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 .
Detailed Action
1. The instant application having Application No. 18/787,333 has claims 1-20 pending filed on 07/29/2024; there are 3 independent claim and 17 dependent claims, all of which are ready for examination by the examiner.
Acknowledgement Of References Cited By Applicant
As required by M.P.E.P. 609(C), the applicant’s submission of the Information Disclosure Statement dated October 15, 2025 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 (2), copy of the PTOL-1449 initialed and dated by the examiner is attached to the instant office action.
Response to Arguments
This Office Action is in response to applicant’s communication filed on November 21, 2025 in response to PTO Office Action dated August 25, 2025. The Applicant’s remarks and amendments to the claims and/or specification were considered with the results that follow.
Claim Rejections
Claim Rejections - 35 USC § 103
35 USC § 103 Rejection of claims 1-20
Applicant's arguments filed on 11/21/2025 with respect to the claims 1-20 have been fully considered but are moot because the arguments do not apply to any of the references being used in the current rejection.
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 1-4, 8-11, 15-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al (US PGPUB 20220121695) in view of Liang et al (CN 114706962 A) and in further view of Li et al (US PGPUB 20240273289).
As per claim 1:
Zhang teaches:
“A method comprising” (Paragraph [0006] (a knowledge graph-based case retrieval method, including))
“accessing communication data on a communication platform associated with an enterprise” (Paragraph [0075] (the historical data and the real-time data (communication data) of a plurality of platforms are obtained through the blockchain structure network))
“extracting a plurality of keywords from the communication data” (Paragraph [0085] (obtaining a keyword of the word embedding vector data and a keyword sequence, and extracting record information about searching))
“creating a knowledge graph comprising a plurality of nodes” (Paragraph [0024] (constructing a legal case knowledge graph by analyzing text information based on a predetermined model, and constructing node set (plurality of nodes) data by analyzing the legal case knowledge graph))
“determining a first keyword node embedding for the first keyword node based at least on information associated with the first keyword node and the one or more keyword nodes using a graph convolutional network algorithm” (Paragraph [0059] and Paragraph [0060] (the first word embedding vector data associated with the to-be-retrieved knowledge graph from the legal case knowledge graph, combines a plurality of data sources of the text information into the same data storage; aggregates the text information, performs word segmentation processing on the text information; performs feature extraction on segmented words to obtain feature information and invokes a predetermined convolutional neural network language model to interpret the feature information as structured data of a knowledge graph by using an Natural Language Processing (NLP) algorithm))
“and attaching the first keyword node embedding to the first keyword node in the knowledge graph” (Paragraph [0136] (the first acquisition unit is further specifically configured to: set a type by using a HashTable function; obtain a keyword of the word embedding vector data and a keyword sequence, and extract record information about searching, deleting and inserting keywords in the hash table by using an address acquisition function)).
Zhang does not EXPLICITLY teach: the plurality of nodes comprising a plurality of keyword nodes; identifying, using a graph random walk algorithm, one or more keyword nodes in the knowledge graph related to a first keyword node; linking the first keyword node with the one or more keyword nodes.
However, in an analogous art, Liang teaches:
“the plurality of nodes comprising a plurality of keyword nodes” (Page 3 Lines 12-14 (according to the keyword in the retrieval content input by the user, obtaining a plurality of associated nodes corresponding to the keyword in the preset associated knowledge map)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Liang and apply them on teachings of Zhang for the method “the plurality of nodes comprising a plurality of keyword nodes”. One would be motivated as to find out the keyword in the retrieval content, then through the preset associated knowledge graph based on enterprise collaborative office platform, keyword matching a plurality of associated nodes in the associated knowledge map are obtained (Liang, Page 7 Line 37-40).
Zhang and Liang do not EXPLICITLY teach: identifying, using a graph random walk algorithm, one or more keyword nodes in the knowledge graph related to a first keyword node; linking the first keyword node with the one or more keyword nodes.
