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 .
The prior art used for rejections are provided below:
1. US20250117998Al (October 10, 2023) to Cheng (hereinafter Cheng).
2. CanvasVAE: Learning to Generate Vector Graphic Documents (August 3, 2021) to Yamaguchi (hereinafter Yamaguchi).
3. US20200183555Al (January 29, 2019) to Al-Sallami (hereinafter Al-Sallami).
4. US10922713B2 (January 3, 2017) to Kulkarni (hereinafter Kulkarni).
5. US8924841B2 (April 25, 2006) to Giannetti (hereinafter Giannetti).
6. US9704106B2 (May 27, 2016) to Welinder (hereinafter Welinder).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 9, 11-14, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Cheng in view of Al-Sallami and Yamaguchi.
Per claim 1, Cheng discloses A system for modifying a database of content resources using machine learning models, the system comprising: one or more processors; one or more non-transitory, computer-readable media comprising instructions that, when executed by the one or more processors, causes operations comprising [Cheng, para. 0002 "An example data processing system according to the disclosure includes a processor and a memory storing executable instructions" (note: Cheng's data processing system is a processor executing instructions held in memory, which constitutes the recited processors and non-transitory media under BRI, a memory being the paradigmatic non-transitory medium)]:
accessing a database of creative content resources associated with an entity [Cheng, para. 0019 "The design content datastore 192 is a persistent datastore in the memory of the application services platform 110 that stores information associated with designs created with and/or modified using the design application. The design content datastore 192 stores brand kit information for users, enterprises, and/or other organizations" (note: Cheng's design content datastore persistently holds the designs created with the design application together with the brand kits of the enterprise that owns them, which constitutes a database of creative content resources associated with an entity)];
obtaining (1) a user prompt for generating a new creative content resource for the entity, wherein the user prompt comprises natural language text indicative of criteria for the new creative content resource and (2) a set of rules indicative of standardized assets and standardized structural specifications for use in the creative content resources associated with the entity [Cheng, para. 0038 and para. 0040 "The natural language prompt and one or more optional samples images are input by the user via a user interface of a design application" and "The request processing unit 150 obtain the brand kit associated with the user from the design content datastore 192" (note: Cheng takes the user's natural-language prompt through the design application and separately retrieves that user's brand kit from the datastore, which constitutes obtaining both the claimed prompt and the claimed set of rules)];
inputting the user prompt and the set of rules into the machine learning model to generate the new creative content resource [Cheng, para. 0042 and para. 0043 "generating intermediate content based on the natural language prompt" and "an operation 214 of generating model-specific prompts to the generative models to customize the intermediate content based on the brand kit" (note: Cheng first generates intermediate content from the prompt and then re-prompts the generative models to conform that content to the brand kit, so the prompt and the rule set are both inputs to the model that emits the finished resource)];
displaying, via a graphical display, the new creative content resource comprising one or more elements of the new creative content resource and one or more structural specifications for composing the one or more elements of the new creative content resource [Cheng, para. 0044 "The process 200 includes an operation 220 of providing the personalized content to the client device 105 of the user and causing the client device 105 to present the personalized content on a user interface of the client device 105" (note: Cheng returns the generated personalized content to the client and renders it on the design application's user interface together with the brand kit that fixes its fonts, palette and logo, which constitutes displaying the new resource with its elements and their structural specifications,)];
Cheng does not expressly disclose, but Cheng combined with Yamaguchi does teach:
obtaining, for each creative content resource of the database, data indicative of (1) elements composing each creative content resource and (2) structural specifications for composing the elements within each creative content resource [Yamaguchi, p. 2, § 3.1 Document structure "Element attributes indicate element-specific configuration, such as position, size, type of the element, opacity, color, or a texture image if the element represents a raster image" (note: Yamaguchi records, for every document in its corpus, both the element's type and the layout attributes that position and style it, which constitutes the claimed element data together with structural specifications)];
training a machine learning model based on the elements and the structural specifications for each creative content resource of the database, wherein the machine learning model is configured to generate new creative content resources [Yamaguchi, Abstract and § 1 "The learned decoder can take randomly sampled latent code to generate a new vector graphic document" (note: Yamaguchi fits its variational auto-encoder to that element-and-attribute representation and the resulting decoder emits documents it has not seen, which constitutes training on the elements and structural specifications to yield a model configured to generate new resources)];
Cheng combined with Yamaguchi does not expressly disclose, but Cheng combined with Al-Sallami does teach:
