DETAILED ACTION
Response to Amendment
Applicant’s amendments filed on 15 December 2025 have been entered. Claim 1 has been amended. Claims 1-20 are still pending in this application, with claims 1, 11 and 15 being independent.
Applicant's request for reconsideration of the finality of the rejection of the last Office action is persuasive and, therefore, the finality of that action is withdrawn.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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.
Claim(s) 1, 2, 4-7, 9, 15, 17 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over UzZaman et al. (US 20240249081 A1), referred herein as UzZaman in view of Wang et al. (US 20250200630 A1), referred herein as Wang.
Regarding Claim 1, UzZaman in view of Wang teaches a method comprising (UzZaman Abst: techniques for generating customizable content based on extracted insights):
generating, by a processing device, a plurality of insights from data extracted from digital content displayed in a user interface (UzZaman [0030] The content-generation application 102 can include various modules or components to transform AI and/or human-extracted insights into customized content for a target recipient. With reference to FIG. 1, the content-generation application 102 can include a data-input module 106 that accesses input data 108 that was transmitted by a user device 110. The input data 108 can include an initial content, which include text, images, and/or other types of data that can be processed and formatted to generate customized content; [0035] In some instances, the content format is identified based on interactions performed by the user of the user device 110. For example, the user can select a particular content format from options displayed on a graphical user interface (e.g., a drop-down menu, radio buttons) of the user device 110; FIG. 1.110: user device);
UzZaman does not, but Wang teaches
producing, by the processing device, a network representation having (Wang [0076] At block 404, the generative AI KG engine uses a generative AI knowledge graph service to generate the product knowledge graph comprising a plurality of products as nodes and a plurality of relationships as edges):
a plurality of nodes based on the plurality of insights (Wang [0061] A node in the product knowledge graph includes a plurality of node attributes and an edge in the product knowledge graph comprises a plurality of edge attributes, where the plurality of node attributes comprise a generative AI graph context that includes in insights on a first node connected to a second node); and
a plurality of connections between corresponding said insights (Wang [0054] The first interface 142 further include edge1 and edge2 that include edge-related information (e.g., product insights) from the product knowledge graph);
receiving, by the processing device, a selection of a subset of nodes from the plurality of nodes (Wang [0003] The product knowledge graph comprises a plurality of products as nodes and a plurality of relationships as edges. The product knowledge graph is associated with a generative AI model. A plurality of recommended products are identified. The plurality of recommended products may be identified using a ranker of the product listing system. The plurality of recommended products are a subset of the plurality of candidate products);
UzZaman in view of Wang further teaches
forming, by the processing device, a prompt by grouping respective said insights from the subset of nodes (UzZaman [0082] FIG. 8 illustrates an example schematic diagram 800 for training and deploying a prompt machine-learning model for generating prompts based on initial content and content format, according to some embodiments. The one or more prompts can thus be generated to specify the content format and customization information that should be included in the customized content; Wang [0038] Product knowledge graph generation data 110D can include prompt templates that are used to generate prompts that are executed on the generative AI model 142. Product knowledge graph generation data 110D can be processed using prompt engineering operations associated with the knowledge graph engine operations 112. In particular, the prompt engineering can support generate the templates that can be converted into prompts based on different types of data from the product knowledge graph data 110D);
generating, by the processing device, an insight summary of the digital content based on the prompt using generative artificial intelligence as implemented using one or more machine-learning models (UzZaman [0027] The present techniques can transform AI and/or human-extracted insights into customized content such as summaries, reports, and responses that are tailored to different personas and industries. The present techniques can include a data input module for receiving AI and/or human-extracted insights, a content format determination module for identifying the relevant persona, industry, report format, or other relevant context, and a report generation module for generating the customized content. The report-generation module can utilize a machine-learning model (e.g., a generative AI language model) to produce natural language text that is tailored for the identified use case and format); and
presenting, by the processing device, the insight summary for output in a user interface. (UzZaman [0033] The content-generation application 102 can include a content-format determination module 112 that determines a content format of customized content 113 to be generated by processing the input data; [0041] The content-generation application 102 can include an output module 126 that outputs the customized content 113. In some instances, the customized content 113 is outputted to a web page, a mobile-application interface, or other platforms that can be accessed by the user device 110)
Wang discloses generative AI knowledge graph engine “generative AI KG engine” of the artificial intelligence system supports providing a knowledge graph in the item listing system. Wang is analogous to the present patent application.
