DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
1. This action is in response to the amendment filed on 9/19/2025. Claims 1 and 11-13 have been amended. Claims 3, 5-8, and 20 have been cancelled. Claims 1-2, 4, and 9-19 remain rejected in the application. Applicant’s amendments to the specification and claims have overcome each and every objection previously set forth in the Non-Final Office Action mailed 6/20/2025.
Response to Arguments
2. Applicant’s arguments are directed toward claim amendments filed on 9/19/2025. Regarding claim 1, and similarly claim 12, with respect to the rejection under 35 U.S.C. 102 regarding that the prior art does not teach the limitation(s): “wherein the significance of each node changes based on a Fibonacci sequence” has been fully considered, but is moot because of new grounds for rejection. Regarding claim 12, and similarly claim 13, with respect to the rejection under 35 U.S.C. 102 regarding that the prior art does not teach the limitation(s): “an ancillary contextual graph” has been fully considered, but are moot because of new grounds for rejection. Claim 1 is now disclosed by Damaraju, Brisbart, and Liang. Claim 12 is now disclosed by Damaraju, Brisbart, Liang, and Goldfarb. Claim 13 is now disclosed by Damaraju, El Rouby, and Zhao.
3. Regarding arguments with respect to claims 2, 4, 9-11, and 14-19, they are dependent on independent claims 1 and 13 respectively. Applicant does not argue anything other than independent claim 1, and similarly claims 12 and 13. The limitations in those claims, in conjunction with combination, has previously been established and explained.
Claim Rejections - 35 USC § 103
4. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
5. Claims 1-11 are rejected under 35 U.S.C. 103 as being unpatentable over Damaraju et al. (US-9911211-B1, hereinafter "Damaraju") in view of Brisbart et al. (US-7788284-B2, hereinafter "Brisbart"), and further in view of Liang et al. (CN-105320719-A, hereinafter "Liang"). (Examiner’s note: Citations to Liang use the original CN-105320719-A document locations.)
6. As per claim 1, Damaraju discloses: A method of simulating configured behaviors applied to a graph comprising the steps of:
providing a plurality of nodes, with each of the plurality of nodes having a link to at least one other node of the plurality of nodes, (Damaraju, column 1, lines 30-34, “Some aspects include … obtaining a graph, the graph having a plurality of nodes and edges connecting the nodes; ...” and Claim 10, “The medium of claim 1, wherein the first transformation comprises: ... adding or removing edges to the graph ...” and column 19, lines 25-28, “Some embodiments may randomly walk the graph, ... in some cases including self-referential edges.”) wherein each node is representative of a concept and each link is representative of a relationship between two concepts; and (Damaraju, column 17, lines 24-31, “In some embodiments, a corresponding graph may be constructed, with documents, paragraphs, entities, sentiments, or terms as nodes, and weighted edges indicating relationships, like similarity, relatedness, species-genus relationships, synonym relationships, possession relationships, relationships in which one node acts on another node, relationships in which one node is an attribute of another, and the like.”)
modifying a position of at least one node or a length of at least one link by applying a force-profile to each of the nodes and links, wherein the nodes are treated as particles; (Damaraju, column 24, lines 59-64, “In some embodiments, the data visualization module 824 may be operative to prepare data visualizations for display on user devices, e.g., visualizations of the graphs described herein. In some cases, such visualizations may include physics-based arrangements of nodes within a display, like a force-directed layout.” and column 25, lines 35-38, “To visualize graph relations, some embodiments of module 824 may arrange vertices (also referred to as nodes) and edges using a physics simulation that mimics the stretching of spider webs.” and column 26, lines 7-11, “Some embodiments may compute the gradient of the modeled system's energy (e.g., based on the forces affecting nodes), integrate that to compute momentum, and move the particles in the simulation representing nodes accordingly.”)
wherein each node has a significance, and wherein the significance is an input to the force-profile; (Damaraju, column 8, lines 61-64, “In some embodiments, the node icons 506 and 508 may be nodes deemed significant according to the techniques described above for the respective cluster.” and column 9, lines 17-23, “In some embodiments, the node icons 508 may be significant in virtue of edge properties of the respective node, like a particularly high score of a topic of a cluster to which the cluster of that respective node connects, a number of edges connected to that node, a median number of edge weights of edges connect to that node, and the like.” and column 9, lines 35-38, “In some cases, the node icons 506 and 508 may be positioned adjacent the respective cluster icon 504, for instance according to a force directed arrangement, such as according to a physics simulation ...”)