obtaining an indication for replacing a recurring asset included in the one or more elements of the new creative content resource with a replacement asset [Al-Sallami, para. 0195 "a merchant can edit the web page 1602 (e.g., by replacing the logo with a new logo). The web page editing user interface 208 can detect the modification and can send an indication to the web server(s) 214" (note: Al-Sallami's logo recurs across the merchant's surfaces, and when the merchant replaces it the editing interface detects the substitution and emits an indication of it to the server, which constitutes obtaining an indication for replacing a recurring asset with a replacement asset)];
determining a subset of creative content resources from the database of creative content resources comprising the recurring asset [Al-Sallami, para. 0073 "an element can be linked to a brand asset in the brand asset database 232 such that when a brand asset is updated, the brand asset corresponding to the element can be updated in real-time or near real-time" (note: each element carries a link to the brand asset it draws on, so the set of resources containing a given asset is resolvable when that asset changes, which constitutes determining the subset comprising the recurring asset]; and
replacing the recurring asset with the replacement asset in each creative content resource of the subset of creative content resources [Al-Sallami, para. 0030 "The updated brand asset can be provided to the brick-and-mortar POS platform such that the brand asset is consistent across the brick-and-mortar POS platform and the web services platform" (note: the updated asset is propagated to every surface that draws on it so the asset is uniform after the substitution, which constitutes replacing the recurring asset across the determined subset,)].
Cheng, Yamaguchi and Al-Sallami are analogous art because all three are from the same field of endeavor of computer-implemented generation and management of an entity's branded visual design content. They are further reasonably pertinent to the same problem with which the inventor was concerned, namely producing and maintaining a body of design content that stays consistent with an entity's standardized assets without manual re-creation.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to further train Cheng’s generative model using Yamaguchi’s disclosed training methodology, on the elements and structural specifications of a set of design documents, so that Cheng’s prompt-conditioned model is also trained on exactly this class of data, and to implement Cheng’s database and content-display functions using Al-Sallami’s centralized brand-asset database architecture and web-based editing interface, so that content generated by Cheng’s model is stored in that database and displayed to the user broken into its constituent elements via that interface. This uses Al-Sallami’s editing and display framework only, not Al-Sallami’s separate recommendation module machine learning, which Al-Sallami confines to cross-merchant similarity recommendations and does not connect to that editing interface.
The suggestion/motivation for doing so would have been that both Yamaguchi and Al-Sallami directly state their own benefits. Yamaguchi states its trained model constitutes a strong baseline for generative modeling of vector graphic documents [Yamaguchi, pg. 1, Abstract “constitutes a strong baseline for generative modeling of vector graphic documents.”]. Al-Sallami states that its centralized database architecture exists because synchronization alleviates the need to manually enter and manage duplicate content across systems [Al-Sallami, para. 0120 “such synchronization alleviates the need for merchants to manually enter and manage”]. Therefore, Al-Sallami’s database and interface architecture lead to an efficiency benefit of avoiding duplicate, manually maintained copies of brand assets across individual creative content resources.
Per claim 2, Cheng discloses A method for modifying a database of creative content resources using machine learning models, the method comprising [Cheng, para. 0018 "The request processing unit 150 is configured to receive requests from a design application implemented by the native application 114 of the client device 105" (note: Cheng's request processing unit receives and services the design-generation requests that drive the method)]:
accessing a database of creative content resources associated with an entity [Cheng, para. 0019 "The design content datastore 192 is a persistent datastore in the memory of the application services platform 110 that stores information associated with designs created with and/or modified using the design application. The design content datastore 192 stores brand kit information for users, enterprises, and/or other organizations" (note: Cheng's design content datastore persistently holds the designs created with the design application together with the brand kits of the enterprise that owns them, which constitutes a database of creative content resources associated with an entity)];
obtaining (1) a user prompt for generating a new creative content resource for the entity, wherein the user prompt comprises natural language text indicative of criteria for the new creative content resource and (2) a set of rules indicative of standardized assets and standardized structural specifications for use in the creative content resources associated with the entity [Cheng, para. 0038 and para. 0040 "The natural language prompt and one or more optional samples images are input by the user via a user interface of a design application" and "The request processing unit 150 obtain the brand kit associated with the user from the design content datastore 192" (note: Cheng takes the user's natural-language prompt through the design application and separately retrieves that user's brand kit from the datastore, which constitutes obtaining both the claimed prompt and the claimed set of rules)];