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified UzZaman to incorporate the teachings of Wang, and applying the generative AI knowledge graph (KG) engine including an edge-node prediction service—where nodes and edges of the knowledge graph are generated based on product knowledge graph generation data (e.g., one or more prompts executed on the generative AI model) into the techniques for generating customizable content based on extracted insights and by applying the content machine-learning model to the one or more prompts.
Doing so would improve operational efficiency, customer engagement, and online shopping by providing content generation (e.g., product descriptions), personalized shopping experiences (e.g., recommendation engines), product discovery (e.g., visual search), and virtual assistants (e.g., chat bots).
Regarding Claim 2, UzZaman in view of Wang teaches the method as described in claim 1, and further teaches wherein the digital content is a user interface configured as a digital dashboard including at least one digital image as a visualization (UzZaman [0028] FIG. 1 illustrates an example computing environment 100 for generating customizable content based on extracted insights, according to some embodiments. In the computing environment 100, a content-generation application 102 of a service provider 104 can be configured to transform AI and/or human-extracted insights into customized content such as summaries, reports, and responses that are tailored to different personas and industries; [0030] The input data 108 can include an initial content, which include text, images, and/or other types of data that can be processed and formatted to generate customized content; FIG. 1: 110).
Regarding Claim 4, UzZaman in view of Wang teaches the method as described in claim 1, and further teaches wherein the grouping is based, at least in part, by correspondence with respective items of a plurality of items that form the digital content (Wang [0042] Mapping rules can include matching rules that are employed to identify and exactly map and partially map closely related products between the product knowledge graph and the product listing system database. These rules encompass category matching, where products within the same or similar categories are considered related; brand matching, associating products from the same brand).
Regarding Claim 5, UzZaman in view of Wang teaches the method as described in claim 1, and further teaches wherein the connections include a layout-based connection, a type-based connection, a topic-based connection, a temporal-based connection, or a score-based connection (UzZaman [0088] each node or interconnection between nodes can have a weight that is a set of parameters derived from training the neural network. For example, an interconnection between nodes can represent a piece of information learned about the interconnected nodes. The interconnection can have a numeric weight that can be tuned (e.g., based on a training dataset), allowing the neural network to be adaptive to inputs and able to learn as more data is processed; Wang [0041] Edges represent relationships between nodes in the knowledge graph. These relationships define how different entities are connected or associated with each other. Edges have labels that describe the nature of the relationship. An example edge can be associated with edge subject “NIKE AIR FORCE 1; and edge object: NIKE HERITAGE BACKPACK).
Regarding Claim 6, UzZaman in view of Wang teaches the method as described in claim 1, and further teaches wherein the selection is based on a ranking (Wang Abst: Using a ranker of the product listing system, a plurality of recommended products are identified. The plurality of recommended products are a subset of the plurality of candidate products).
Regarding Claim 7, UzZaman in view of Wang teaches the method as described in claim 6, and further teaches wherein the ranking is based on weighting of types exhibited by respective said connections associated with the plurality of nodes (Wang [0044] machine learning models may be trained to predict product relationships based on historical data… An algorithm assigns scores or ranks to potential matches based on how well they satisfy the matching rules. For example, products from the same category might receive a higher score).
Regarding Claim 9, UzZaman in view of Wang teaches the method as described in claim 1, and further teaches further comprising identifying, by the processing device, the plurality of connections between the corresponding said insights (Wang [0044] In one embodiment, machine learning models may be trained to predict product relationships based on historical data. For instance, a collaborative filtering model might learn from past user behaviors to predict which products are likely to be related. The matching rules, as described earlier (category matching, brand matching, etc.), are implemented in the form of logical conditions within the algorithm. These rules guide the system in identifying closely related products).
Regarding Claims 15, 17 and 18, UzZaman in view of Wang teaches one or more computer-readable storage media storing instructions that, responsive to execution by a processing device (UzZaman Abst: techniques for generating customizable content based on extracted insights). The metes and bounds of the claims substantially correspond to the limitations set forth in claims 1, 2, 5 and 9; thus they are rejected on similar grounds and rationale as their corresponding limitations.