[[wherein each link adds an amount of energy to the graph;]]
[[wherein the energy in the graph flows to and collects at nodes;]]
[[wherein the significance of a particular node is based on the collection of energy at that particular node;]]
wherein the significance of each node is displayed as a particular size, shape or color; and (Damaraju, column 5, lines 41-58, “For example, nodes, edges, clusters, and links between clusters may have various forms of metadata, and some embodiments may change the way these elements are depicted in a visualization based on the value of the metadata field selected in a transformation. ... In another example, nodes or clusters may represent companies, and shapes of icons corresponding to the nodes or clusters may be changed based on market share of the companies. A variety of different visual attributes may be changed including sizes of icons, colors of icons, orientation of icons, transparency of icons, drop shadows of icons, positions of icons, frequency or amplitude of vibratory movement of icons, and the like.”)
[[wherein the significance of each node changes based on a Fibonacci sequence.]]
7. Damaraju doesn't explicitly disclose but Brisbart discloses: wherein each link adds an amount of energy to the graph; (Brisbart, column 3, lines 51-53, “Next, a keyword graph is traversed. … These nodes are assigned energy weights, 230.” and column 5, lines 60-64, “Energy weights are assigned based on how the prisma terms relate to one another on the keyword graph. A single energy weight is normalized to the sum of all energy connections on the graph.”)
wherein the energy in the graph flows to and collects at nodes; (Brisbart, column 3, line 67-column 4 line 4, “The nodes of the graph are in separate energy levels and energy “flows” in one direction and never lead back to themselves. The nodes at the top energy level, 300, are where all the energy flows to and hence represent the most common and generic topics.”)
wherein the significance of a particular node is based on the collection of energy at that particular node; (Brisbart, column 4 lines 2-5, “The nodes at the top energy level, 300, are where all the energy flows to and hence represent the most common and generic topics. The nodes at lowest energy level, 320, represent the most topic specific terms.”)
8. Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to modify the method of Damaraju to include the disclosure of adding an amount of energy with each link to the graph, where energy flows to and collects at nodes, and the significance of a node is based on the collection of energy at that particular node, of Brisbart. The motivation for this modification could have been to provide a way to put a numerical value or weight on concepts as a means to explore their value in relation to other concepts and their contribution to each other.
9. Damaraju in view of Brisbart doesn't explicitly disclose but Liang discloses: wherein the significance of each node changes based on a Fibonacci sequence. (Liang, page 6, [0024], “Furthermore, in step 1) of the tree-based tag recommendation, user behavior is mapped to its impact on the node weights of the tree tags. User behavior not only affects the direct tags of the project itself, but also the indirect tags of the project. The proportion of the indirect tags' influence is determined by a Fibonacci-like sequence, and the final weight is related to the level of the tag node and the length of the project tag path. Finally, projects are recommended to the user based on the project attributes and the node weights of the tree tags.” and page 5, [0013], “However, on crowdfunding websites, projects often belong directly or indirectly to multiple tags, and traditional recommendation methods often ignore the constraints between these tags. In addition, due to the various connections between users (friendships, collaborations, etc.) and between users and projects (browsing, donating, saving, etc.), these nodes and connections constitute a relatively complex graph structure, which is quite different from other types of websites.”; Examiner’s note: Liang discloses building a graph/tree style relationship between crowdfunding website projects and determine which to recommend based on a final weight. A Fibonacci-like sequence is used to help determine the final node weights.)
10. Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to modify the method of Damaraju in view of Brisbart to include the disclosure of changing a node’s significance based on a Fibonacci sequence, of Liang. The motivation for this modification could have been to provide a “growth” functionality for individual nodes and help indicate what “level” (significance) they are at. For instance, as a node increases its links, the total link number could match up to that of a Fibonacci sequence. For any individual node, it would be fairly easy to “level up” a few levels initially but become more difficult for higher level nodes (for example, 13 vs 21 links). If expressed visually, a user could quickly determine that a larger node’s significance is much greater that the smaller nodes due to the node’s “growth” from a Fibonacci sequence.