inputting the user prompt and the set of rules into a machine learning model to obtain the new creative content resource,… [Cheng, para. 0042 and para. 0043 "generating intermediate content based on the natural language prompt" and "an operation 214 of generating model-specific prompts to the generative models to customize the intermediate content based on the brand kit" (note: Cheng first generates intermediate content from the prompt and then re-prompts the generative models to conform that content to the brand kit, so the prompt and the rule set are both inputs to the model that emits the finished resource)];
…wherein the machine learning model is trained using the elements and the structural specifications of creative content resources of the database and wherein the machine learning model is configured to generate new creative content resources [Yamaguchi, Abstract, "train variational auto-encoders to learn the representation of the documents” (note: Yamaguchi fits its auto-encoder to the document's element and attribute representation, which constitutes training the model on the elements and structural specifications);
Abstract and § 1 "The learned decoder can take randomly sampled latent code to generate a new vector graphic document" (note: Yamaguchi fits its variational auto-encoder to that element-and-attribute representation and the resulting decoder emits documents it has not seen, which constitutes training on the elements and structural specifications to yield a model configured to generate new resources)];
Cheng combined with Yamaguchi does not expressly disclose, but combined with Al-Sallami does teach:
obtaining an indication for replacing a recurring asset included in the new creative content resource with a replacement asset [Al-Sallami, para. 0195 "a merchant can edit the web page 1602 (e.g., by replacing the logo with a new logo). The web page editing user interface 208 can detect the modification and can send an indication to the web server(s) 214" (note: Al-Sallami's logo recurs across the merchant's surfaces, and when the merchant replaces it the editing interface detects the substitution and emits an indication of it to the server, which constitutes obtaining an indication for replacing a recurring asset with a replacement asset)]; and
replacing the recurring asset with the replacement asset in each creative content resource of a subset of creative content resources from the database [Al-Sallami, para. 0030 "The updated brand asset can be provided to the brick-and-mortar POS platform such that the brand asset is consistent across the brick-and-mortar POS platform and the web services platform" (note: the updated asset is propagated to every surface that draws on it so the asset is uniform after the substitution, which constitutes replacing the recurring asset across the determined subset,)].
The rationale to combine Cheng, Yamaguchi and Al-Sallami as set forth for claim 1 applies to claim 2, which recites the method corresponding to the system of claim 1 over the same combination.
Per claim 3, Cheng-Yamaguchi-Al-Sallami disclose claim 2.
Cheng does not expressly disclose, but with Al-Sallami does teaches: determining the subset of creative content resources from the database of creative content resources comprising the recurring asset. [Al-Sallami, para. 0073 "an element can be linked to a brand asset in the brand asset database 232 such that when a brand asset is updated, the brand asset corresponding to the element can be updated in real-time or near real-time" (note: each element carries a link to the brand asset it draws on, so the set of resources containing a given asset is resolvable when that asset changes, which constitutes determining the subset comprising the recurring asset]
The rationale to combine Cheng with Al-Sallami is the same as per claim 2.
Per claim 4, Cheng-Yamaguchi-Al-Sallami disclose claim 2.
Cheng does not expressly disclose, but with Yamaguchi does teach: training the machine learning model based on the elements and the structural specifications for each creative content resource of the database. [Yamaguchi, Abstract, "train variational auto-encoders to learn the representation of the documents” (note: Yamaguchi fits its auto-encoder to the document's element and attribute representation, which constitutes training the model on the elements and structural specifications);
Abstract and § 1 "The learned decoder can take randomly sampled latent code to generate a new vector graphic document" (note: Yamaguchi fits its variational auto-encoder to that element-and-attribute representation and the resulting decoder emits documents it has not seen, which constitutes training on the elements and structural specifications to yield a model configured to generate new resources)];
The rationale to combine Cheng with Yamaguchi is the same as per claim 2.
Per claim 9, Cheng-Yamaguchi-Al-Sallami disclose claim 2.
Cheng further teaches: generating, using a second machine learning model, the set of rules indicative of standardized assets and standardized structural specifications for use in creative content resources associated with the entity based on the elements and the structural specifications for each creative content resource. [Yamaguchi, 3.1 Document structure, "Element attributes indicate element-specific configuration, such as position, size, type of the element, opacity, color" (note: this discloses the per-resource feature inputs to the rule generation combination.);
Cheng, para. 0023 "the language model 124 is implemented using a large language model (LLM)... Examples of such models include... GPT-3... or GPT-4" (note: this identifies language model 124 as a second, distinct model from Yamaguchi's CanvasVAE, cited for claim 2);
para. 0021 "content created by the user can be accessed and analyzed by the AI services 120 when generating a brand kit for the user." (note: Cheng disclose automatically generating a standardized rule (a brand kit) for a specific entity by analyzing that entity's own previously created content.)]
