DETAILED ACTION
Claims 1-20 are pending in the present application and are under examination on the merits. This communication is the first action on the merits (FAOM).
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 .
Information Disclosure Statement
Applicant filed an Information Disclosure Statement (IDS) on 9/15/2025. This filing is in compliance with 37 C.F.R. 1.97.
As required by M.P.E.P. 609(C), the applicant's submission of the Information Disclosure Statement is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P. 609(C), a copy of the PTOL -1449 form, initialed and dated by the examiner, is attached to the instant office action.
Drawings
The drawings filed on 4/9/2025 are acceptable as filed.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Here, under considerations of the broadest reasonable interpretation of the claimed invention, Examiner finds that the Applicant invented a method and system for receiving and generating graph data to construct associations in a graphical setting and determine relationships therefrom. Examiner formulates an abstract idea analysis, following the framework described in the MPEP as follows:
Step 1: The claims are directed to a statutory category, namely a "method" (claims 8-14) and "system" (claims 1-7 and 15-20).
Step 2A - Prong 1: The claims are found to recite limitations that set forth the abstract idea(s), namely, regarding claim 1:
receive graph structure data characterizing associations between a plurality of entities and a plurality of propositions;
receive goal data characterizing a plurality of goals;
input the graph structure data and the goal data to an analytics model and, in response, generate graph data characterizing associations between the plurality of propositions and the plurality of goals;
input market data for the plurality of entities and the graph data to a large language model and, in response, generate resolution data characterizing at least one of the plurality of propositions;
Independent claims 8 and 15 recites substantially similar claim language.
Dependent claims 2-7, 9-14, and 16-20 recite the same or similar abstract idea(s) as independent claims 1, 8, and 15 with merely a further narrowing of the abstract idea(s) to particular data characterization and/or additional data analyses performed as part of the abstract idea.
The limitations in claims 1-20 above falling well-within the groupings of subject matter identified by the courts as being abstract concepts, specifically the claims are found to correspond to the category of:
"Certain methods of organizing human activity- fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)" as the limitations identified above are directed to receiving and generating graph data to construct associations in a graphical setting and determine relationships therefrom and thus is a method of organizing human activity including at least commercial or business interactions or relations and/or a management of user personal behavior; and/or
"Mental processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)" as the limitations identified above include mere data observations, evaluations, judgements, and/or opinions, e.g. including user observation and evaluation of receiving and generating graph data to construct associations in a graphical setting and determine relationships therefrom, which is capable of being performed mentally and/or using pen and paper.
Step 2A - Prong 2: Claims 1-20 are found to clearly be directed to the abstract idea identified above because the claims, as a whole, fail to integrate the claimed judicial exception into a practical application, specifically the claims recite the additional elements of:
" receive input data from a user interface" (claims 6 and 13) however the aforementioned elements directed to the receiving of user input/selection of data to view via a dashboard and displaying corresponding data via the dashboard merely amount to generic GUI elements of a general purpose computer used to "apply" the abstract idea (MPEP 2106.05(f)) and/or is merely an attempt at limiting the abstract idea of receiving and generating graph data to construct associations in a graphical setting and determine relationships therefrom to a particular field of use/technological environment of a GUI dashboard (MPEP 2106.05(h)) and therefore the GUI dashboard input and display of data fails to integrate the abstract idea into a practical application;
" An apparatus comprising: a memory storing instructions; and a processor communicatively coupled to the memory and configured to execute the instructions to:… store the resolution data in a data repository. / An apparatus comprising: a memory storing instructions; and a processor communicatively coupled to the memory and configured to execute the instructions to:" (claims 1, 8, and 15) “input the graph data to a trained artificial intelligence (Al) model and, based on the inputting the graph data to the trained AI model, generate query data characterizing one or more queries,” (claims 4, 11, and 18) however the aforementioned elements merely amount to generic components of a general purpose computer used to "apply" the abstract idea (MPEP 2106.0S(f)) and thus fails to integrate the recited abstract idea into a practical application, furthermore the high-level recitation of receiving data from a generic computer "apparatus" is at most an attempt to limit the abstract to a particular field of use (MPEP 2106.0S(h), e.g.: "For instance, a data gathering step that is limited to a particular data source (such as the Internet) or a particular type of data (such as power grid data or XML tags) could be considered to be both insignificant extra-solution activity and a field of use limitation. See, e.g., Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (limiting use of abstract idea to the Internet); Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data); Intellectual Ventures I LLC v. Erie lndem. Co., 850 F.3d 1315, 1328-29, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017) (limiting use of abstract idea to use with XML tags).") and/or merely insignificant extra-solution activity (MPE 2106.05(g)) and thus further fails to integrate the abstract idea into a practical application;
Step 2B: Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 merely amount to a general purpose computer that attempts to apply the abstract idea in a technological environment (MPEP 2106.0S(f)), including merely limiting the abstract idea to a particular field of use of analysis of a "graph structure" via a GUI "interface", as explained above, and/or performs insignificant extra-solution activity, e.g. data gathering or output, (MPEP 2106.0S(g)), as identified above, which is further found under step 2B to be merely well-understood, routine, and conventional activities as evidenced by MPEP 2106.0S(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, electronically scanning or extracting data from a physical document, and a web browser's back and forward button functionality). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea directed to receiving and generating graph data to construct associations in a graphical setting and determine relationships therefrom.