11. As per claim 2, Damaraju in view of Brisbart, and further in view of Liang discloses: The method of simulating configured behaviors applied to a graph of claim 1, wherein the length of each link is determined by the application of the force-profile. (Damaraju, column 25, lines 35-41, “To visualize graph relations, some embodiments of module 824 may arrange vertices (also referred to as nodes) and edges using a physics simulation that mimics the stretching of spider webs. Some spider-web-inspired representations may model interactions between each pair of vertices as a Coulomb-like repulsion and an additional Hooke-like attraction in the presence of an edge between the pair.”)
12. As per claim 4, Damaraju in view of Brisbart, and further in view of Liang discloses: The method of simulating configured behaviors applied to a graph of claim 3, wherein the significance of each node is determined by the number of links a particular node has. (Damaraju, column 9, lines 14-23, “In some cases, node icons 506 correspond to nodes that are significant in virtue of metadata or unstructured text attributes of the respective node, such as according the techniques described above. In some embodiments, the node icons 508 may be significant in virtue of edge properties of the respective node, like a particularly high score of a topic of a cluster to which the cluster of that respective node connects, a number of edges connected to that node, a median number of edge weights of edges connect to that node, and the like.”)
13. As per claim 9, Damaraju in view of Brisbart, and further in view of Liang discloses: The method of simulating configured behaviors applied to a graph of claim 1, wherein the force-profile is physics based. (Damaraju, column 24, lines 59-64, “In some embodiments, the data visualization module 824 may be operative to prepare data visualizations for display on user devices, e.g., visualizations of the graphs described herein. In some cases, such visualizations may include physics-based arrangements of nodes within a display, like a force-directed layout.”)
14. As per claim 10, Damaraju in view of Brisbart, and further in view of Liang discloses: The method of simulating configured behaviors applied to a graph of claim 1, wherein the force-profile includes at least one of the following forces:
gravity, charge, centering, radial, positioning, spring, and collision. (Damaraju, column 9, lines 35-40, “In some cases, the node icons 506 and 508 may be positioned adjacent the respective cluster icon 504, for instance according to a force directed arrangement, such as according to a physics simulation, like those described below, in which the respective cluster icon 504 has a mass with gravity or a charge that attracts the node icons ...” and column 9, lines 51-57, “In the illustrated graph, each of the clusters 504 may be positioned relative to one another according to a force directed layout as well. For example, the lines 510 connecting the respective clusters may be modeled according to a physics model, like according to springs, with the clusters 504 being modeled is having a repellent force, placing the springs intention.” and column 25, lines 20-27, “In some cases ... properties of physical models, like inertia, friction, attractive forces, repulsive forces, momentum, frequency of oscillation, and the like, may be mapped to different dimensions like those discussed above, e.g., similarity, relatedness, sentiment, and the like.”)
15. As per claim 11, Damaraju in view of Brisbart, and further in view of Liang discloses: The method of simulating configured behaviors applied to a graph of claim 1, wherein the force-profile includes at least one user-configured or created force. (Damaraju, column 25, lines 41-52, “A relatively weak gravitation-like force may be modeled to prevent separate components and isolated vertices from venturing too far from the network's center of mass. ... In some cases, the parameters and initial conditions of the physics based model may be determined by module 824, and instructions for executing the model and adjusting the model based on user input may be sent to the user device, e.g., in the form of JavaScript™ instructions that model, for instance, a user selecting and dragging a node as a force applied to the physics model.”)
16. Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Damaraju et al. (US-9911211-B1, hereinafter "Damaraju") in view of Brisbart et al. (US-7788284-B2, hereinafter "Brisbart"), further in view of Liang et al. (CN-105320719-A, hereinafter "Liang"), and further in view of Goldfarb (US-2020/0320106-A1).
17. Claim 12 is similar in scope to claim 1 except for additional limitations that Damaraju in view of Brisbart, and further in view of Liang discloses: A method of generating a contextual graph and simulating configured behaviors applied to the contextual graph comprising the steps of:
receiving user inputs via an interface of one or more concepts; (Damaraju, column 1, lines 8-10,
“The present disclosure relates generally to graph analysis and, more specifically, to user interfaces to manipulate visual representations of graphs.” and column 1, lines 20-23, “Other examples include graphs characterizing relationships between other entities, like between companies, countries, or people, such as graphs relating exchanges there between or similarities there between.”)