Per claim 11, Cheng-Yamaguchi-Al-Sallami disclose claim 2.
Cheng does not expressly disclose, but with Al-Sallami does teaches: displaying, via a graphical display, the new creative content resource comprising one or more elements of the new creative content resource and one or more structural specifications for composing the one or more elements of the new creative content resource; [Cheng, para. 0044 "The process 200 includes an operation 220 of providing the personalized content to the client device 105 of the user and causing the client device 105 to present the personalized content on a user interface of the client device 105" (note: Cheng returns the generated personalized content to the client and renders it on the design application's user interface together with the brand kit that fixes its fonts, palette and logo, which constitutes displaying the new resource with its elements and their structural specifications,)]
receiving one or more user inputs indicative of a modification of the new creative content resource; and [Al-Sallami, para. 0076 "the web page editing user interface 208 can present one or more options 328 for customizing the content and/or design of the element." (note: this discloses receiving user input indicative of a modification.)]
updating the new creative content resource based on the one or more user inputs. [Al-Sallami, para. 0076 "the web page editing user interface 308 can update the corresponding element in the second panel 308 in the graphical user interface 324." (note: Updating the resource based on the input. No new reference is introduced by this claim.)]
The rationale to combine Cheng with Al-Sallami is the same as per claim 2.
Per claim 12, Cheng-Yamaguchi-Al-Sallami disclose claim 2.
Cheng does not expressly disclose, but with Al-Sallami does teaches: updating the database of creative content resources by adding the new creative content resource to the database. [Al-Sallami, para. 0194 "the brand asset management module 230 can update the brand asset (e.g., instructions associated therewith) in the brand asset database 232." (note: this discloses updating the database with new and changed resource data)]
The rationale to combine Cheng with Al-Sallami is the same as per claim 2.
Per claim 13, Cheng-Yamaguchi-Al-Sallami disclose claim 2.
Cheng does not expressly disclose, but with Al-Sallami does teaches: the user prompt further comprises one or more files comprising data regarding an item [Cheng, para. 0038 “The natural language prompt and one or more optional samples images are input by the user via a user interface of a design application” (note: Cheng discloses the prompt submission already accepts accompanying files (sample images));
Al-Sallami, para. 0058 “The inventory data, for example, can include a description of an item, a price of the item, a discount available for the item, taxes applicable to the item, a location of the item, image(s) of the item, characteristic(s) of the item, etc.” (note: Al-Sallami shows the specific kind of structured item data that could be supplied that way. User prompt is anchored to Cheng throughout, Al-Sallami supplies content-type detail only)] and wherein the machine learning model is further configured to access the data from the one or more files and process the data to include as an element of the new creative content resource. [Cheng, para. 0055 “The prompt construction layer 140 can provide these sample images as an input to one or more of the generative models.” (note: The same combined model from claim 2 is shown processing accompanying files as model input, extending that same mechanism to item data files)]
The rationale to combine Cheng with Al-Sallami is the same as per claim 2.
Claims 14, 18, 19, and 20 are substantially similar in scope and spirit to claims 2, 9, 11, and 13, respectively. Therefore, the rejection of claims 2, 9, 11, and 13 is applied accordingly. Al-Sallami also shows the method being implemented by an article of manufacture [Al-Sallami, para. 0245 " Depending on the configuration of the user computing device(s) 2100, the computer readable media 2104 can be a type of computer-readable storage media and/or can be a tangible non-transitory media to the extent that when mentioned, non-transitory computer readable media "]
Claims 5, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Cheng in view of Al-Sallami, Yamaguchi, and Giannetti.
Per claim 5, Cheng-Yamaguchi-Al-Sallami disclose claim 2.