Claims 1-20 are accordingly rejected under 35 USC§ 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea(s)) without significantly more.
Note: The analysis above applies to all statutory categories of invention. As such, the presentment of any claim otherwise styled as a machine or manufacture, for example, would be subject to the same analysis.
For further authority and guidance, see:
MPEP § 2106
https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility
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 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 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 1-6, 8-13, 15, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2025/0278770 to Unnikrishnan (hereafter referred to as Unnikrishnan) in view of U.S. Patent Application Publication Number 2019/0114549 to Olsher (hereafter referred to as Olsher).
As per claim 1, Unnikrishnan teaches:
An apparatus comprising: a memory storing instructions; and a processor communicatively coupled to the memory and configured to execute the instructions to: (Paragraph Number [0097] teaches the one or more servers 510 implements a content delivery apparatus 502. In one embodiment, the content delivery apparatus 502 includes at least one processor; at least one memory including instructions executable by the at least one processor; and a machine learning model comprising parameters stored in the at least one memory, wherein the machine learning model comprises a GNN model 304).
receive graph structure data characterizing associations between a plurality of entities and a plurality of propositions (Paragraph Number [0068] teaches the GNN model 304 to learn and utilize meaningful spatial representations that can facilitate various graph-based learning tasks such as node classification, link prediction, and graph analysis. Node embeddings 324 obtained from a well-trained GNN can serve as valuable feature representations, enabling downstream applications such as recommendation systems, graph clustering, and knowledge graph reasoning. The quality and effectiveness of the node embeddings strongly influence the GNN's overall performance in understanding and leveraging the complex network structures and relationships. Paragraph Number [0034] teaches when using a buyer journey graph, the touchpoint content adaptation system uses an ML model designed to use graph-structured data, such as a Graph Neural Network (GNN). A GNN is a type of neural network designed to operate on graph-structured data. Unlike traditional neural networks that operate on grid-like data such as images or sequences, GNNs are specifically tailored to handle data represented as graphs, where nodes and edges encapsulate entities and their relationships. GNNs are adept at capturing dependencies and relationships between entities in a graph, making them well-suited for tasks involving relational data, network analysis, social network modeling, and recommendations. (See Paragraph Number [0052] and [0099] in regard to gathering recommendations and compiling value propositions to provide to a customer) (See also Paragraph Numbers [0070] and [0076] which further teaches and defines what the nodes and edges of the graph represents)).
input the graph structure data ...to an analytics model and, in response, generate graph data characterizing associations between the plurality of propositions and the plurality of goals (Paragraph Number [0042] teaches as used herein, the term “buyer journey graph” refers to a knowledge graph generated using domain specific information associated with informational stages, evaluation stages, and transactional stages, such as buyer journey data, user data, company data, business data, touchpoints, events, decision stages, transactions, user profile information, relationships, properties, attributes, or other graph-structured data. Paragraph Number [0034] teaches when using a buyer journey graph, the touchpoint content adaptation system uses an ML model designed to use graph-structured data, such as a Graph Neural Network (GNN). A GNN is a type of neural network designed to operate on graph-structured data. Unlike traditional neural networks that operate on grid-like data such as images or sequences, GNNs are specifically tailored to handle data represented as graphs, where nodes and edges encapsulate entities and their relationships. GNNs are adept at capturing dependencies and relationships between entities in a graph, making them well-suited for tasks involving relational data, network analysis, social network modeling, and recommendations. (See Paragraph Number [0052] in regard to gathering recommendations and compiling value propositions to provide to a customer)).
input market data for the plurality of entities and the graph data to a large language model and, in response, generate resolution data characterizing at least one of the plurality of propositions (Paragraph Number [0042] teaches as used herein, the term “buyer journey graph” refers to a knowledge graph generated using domain specific information associated with informational stages, evaluation stages, and transactional stages, such as buyer journey data, user data, company data, business data, touchpoints, events, decision stages, transactions, user profile information, relationships, properties, attributes, or other graph-structured data. Paragraph Number [0112] teaches the logic flow 700 receives activity data associated with a user from a device. In block 704, the logic flow 700 generates a touchpoint embedding and a decision embedding using a graph neural network (GNN) model based on the activity data, the GNN model trained using a knowledge graph. In block 706, the logic flow 700 predicts a touchpoint using a first classifier based on the touchpoint embedding. In block 708, the logic flow 700 predicts a decision stage using a second classifier based on the decision embedding. In block 710, the logic flow 700 generates personalized content for the touchpoint based on the decision stage using a large language model (LLM). (See also Paragraph Number [0099] in regard to content generated by the model which encompasses data such as messages, predictions, recommendations, advertisements, or suggestions to improve user experience. Examiner asserts that this output constitutes propositions as described by the Specification Paragraph Number [0027] where propositions are equated to business solutions)).
