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 .
Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 3 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With respect to claim 3, it is unclear what the limitation “the group consisting of databases, tables, analysis workbooks, users, data sources, and analysis.” [line 2] refers to. Claim 3 is depended on claim 1. However, claim 1 never recites any particular group that an asset type for each of the plurality of analytic assets is selected from. For the purposes of examination, Examiner will interpret the limitation as “a group consisting of databases, tables, analysis workbooks, users, data sources, and analysis.”
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 an abstract idea without significantly more.
Independent claims
Step 1
Claim 1 is drawn to a method, claim 15 is drawn to a computer system for visual analysis of datasets, and claim 20 is drawn to a non-transitory computer readable storage medium storing one or more programs that comprising instructions to execute the method of claim 1. Therefore, each of these claim groups falls under one of four categories of statutory subject matter (process/method, machines/product/apparatus, manufactures, and composition of matter).
Step 2A – Prong 1
Claims 1, 15 and 20 are directed to a judicially recognized exception of an abstract idea without significantly more.
Claims 1, 15 and 20 recite a method of predicting a link between the two nodes of the data graph based on the corresponding node embeddings that under its broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with or without the physical aid such as pen and paper, to analyze such information to make a prediction. Therefore, the step of predicting a link between the two nodes of data graph is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Claims 1, 15 and 20 recite a method of generating a recommendation for an analytic asset in accordance with a determination that a probability for the link is above a predetermined threshold that under its broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with or without the physical aid such as pen and paper, to present a recommendation based on the analysis. Therefore, the step of generating a recommendation for an analytic asset is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Step 2A – Prong 2
Claims 1, 15 and 20 recite further obtaining a data graph that includes a plurality of nodes, wherein each node stores metadata for a respective analytic asset of a plurality of analytic assets, and the data graph encodes relationships between the plurality of analytic assets that fails to integrate the abstract idea into a practical application. The step of obtaining a data graph is a form of insignificant input and output solution activities, where obtaining a data graph including a plurality of nodes is necessary for all uses of the judicial exception. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Claims 1, 15 and 20 recite further extracting a set of features for each node of the data graph that fails to integrate the abstract idea into a practical application. The step of extracting node features is a form of insignificant input and output solution activities, where extracting a set of features is necessary for all uses of the judicial exception. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Claims 1, 15 and 20 recite further deriving corresponding node embeddings for two nodes of the data graph using a two-layer graph neural network based on the data graph and the set of features that fails to integrate the abstract idea into a practical application. The step of deriving node embeddings is a form of insignificant input and output solution activities, where deriving corresponding node embeddings is necessary for all uses of the judicial exception. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Step 2B
The additional elements in step 2A-Prong 2 those are a form of insignificant extra-solution activities, do not amount to significantly more than an abstract idea because the court decision has determined that these additional elements of obtaining a data graph including a plurality of nodes; extracting a set of features; and deriving corresponding node embeddings to be well-understood, routine, and conventional when claimed in a merely generic manner (MPEP 2106.05(d)(II)).
As such, claims 1, 15 and 20 are not patent eligible.
Dependent claims
Claims 2-14 and 16-19 merely narrow the previously recited abstract idea limitations. For the reasons described above with respect to claims 1 and 15, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claims above and do not provide anything more than the mental process that are practically capable of being performed in the human mind with the assistance of pen and paper. Therefore, claims 2-14 and 16-19 also recite abstract ideas that do not integrate into a practical application or amount to significantly more than the judicial exception, and are rejected under U.S.C. 101.
Step 1
Claims 2-14 are drawn to a method and claims 16-19 are drawn to a computer system for visual analysis of datasets. Therefore, each of these claim groups falls under one of four categories of statutory subject matter (process/method, machines/product/apparatus, manufactures, and composition of matter).