However, in an analogous art, Li teaches:
“identifying, using a graph random walk algorithm, one or more keyword nodes in the knowledge graph related to a first keyword node” (Paragraph [0077] and Paragraph [0093] (the processor(s) use a weighted random walk algorithm and a double forward propagation algorithm to train the heterogeneous Graphical Neural Networks (GNN) with the multiple sub-graph inputs, builds the keyword-document bipartite graph to include two sets of nodes and edges that are linked together in the graph structure where one set of nodes represent the documents of the text corpus with each document node representing a different document of the text corpus and the other set of nodes represent keywords extracted for the text corpus with each keyword node representing a different extracted keyword))
“linking the first keyword node with the one or more keyword nodes” (Paragraph [0093] (each edge extends and represents a relationship between two of the nodes and through commonly shared keyword node(s), different text documents are linked together within the structure of the keyword-document bipartite graph)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Li and apply them on teachings of Zhang and Liang for the method “identifying, using a graph random walk algorithm, one or more keyword nodes in the knowledge graph related to a first keyword node; linking the first keyword node with the one or more keyword nodes”. One would be motivated as homogeneous graphs include only one type of node and one type of edge, homogeneous graph neural networks GNNs are able to compute embeddings for all of the nodes of the graph in the same coordinate space and in turn, homogeneous GNNs may be relatively computationally efficient (Li, Paragraph [0003]).
As per claim 2:
Zhang, Liang and Li teach the method of claim 1 above.
Zhang further teaches:
“receiving a user search query” (Paragraph [0004] (query content input by a user is received))
“determining a query embedding based on the user search query” (Paragraph [0011] (retrieving case information and first word embedding vector data associated with the to-be-retrieved knowledge graph from the legal case knowledge graph))
“identifying from the knowledge graph a group of keyword nodes related to user query by comparing the query embedding to a plurality of keyword node embeddings associated with the plurality of keyword nodes in the knowledge graph” (Paragraph [0020] and Paragraph [0085] (retrieve case information and first word embedding vector data associated with the to-be-retrieved knowledge graph from the legal case knowledge graph, and obtain second word embedding vector data of the to-be-retrieved knowledge graph where obtaining the word embedding vector data, includes obtaining a keyword of the word embedding vector data)).
Also, Liang further teaches:
“retrieving a set of communication data related to the group of keyword nodes” (Page 4 Lines 1-3 (a node matching module, configured to according to the keyword in the retrieval content input by the user, obtaining a plurality of associated nodes corresponding to the keyword in the pre-set associated knowledge map))
“and providing an answer to the user search query based on the set of communication data” (Page 4 Lines 14-15 (an information feedback module, configured to screen a plurality of search results in the search result set, and returning the selected search result to the user)).
As per claim 3:
Zhang, Liang and Li teach the method of claim 2 above.
Liang further teaches:
“determining a similarity score for a keyword node” (Paragraph [0012] (obtaining the content information corresponding to the n associated nodes and the matching score (similarity score) of the associated node))
Also, Zhang further teaches:
“determining a similarity score for a keyword node by comparing the query embedding and the keyword node embedding corresponding to the keyword node in the knowledge graph” (Paragraph [0012] (calculating a first similarity and a second similarity of the case information based on the first word embedding vector data and the second word embedding vector data, and adding the first similarity and the second similarity to obtain a target similarity))
“and in response to determining the similarity score is greater than a threshold value, retrieving a subset of communication data associated with the keyword node” (Paragraph [0102] (whether the first similarity is greater than the first predetermined threshold is determined, and the first similarity that is greater than the first predetermined threshold is selected; and whether the second similarity is greater than the second predetermined threshold is determined, and the second similarity that is greater than the second predetermined threshold is selected)).
As per claim 4:
Zhang, Liang and Li teach the method of claim 1 above.
Li further teaches:
“updating the knowledge graph based on updates in communication data associated with the enterprise periodically” (Paragraph [0067] and Paragraph [0068] (the communication module of the system is configured to communicate with a computer of a user where the interface is configured to enable the user to provide input(s) for a Graphical Neural Networks (GNN) of the system and enables to update, intermittently and/or at predefined intervals, samples of the training database for updating the GNN)).