store the resolution data in a data repository (Paragraph Number [0039] teaches the term “knowledge graph” refers to a structured representation of knowledge that captures information about entities (e.g., people, places, things, etc.) and the relationships between them. A knowledge graph is a type of knowledge base that uses a graph structure to organize data, where nodes represent entities (with associated properties) and edges represent relationships between these entities. Knowledge graphs store and represent knowledge in a way that is both human-readable and machine-understandable. Paragraph Number [0086] teaches the context orchestrator 420 queries supplemental content 422 specific to the decision stage 416 from vector data sources stored by a database 424. Paragraph Number [0105] teaches the input 612 and the output 614 are stored in a data repository 616).
Unnikrishnan teaches receiving and generating graph data to construct associations in a graphical setting and determine relationships therefrom but does not explicitly teach gathering goal data for use in a graph that provides for associations which is taught by the following citations from Olsher:
receive goal data characterizing a plurality of goals (Paragraph Number [0080] teaches the input data knowledge that represents a nuanced knowledge is a nuanced knowledge selected from the group consisting of the group consisting of a key word, an interest, a goal, a trait, a view, an opinion, a symbol, a semantic, a meaning, an inflection, and an interpretation. Paragraph Number [0103] teaches the input systems 106 can include input subcomponents or systems 200 such as a set of questions, goals and concerns 202, real world data 204, stakeholder interview results or “brain dumps” 206, as well as user data, OSINT, briefing data, natural language text, social media feeds and posts, medical data, all referred here as user data 208. The output system 110 can include controls for useful actions 210, recommendations 212 in the form of text or data, a GUI in the form of a system dashboard 214, predictive data 216 and control messages 218, by way of examples. Generally, these are referred herein as controlled actions 210).
input ... the goal data to an analytics model and, in response, generate graph data characterizing associations between the plurality of propositions and the plurality of goals (Paragraph Number [0103] teaches the input systems 106 can include input subcomponents or systems 200 such as a set of questions, goals and concerns 202, real world data 204, stakeholder interview results or “brain dumps” 206, as well as user data, OSINT, briefing data, natural language text, social media feeds and posts, medical data, all referred here as user data 208. The output system 110 can include controls for useful actions 210, recommendations 212 in the form of text or data, a GUI in the form of a system dashboard 214, predictive data 216 and control messages 218, by way of examples. Generally, these are referred herein as controlled actions 210. Paragraph Number [0221] teaches in order to maximize overall representation nuance (ψoverall), the system and method can be arranged with desired primitives with minimal semantic entropy, primitives that best fit the data, and graphs containing highly distributed information (with many edges). Paragraph Number [0336] teaches the graph structure enables following the graph in a semantic process that is considerably deeper than a semantic network itself. Graph traversal is a system 100 semantic operation and process that can use the semantic edge guided transversal. Paragraph Number [0630] teaches by discovering concepts such that, when energy is introduced into them and propagated throughout the knowledge substrate, positive and negative energy, respectively, is introduced where desired in the graph (as determined by matched target scores, minimal clashes, and other measures), the system is able to discover intermediate concepts that should be promoted or avoided. In a related embodiment, by then running forward propagation from various potential options and observing their effects on said intermediate concepts, the system can discover actions that should be promoted or avoided).
Both Unnikrishnan and Olsher are directed to directed graphs that display relationships between types of data. Unnikrishnan discloses receiving and generating graph data to construct associations in a graphical setting and determine relationships therefrom. Olsher improves upon Unnikrishnan by disclosing gathering goal data for use in a graph that provides for associations. One of ordinary skill in the art would be motivated to further include gathering goal data for use in a graph that provides for associations, to efficiently utilize goal data as a driver for associations in a directed graph and to improve the analysis of a large language model with additional relevant data.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of receiving and generating graph data to construct associations in a graphical setting and determine relationships therefrom in Unnikrishnan to further utilize gathering goal data for use in a graph that provides for associations as disclosed in Olsher, since the claimed invention is merely a combination of old elements, and in 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.
As per claim 8, claim 8 recites a method that is substantially similar to the steps performed by the system in claim 1 and is rejected for the same reasons put forth in regard to claim 1.