converting each concept into an individual node, wherein each node is configured to be responsive to forces like a particle; (Damaraju, column 1, lines 20-23, “Other examples include graphs characterizing relationships between other entities, like between companies, countries, or people, such as graphs relating exchanges there between or similarities there between.” and column 1, lines 30-34, “Some aspects include a process of adjusting a visualization of a graph in response to user interactions with the visualization, the process including: obtaining a graph, the graph having a plurality of nodes and edges connecting the nodes ...” and column 26, lines 7-11, “Some embodiments may compute the gradient of the modeled system's energy (e.g., based on the forces affecting nodes), integrate that to compute momentum, and move the particles in the simulation representing nodes accordingly.”)
linking nodes together based on a user linking input; (Damaraju, column 1, lines 30-47, “Some aspects include a process of adjusting a visualization of a graph in response to user interactions with the visualization, the process including: ... receiving a first user input requesting that a first lens be applied to the visualization, wherein: the first lens defines a first region of the display area ... the first lens specifies a first transformation that affects the visualization differently in the first region than outside the first region of the display area ... transforming the first portion of the graph with the first transformation specified by the first lens ...” and Claim 10, lines 1-6, “The medium of claim 1, wherein the first transformation comprises: ... adding or removing edges to the graph in the first portion …”)
displaying each node and link as it is received; (Damaraju, column 1, lines 30-36, “Some aspects include a process of adjusting a visualization of a graph in response to user interactions with the visualization, the process including: obtaining a graph, the graph having a plurality of nodes and edges connecting the nodes; causing a visualization of the graph to be presented on one or more displays having a display area in which the visualization is presented …”)
modifying a position of at least one node or a length of at least one link by applying a force-profile to each of the nodes and links each time a new user input or user linking input is received; (Damaraju, column 1, lines 30-43, “Some aspects include a process of adjusting a visualization of a graph in response to user interactions with the visualization, the process including: ... receiving a first user input requesting that a first lens be applied to the visualization, wherein: the first lens defines a first region of the display area ... the first lens specifies a first transformation that affects the visualization differently in the first region than outside the first region of the display area ...” and column 5, lines 66-column 6, line 6, “In some embodiments, the transformation relates to changes to physical parameters in a physics model by which the graph visualization is generated, for example, in a force directed graph. For example, a transformation may change a repelling force in such a model, for example, increasing the repelling force to make nodes separate and be easier to be seen, or decreasing the repelling force to make node groupings easier to see.” and column 25, lines 35-38, “To visualize graph relations, some embodiments of module 824 may arrange vertices (also referred to as nodes) and edges using a physics simulation that mimics the stretching of spider webs.”)
…
[[the method further comprising generating an ancillary contextual graph, wherein the ancillary contextual graph is used to identify an anomaly in the contextual graph.]]
See claim 1 rejection for reason to combine.
18. Damaraju in view of Brisbart, and further in view of Liang doesn't explicitly disclose but Goldfarb discloses: the method further comprising generating an ancillary contextual graph, wherein the ancillary contextual graph is used to identify an anomaly in the contextual graph. (Goldfarb, [0003], “The processor is configured to obtain a graph that represents the entities by respective nodes and, via multiple edges, interconnects each pair of the nodes that represents a respective pair of the entities that are related to one another. The processor is further configured to receive behavior-indicating data via the communication interface, and, based on the behavior-indicating data, to compute respective single-entity anomaly scores (SEASs) for the entities, each of the SEASs quantifying a first degree to which first behavior of a respective one of the entities is anomalous. The processor is further configured to, in response to any particular one of the SEASs, for any particular one of the entities, exceeding a predefined SEAS threshold, identify a subgraph of the graph, which represents a subset of the entities that includes the particular one of the entities, and compute a subgraph anomaly score (SAS) that quantifies a second degree to which second behavior of the subset of the entities is anomalous. The processor is further configured to, in response to the SAS exceeding a predefined SAS threshold, generate an alert.”)
19. Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to modify the method of Damaraju in view of Brisbart, and further in view of Liang to include the disclosure of generating an ancillary contextual graph that is used to help identify an anomaly in the contextual graph, of Goldfarb. The motivation for this modification could have been to utilize behavior from other ancillary “subgraphs” as a comparison against the rest of the contextual graph. Depending on the nature of the graph, sometimes certain graph behaviors or patterns appear as the graph continues to grow. By analyzing ancillary “subgraphs,” it may become apparent that certain parts of the graph are deviating from typical behavior of the rest of the graph. Or, there may be a part of the subgraph that behaves fundamentally different from the rest of the graph. The ability to compare various ancillary “subgraphs” helps to indicate parts of the subgraph that are exhibiting different behaviors and may even be causing an anomaly.