Cheng does not expressly disclose, but with Giannetti does teach: obtaining data indicative of (1) elements composing each creative content resource and (2) structural specifications for composing the elements within each creative content resource comprises:
obtaining programmatic code configured to generate each creative content resource upon execution; and [Giannetti, column 3, line 57 "The document template 230 comprises a layout of copy-holes... is passed to a processor 260 to be merged with input data." (note: Giannetti's document template is program markup that generates a rendered document instance on processing, which is programmatic code configured to generate each creative content resource upon execution.)]
extracting, from the programmatic code, elements and structural specifications of each creative content resource. [Giannetti, column 6, line 1 "the program code uses standard XSL tags”;
column 5, line 59 “Group 1 contains an image, defined by the file LeftPoints.jpg... marked with an (X,Y) coordinate position." (note: Giannetti's discloses parsing markup tags out of the template code to identify an image element together with its coordinate position (structural specification), which is element and structural specifications extraction from code.)]
Cheng and Giannetti are analogous art because they are from the same field of endeavor of automated content generation and production. They are further reasonably pertinent to the same problem of producing and maintaining consistent content without manual effort per-item.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to add Giannetti’s code parsing and automatic publication techniques to Cheng’s content-generation system, as an alternative data-acquisition and deployment path.
The suggestion/motivation for doing so would have been improved efficiency as stated by Giannetti [Giannetti, column 7, line 43 "One advantage is in the reduction of the number of templates and the resultant alleviating of concerns associated with multiple updates"].
Per claim 10, Cheng-Yamaguchi-Al-Sallami disclose claim 2.
Cheng does not expressly disclose, but with Giannetti does teach: automatically deploying each creative content resource of the subset of creative content resources responsive to an indication of successful replacement of the recurring asset with the replacement asset. [Giannetti, column 3, line 51 "the consumer 130 publishes documents 100 defined by the template 110." (note: Giannetti's publishes documents once the template is complete, deployment responsive to an indication of successful production of the output)]
The rationale to combine Cheng with Giannetti is the same as per claim 5.
Claim 17 is substantially similar in scope and spirit to claim 10. Therefore, the rejection of claim 10 is applied accordingly. Al-Sallami also shows the method being implemented by an article of manufacture [Al-Sallami, para. 0245 " Depending on the configuration of the user computing device(s) 2100, the computer readable media 2104 can be a type of computer-readable storage media and/or can be a tangible non-transitory media to the extent that when mentioned, non-transitory computer readable media "]
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Cheng in view of Al-Sallami, Yamaguchi, and Kulkarni.
Per claim 6, Cheng-Yamaguchi-Al-Sallami disclose claim 4.
Cheng does not expressly disclose, but with Kulkarni does teach: determining that a first element of the elements of a creative content resource is a textual element; and [Kulkarni, column 11, line 42 "for textual creatives such as description, call_to_action_type, and caption and body text, the creative analysis module 210 uses textual analysis methods." (note: Kulkarni's pipeline branches on creative type, applying textual analysis when the creative is a textual element.)]
responsive to determining that the first element is a textual element, extracting structural specifications including a font name associated with the first element, a font size associated with the first element, a font color associated with the first element, a background color associated with the first element and/or a position of the first element. [Kulkarni, column 11, line 53 "further extracted features may include font size, font color, and number of words associated with a textual component." (note: font size and font color are extracted)]
Cheng and Kulkarni are analogous art because they are from the same field of endeavor of analysis and generation of structured creative content. They are further reasonably pertinent to the same problem of managing individual content components rather than treating content as an unanalyzable whole.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to apply Kulkarni’s per-element feature extraction to the content resources managed by Cheng’s system.
The suggestion/motivation for doing so would have been that Kulkarni’s per-element feature extraction lets the system analyze individual brand-asset elements rather than treating each creative resource as an unanalyzable whole. [Kulkarni, column 1, line 39 “Content providers cannot "see inside a content item" to understand which components of the content item did not perform well for their objectives or target audience.”]
Per claim 7, Cheng-Yamaguchi-Al-Sallami disclose claim 4.
Cheng does not fully disclose, but with Kulkarni does teach: determining that a first element of the elements of a creative content resource is an image element; and [Kulkarni, column 11, line 56 "the creative analysis module 210 extracts various image features associated with an image creative." (note: Kulkarni branches its extraction pipeline specifically for image creatives, which determines a first element is an image element.)]
responsive to determining that the first element is an image element, extracting structural specifications including a color space associated with the first element, a resolution associated with the first element, a position of the first element, and/or an aspect ratio associated with the first element. [Kulkarni, column 11, line 56 " The creative analysis module 210 extracts various image features associated with an image creative such as dominant color of the image, background color of the image, size of the image (e.g., width and length of the image)." (note: Kulkarni extracts image size (from which aspect ratio is directly derived) and dominant/background color (color space))]
The rationale to combine Cheng with Kulkarni is the same as per claim 6.