As per claim 15, Unnikrishnan teaches:
An apparatus comprising: a memory storing instructions; and a processor communicatively coupled to the memory and configured to execute the instructions to: (Paragraph Number [0097] teaches the one or more servers 510 implements a content delivery apparatus 502. In one embodiment, the content delivery apparatus 502 includes at least one processor; at least one memory including instructions executable by the at least one processor; and a machine learning model comprising parameters stored in the at least one memory, wherein the machine learning model comprises a GNN model 304).
The remainder of the claim limitations are substantially similar to the claim language recited in regard to claim 1 and are rejected for the same reasons put forth in regard to claim 1.
As per claims 2 and 9, the combination of Unnikrishnan and Olsher teaches each of the limitations of claims 1 and 8 respectively.
In addition, Unnikrishnan teaches:
wherein the processor is configured to execute the instructions to receive information from a plurality of sources (Paragraph Number [0076] teaches the touchpoint content adaptation system 400 includes an input structure 302 comprising a buyer journey graph 200. As previously described, buyer journey data is encoded into a buyer journey graph 200 by combining information from sources such as omni-channel transaction graphs, application logs, session data, time-series data of touchpoints of buyers, and other data sources).
and aggregate the information as the market data in the data repository wherein the market data comprises various input modalities. (Paragraph Number [0066] teaches the GNN system 300 generally comprises several key components, which collectively enable the network to operate effectively on graph-structured data. Some of the fundamental components of a GNN system 300 include the representation of nodes as feature vectors, capturing the attributes or properties associated with each node in the graph, message passing mechanisms to propagate information across the graph thereby enabling the aggregation of neighboring nodes' features and the incorporation of relational information into the node representations, aggregation functions to combine and summarize information collected from neighboring nodes during the message passing process thereby generating aggregated representations for each node, an update function is used to update the node representations based on the aggregated information obtained from neighboring nodes thereby enabling the incorporation of the graph structure and relational dependencies, and an output function that processes the updated node representations to produce the final output, which may involve node classification, link prediction, graph classification, or other graph-based learning tasks. (See also Paragraph Numbers [0034], [0068], [0052], [0070], and [0076] in regard to determining market data from various sources and of various types of data)).
As per claims 3 and 10, the combination of Unnikrishnan and Olsher teaches each of the limitations of claims 1 and 8 respectively.
In addition, Unnikrishnan teaches:
wherein the graph structure data associates each of the plurality of propositions to one or more entities (Paragraph Number [0042] teaches as used herein, the term “buyer journey graph” refers to a knowledge graph generated using domain specific information associated with informational stages, evaluation stages, and transactional stages, such as buyer journey data, user data, company data, business data, touchpoints, events, decision stages, transactions, user profile information, relationships, properties, attributes, or other graph-structured data. Paragraph Number [0034] teaches when using a buyer journey graph, the touchpoint content adaptation system uses an ML model designed to use graph-structured data, such as a Graph Neural Network (GNN). A GNN is a type of neural network designed to operate on graph-structured data. Unlike traditional neural networks that operate on grid-like data such as images or sequences, GNNs are specifically tailored to handle data represented as graphs, where nodes and edges encapsulate entities and their relationships. GNNs are adept at capturing dependencies and relationships between entities in a graph, making them well-suited for tasks involving relational data, network analysis, social network modeling, and recommendations. (See Paragraph Number [0052] in regard to gathering recommendations and compiling value propositions to provide to a customer)).
and the resolution data characterizes a corresponding one of the one or more entities (Paragraph Number [0034] teaches when using a buyer journey graph, the touchpoint content adaptation system uses an ML model designed to use graph-structured data, such as a Graph Neural Network (GNN). A GNN is a type of neural network designed to operate on graph-structured data. Unlike traditional neural networks that operate on grid-like data such as images or sequences, GNNs are specifically tailored to handle data represented as graphs, where nodes and edges encapsulate entities and their relationships. GNNs are adept at capturing dependencies and relationships between entities in a graph, making them well-suited for tasks involving relational data, network analysis, social network modeling, and recommendations. (See Paragraph Number [0052] in regard to gathering recommendations and compiling value propositions to provide to a customer)).
As per claims 4, 11, and 18, the combination of Unnikrishnan and Olsher teaches each of the limitations of claims 1, 8, and 15 respectively.
