DETAILED ACTION
This is a non-final, first office action on the merits. Claims 1-20 are pending. 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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-20 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claims 1, 16, and 19 recite an abstract idea. Claims 1, 16, and 19 include “obtaining a graphical inference generated using the generative inference model and ingest data, populating a structured representation for the graphical inference using a schema; making a determination regarding whether the structured representation for the graphical inference indicates that the graphical inference exceeds a predetermined level of similarity with respect to reference works, in a first instance of the determination where the structured representation for the graphical inference indicates that the graphical inference does not exceed the predetermined level of similarity: providing the graphical inference to a downstream consumer as an implemented service, and in a second instance of the determination where the structured representation for the graphical inference indicates that the graphical inference exceeds the predetermined level of similarity: initiating performance of an action set to manage an impact of similarities between the graphical inference and the reference works”.
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the elements above recite mental processes-concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because the elements describe a process for managing a generative inference model. As a result, claims 1, 16, and 19 recite an abstract idea under Step 2A Prong One.
Claims 2-15, 17-18, and 20 further describe the process for managing a generative inference model. As a result, claims 2-15, 17-18, and 20 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claims 1, 16, and 19.
With respect to Step 2A Prong Two of the framework, claims 1, 16, and 19 do not include additional elements that integrate the abstract idea into a practical application. Claims 1, 16, and 19 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1, 16, and 19 include a generative inference model, a non-transitory machine-readable medium, a processor, and a memory coupled to the processor to store instructions. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 1, 16, and 19 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 3-15 and 18 do not include any additional elements beyond those recited with respect to claims 1, 16, and 19. As a result, claims 3-15 and 18 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above with respect to claims 1, 16, and 19.
Claims 2, 17, and 20 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 2, 17, and 20 include human interpretable objects and a generative inference model. When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claims 2, 17, and 20 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claims 1, 16, and 19 do not include additional elements amounting to significantly more than the abstract idea. As noted above, claims 1, 16, and 19 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1, 16, and 19 include a generative inference model, a non-transitory machine-readable medium, a processor, and a memory coupled to the processor to store instructions. The additional elements do not amount to significantly more than the abstract idea because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, independent claims 1, 16, and 19 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 3-15 and 18 do not include any additional elements beyond those recited with respect to claims 1, 16, and 19. As a result, claims 3-15 and 18 do not include additional elements that amount to significantly more than the abstract idea under Step 2B for the same reasons as stated above with respect to claims 1, 16, and 19.
Claims 2, 17, and 20 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 2, 17, and 20 include human interpretable objects and a generative inference model. The additional elements do not amount to significantly more than the abstract idea because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 2, 17, and 20 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1, 8-9, 13-16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Bourgin et al. (US Pub No. 2024/0403557) (hereinafter Bourgin et al.), in view of Tran et al. (US Pub No. 2023/0252224) (hereinafter Tran et al.), and further in view of Ren, Jie, et al. "Copyright Protection in Generative Al: A Technical Perspective." arXiv preprint arXiv:2402.02333 (2024) (hereinafter Jie et al.).
Regarding claims 1, 16, and 19, Bourgin in view of Tran and Jie discloses a method for managing a generative inference model, the method comprising:
obtaining a graphical inference generated using the generative inference model and ingest data (see Bourgin, para [0115], wherein obtain a visual structure of a digital design document for resizing the digital design document to another size (e.g., converting a widescreen banner to a portrait post size); para [0051], wherein trains models (e.g., artificial intelligence models) to generate a recommended revision to the digital design document; and para [0085], wherein ingestion by downstream artificial intelligence models or algorithms);
populating a structured representation for the graphical inference using a schema (see Bourgin, para [0055], wherein mapping structural information, artificial intelligence, or machine learning services. Moreover, the digital design system 102 utilizes the design representation 204 for downstream chaining of machine learning services to add further information such as design element semantics or to return layout suggestions; para [0166], wherein generate a structural representation such as a visual structure inference (e.g., flow relationships and structure in an inferred structure tree). Moreover, the digital design system 102 utilizes the visual structure inference to build the spring-based constraint system; and para [0056], wherein a digital design graph includes a representation of component relationships with one or more edges between a pair of nodes (i.e., mapping data points as nodes and edges, systems apply logical rules defined in the schema));
providing the graphical inference to a downstream consumer as a computer-implemented service (see Bourgin, abstract, wherein generate a structural representation based on the digital design multigraph for downstream applications); and
in a second instance of the determination where the structured representation for the graphical inference indicates that the graphical inference exceeds the predetermined level of similarity: initiating performance of an action set to manage an impact of similarities between the graphical inference and the reference works.
Bourgin et al. fails to explicitly disclose making a determination regarding whether the structured representation for the graphical inference indicates that the graphical inference exceeds a predetermined level of similarity with respect to reference works; in a first instance of the determination where the structured representation for the graphical inference indicates that the graphical inference does not exceed the predetermined level of similarity.