20. Claims 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over Damaraju et al. (US-9911211-B1, hereinafter "Damaraju") in view of El Rouby et al. (US-2022/0414210-A1, hereinafter "El Rouby"), and further in view of Zhao (WO-2021/189730-A1). (Examiner’s note: Citations to Zhao use the original WO-2021/189730-A1 document locations.)
21. As per claim 13, Damaraju discloses: A computer system for generating contextual graphs, comprising:
a monitor with a graphical user interface; (Damaraju, column 27, lines 25-27, “I/O devices 1060 may include, for example, graphical user interface presented on displays (e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor) ...”)
a computing device connected to the monitor, the computing device having a central processing unit and a memory; (Damaraju, Figure 11; column 26, lines 52-60, “Computing system 1000 may include one or more processors (e.g., processors 1010a-1010n) coupled to system memory 1020, an input/output I/O device interface 1030, ... A processor may include a central processing unit (CPU) ...” and column 27, lines 21-26, “I/O device interface 1030 may provide an interface for connection of one or more I/O devices 1060 to computer system 1000. ... I/O devices 1060 may include, for example, graphical user interface presented on displays …”)
wherein the central processing unit is operable to execute a plurality of instructions stored on the memory in order to perform a method for generating a contextual graph; (Damaraju, column 26, line 59-column 27, line 2, “A processor may include a central processing unit (CPU) that carries out program instructions ... A processor may receive instructions and data from a memory …” and column 1, lines 30-34, “Some aspects include a process of adjusting a visualization of a graph in response to user interactions with the visualization, the process including: obtaining a graph, the graph having a plurality of nodes and edges connecting the nodes ...”)
a plurality of user input devices connected to the computing device; and (Damaraju, column 27, lines 25-31, “I/O devices 1060 may include, for example, graphical user interface presented on displays (e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor), pointing devices (e.g., a computer mouse or trackball), keyboards, keypads, touchpads, scanning devices, voice recognition devices, gesture recognition devices, printers, audio speakers, microphones, cameras, or the like.”)
wherein the central processing unit is operable to display a plurality of nodes and a plurality of links on the graphical user interface. (Damaraju, column 3, lines 17-20, “In some embodiments, the process 10 may power a user interface by which a user may relatively quickly apply different views to a data set in a visualization.” and column 1, lines 30-36, “Some aspects include a process of adjusting a visualization of a graph in response to user interactions with the visualization, the process including: obtaining a graph, the graph having a plurality of nodes and edges connecting the nodes; causing a visualization of the graph to be presented on one or more displays having a display area in which the visualization is presented ...”)
wherein the central processing unit is operable [[to identify and generate a prompt to explore at least one of a connection or an anomaly in the contextual graph;]]
[[wherein the prompt is generated from a learning algorithm, the learning algorithm based on a comparison of an ancillary contextual graph to the contextual graph.]]
22. Damaraju doesn't explicitly disclose but El Rouby discloses: [[wherein the central processing unit is operable]] to identify and generate a prompt to explore at least one of a connection or an anomaly in the contextual graph; (El Rouby, ¶ [0046], lines 1-6, “Using all of the information collated by the graph service, anomalies and risky access patterns may then be identified. The malicious behavior service may proactively prompt the graph service to populate data into a graph from access requests which are flagged by the governance control plane, metadata control plane or catalog as ‘risky.’” and ¶ [0046], lines 1-6, “With regard to 1., contextual identity information may be included the graph, and in the case of 2., events related to attempts by a user to interact with metadata or data may be included in the graph.” and ¶ [0028], lines 13-16, “Such embodiments may employ information about the user, such as UserID, Group, and Location, for example, who is trying to access the asset, metadata about the asset itself, such as Sensitivity, Category, and Location, for example, as well as metadata about the access attempt, such as IP Address, Location, Time, and Date, to make a final decision of either allowing or denying access.”)
23. Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to modify the method of Damaraju to include the disclosure of identifying and generating a prompt to explore at least one of a connection or an anomaly in the contextual graph, of El Rouby. The motivation for this modification could have been to draw a user’s attention to unique aspects of a complex graph’s behavior or makeup, particularly if there are aspects of the graph that are unusual and need to be addressed.