In addition, Unnikrishnan teaches:
input the graph data to a trained artificial intelligence (Al) model and, based on the inputting the graph data to the trained AI model, generate query data characterizing one or more queries (Paragraph Number [0032] teaches improved AI techniques to assist in data-driven analysis. Some embodiments are particularly directed to using AI techniques to support a touchpoint content adaptation system. The touchpoint content adaptation system encodes knowledge from buyer decision journeys for ingest by a ML model. Further, the touchpoint content adaptation system uses an improved ML model trained to predict a decision phase of a buyer and their next touchpoint based on a domain-specific knowledge graph, referred to as a buyer journey graph. Paragraph Number [0086] teaches the context orchestrator 420 queries supplemental content 422 specific to the decision stage 416 from vector data sources stored by a database 424. For example, when the decision stage 416 is an informational stage, the context orchestrator 420 queries content 422 such as information on product usage. When the decision stage 416 is a comparative stage, context orchestrator 420 queries content 422 such as price comparisons, with similar products or cross sell rules. When the decision stage 416 is a transaction stage, the context orchestrator 420 queries content 422 such as information on product pricing, taxes, delivery options, payment terms, and so forth. The content 422 is output to the prompt builder 426).
input the query data to the large language model and, in response, generate the resolution data characterizing at least one of the plurality of propositions (Paragraph Number [0086] teaches the context orchestrator 420 queries supplemental content 422 specific to the decision stage 416 from vector data sources stored by a database 424. For example, when the decision stage 416 is an informational stage, the context orchestrator 420 queries content 422 such as information on product usage. When the decision stage 416 is a comparative stage, context orchestrator 420 queries content 422 such as price comparisons, with similar products or cross sell rules. When the decision stage 416 is a transaction stage, the context orchestrator 420 queries content 422 such as information on product pricing, taxes, delivery options, payment terms, and so forth. The content 422 is output to the prompt builder 426. Paragraph Number [0088] teaches the prompt 428 is for a generative AI to produce the content 422 in a natural human language from a language model (LM) or large language model (LLM). Paragraph Number [0091] teaches the prompt builder 426 uses the buyer encodings to select among different prompts 428 through a relevancy scoring process. In one embodiment, the relevancy scoring process may score the relevancy of products or services according to a set of user preferences and past interactions with a company, such as through encodings of metadata for the user 104 during a loyalty loop 124, thereby enabling personalized recommendations. (See also Paragraph Number [0099] in regard to content generated by the model which encompasses data such as messages, predictions, recommendations, advertisements, or suggestions to improve user experience. Examiner asserts that this output constitutes propositions as described by the Specification Paragraph Number [0027] where propositions are equated to business solutions)).
As per claims 5 and 12, the combination of Unnikrishnan and Olsher teaches each of the limitations of claims 1 and 4, and 8 and 11 respectively.
In addition, Unnikrishnan teaches:
wherein the processor is configured to execute the instructions to, based on the graph data and the query data, generate matching data charactering associations between the plurality of propositions and the one or more queries. (Paragraph Number [0069] teaches in the GNN model 304, edge embeddings 326 refer to a vector representation that captures the essential features and characteristics of the relationships between nodes in the graph. Similar to node embeddings 324, edge embeddings 326 are obtained through the GNN's learning process, which focuses on updating and refining the representations of edges based on the connected nodes and their interactions. The goal of edge embeddings 326 is to encode information about the structural, semantic, and contextual aspects of the relationships between nodes in the graph. By capturing pertinent information about the edges, the GNN model 304 can effectively learn and utilize meaningful representations that convey the nuanced interactions and dependencies within the graph. Edge embeddings 326 play a crucial role in various graph-based learning tasks, including link prediction, graph classification, and graph analysis. Paragraph Number [0091] teaches the prompt builder 426 uses the buyer encodings to select among different prompts 428 through a relevancy scoring process. In one embodiment, the relevancy scoring process may score the relevancy of products or services according to a set of user preferences and past interactions with a company, such as through encodings of metadata for the user 104 during a loyalty loop 124, thereby enabling personalized recommendations. (See also Paragraph Number [0099] in regard to content generated by the model which encompasses data such as messages, predictions, recommendations, advertisements, or suggestions to improve user experience. Examiner asserts that this output constitutes propositions as described by the Specification Paragraph Number [0027] where propositions are equated to business solutions)).
As per claims 6 and 13, the combination of Unnikrishnan and Olsher teaches each of the limitations of claims 1, 4, and 5, and 8, 11, and 12 respectively.
In addition, Unnikrishnan teaches:
receive input data from a user interface (Paragraph Number [0098] teaches the servers 510 may include content delivery apparatus 502 implementing touch point content adaptation system 400 that is designed for performing targeted content delivery. In an example process, the content delivery apparatus 502 obtains activity data 508 from a user 104 via the device 436. The user 104 interacts with the content delivery apparatus 502 via a user interface of the content delivery apparatus 502. In some cases, portions of the user interface are displayed on a personal machine or device 436 of the user 104).