Step 2A – Prong 1
Dependent claims 14 and 18 recite further the mental process by wherein each analytic asset type is associated with a corresponding predetermined threshold, the method further comprising: generating the recommendation in accordance with a determination that a corresponding probability for a node is above its corresponding predetermined threshold that is based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Step 2A – Prong 2
Dependent claim 2 recites further the insignificant extra solution activities by wherein each of the plurality of analytic assets includes a respective asset type, a respective set of asset authors, and respective creation data. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 3 recites further the insignificant extra solution activities by wherein the asset type for each of the plurality of analytic assets is selected from the group consisting of databases, tables, analysis workbooks, users, data sources, and analysis. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 4 recites further the insignificant extra solution activities by wherein the asset authors include end-user details. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 5 recites further the insignificant extra solution activities by wherein the data graph includes a node for a dataset that has a lineage relationship with a node for an analysis workbook. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 6 recites further the insignificant extra solution activities by wherein the data graph includes a node for a curated data source for telemetry and usage data that identify when and by whom analytic assets were created and viewed, respectively. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 7 recites further the insignificant extra solution activities by wherein the data graph includes one or more nodes for data sources for producing personalized recommendations. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 8 recites further the insignificant extra solution activities by wherein the set of features include asset type, community clustering, centrality, and node degree. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 9 recites further the insignificant extra solution activities by wherein the two-layer graph neural network includes two layers, each of which is a GraphSAGE convolution. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 10 recites further the insignificant extra solution activities by wherein the plurality of nodes includes one or more nodes for databases, one or more nodes for tables, one or more nodes for curated data sources, one or more nodes for users, one or more nodes for analysis workbooks, and one or more nodes for analysis. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claims 11 and 17 recite further the insignificant extra solution activities by wherein the data graph includes connections between (i) a node for a database and a node for a table, (ii) the node for the table and a node for a user, (iii) the node for the table and a node for a curated data source, (iv) the node for the table and a node for an analysis workbook, (v) the node for the curated data source and the node for the analysis workbook, (vi) the node for the user and the node for the analysis workbook, and (vii) the node for the analysis workbook and a node for analysis. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claims 12 and 19 recite further the insignificant extra solution activities by wherein the two-layer graph neural network is trained by batch training over ten epochs. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 13 recites further the insignificant extra solution activities by wherein each batch samples only direct neighbors and one random node for each node. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 16 recites further the insignificant extra solution activities by wherein (i) each of the plurality of analytic assets includes a respective asset type, a respective set of asset authors, and respective creation data, (ii) the asset type for each of the plurality of analytic assets is selected from the group consisting of databases, tables, analysis workbooks, users, data sources, and analysis, and (iii) the asset authors include end-user details. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
As such, dependent claims 2-14 and 16-19 are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Song et al (US 12620485 B2) hereafter Song, and further in view of Mori et al (US 20250125036 A1) hereafter Mori.
With respect to claim 1, Song teaches a method of generating analytic asset recommendations using graph neural networks (a graph neural network is used to recognize patterns in the input data graph based on a set of rules. The network can use stored values or node and weights derived from historical data to make predictions on new input data [col. 9, lines 10-20]), comprising:
at a computing system having one or more processors and memory storing one or more programs configured for execution by the one or more processors (the invention comprises a system for predicting and recommending a particular healthcare facility or provider for the members or patients needing a particular medical procedure or other health intervention, a computer processor, and a non-transitory computer-readable medium [col. 2, lines 10-30]):
obtaining a data graph that includes a plurality of nodes, wherein each node stores metadata for a respective analytic asset of a plurality of analytic assets, and the data graph encodes relationships between the plurality of analytic assets (graph analytic is the analysis of relationships among multiple entities. A graph is composed of a set of nodes and a set of edges. Graph embedding is a technique to transform a graph to a vector or set of vectors. The graph is comprised of a heterogeneous graph extracted from the historical claims data where the graph comprises at least 2 types of nodes selected from the group comprising a member node, a healthcare facility node, and a provider node [col. 1, line 10 – col. 2, line 5]);
extracting a set of features for each node of the data graph (data sources can be identified and chosen to create features to represent characteristics of individual healthcare entity and interactions between different healthcare entities. Healthcare applications apply multiple steps including identifying data sources to create features to represent characteristics of individual healthcare entity and interactions between different healthcare entities, preparing node features and preparing edge features [col. 4, lines 5-50]);
deriving corresponding node embeddings for two nodes of the data graph using a two-layer graph neural network based on the data graph and the set of features (An example of HinSAGELinkGenerator function from StellaGraph package specifying a two-layer HinSage model layer to produce a vector output is used to concatenate two vectors as a combined two length vector. Healthcare applications also apply a step of embedding the graph data into vectors using a graph embedding technique using GraphSAGE [col. 4, lines 30-45; col. 6, lines 25-45]);
predicting a link between the two nodes of the data graph based on the corresponding node embeddings (GraphSAGE is a representation learning technique capable of predicting embedding of a new node. The embedding process assumes that nodes that reside in the same neighborhood should have similar embeddings. A neighborhood embedding is created for each node and concatenated it with the existing embedding of the node. A graph can represent a social network having nodes are members of the network and the edges connecting them represent their network or friend links between members. Graph embedding and link prediction can be utilized on other healthcare AI problems to enrich the architecture and performance [col. 7, line 55 - col. 8, line 55; col. 9, lines 30-50]).