As per claim 8:
Zhang teaches:
“A system comprising” (Paragraph [0014] (a knowledge graph-based case retrieval device, where the retrieval device has a function of implementing the knowledge graph-based case retrieval method, including))
“a communication interface” (Paragraph [0142] (the knowledge graph-based case retrieval equipment may further include one or more wired or wireless network interfaces (communication interface)))
“a non-transitory computer-readable medium” (Paragraph [0031] (a computer-readable storage medium including instructions))
“and one or more processors communicatively coupled to the communications interface and the non-transitory computer-readable medium, the one or more processors configured to execute processor-executable instructions stored in the non- transitory computer-readable medium to” (Paragraph [0144] and Paragraph [0146] (the processor is a control center of the knowledge graph-based case retrieval equipment, and can perform processing based on the computer program instructions are loaded and executed on a computer, the computer may be a general-purpose computer, a special-purpose computer, a computer network, or another programmable equipment and the computer instructions may be stored in a computer-readable storage medium where the computer-readable storage medium may be non-volatile or volatile))
“access communication data on a communication platform associated with an enterprise” (Paragraph [0075] (the historical data and the real-time data (communication data) of a plurality of platforms are obtained through the blockchain structure network))
“extract a plurality of keywords from the communication data” (Paragraph [0085] (obtaining a keyword of the word embedding vector data and a keyword sequence, and extracting record information about searching))
“create a knowledge graph comprising a plurality of nodes” (Paragraph [0024] (constructing a legal case knowledge graph by analyzing text information based on a predetermined model, and constructing node set (plurality of nodes) data by analyzing the legal case knowledge graph))
“determine a first keyword node embedding for the first keyword node based at least on information associated with the first keyword node and the one or more keyword nodes using a graph convolutional network algorithm” (Paragraph [0059] and Paragraph [0060] (the first word embedding vector data associated with the to-be-retrieved knowledge graph from the legal case knowledge graph, combines a plurality of data sources of the text information into the same data storage; aggregates the text information, performs word segmentation processing on the text information; performs feature extraction on segmented words to obtain feature information and invokes a predetermined convolutional neural network language model to interpret the feature information as structured data of a knowledge graph by using an Natural Language Processing (NLP) algorithm))
“and attach the first keyword node embedding to the first keyword node in the knowledge graph” (Paragraph [0136] (the first acquisition unit is further specifically configured to: set a type by using a HashTable function; obtain a keyword of the word embedding vector data and a keyword sequence, and extract record information about searching, deleting and inserting keywords in the hash table by using an address acquisition function)).
Zhang does not EXPLICITLY teach: the plurality of nodes comprising a plurality of keyword nodes; identify, using a graph random walk algorithm, one or more keyword nodes in the knowledge graph related to a first keyword node; link the first keyword node with the one or more keyword nodes.
However, in an analogous art, Liang teaches:
“the plurality of nodes comprising a plurality of keyword nodes” (Page 3 Lines 12-14 (according to the keyword in the retrieval content input by the user, obtaining a plurality of associated nodes corresponding to the keyword in the preset associated knowledge map)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Liang and apply them on teachings of Zhang for the system “the plurality of nodes comprising a plurality of keyword nodes”. One would be motivated as to find out the keyword in the retrieval content, then through the preset associated knowledge graph based on enterprise collaborative office platform, keyword matching a plurality of associated nodes in the associated knowledge map are obtained (Liang, Page 7 Line 37-40).
Zhang and Liang do not EXPLICITLY teach: identify, using a graph random walk algorithm, one or more keyword nodes in the knowledge graph related to a first keyword node; link the first keyword node with the one or more keyword nodes.
However, in an analogous art, Li teaches:
“identify, using a graph random walk algorithm, one or more keyword nodes in the knowledge graph related to a first keyword node” (Paragraph [0077] and Paragraph [0093] (the processor(s) use a weighted random walk algorithm and a double forward propagation algorithm to train the heterogeneous Graphical Neural Networks (GNN) with the multiple sub-graph inputs, builds the keyword-document bipartite graph to include two sets of nodes and edges that are linked together in the graph structure where one set of nodes represent the documents of the text corpus with each document node representing a different document of the text corpus and the other set of nodes represent keywords extracted for the text corpus with each keyword node representing a different extracted keyword))
“link the first keyword node with the one or more keyword nodes” (Paragraph [0093] (each edge extends and represents a relationship between two of the nodes and through commonly shared keyword node(s), different text documents are linked together within the structure of the keyword-document bipartite graph)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Li and apply them on teachings of Zhang and Liang for the system “identify, using a graph random walk algorithm, one or more keyword nodes in the knowledge graph related to a first keyword node; link the first keyword node with the one or more keyword nodes”. One would be motivated as homogeneous graphs include only one type of node and one type of edge, homogeneous graph neural networks GNNs are able to compute embeddings for all of the nodes of the graph in the same coordinate space and in turn, homogeneous GNNs may be relatively computationally efficient (Li, Paragraph [0003]).
As per claim 9, the claim is rejected based upon the same rationale given for the parent claim 8 and the claim 2 above.
As per claim 10, the claim is rejected based upon the same rationale given for the parent claim 9 and the claim 3 above.
As per claim 11, the claim is rejected based upon the same rationale given for the parent claim 8 and the claim 4 above.