24. Damaraju in view of El Rouby doesn't explicitly disclose but Zhao discloses: wherein the prompt is generated from a learning algorithm, the learning algorithm based on a comparison of an ancillary contextual graph to the contextual graph. (Zhao, page 6, lines 1-8, “105: Anomaly detection is performed on the high-density subgraph by using the anomaly detection model in combination with the target derivative feature, to obtain the target abnormal high-density subgraph. The server constructs an anomaly detection model, where the anomaly detection model is a combination model integrating a plurality of performance models, performs screening on sample data (sample data with derived features) in the anomaly detection model by an expert rule to obtain initial sample data, performs risk prediction on the initial sample data to obtain a risk value, determines whether the risk value is greater than a preset value, obtains initial sample data whose risk value is greater than a preset value, obtains candidate sample data, and performs normal distribution analysis on the candidate sample data in an anomaly detection algorithm based on a Gaussian (normal) distribution in the unsupervised learning algorithm, to obtain a target abnormal high-density subgraph corresponding to the abnormality in the target derivative feature, to complete training of the target anomaly detection model to obtain a final target anomaly detection model, and perform anomaly detection on the high-density subgraph by using the anomaly detection model in combination with the target derivation feature.”)
25. Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to modify the computer system of Damaraju in view of El Rouby to include the disclosure of using a learning algorithm to compare an ancillary contextual graph to the contextual graph as a means to generate a graph exploration or anomaly prompt, of Zhao. The motivation for this modification could have been to utilize behavior from other ancillary “subgraphs” as a comparison against the rest of the contextual graph. Depending on the nature of the graph, sometimes certain graph behaviors or patterns appear as the graph continues to grow. These patterns can train a machine learning model to recognize these graph behaviors. Then, by using the machine learning model to analyze ancillary “subgraphs,” it may become apparent that certain parts of the graph are deviating from typical behavior of the rest of the graph. Or, there may be a part of the subgraph that behaves fundamentally different from the rest of the graph. The ability to compare various ancillary “subgraphs” through machine learning processes helps to quickly indicate parts of the subgraph that are exhibiting different behaviors and may even be causing an anomaly. The machine learning model may also indicate less obvious deviations in the graph that might have otherwise gone unnoticed.
26. As per claim 14, Damaraju in view of El Rouby, and further in view of Zhao discloses: The computer system of claim 13 wherein the plurality of user input devices comprises at least one of a keyboard, a mouse, or a touchscreen for user interaction. (Damaraju, column 4, lines 56-57, “For example, in some cases, inputs may be received on a touchscreen of a touchscreen display.” and column 27, lines 25-31, “I/O devices 1060 may include, for example, graphical user interface presented on displays (e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor), pointing devices (e.g., a computer mouse or trackball), keyboards, keypads, touchpads, scanning devices, voice recognition devices, gesture recognition devices, printers, audio speakers, microphones, cameras, or the like.”)
27. As per claim 15, Damaraju in view of El Rouby, and further in view of Zhao discloses: The computer system of claim 13 wherein the plurality of nodes represents a plurality of concepts or terms, and the plurality of links represents relationships between the plurality of concepts or terms. (Damaraju, column 17, lines 24-31, “In some embodiments, a corresponding graph may be constructed, with documents, paragraphs, entities, sentiments, or terms as nodes, and weighted edges indicating relationships, like similarity, relatedness, species-genus relationships, synonym relationships, possession relationships, relationships in which one node acts on another node, relationships in which one node is an attribute of another, and the like.”)
28. As per claim 16, Damaraju in view of El Rouby, and further in view of Zhao discloses: The computer system of claim 13 wherein a plurality of forces affect a position or a significance of the plurality of nodes on the graphical user interface. (Damaraju, column 9, lines 35-40, “In some cases, the node icons 506 and 508 may be positioned adjacent the respective cluster icon 504, for instance according to a force directed arrangement, such as according to a physics simulation, like those described below, in which the respective cluster icon 504 has a mass with gravity or a charge that attracts the node icons ...” and column 9, lines 51-57, “In the illustrated graph, each of the clusters 504 may be positioned relative to one another according to a force directed layout as well. For example, the lines 510 connecting the respective clusters may be modeled according to a physics model, like according to springs, with the clusters 504 being modeled is having a repellent force, placing the springs intention.” and column 25, lines 20-27, “In some cases ... properties of physical models, like inertia, friction, attractive forces, repulsive forces, momentum, frequency of oscillation, and the like, may be mapped to different dimensions like those discussed above, e.g., similarity, relatedness, sentiment, and the like.”)