However, Song does not disclose generating a recommendation for an analytic asset in accordance with a determination that a probability for the link is above a predetermined threshold.
In the same field of endeavor, Mori teaches generating a recommendation for an analytic asset in accordance with a determination that a probability for the link is above a predetermined threshold (a link prediction section identifies a second user who has a predetermined relationship with the subject user by link prediction using a subject user graph and a plurality of second user graphs. The evaluation section generates response information including information pertaining to a meal menu related to the second user who has been identified by the link prediction section. The generation section may generate response information for recommending a meal menu for which the recommendation level calculated by the evaluation section is equal to or more than a predetermined value. In the flow of process, the link prediction calculates a probability that a node conforms to the request received in links to a node included in the subject user graph. The graph updating section determines whether or not the probability calculated is equal to or more than a threshold. If the probability is equal to or more than the threshold, the process proceeds to step 406, otherwise, it proceeds to step 405 [par. 0153-0160, 0192-0202 and FIG. 12]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of recommending a meal menu suitable for a user based on the physical information or health condition of the subject user as suggested by Mori into the concept of using a graph-based data structure to capture complex relations between different healthcare entities, analyze and mine healthcare data as suggested by Song because both of these systems addressing the process of predicting a link for a certain healthcare conditions/tasks by generating the graph embedding to generate a recommendation. Doing so would be desirable because the concept of Song would be more efficient by calculating a probability of a node such that the probability is equal to or more than a predetermined threshold to continue the generation process (Mori, [par. 0195-0197]).
With respect to claim 2, the combination of Song and Mori teaches wherein each of the plurality of analytic assets includes a respective asset type, a respective set of asset authors, and respective creation data (Song, the graph is comprised of at least two types of nodes selected from the group comprising a member node, a healthcare facility node, and a provider node [col. 2, lines 25-45]).
With respect to claim 3, the combination of Song and Mori teaches wherein the asset type for each of the plurality of analytic assets is selected from the group consisting of databases, tables, analysis workbooks, users, data sources, and analysis (Song, the system comprises a database for storing historical claims data, a plurality of concatenated vectors and either a first healthcare facility vector or provider vector concatenated together. Various data sources can be identified and chosen to create features to represent characteristics of individual healthcare entity and interactions [col. 2, lines 10-45; col. 4, lines 5-10]).
With respect to claim 4, the combination of Song and Mori teaches wherein the asset authors include end-user details (Song, a visualization dashboard lays out the ARM model output for presentation to business users. The end users are the providers for those members who needs to have EGDs in an ASC setting [col. 10, lines 30-55]).
With respect to claim 5, the combination of Song and Mori teaches wherein the data graph includes a node for a dataset that has a lineage relationship with a node for an analysis workbook (Song, a graph is composed of a set of nodes and a set of edges. Nodes can represent different entities, and edges can represent the relationship between a pair of nodes. A graph can represent a social network having nodes are members of the network and the edges connecting them represent their network or “friend” links between members [col. 1, lines 40-50; col. 8, lines 30-60]).
With respect to claim 6, the combination of Song and Mori teaches wherein the data graph includes a node for a curated data source for telemetry and usage data that identify when and by whom analytic assets were created and viewed, respectively (Song, node features such as age, gender and facility performance are used in the model scoring process. The embedding process assumes that nodes that reside in the same neighborhood should have similar embeddings. A neighborhood embedding is created for each node and concatenated it with the existing embedding of the node. The connection between member and ASCs is extracted from historic medical claims. If a member has visited a certain ASC in the past, a link (or edge) is created between this member and the ASC [col. 8, lines 15-40; col. 9, line 50 – col. 10, line 20]).
With respect to claim 7, the combination of Song and Mori teaches wherein the data graph includes one or more nodes for data sources for producing personalized recommendations (Mori, the link prediction section may predict from among nodes that indicate meal menus and are included in a second user graph satisfying the condition, a node which links to a node included in the subject user graph. The evaluation section may evaluate a recommendation level for the subject user of a meal menu indicated by the node [par. 0092-0095]).
