysis of the selected text element to identify text style suggestions that are compatible with the context of the user's design. The identified compatible text styles are mapped to different positions of style wheel 116 (e.g., which may correspond to style wheel 250 of FIG. 2), and the user may scroll through the positions of style wheel 116 to iterate through the compatible style suggestions (i.e. iterating through text style suggestions interpreted as selecting a first animation for the first design element));
-and applying the first animation to the first design element (Kraus, para [0045]; Reference discloses the identified compatible text styles are mapped to different positions of style wheel 116 (e.g., which may correspond to style wheel 250 of FIG. 2), and the user may scroll through the positions of style wheel 116 to iterate through the compatible style suggestions. Upon selection of a particular text style suggestion (e.g., corresponding to the position of indicator 255 of style wheel 250 of FIG. 2), style preview component previews and/or applies the text suggestion to the user's design (i.e. applying the first animation to the first design element)).
In regards to claim 18. Kraus discloses one or more non-transitory computer-readable media storage storing instructions executable by one or more computer processing units to cause the one or more computer processing units to perform a method for automatically animating a set of design elements (Kraus, Abstract), the method including:
-generating a set of categoriser pre-inputs based on the set of design elements, wherein generating the set of categoriser pre-inputs includes processing a first design element in the set of design elements to generate a first categoriser pre-input (Kraus, Fig. 1 and para [0045]; Reference discloses in FIG. 1, text style suggestion tool 110 includes metadata collector 112, style discriminator 114, style wheel 116, and style preview component 118. At a high-level, when invoked, text style suggestion tool 110 generates context-specific text style suggestions by collecting contextual metadata (i.e. generated set of categorizer pre-inputs) about a selected text element (e.g., metadata collector 112) (i.e. set of design elements) and providing a representation of the collected metadata with a call to text style suggestion service 180 (i.e. generating the set of categoriser pre-inputs includes processing a first design element in the set of design elements to generate a first categoriser pre-input);
-generating a categoriser input based on the set of categoriser pre-inputs (Kraus, Fig. 1 and para [0045]; Reference discloses at a high-level, when invoked, text style suggestion tool 110 generates context-specific text style suggestions by collecting contextual metadata about a selected text element (e.g., metadata collector 112) and providing a representation of the collected metadata with a call to text style suggestion service 180 (i.e. generated categoriser input based on the set of categoriser pre-inputs). Upon receiving the call, text style suggestion service 180 (e.g., style selector 182 of text style suggestion service 180) identifies text style suggestions (e.g., from representative styles 194) based on the collected metadata and returns the identified text style suggestions to text style suggestion tool 110);
-determining a category by processing the categoriser input (Kraus, para [0045]; Reference discloses style discriminator 114 performs a contextual analysis of the selected text element to identify text style suggestions that are compatible with the context of the user's design (i.e. contextual analysis interpreted as the determining category processing by processing categorizer input regarding the text element);
-and automatically applying one or more animations to the set of design elements (Kraus, para [0025]; Reference discloses In some cases, the present techniques may be used to automatically generate designs or design elements without user intervention. In these cases, the user no longer needs to figure out a font, shape, arrangement, emphasis, or the like, as these decisions may be made for them by automatically mapping context to good design) , wherein automatically applying the one or more animations to the set of design elements includes: determining, based on the category, a first animation for the first design element (Kraus, para [0042] and [0045]; Reference at [0042] discloses turning now to design application 104 on client device, a user may use any known type of design tool(s) 106 to select or generate a design with any number and arrangement of text and/or graphic elements, and to apply desired styles (e.g., color, effects, animation, etc.) and other design features. Para [0045] discloses style discriminator 114 performs a contextual analysis of the selected text element to identify text style suggestions that are compatible with the context of the user's design. The identified compatible text styles are mapped to different positions of style wheel 116 (e.g., which may correspond to style wheel 250 of FIG. 2), and the user may scroll through the positions of style wheel 116 to iterate through the compatible style suggestions (i.e. iterating through text style suggestions interpreted as selecting a first animation for the first design element));
-and applying the first animation to the first design element (Kraus, para [0045]; Reference discloses the identified compatible text styles are mapped to different positions of style wheel 116 (e.g., which may correspond to style wheel 250 of FIG. 2), and the user may scroll through the positions of style wheel 116 to iterate through the compatible style suggestions. Upon selection of a particular text style suggestion (e.g., corresponding to the position of indicator 255 of style wheel 250 of FIG. 2), style preview component previews and/or applies the text suggestion to the user's design (i.e. applying the first animation to the first design element)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: See the Notice of References Cited (PTO-892).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TERRELL M ROBINSON whose telephone number is (571)270-3526. The examiner can normally be reached 8am-5pm.
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, KENT CHANG can be reached at 571-272-7667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TERRELL M ROBINSON/Primary Examiner, Art Unit 2614
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