determine, based on the matching data, at least one of the plurality of propositions and associated queries (Paragraph Number [0069] teaches in the GNN model 304, edge embeddings 326 refer to a vector representation that captures the essential features and characteristics of the relationships between nodes in the graph. Similar to node embeddings 324, edge embeddings 326 are obtained through the GNN's learning process, which focuses on updating and refining the representations of edges based on the connected nodes and their interactions. The goal of edge embeddings 326 is to encode information about the structural, semantic, and contextual aspects of the relationships between nodes in the graph. By capturing pertinent information about the edges, the GNN model 304 can effectively learn and utilize meaningful representations that convey the nuanced interactions and dependencies within the graph. Edge embeddings 326 play a crucial role in various graph-based learning tasks, including link prediction, graph classification, and graph analysis. Paragraph Number [0091] teaches the prompt builder 426 uses the buyer encodings to select among different prompts 428 through a relevancy scoring process. In one embodiment, the relevancy scoring process may score the relevancy of products or services according to a set of user preferences and past interactions with a company, such as through encodings of metadata for the user 104 during a loyalty loop 124, thereby enabling personalized recommendations. (See also Paragraph Number [0099] in regard to content generated by the model which encompasses data such as messages, predictions, recommendations, advertisements, or suggestions to improve user experience. Examiner asserts that this output constitutes propositions as described by the Specification Paragraph Number [0027] where propositions are equated to business solutions)).
generate response data based on the at least one of the plurality of propositions and associated queries (Paragraph Number [0100] teaches the personalized content 434 is delivered through one or more of the media channels 518. A media channel refers to a specific platform or medium through which targeted content, such as advertisements, are disseminated to a target user. Media channels 518 can include various forms of digital and traditional media such as websites, mobile applications, social media platforms, television, radio, print publications, and outdoor advertising spaces. Each media channel possesses its own unique characteristics and user demographics, allowing advertisers to tailor their messages to reach the desired target user effectively. message provider, such as advertisers, often choose certain media channels based on factors such as user engagement, reach, cost, and the compatibility of the channel with their target market. An example of the media channel 518 is a social media platform, such as Google or Meta, or some other mode of information transfer within the platform).
transmit the response data (Paragraph Number [0101] teaches the content delivery apparatus 502 or components thereof are implemented on a server. A server provides one or more functions to users linked by way of one or more of the various networks. In some cases, the server includes a single microprocessor board, which includes a microprocessor responsible for controlling all aspects of the server. In some cases, a server uses microprocessor and protocols to exchange data with other devices/users on one or more of the networks via hypertext transfer protocol (HTTP), and simple mail transfer protocol (SMTP), although other protocols such as file transfer protocol (FTP), and simple network management protocol (SNMP) can also be used).
As per claim 19, the combination of Unnikrishnan and Olsher teaches each of the limitations of claims 15.
In addition, Unnikrishnan teaches:
receive market data for the plurality of entities (Paragraph Number [0042] teaches as used herein, the term “buyer journey graph” refers to a knowledge graph generated using domain specific information associated with informational stages, evaluation stages, and transactional stages, such as buyer journey data, user data, company data, business data, touchpoints, events, decision stages, transactions, user profile information, relationships, properties, attributes, or other graph-structured data. Paragraph Number [0112] teaches the logic flow 700 receives activity data associated with a user from a device. In block 704, the logic flow 700 generates a touchpoint embedding and a decision embedding using a graph neural network (GNN) model based on the activity data, the GNN model trained using a knowledge graph. In block 706, the logic flow 700 predicts a touchpoint using a first classifier based on the touchpoint embedding. In block 708, the logic flow 700 predicts a decision stage using a second classifier based on the decision embedding. In block 710, the logic flow 700 generates personalized content for the touchpoint based on the decision stage using a large language model (LLM). (See also Paragraph Number [0099] in regard to content generated by the model which encompasses data such as messages, predictions, recommendations, advertisements, or suggestions to improve user experience. Examiner asserts that this output constitutes propositions as described by the Specification Paragraph Number [0027] where propositions are equated to business solutions)).
inputting the market data … to the large language model and, in response, generating resolution data characterizing a recommended one of the plurality of propositions (Paragraph Number [0042] teaches as used herein, the term “buyer journey graph” refers to a knowledge graph generated using domain specific information associated with informational stages, evaluation stages, and transactional stages, such as buyer journey data, user data, company data, business data, touchpoints, events, decision stages, transactions, user profile information, relationships, properties, attributes, or other graph-structured data. Paragraph Number [0034] teaches when using a buyer journey graph, the touchpoint content adaptation system uses an ML model designed to use graph-structured data, such as a Graph Neural Network (GNN). A GNN is a type of neural network designed to operate on graph-structured data. Unlike traditional neural networks that operate on grid-like data such as images or sequences, GNNs are specifically tailored to handle data represented as graphs, where nodes and edges encapsulate entities and their relationships. GNNs are adept at capturing dependencies and relationships between entities in a graph, making them well-suited for tasks involving relational data, network analysis, social network modeling, and recommendations. (See Paragraph Number [0052] in regard to gathering recommendations and compiling value propositions to provide to a customer)).
providing the resolution data for display (Paragraph Number [0092] teaches once the multi-model encoder/decoder 432 adapts the content 422 in response to the prompt 428, it outputs personalized content 434 for the user 104. The touch point content adapter 418 then forwards the personalized content 434 at the touchpoint 412 for presentation on an electronic display of an electronic device 436).