With respect to claim 8, the combination of Song and Mori teaches wherein the set of features include asset type, community clustering, centrality, and node degree (Song, the graph is comprised of at least two types of nodes selected from the group comprising a member node, a healthcare facility node, and a provider node. Providers can be segmented into distinct groups based on member/provider networks, contract, affiliation, and historic engagement [col. 2, lines 25-45; col. 10, line 55 – col. 11, line 10]).
With respect to claim 9, the combination of Song and Mori teaches wherein the two-layer graph neural network includes two layers, each of which is a GraphSAGE convolution (Song, GraphSAGE is a representation learning technique capable of predicting embedding of a new node. The embedding process assumes that nodes that reside in the same neighborhood should have similar embeddings. A neighborhood embedding is created for each node and concatenated it with the existing embedding of the node. A graph can represent a social network having nodes are members of the network and the edges connecting them represent their network or friend links between members. Graph embedding and link prediction can be utilized on other healthcare AI problems to enrich the architecture and performance [col. 7, line 55 - col. 8, line 55; col. 9, lines 30-50]).
With respect to claim 10, the combination of Song and Mori teaches wherein the plurality of nodes includes one or more nodes for databases, one or more nodes for tables, one or more nodes for curated data sources, one or more nodes for users, one or more nodes for analysis workbooks, and one or more nodes for analysis (Song, the system comprises a database for storing historical claims data, a plurality of concatenated vectors and either a first healthcare facility vector or provider vector concatenated together. Various data sources can be identified and chosen to create features to represent characteristics of individual healthcare entity and interactions. Nodes can be identified as individual entities [col. 2, lines 10-45; col. 4, lines 5-10]).
With respect to claim 11, the combination of Song and Mori teaches wherein the data graph includes connections between (i) a node for a database and a node for a table, (ii) the node for the table and a node for a user, (iii) the node for the table and a node for a curated data source, (iv) the node for the table and a node for an analysis workbook, (v) the node for the curated data source and the node for the analysis workbook, (vi) the node for the user and the node for the analysis workbook, and (vii) the node for the analysis workbook and a node for analysis (Song, graph embedding captures the information of graph topology, node attributes and neighborhood attributes. Graph analytics, also called network analysis, is the analysis of relationships among multiple entities. Nodes can represent different entities, and edges can represent the relationship between a pair of nodes [col. 1, lines 30-55]).
With respect to claim 14, the combination of Song and Mori teaches wherein each analytic asset type is associated with a corresponding predetermined threshold, the method further comprising: generating the recommendation in accordance with a determination that a corresponding probability for a node is above its corresponding predetermined threshold (Mori, a link prediction section identifies a second user who has a predetermined relationship with the subject user by link prediction using a subject user graph and a plurality of second user graphs. The evaluation section generates response information including information pertaining to a meal menu related to the second user who has been identified by the link prediction section. The generation section may generate response information for recommending a meal menu for which the recommendation level calculated by the evaluation section is equal to or more than a predetermined value. In the flow of process, the link prediction calculates a probability that a node conforms to the request received in links to a node included in the subject user graph. The graph updating section determines whether or not the probability calculated is equal to or more than a threshold. If the probability is equal to or more than the threshold, the process proceeds to step 406, otherwise, it proceeds to step 405 [par. 0153-0160, 0192-0202 and FIG. 12]).
With respect to claim 15, it is a computer system claim that is corresponding to the method of claim 1. Therefore, it is rejected for the same reason as claimed in claim 1 above.
With respect to claim 16, the combination of Song and Mori teaches wherein (i) each of the plurality of analytic assets includes a respective asset type, a respective set of asset authors, and respective creation data, (ii) the asset type for each of the plurality of analytic assets is selected from the group consisting of databases, tables, analysis workbooks, users, data sources, and analysis, and (iii) the asset authors include end-user details (Song, the graph is comprised of at least two types of nodes selected from the group comprising a member node, a healthcare facility node, and a provider node. The system comprises a database for storing historical claims data, a plurality of concatenated vectors and either a first healthcare facility vector or provider vector concatenated together. Various data sources can be identified and chosen to create features to represent characteristics of individual healthcare entity and interactions. a visualization dashboard lays out the ARM model output for presentation to business users. The end users are the providers for those members who needs to have EGDs in an ASC setting [col. 2, lines 25-45; col. 4, lines 5-10; col. 10, lines 30-55]).