29. As per claim 17, Damaraju in view of El Rouby, and further in view of Zhao discloses: The computer system of claim 13 wherein the method for generating a contextual graph comprises applying a force-profile to modify a layout and an appearance of the plurality of nodes and the plurality of links. (Damaraju, column 24, lines 59-64, “In some embodiments, the data visualization module 824 may be operative to prepare data visualizations for display on user devices, e.g., visualizations of the graphs described herein. In some cases, such visualizations may include physics-based arrangements of nodes within a display, like a force-directed layout.” and column 25, lines 35-38, “To visualize graph relations, some embodiments of module 824 may arrange vertices (also referred to as nodes) and edges using a physics simulation that mimics the stretching of spider webs.” and column 25, lines 20-27, “In some cases, displays may be relatively high dimensional, e.g., various visual attributes, like line weight, icon size, color, transparency, drop shadow offsets, or properties of physical models, like inertia, friction, attractive forces, repulsive forces, momentum, frequency of oscillation, and the like, may be mapped to different dimensions like those discussed above, e.g., similarity, relatedness, sentiment, and the like.”)
30. As per claim 18, Damaraju in view of El Rouby, and further in view of Zhao discloses: The computer system of claim 13 wherein the method for generating a contextual graph comprises an input selecting and highlighting at least one node out of the plurality nodes or a link path within the contextual graph. (Damaraju, column 7, lines 5-25, “For example, the user may freehand draw a region of the display screen by dragging their finger in a closed shape to define a perimeter, and some embodiments may decompose that shape into an approximating polygon having vertices and display space. Some embodiments may then determine with a point-in polygon algorithm whether each graph element is within the corresponding region of the display. ... Next, some embodiments may transform the first portion of the graph, as indicated by Block 20. In some cases, this includes not transforming other portions of the graph, such that the transformation is selectively applied to a subset of the graph elements selected by the user when sizing and positioning the lens.” and column 5, lines 41-58, “For example, nodes, edges, clusters, and links between clusters may have various forms of metadata, and some embodiments may change the way these elements are depicted in a visualization based on the value of the metadata field selected in a transformation. ... In another example, nodes or clusters may represent companies, and shapes of icons corresponding to the nodes or clusters may be changed based on market share of the companies. A variety of different visual attributes may be changed including sizes of icons, colors of icons, orientation of icons, transparency of icons, drop shadows of icons, positions of icons, frequency or amplitude of vibratory movement of icons, and the like.”)
31. As per claim 19, Damaraju in view of El Rouby, and further in view of Zhao discloses: The computer system of claim 13 wherein the central processing unit is operable to add, modify, or delete at least one of the plurality of nodes or one of the plurality of links in real-time on the graphical user interface. (Damaraju, column 1, lines 30-47, “Some aspects include a process of adjusting a visualization of a graph in response to user interactions with the visualization, the process including: ... receiving a first user input requesting that a first lens be applied to the visualization, wherein: the first lens defines a first region of the display area ... the first lens specifies a first transformation that affects the visualization differently in the first region than outside the first region of the display area ... transforming the first portion of the graph with the first transformation specified by the first lens ...” and Claim 10, lines 1-6, “The medium of claim 1, wherein the first transformation comprises: ... adding or removing edges to the graph in the first portion …” and column 5, lines 20-24, “In some embodiments, the transformations are transformations to the graph. For example, in some embodiments, the transformations change parameters of algorithms by which the graph is formed.”)
Conclusion
32. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Scarr (US-2019/0121914-A1), [0047]-[0048], discloses hierarchical weighted tiers of nodes based on a Fibonnaci-like sequence. This hierarchy can be used to describe relationships between concepts, such as relationships between food/drink categories for example.
33. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
34. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW CLOTHIER whose telephone number is (571)272-4667. The examiner can normally be reached Mon-Fri 8:00am-4:00pm.
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/MATTHEW CLOTHIER/Examiner, Art Unit 2614
/KENT W CHANG/Supervisory Patent Examiner, Art Unit 2614