Analogous art Tran discloses using a schema (see Tran, para [0266], wherein Generate Semantic Knowledge Mapping and schema markup for crawlers to use);
Analogous art Tran discloses making a determination regarding whether the structured representation for the graphical inference indicates that the graphical inference exceeds a predetermined level of similarity with respect to reference works (see Tran, para [0063], wherein dragged and dropped into the tree structure to generate graphs with noun; para [0038], wherein the system provides a Visual and intuitive user interface with built-in semantic and technical understanding, automatic relevant passage suggestions; paras [0023]-[0024], wherein applying an artificial intelligence software to detect similarity of functions. detecting plagiarism in the document by matching the document text to text crawled from the Internet; and para [0098], wherein word vectors obtained via co-occurrence statistics consider two factors: syntactic, and semantic similarity so if a small window of context has been used then words like bad, good have very similar representation);
Analogous art Tran discloses in a first instance of the determination where the structured representation for the graphical inference indicates that the graphical inference does not exceed the predetermined level of similarity (see Tran, para [0063], wherein dragged and dropped into the tree structure to generate graphs with noun; para [0038], wherein the system provides a Visual and intuitive user interface with built-in semantic and technical understanding, automatic relevant passage suggestions; paras [0023]-[0024], wherein applying an artificial intelligence software to detect similarity of functions. detecting plagiarism in the document by matching the document text to text crawled from the Internet; and para [0411], wherein If no matching occurrence for current claim text and if claim is a dependent claim, search parent claims for antecedent basis).
Bourgin directed to a system for generating a structural representation for a digital design document from the digital design multigraph. Tran directed to generating long text or video using the neural network architectures. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Bourgin, regarding the System for Constructing Digital Design Graphs for Generating Structural Representations of Digital Design Documents, to have included making a determination regarding whether the structured representation for the graphical inference indicates that the graphical inference exceeds a predetermined level of similarity with respect to reference works; in a first instance of the determination where the structured representation for the graphical inference indicates that the graphical inference does not exceed the predetermined level of similarity because both inventions teach improving the context of the text and the language patterns. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Bourgin et al. fails to explicitly disclose in a second instance of the determination where the structured representation for the graphical inference indicates that the graphical inference exceeds the predetermined level of similarity: initiating performance of an action set to manage an impact of similarities between the graphical inference and the reference works.
Analogous art Jie discloses in a second instance of the determination where the structured representation for the graphical inference indicates that the graphical inference exceeds the predetermined level of similarity: initiating performance of an action set to manage an impact of similarities between the graphical inference and the reference works (see Jie, pages 15-16, columns 1-2, wherein evaluates the visual similarity of two images. The lower and upper bound of &i, a 1,i and au,i, are chosen based on empirical observation…a deep-learning-based structure for watermark (or "fingerprints" in their paper) embedding and extraction. As shown in Figure 14 Pipeline for Tree-Ring Watermarking…; page 19, columns 2, wherein identify potential document matches, followed by rigorous similarity checks or direct deletion of duplicates. This process, which includes sorting n-grams of each document and applying the Jaccard Index for similarity, significantly reduces model memorization, decreases training time, and improves evaluation accuracy by reducing train-test overlap. Kandpal et aL (2022) found that language models' likelihood of regenerating training sequences superlinearly correlates with their frequency in the dataset; page 20, columns 1, wherein the framework uses negative similarity, specifically a BERTScore metric, as a reward signal. By inputting prefixes from the original pre-training dataset into the LM to generate suffixes, the dissimilarity between the true and generated suffixes is calculated. This score is then maximized during training to ensure that the LM's tendency to replicate verbatim memorization is diminished. Besides, they found that when model size increases, both the convergence rate and dissimilarity score increase, suggesting that larger models may tend to "forget" the memorized data faster; and page 13, columns 1-2, wherein two frameworks for watermark embedding based on the Human Visual System (HVS) to reduce the impact of the watermark on the visual quality of the generated images….Triggered-based watermarking secretly incorporates a trigger to the protected model such that an image with copyright information will be generated once the trigger is activated).
Bourgin directed to a system for generating a structural representation for a digital design document from the digital design multigraph. Jie directed to creating synthesized content such as text, images, audio, and code. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Bourgin, regarding the System for Constructing Digital Design Graphs for Generating Structural Representations of Digital Design Documents, to have included in a second instance of the determination where the structured representation for the graphical inference indicates that the graphical inference exceeds the predetermined level of similarity: initiating performance of an action set to manage an impact of similarities between the graphical inference and the reference works because both inventions teach improving the context of the text and the language patterns. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 16 discloses additional feature such as a non-transitory machine-readable medium (see Bourgin, para [0194]).
Regarding claim 19 discloses additional feature such as a processor, and
a memory (see Bourgin, para [0194]).