With respect to claim 17, it is a computer system claim that is corresponding to the method of claim 11. Therefore, it is rejected for the same reason as claimed in claim 11 above.
With respect to claim 18, it is a computer system claim that is corresponding to the method of claim 14. Therefore, it is rejected for the same reason as claimed in claim 14 above.
With respect to claim 19, it is a computer system claim that is corresponding to the method of claim 12. Therefore, it is rejected for the same reason as claimed in claim 12 above.
With respect to claim 20, it is a non-transitory computer readable storage medium claim that is corresponding to the method of claim 1. Therefore, it is rejected for the same reason as claimed in claim 1 above.
Claims 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Song et al (US 12620485 B2) hereafter Song, further in view of Mori et al (US 20250125036 A1) hereafter Mori, as claimed in claim 1 above, and further in view of Vinay et al (US 20240135197 A1) hereafter Vinay.
With respect to claim 12, the combination of Song and Mori teaches all limitations as claimed in claim 1 above.
However, the combination of Song and Mori does not disclose wherein the two-layer graph neural network is trained by batch training over ten epochs.
In the same field of endeavor, Vinay teaches wherein the two-layer graph neural network is trained by batch training over ten epochs (a node predictor module is trained until the error determined by the comparator is within a certain threshold (or a threshold number of batches, epochs, or iterations have been reached). The error is used to adjust the weights of the edge predictor module. The error may be calculated each iteration, batch, and/or epoch, and propagated through all of the algorithmic weights in the edge predictor module [par. 0073-0077]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of generating a scene sequence for the scene graph based on the clustered subgraphs as suggested by Vinay into the combination of Song and Mori because all of these systems addressing the process of predicting a link for a certain healthcare conditions/tasks by generating the graph embedding to generate a recommendation. Doing so would be desirable because the combination of Song and Mori would be more efficient by providing recommendations to a seed of objects and corresponding relationship such that the scene enrichment system expands scene graphs as a sequential prediction task including predicting a new node of the scene graph and subsequently predicting relationships between a new node and a previous node in the graph (Vinay, [par. 0002-0007]).
With respect to claim 13, the combination of Song, Mori and Vinay teaches wherein each batch samples only direct neighbors and one random node for each node (Vinay, the scene graph processing module concatenates each of the clustered subgraph sequences to generate a single scene sequence. Concatenating the clustered subgraph sequences in a random order introduces robustness with respect to the input [par. 0035, 0035]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Qin et al (US 12505350 B2) disclosed a graph neural network (GNN) training method, system, and computer program product in a graph, include generating, by the computing device, one or more one or more hypothetical edges between two or more nodes of a plurality of nodes of a graph neural network, testing, by the computing device, to determine whether the one or more generated hypothetical edges should be connected by using negative sampling, and permanently connecting, by the computing device, the one or more tested hypothetical edges if the negative sampling indicates the connectivity.
Sarkhel et al (US 20240037149 A1) disclosed a method includes accessing a graph. The graph includes video nodes representing videos, historical hashtag nodes representing historical hashtags, and edges indicating associations among the video nodes and the historical hashtag nodes. A trending hashtag is identified. An edge is added to the graph between a historical hashtag node representing a historical hashtag and a trending hashtag node representing the trending hashtag, based on a semantic similarity between the historical hashtag and the trending hashtag. A new video node representing a new video is added to the video nodes of the graph. A graph neural network (GNN) is applied to the graph, and the GNN predicts a new edge between the trending hashtag node and the new video node. The trending hashtag is recommended for the new video based on prediction of the new edge.
Shrivastava et al (US 20230394722 A1) disclosed a method for utilizing an interactive graphing system to achieve improved dataset exploration utilizing an intelligent workflow and an interactive user interface. More specifically, the interactive graphing system facilitates generating updated network graphs that include inferred user influences based on implicit user action. Indeed, the interactive graphing system can automatically generate and present a user with an updated network graph that includes added, removed, or subsetted elements and relationships that are otherwise hidden from a user. Additionally, the interactive graphing system facilitates network graph exploration and processing of customized combined network graphs that join otherwise separate network graphs.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Quoc Phung whose telephone number is (703) 756 1330. The examiner can normally be reached on Monday through Friday from 9am to 5pm PT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached on 571-272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Q.L.P./Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
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