Claim(s) 3, 11-13 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over UzZaman et al. (US 20240249081 A1), referred herein as UzZaman in view of Wang et al. (US 20250200630 A1), referred herein as Wang and Gusarov et al. (US 20250148218 A1), referred herein as Gusarov.
Regarding Claim 3, UzZaman in view of Wang teaches the method as described in claim 2, but does not teach the claimed limitation herein. However, Gusarov teaches
wherein at least one said insight is generated based on the at least one digital image as a caption using a machine-learning model (Gusarov [0085] Image captioning: the visual-semantic machine learning model receives an input image and generates a natural language text description or caption of the image; [0127] In some examples, the prompting component 410 implements VQA and/or directed image captioning).
Gusarov discloses methods for automatically generating descriptions of augmented reality effects. Gusarov analogous to the present patent application.
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified UzZaman to incorporate the teachings of Gusarov, and applying the directed image captioning/describing functionality of a machine learning model for generating customizable content based on extracted insights and by applying the content machine-learning model to the one or more prompts.
Doing so, the relevance or accuracy of search results can be improved by improving the quality of descriptions (e.g., by providing descriptions that focus specifically on the visual effects of augmentations based on a “before-and-after” analysis).
Regarding Claims 11-13, UzZaman in view of Wang and Gusarov teaches a computing device (UzZaman Abst: techniques for generating customizable content based on extracted insights). The metes and bounds of the claims substantially correspond to the limitations set forth in claims 1-3, 5 and 9; thus they are rejected on similar grounds and rationale as their corresponding limitations.
Regarding Claim 16, UzZaman in view of Wang teaches the one or more computer-readable storage media as described in claim 15. The metes and bounds of the claims substantially correspond to the limitations set forth in claim 2; thus they are rejected on similar grounds and rationale as their corresponding limitations.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over UzZaman et al. (US 20240249081 A1), referred herein as UzZaman in view of Wang et al. (US 20250200630 A1), referred herein as Wang and Birru et al. (US 20230316096 A1), referred herein as Birru.
Regarding Claim 8, UzZaman in view of Wang teaches the method as described in claim 1, but does not teach the claimed limitation herein. However, Birru teaches
wherein the selection is received via a user interface that includes output of the plurality of nodes and the selection selects the subset (Birru [0242] At step 1604, a box select tool is provided to a user. A user selects a portion of the displayed Knowledge Graph using the box select tool at step 1606. At step 1608 the user is allowed to select the nodes within the box and hide the nodes outside the box the display of the Knowledge Graph. The method stops at step 1610; Claim 12. The method of claim 1, further comprising providing a box select tool that allows the user to select a subset of nodes of the Knowledge Graph).
Birru discloses a system, a method, and a computer program product for displaying a Knowledge Graph at a client device. Birru is analogous to the present patent application.
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified UzZaman to incorporate the teachings of Birru, and applying the Knowledge Graph user interface into the techniques for generating customizable content based on extracted insights and by applying the content machine-learning model to the one or more prompts.
Doing so would provide an improved systems and methods for optimally displaying a Knowledge Graph. Specifically, there is a need to retrace the navigation selection by a user.
Claim(s) 10, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over UzZaman et al. (US 20240249081 A1), referred herein as UzZaman in view of Wang et al. (US 20250200630 A1), referred herein as Wang and Annamalai et al. (US 20250165529 A1), referred herein as Annamalai.
Regarding Claim 10, UzZaman in view of Wang teaches the method as described in claim 1, but does not teach all the claimed limitation herein. However, further in view of Annamalai, the prior art teaches further comprising:
receiving a request to expand the insight summary (UzZaman [0044] (3) automated response generation systems that can be used by organizations and individuals to generate automated responses to various types of requests; Annamalai [0012] The augmented knowledge graph expands the knowledge graph with additional entities and additional relationships between the additional entities using additional data that is obtained from a source external to the multimedia data);
selecting at least one additional node from the plurality of nodes (Annamalai [0017] A user's query itself can be used to expand a knowledge graph by extracting entities from the query, obtaining data about those entities, and integrating that data into the knowledge graph; [0033] a knowledge graph is augmented based on a user query: any entities and/or relationships that are extracted from the user query are used to obtain additional data sourced externally from the multimedia data, and the additional data is processed into a knowledge graph that is integrated into the knowledge graph that corresponds to the multimedia data), and
generating an expanded insight summary based on the subset and the at least one additional node (UzZaman [0044] (5) software applications or services that can be used to implement the invention and generate customized content; and (6) an API service that can be integrated with other systems to provide the users automated customizable content generation functionality).