As per claim 15:
Zhang teaches:
“A non-transitory computer-readable medium comprising” (Paragraph [0031] (a computer-readable storage medium including instructions))
“processor-executable instructions configured to cause one or more processors to” (Paragraph [0146] (the computer instructions may be stored in a computer-readable storage medium and the computer program instructions are loaded and executed on a computer, all or some of the processes or functions include))
“access communication data on a communication platform associated with an enterprise” (Paragraph [0075] (the historical data and the real-time data (communication data) of a plurality of platforms are obtained through the blockchain structure network))
“extract a plurality of keywords from the communication data” (Paragraph [0085] (obtaining a keyword of the word embedding vector data and a keyword sequence, and extracting record information about searching))
“create a knowledge graph comprising a plurality of nodes” (Paragraph [0024] (constructing a legal case knowledge graph by analyzing text information based on a predetermined model, and constructing node set (plurality of nodes) data by analyzing the legal case knowledge graph))
“determine a first keyword node embedding for the first keyword node based at least on information associated with the first keyword node and the one or more keyword nodes using a graph convolutional network algorithm” (Paragraph [0059] and Paragraph [0060] (the first word embedding vector data associated with the to-be-retrieved knowledge graph from the legal case knowledge graph, combines a plurality of data sources of the text information into the same data storage; aggregates the text information, performs word segmentation processing on the text information; performs feature extraction on segmented words to obtain feature information and invokes a predetermined convolutional neural network language model to interpret the feature information as structured data of a knowledge graph by using an Natural Language Processing (NLP) algorithm))
“and attach the first keyword node embedding to the first keyword node in the knowledge graph” (Paragraph [0136] (the first acquisition unit is further specifically configured to: set a type by using a HashTable function; obtain a keyword of the word embedding vector data and a keyword sequence, and extract record information about searching, deleting and inserting keywords in the hash table by using an address acquisition function)).
Zhang does not EXPLICITLY teach: the plurality of nodes comprising a plurality of keyword nodes; identify, using a graph random walk algorithm, one or more keyword nodes in the knowledge graph related to a first keyword node; link the first keyword node with the one or more keyword nodes.
However, in an analogous art, Liang teaches:
“the plurality of nodes comprising a plurality of keyword nodes” (Page 3 Lines 12-14 (according to the keyword in the retrieval content input by the user, obtaining a plurality of associated nodes corresponding to the keyword in the preset associated knowledge map)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Liang and apply them on teachings of Zhang for the system “the plurality of nodes comprising a plurality of keyword nodes”. One would be motivated as to find out the keyword in the retrieval content, then through the preset associated knowledge graph based on enterprise collaborative office platform, keyword matching a plurality of associated nodes in the associated knowledge map are obtained (Liang, Page 7 Line 37-40).
Zhang and Liang do not EXPLICITLY teach: identify, using a graph random walk algorithm, one or more keyword nodes in the knowledge graph related to a first keyword node; link the first keyword node with the one or more keyword nodes.
However, in an analogous art, Li teaches:
“identify, using a graph random walk algorithm, one or more keyword nodes in the knowledge graph related to a first keyword node” (Paragraph [0077] and Paragraph [0093] (the processor(s) use a weighted random walk algorithm and a double forward propagation algorithm to train the heterogeneous Graphical Neural Networks (GNN) with the multiple sub-graph inputs, builds the keyword-document bipartite graph to include two sets of nodes and edges that are linked together in the graph structure where one set of nodes represent the documents of the text corpus with each document node representing a different document of the text corpus and the other set of nodes represent keywords extracted for the text corpus with each keyword node representing a different extracted keyword))
“link the first keyword node with the one or more keyword nodes” (Paragraph [0093] (each edge extends and represents a relationship between two of the nodes and through commonly shared keyword node(s), different text documents are linked together within the structure of the keyword-document bipartite graph)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Li and apply them on teachings of Zhang and Liang for the system “identify, using a graph random walk algorithm, one or more keyword nodes in the knowledge graph related to a first keyword node; link the first keyword node with the one or more keyword nodes”. One would be motivated as homogeneous graphs include only one type of node and one type of edge, homogeneous graph neural networks GNNs are able to compute embeddings for all of the nodes of the graph in the same coordinate space and in turn, homogeneous GNNs may be relatively computationally efficient (Li, Paragraph [0003]).
As per claim 16, the claim is rejected based upon the same rationale given for the parent claim 15 and the claim 2 above.
As per claim 17, the claim is rejected based upon the same rationale given for the parent claim 16 and the claim 3 above.
As per claim 20, the claim is rejected based upon the same rationale given for the parent claim 15 and the claim 4 above.
Claims 5-7, 12-14 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al (US PGPUB 20220121695) in view of Liang et al (CN 114706962 A) and in further view of Li et al (US PGPUB 20240273289) and Cai et al (CN 116757246 A).