Regarding claim 8, Bourgin in view of Tran and Jie discloses the method of claim 1, wherein the schema, as set forth above with claim 1.
Bourgin et al. fails to explicitly disclose usable to identify information regarding stylistic elements present in a depiction of a scene defined by the graphical inference.
Analogous art Tran discloses usable to identify information regarding stylistic elements present in a depiction of a scene defined by the graphical inference (see Tran, para [0003], wherein generating tunable stylized text (such as, for example, one or more sentences) by transforming received user text input and one or more user-originated stylistic parameters (directed to polarity of subjective opinion; para [0065], wherein deductive synthesis can be used to generate solution proposals for the user, where the idea is to start with a high-level specification and refine it to a low-level implementation by applying deductive rules or semantics preserving transformations; para [0038], wherein the system helps the users with diagrams or schematics where they add value and increase reader comprehension. When used, the diagrams are directly referenced within the text and clearly explained in the text. The system provides a Visual and intuitive user interface with built-in semantic and technical understanding, automatic relevant passage suggestions).
One of ordinary skill in the art would have recognized that applying the known technique of Tran would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Regarding claim 9, Bourgin in view of Tran and Jie discloses the method of claim 8, wherein the information comprises:
a color scheme present in the scene (see Bourgin, para [0116], wherein generates a style recommendation based on the feature representation to replace at least one of text, font, color, or shapes within the digital design document).
Bourgin et al. fails to explicitly disclose a pattern present in the scene.
Analogous art Tran discloses a pattern present in the scene (see Tran, para [0234], wherein recognize emotional intent patterns in human text, speech and facial expressions and respond to those cues in appropriate, empathetic ways-such as offering directions or information).
One of ordinary skill in the art would have recognized that applying the known technique of Tran would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Bourgin et al. fails to explicitly disclose a perspective of the scene.
Analogous art Jie discloses a perspective of the scene (see Jie, page 20, column 1, wherein LLM alignment strategies, such as RLHF, have been widely applied for constructing better LLMs aligned with human values, such as enhancing the reliability of generated outputs, and improving ethical decision-making. Therefore, the similar pipeline of model alignment can also be investigated from the perspective of copyright protection).
One of ordinary skill in the art would have recognized that applying the known technique of Jie would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Regarding claim 13, Bourgin in view of Tran and Jie discloses the method of claim 1, wherein the action set comprises obtaining a description of the similarities between the graphical inference and the reference works (see Bourgin, para [0120], wherein generate feature representations for a set of digital design documents from additional digital design multigraphs. The digital design system 102 can compare these feature representations to a feature representation of a particular digital design document to select a similar digital design document (e.g., the closest digital design document). Thus, the digital design system 102 can determine similarity with other digital design document based on layout, colors, and other digital design properties reflected in a digital design multigraph).
Regarding claim 14, Bourgin in view of Tran and Jie discloses the method of claim 1, wherein the action set, as set forth above with claim 1.
Bourgin et al. fails to explicitly disclose preventing provision of the graphical inference to the downstream consumer.
Analogous art Jie discloses preventing provision of the graphical inference to the downstream consumer (see Jie, page 15, columns 2, wherein the watermark is embedded into the initial noise vector used for sampling. In order to ensure that the watermark can achieve a better robustness against multiple image modification such as cropping, dilation, flipping, and rotation, they suggest to encode the watermark patterns to the Fourier space of the image).
One of ordinary skill in the art would have recognized that applying the known technique of Jie would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Regarding claim 15, Bourgin in view of Tran and Jie discloses the method of claim 1, wherein the action set comprises at least one action that, when performed, modifies operation and/or use of the generative inference model to reduce a likelihood that a future graphical inference generated using the generative inference model and the ingest data plagiarizes the reference works (see Bourgin, para [0036], wherein the structural representations allow for generating a recommended revision to the digital design document, generating a modified digital design document from the digital design document, or identifying an additional digital design document corresponding to the digital design document that considers various design elements and interactions between the design elements across the digital design document as a whole).
Allowable Subject Matter
Regarding claims 2-7, 10-12, 17-18, and 20 objected to as being dependent upon a rejected base claim, but it appears they would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, and rewritten to overcome the 35 USC 101 rejection.
Conclusion
The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure. (US Pub No. 2011/0206277; US Pat No. 6,268,868; US Pub No. 2025/0021820; US Pub No. 2025/0139378; US Pub No. 2025/0209300; US Pub No. 2018/0365576; US Pub No. 2023/0077508; US Pub No. 2024/0249165; US Pub No. 2019/0213484; and B Yang (Perceptual similarity measurement based on generative adversarial neural networks in graphics design) - Applied Soft Computing, 2021 - Elsevier.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAFIZ A KASSIM whose telephone number is (571)272-8534. The examiner can normally be reached 9:00 - 5:00 PM.
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/HAFIZ A KASSIM/Primary Examiner, Art Unit 3623 08/14/2026
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