Unnikrishnan teaches receiving and generating graph data to construct associations in a graphical setting and determine relationships therefrom but does not explicitly teach gathering goal data for use in a graph that provides for associations which is taught by the following citations from Olsher:
inputting … the goal data to the large language model and, in response, generating resolution data characterizing a recommended one of the plurality of propositions (Paragraph Number [0103] teaches the input systems 106 can include input subcomponents or systems 200 such as a set of questions, goals and concerns 202, real world data 204, stakeholder interview results or “brain dumps” 206, as well as user data, OSINT, briefing data, natural language text, social media feeds and posts, medical data, all referred here as user data 208. The output system 110 can include controls for useful actions 210, recommendations 212 in the form of text or data, a GUI in the form of a system dashboard 214, predictive data 216 and control messages 218, by way of examples. Generally, these are referred herein as controlled actions 210. Paragraph Number [0221] teaches in order to maximize overall representation nuance (ψoverall), the system and method can be arranged with desired primitives with minimal semantic entropy, primitives that best fit the data, and graphs containing highly distributed information (with many edges). Paragraph Number [0336] teaches the graph structure enables following the graph in a semantic process that is considerably deeper than a semantic network itself. Graph traversal is a system 100 semantic operation and process that can use the semantic edge guided transversal. Paragraph Number [0630] teaches by discovering concepts such that, when energy is introduced into them and propagated throughout the knowledge substrate, positive and negative energy, respectively, is introduced where desired in the graph (as determined by matched target scores, minimal clashes, and other measures), the system is able to discover intermediate concepts that should be promoted or avoided. In a related embodiment, by then running forward propagation from various potential options and observing their effects on said intermediate concepts, the system can discover actions that should be promoted or avoided).
A person of ordinary skill in the art would have been motivated to combine these references as described in regard to claim 1.
Claims 7, 14, 16, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2025/0278770 to Unnikrishnan (hereafter referred to as Unnikrishnan) in view of U.S. Patent Application Publication Number 2019/0114549 to Olsher (hereafter referred to as Olsher) and in further view of U.S. Patent Application Publication Number 2023/0138410 to Le et al. (hereafter referred to as Le).
As per claims 7, 14, and 20, the combination of Unnikrishnan and Olsher teaches each of the limitations of claims 1, 8, and 15 respectively.
In addition, Unnikrishnan teaches:
provide the subset of the plurality of propositions for display (Paragraph Number [0092] teaches once the multi-model encoder/decoder 432 adapts the content 422 in response to the prompt 428, it outputs personalized content 434 for the user 104. The touch point content adapter 418 then forwards the personalized content 434 at the touchpoint 412 for presentation on an electronic display of an electronic device 436).
Unnikrishnan teaches receiving and generating graph data to construct associations in a graphical setting and determine relationships therefrom but does not explicitly teach generating a ranking score for each of the associations between the plurality of data types which is taught by the following citations from Le:
generate a ranking score for each of the associations between the plurality of propositions and the plurality of goals based on applying a path finding algorithm to the graph data (Paragraph Number [0132] teaches where the activity feature indicates recent views of feed items of a particular topic by the user and/or the user's first-degree connections, the application system uses the machine learning model output to configure a recommendation portion of the user interface to rank content items that belong to that topic higher in the user's feed. In yet another example, where the activity feature indicates that recent activities of a user (connect, follow, profile view, interaction with feed updates, etc.) relate to certain topics, the application system uses the machine learning output to formulate search suggestions based on the user's recent activities with respect to those topics when the user enters a search query).
based on the ranking scores, determine a subset of the plurality of propositions (Paragraph Number [0131] teaches the processing device generates, by the application system, user interface output based on the model output provided to the application system in operation 318. For example, a recommendation component of the application system ranks, groups, sorts, or filters a set of recommendations, such as recommended content items, based on the machine learning model output. For instance, where the activity feature indicates a high number of view job events within the time window, the application system uses the machine learning model output to configure a recommendation portion of the user interface to include a recommendation to submit a job application for a particular job).
Both the combination of Unnikrishnan and Olsher and Le are directed to directed graphs that display relationships between types of data. The combination of Unnikrishnan and Olsher discloses receiving and generating graph data to construct associations in a graphical setting and determine relationships therefrom. Le improves upon the combination of Unnikrishnan and Olsher by disclosing generating a ranking score for each of the associations between the plurality of data types. One of ordinary skill in the art would be motivated to further include generating a ranking score for each of the associations between the plurality of data types, to efficiently determine which specific association is more related to the input data and to determine which data set is the best to implement in a recommendation.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of receiving and generating graph data to construct associations in a graphical setting and determine relationships therefrom in the combination of Unnikrishnan and Olsher to further utilize generating a ranking score for each of the associations between the plurality of data types as disclosed in Le, since the claimed invention is merely a combination of old elements, and in 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.
