Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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.
Claim 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
MPEP 2106 (Ill) sets out steps for evaluating whether a claim is drawn to patent-eligible subject matter. The analysis of claims 1-20, in accordance with these steps, follows.
Step 1 Analysis:
Claims 1-20 are directed to method (processes). Therefore, claims 1-20 fall into one of four statutory categories (i.e., process, machine, article of manufacture).
As to claim 1,
Step 2A Prong 1: this claim recites the following abstract ideas:
receiving a query that identifies a node of the knowledge graph; (This limitation describes the receiving of a question to identify a node which is a mental process implemented in the human mind.)
identify a path in the knowledge graph relevant to the node in the query; and (this limitation describes determining a path in the knowledge graph that is relevant to the node in the query, which is a mental process implemented in the human mind)
providing an output of the path in the knowledge graph in response to the query. (this limitation describes providing an answer to the question with the paths of the knowledge graph, which is a mental process that can be implemented using pen and paper)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
training a probabilistic graphical model using a knowledge graph and a node-path matrix; (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
using the probabilistic graphical model to (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 2,
Step 2A Prong 1: this claim recites the following abstract ideas:
determine a probability distribution of other nodes in the knowledge graph conditioned on the node in the query; and (this limitation describes determining how likely other nodes are to be related to a selected node, which is a mental process that can be implemented in the human mind)
using the probability distribution to identify the path relevant to the node in the query. (this claim limitation describes the calculation of probability which a mental process that is implemented with a pen and paper)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
using the probabilistic graphical model to (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 3,
Step 2A Prong 1: this claim recites the following abstract ideas:
wherein the path includes the node in the query and other nodes with a probability value over a threshold of being on the path. (this limitation describes applying a threshold to decide which nodes to include, which is a mental process implemented in the human mind.)
Step 2A Prong 2 and 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
As to claim 4,
Step 2A Prong 1: this claim recites the following abstract ideas:
providing the output of the plurality of paths in the knowledge graph in response to the query. (this limitation describes providing an answer to the question with the paths of the knowledge graph, which is a mental process that can be implemented using pen and paper)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
using the probabilistic graphical model to identify a plurality of paths in the knowledge graph relevant to the node in the query; and (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 5,
Step 2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on claim 1.
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
wherein the probabilistic graphical model learns a joint distribution over the knowledge graph and a marginal probability represents a distribution of node importance values. (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 6,
Step 2A Prong 1: this claim recites the following abstract ideas:
wherein the node-path matrix identifies different connections simulated between subsets of nodes during path-based simulations on the knowledge graph and nodes identified on the different connections simulated during the path-based simulations. (this limitation describes recording a a simulated graph connection in a table, which is a mental process that can be performed using a pen and paper)
Step 2A Prong 2 and 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
As to claim 7,
Step 2A Prong 1: this claim recites the following abstract ideas:
wherein the path-based simulations randomly select two nodes in the knowledge graph as a subset of nodes and records node occurrence of the nodes identified on multiple simulated connections between the two nodes in the node-path matrix. (this limitation describes randomly selecting two nodes, and tallying node occurrences in a table, which is a mental process implemented using a pen and paper)
Step 2A Prong 2 and 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
As to claim 8,
Step 2A Prong 1: this claim recites the following abstract ideas:
wherein the query identifies a set of nodes of the knowledge graph, and the method further comprises: (this limitation describes determining multiple nodes based on the question, which is a mental process implemented in the human mind)
identify one or more paths in the knowledge graph relevant to the set of nodes in the query; and (this limitation describes determining paths relevant to multiple selected nodes, which is a mental process that can be implemented using pen and paper)
providing the output of the one or more paths in the knowledge graph in response to the query. (this limitation describes providing an answer to the question with the paths of the knowledge graph, which is a mental process that can be implemented using pen and paper)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
using the probabilistic graphical model to (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 9,
Step 2A Prong 1: this claim recites the following abstract ideas:
identify one or more paths in the knowledge graph without the node; and (this limitation describes determining one or more paths in the knowledge graph without a node, which is a mental process implemented in the human mind)
providing the output of the one or more paths in the knowledge graph in response to the query. (this limitation describes providing an answer to the question with the paths of the knowledge graph, which is a mental process that can be implemented using pen and paper)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
using the probabilistic graphical model to (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 10,
Step 2A Prong 1: this claim recites the following abstract ideas:
receiving a selection of a knowledge graph; (this limitation describes the selection of a knowledge graph which is a mental process implemented using a pen and paper.)