Claims 8 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Cheng in view of Al-Sallami, Yamaguchi, and Welinder.
Per claim 8, Cheng-Yamaguchi-Al-Sallami disclose claim 2.
Cheng does not expressly disclose, but with Welinder does teach: wherein obtaining data indicative of (1) elements composing each creative content resource and (2) structural specifications for composing the elements within each creative content resource comprises:
transmitting, to a remote device, a request for labelling portions of each creative content resource of the database; and [Welinder, column 11, line 67, "the annotation tasks along with one or more pieces of the obtained (310) source data are transmitted to one or more annotators." (note: An annotation (label) task together with the data to be labelled is transmitted out to a remote annotation device.);
column 8, line 2 "distributed data annotation system 100 includes distributed data annotation server system 110 connected to source data database 120 and one or more data annotation devices 130 via network 140." (note: Welinder discloses the server system and annotation devices are explicitly separate components connected over a network, which is a remote device)]
receiving, from a remote device, one or more labels corresponding to elements and structural specifications of each creative content resource. [Welinder, column 8, line 44 “Data annotation devices 130 transmit annotated source data to distributed data annotation server system 110.” (note: this discloses receiving from a remote device);
column 12, line 12 "The annotations include a confidence label and/or one or more labels describing one or more features of the piece of source data based on the determined (312) labeling task." (note: this shows that what is received is one or more labels describing features)]
Cheng and Welinder are analogous art because they are from the same field of endeavor of data annotation and content analysis. They are further reasonably pertinent to the same problem of obtaining structural data about content difficult to extract by automated local parsing.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to include Welinder's remote labelling to Cheng’s system as an alternative data-acquisition path.
The suggestion/motivation for doing so would have been to distribute the annotation load to remote annotation devices and improve scalability. [Welinder, column 5, line 34 "a large variety of source data... can be generated and/or obtained";
column 6, line 38 "Data annotation devices include human annotators, machine-based categorization devices, and/or a combination of machine and human annotation"]
Claim 15 is substantially similar in scope and spirit to claim 8. Therefore, the rejection of claim 8 is applied accordingly. Al-Sallami also shows the method being implemented by an article of manufacture [Al-Sallami, para. 0245 " Depending on the configuration of the user computing device(s) 2100, the computer readable media 2104 can be a type of computer-readable storage media and/or can be a tangible non-transitory media to the extent that when mentioned, non-transitory computer readable media "]
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Cheng in view of Al-Sallami, Yamaguchi, Welinder, and Giannetti.
Per claim 16, Cheng-Yamaguchi-Al-Sallami-Welinder disclose claim 15.
Cheng does not expressly disclose, but with Giannetti does teach: obtaining data indicative of (1) elements composing each creative content resource and (2) structural specifications for composing the elements within each creative content resource comprises:
obtaining programmatic code configured to generate each creative content resource upon execution; and [Giannetti, column 3, line 57 "The document template 230 comprises a layout of copy-holes... is passed to a processor 260 to be merged with input data." (note: Giannetti's document template is program markup that generates a rendered document instance on processing, which is programmatic code configured to generate each creative content resource upon execution.)]
extracting, from the programmatic code, elements and structural specifications of each creative content resource. [Giannetti, column 6, line 1 "the program code uses standard XSL tags”;
column 5, line 59 “Group 1 contains an image, defined by the file LeftPoints.jpg... marked with an (X,Y) coordinate position." (note: Giannetti's discloses parsing markup tags out of the template code to identify an image element together with its coordinate position (structural specification), which is element and structural specifications extraction from code.)]
Cheng and Giannetti are analogous art because they are from the same field of endeavor of automated content generation and production. They are further reasonably pertinent to the same problem of producing and maintaining consistent content without manual effort per-item.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to add Giannetti’s code parsing and automatic publication techniques to Cheng’s content-generation system, as an alternative data-acquisition and deployment path.
The suggestion/motivation for doing so would have been improved efficiency as stated by Giannetti [Giannetti, column 7, line 43 "One advantage is in the reduction of the number of templates and the resultant alleviating of concerns associated with multiple updates"].
Conclusion
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/SAYED MUNEER SHAH/Examiner, Art Unit 2124
/MIRANDA M HUANG/Supervisory Patent Examiner, Art Unit 2124
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