As per claim 16, the combination of Unnikrishnan and Olsher teaches each of the limitations of claim 15.
Unnikrishnan teaches receiving and generating graph data to construct associations in a graphical setting and determine relationships therefrom but does not explicitly teach generating a ranking score for each of the associations between the plurality of data types which is taught by the following citations from Le:
wherein the first of the plurality of propositions is associated with a first of the plurality of entities that is higher on an organization chart than a second of the plurality of entities that is not associated with the first of the plurality of entities, the resolution data indicating that the first of the plurality of propositions should be proposed (Paragraph Number [0132] teaches where the activity feature indicates recent views of feed items of a particular topic by the user and/or the user's first-degree connections, the application system uses the machine learning model output to configure a recommendation portion of the user interface to rank content items that belong to that topic higher in the user's feed. In yet another example, where the activity feature indicates that recent activities of a user (connect, follow, profile view, interaction with feed updates, etc.) relate to certain topics, the application system uses the machine learning output to formulate search suggestions based on the user's recent activities with respect to those topics when the user enters a search query).
A person of ordinary skill in the art would have been motivated to combine these references as described in regard to claim 7.
As per claim 17, the combination of Unnikrishnan and Olsher teaches each of the limitations of claim 15.
In addition, Unnikrishnan teaches:
determine that that the first of the plurality of propositions is associated with at least a first of the plurality of entities, and not associated with a second of the plurality of entities (Paragraph Number [0077] teaches the touchpoint content adaptation system 400 further includes a GNN model 304. During a training phase, the GNN model 304 is trained using a training dataset derived from the buyer journey graph 200. During each training iteration, the GNN model 304 takes as input structure 302 various sampled subgraphs from the buyer journey graph 200 to generate two types of embeddings comprising a touchpoint embedding 402 and a decision embedding 404. The touchpoint embedding 402 comprises a first set of node embeddings 324 and edge embeddings 326 representing relationships between a user node 202 and a touchpoint node 204, where the touchpoint node 204 represents a touchpoint for the user node 202. The decision embedding 404 comprises a second set of node embeddings 324 and edge embeddings 326 representing relationships between a user node 202 and an event node 206, where the event node 206 represents a decision stage in the buyer decision journey 102).
input the first of the plurality of propositions and the graph structure data to the large language model based on the determination (Paragraph Number [0069] teaches in the GNN model 304, edge embeddings 326 refer to a vector representation that captures the essential features and characteristics of the relationships between nodes in the graph. Similar to node embeddings 324, edge embeddings 326 are obtained through the GNN's learning process, which focuses on updating and refining the representations of edges based on the connected nodes and their interactions. The goal of edge embeddings 326 is to encode information about the structural, semantic, and contextual aspects of the relationships between nodes in the graph. By capturing pertinent information about the edges, the GNN model 304 can effectively learn and utilize meaningful representations that convey the nuanced interactions and dependencies within the graph. Edge embeddings 326 play a crucial role in various graph-based learning tasks, including link prediction, graph classification, and graph analysis. Paragraph Number [0091] teaches the prompt builder 426 uses the buyer encodings to select among different prompts 428 through a relevancy scoring process. In one embodiment, the relevancy scoring process may score the relevancy of products or services according to a set of user preferences and past interactions with a company, such as through encodings of metadata for the user 104 during a loyalty loop 124, thereby enabling personalized recommendations. (See also Paragraph Number [0099] in regard to content generated by the model which encompasses data such as messages, predictions, recommendations, advertisements, or suggestions to improve user experience. Examiner asserts that this output constitutes propositions as described by the Specification Paragraph Number [0027] where propositions are equated to business solutions)).
Unnikrishnan teaches receiving and generating graph data to construct associations in a graphical setting and determine relationships therefrom but does not explicitly teach generating a ranking score for each of the associations between the plurality of data types which is taught by the following citations from Le:
determine the first of the plurality of entities has a higher rank than the second of the plurality of entities (Paragraph Number [0131] teaches the processing device generates, by the application system, user interface output based on the model output provided to the application system in operation 318. For example, a recommendation component of the application system ranks, groups, sorts, or filters a set of recommendations, such as recommended content items, based on the machine learning model output. For instance, where the activity feature indicates a high number of view job events within the time window, the application system uses the machine learning model output to configure a recommendation portion of the user interface to include a recommendation to submit a job application for a particular job).
A person of ordinary skill in the art would have been motivated to combine these references as described in regard to claim 7.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW H DIVELBISS whose telephone number is (571)270-0166. The examiner can normally be reached on 7:30 am - 6:00 PM. 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, Jerry O'Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000).
/MATTHEW H DIVELBISS/Examiner, Art Unit 3624
Prosecution Insights