As per claim 5:
Zhang, Liang and Li teach the method of claim 1 above.
Zhang, Liang and Li do not EXPLICITLY teach: wherein the plurality of nodes further comprises a plurality of user nodes corresponding to a plurality of user accounts associated with the enterprise on the communication platform; and a plurality of entity nodes corresponding to a plurality of functionalities on the communication platform.
However, in an analogous art, Cai teaches:
“wherein the plurality of nodes further comprises a plurality of user nodes corresponding to a plurality of user accounts associated with the enterprise on the communication platform” (Page 3 Lines 14-19 (constructing a knowledge map based on the association between the contents related to the user attribute and the interaction between the user and the content; based on knowledge map, determining all reachable meta-path types between any two users, generating vector representations of all user nodes corresponding to each meta-path type under each time slice))
“and a plurality of entity nodes corresponding to a plurality of functionalities on the communication platform” (Page 8 Lines 33-36 (the term "Knowledge Graph" (KG for short) is a networked knowledge representation form, which expresses knowledge and connection between knowledge through the relationship between entities, the node of which represents an entity (or concept))).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Cai and apply them on teachings of Zhang, Liang, and Li for the method “wherein the plurality of nodes comprises a plurality of user nodes corresponding to a plurality of user accounts associated with the enterprise on the communication platform; and a plurality of entity nodes corresponding to a plurality of functionalities on the communication platform”. One would be motivated as it can accurately identify the institution client or the institution client related information represented by the user and has important meaning for the enterprise to develop the cross service of the same institution client and the potential client expansion (Cai, Page 2 Lines 41-44).
As per claim 6:
Zhang, Liang, Li and Cai teach the method of claim 5 above.
Cai further teaches:
“wherein the knowledge graph is a weighted graph, wherein links between the plurality of user nodes and the plurality of entity nodes are weighted based on organization data associated with the enterprise and the communication data” (Page 3 Lines 19-22 (determining a final vector representation of the user node, wherein the final vector representation of each user node is a weighted splice of all vector representations of corresponding user nodes corresponding to all meta-path types of each user node under all time slices)).
As per claim 7:
Zhang, Liang, Li and Cai teach the method of claim 6 above.
Cai further teaches:
“wherein identifying, using a graph random walk algorithm, one or more keyword node in the knowledge graph related to a first keyword node to link the first keyword node with the one or more keyword nodes comprises” (Page 3 Lines 19-22 (determining a final vector representation of the user node, wherein the final vector representation of each user node is a weighted splice of all vector representations of corresponding user nodes corresponding to all meta-path types of each user node under all time slices))
“creating a walk path to a second keyword node within a predetermined
degree of separation from the first keyword node in the weighted graph based on linkage weights of links associated with user nodes and entity nodes between the first keyword node and the second keyword node” (Page 15 Lines 38-45 and Page 16 Lines 3-14 (for each user similarity relationship graph generated, a random walk is performed with side weights to generate a plurality of user node sequences wherein for each of the N nodes obtained, a sequence generation step is performed as a starting node to determine the next node of the starting node where the next node is used as a new starting node, and the sequence generation step is repeated until the number of nodes in the current user node sequence reaches a predetermined value and the probability of random walk from the starting node to each adjacent node is calculated )).
As per claim 12, the claim is rejected based upon the same rationale given for the parent claim 9 and the claim 5 above.
As per claim 13, the claim is rejected based upon the same rationale given for the parent claim 12 and the claim 6 above.
As per claim 14, the claim is rejected based upon the same rationale given for the parent claim 13 and the claim 7 above.
As per claim 18, the claim is rejected based upon the same rationale given for the parent claim 15 and the claims 5 and 6 above.
As per claim 19, the claim is rejected based upon the same rationale given for the parent claim 18 and the claim 7 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gramatica Ruggero , (US PGPUB 20170024476), a machine-implemented method of relating information nodes in an information network, comprising the steps of: processing a plurality of data objects according to a predefined dictionary containing a plurality of information units and a plurality of correlation-indicating elements to defect in the plurality of data objects the presence of a correlation between respective information units; establishing an information network with a plurality of information nodes and links between the information nodes, said information nodes being related to said information units and said links being related to said detected correlations.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. 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).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAMAL K DEWAN whose telephone number is (571)272-2196. The examiner can normally be reached on Mon-Fri 8:00 AM – 5:00 PM (EST). If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, TONY MAHMOUDI can be reached on 571-272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALEX GOFMAN/Primary Examiner, Art Unit 2163
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