Annamalai discloses a method, computer system, and computer program product for real-time video searching based on augmented knowledge graphs that are generated using machine learning models. Annamalai is analogous to the present patent application.
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified UzZaman to incorporate the teachings of Annamalai, and applying the augmented knowledge graph by obtaining additional entities by users into the techniques for generating customizable content based on extracted insights and by applying the content machine-learning model to the one or more prompts.
Doing so would be able to automatically generate highlights enhances the viewing experience by providing a convenient way to quickly identify the most interesting and impactful parts of a video.
Regarding Claim 19, UzZaman in view of Wang teaches the one or more computer-readable storage media as described in claim 15. The metes and bounds of the claims substantially correspond to the limitations set forth in claim 10; thus they are rejected on similar grounds and rationale as their corresponding limitations.
Regarding Claim 20, UzZaman in view of Wang teaches the one or more computer-readable storage media as described in claim 15,
In view of Annamalai, the prior art further teaches
wherein the displaying of the insight summary is performed along with the respective said nodes (Annamalai [0052] As depicted, knowledge graph user interface 600 provides an interface through which a user can interact to obtain information about any desired entity, including relationships between entities. The knowledge graph may be zoomable or otherwise interactive so that a user can explore specific subsets of entities, such as by toggling the display of only related entities, only entities related by a threshold number of “hops” or connections, and the like. When a user selects a node (e.g., node 601A), user interface elements may display information about that node. As depicted, the node type element 606 indicates that the node is an entity, and node identifier element 608 provides a unique identifier for the node. Attribute element 610 indicates an attribute of the entity, and link element 612 provides a link to a resource for obtaining additional information about the entity. Finally, name element 614 indicates a name for the class of entity (in this case, a vehicle)).
The same motivation as claim 10 applies here.
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over UzZaman et al. (US 20240249081 A1), referred herein as UzZaman in view of Wang et al. (US 20250200630 A1), referred herein as Wang, Gusarov et al. (US 20250148218 A1), referred herein as Gusarov and Annamalai et al. (US 20250165529 A1), referred herein as Annamalai.
Regarding Claims 14, UzZaman in view of Wang and Gusarov teaches the computing device as described in claim 11. The metes and bounds of the claims substantially correspond to the limitations set forth in claim 10; thus they are rejected on similar grounds and rationale as their corresponding limitations.
Response to Arguments
Applicant’s arguments, see page 8, filed on 15 December 2025, with respect to the rejection(s) of claim(s) 3, 11-14 and 16 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of US 20250148218 A1
Applicant's arguments filed on 15 December 2025, with respect to the 103 rejection have been fully considered but they are not persuasive.
On page 9, Applicant's Remarks, with respect to claims 1, 11 and 15, the applicant argues “UzZaman is silent with respect to a ‘user interface’ much less a ‘’digital dashboard’." Examiner actually agrees with this statement. Paragraph [0026], [0035] and FIG. 1 of UzZaman explicitly disclosed a graphical user interface and a content-generation application to generate customized content such as summaries, reports, and responses. Furthermore, in computer information systems, a dashboard is a type of graphical user interface which often provides at-a-glance views of data relevant to a particular objective or process through a combination of visualizations and summary information. The content-generation application on the user device 110 performs the exact same function as a digital dashboard as claimed. The customized content generated by application is the same result achieved by the user interface as claimed. Regarding this argument, it is respectfully noted that, UzZaman in view of Wang and Gusarov teaches the limitation of digital content displayed in a user interface, and a digital dashboard as claimed.
On page 10 of Applicant’s Remarks, the Applicant argues the corresponding dependent claims are not taught by the prior art, insomuch as they depend from claims that are not taught by the prior art. Examiner respectfully disagrees with these arguments, for the reasons discussed above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Samantha (Yuehan) Wang whose telephone number is (571)270-5011. The examiner can normally be reached Monday-Friday, 8am-5pm.
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/Samantha (YUEHAN) WANG/
Primary Examiner
Art Unit 2617