running a plurality of path-based simulations on the knowledge graph; (this limitation describes tracing multiple paths through a graph which is a mental process implemented using pen and paper)
generating a normalized node-path matrix in response to running the plurality of path-based simulations on the knowledge graph; and (this limitation describes recoding path information in a table and normalizing the values, which is a mental process implemented using pen and paper)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
training a probabilistic graphical model using the normalized node-path matrix and the knowledge graph. (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 11,
Step 2A Prong 1: this claim recites the following abstract ideas:
wherein the plurality of path-based simulations simulate different connections between a set of nodes in the knowledge graph and identify nodes in the knowledge graph on different connections in a node-path matrix. (this limitation describes tracing different possible connection between nodes and record which nodes appear on that connections, which is a mental process implemented using a pen and paper)
Step 2A Prong 2 and 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
As to claim 12,
Step 2A Prong 1: this claim recites the following abstract ideas:
wherein the plurality of path-based simulations randomly select two nodes in the knowledge graph and records nodes identified on simulated connections the two nodes in a node-path matrix. ( this limitation describes randomly selecting two nodes that are on the tracing path and recording nodes that appear on the traced path, which is a mental process implemented using pen and paper)
Step 2A Prong 2 and 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
As to claim 13,
Step 2A Prong 1: this claim recites the following abstract ideas:
wherein the plurality of path-based simulations continue to randomly select two nodes until each node pair in the knowledge graph is selected or a compute limit is reached. (the limitation describes stopping condition once the tracing of all pair nodes are completed, which is a mental process implemented in the human mind)
Step 2A Prong 2 and 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
As to claim 14,
Step 2A Prong 1: this claim recites the following abstract ideas:
wherein the plurality of path-based simulations include a single-exclusion shortest path, a personalized page-rank, and a random walk based traversal. (this limitation describes finding the shortest path, graph ranking technique based on personalized relevance and traversing edge in a graph, which is a mental process implemented in the human mind)
Step 2A Prong 2 and 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
As to claim 15,
Step 2A Prong 1: this claim recites the following abstract ideas:
wherein the normalized node-path matrix identifies different connections simulated between nodes in the knowledge graph during the plurality of path-based simulations on the knowledge graph and a node occurrence of nodes identified on the different connections during the plurality of path-based simulations. (this limitation describes recording traced graph connections in a matrix, and recording the node occurrence values for those connections, which is a mental process implemented using pen and paper)
Step 2A Prong 2 and 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
As to claim 16,
Step 2A Prong 1: this claim recites the following abstract ideas:
wherein the normalized node-path matrix combines values of the node occurrence of the nodes on the different connections simulated during the plurality of path-based simulations on the knowledge graph. (this limitation describes aggregating node occurrence value across traced paths, which is a mental process implemented using pen and paper)
Step 2A Prong 2 and 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
As to claim 17,
Step 2A Prong 1: this claim recites the following abstract ideas:
wherein the knowledge graph merges a plurality of knowledge graphs selected into a single knowledge graph. (this limitation describes combining multiple graphs into one, which is a mental process implemented using pen and paper)
Step 2A Prong 2 and 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
As to claim 18,
Step 2A Prong 1: this claim recites the following abstract ideas:
wherein the plurality of knowledge graphs are from different domains. (this limitation merely specify that the graphs being merged come from different sources, the limitation concerns the content of information not a technology improvement)
Step 2A Prong 2 and 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
As to claim 19,
Step 2A Prong 1: this claim recites the following abstract ideas:
wherein the plurality of knowledge graphs include different content or data types. (this limitation merely specifies that the graph include different types of content or data)
Step 2A Prong 2 and 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
As to claim 20,
Step 2A Prong 1: this claim recites the following abstract ideas:
receiving a query that identifies one or more nodes of the knowledge graph; (This limitation describes the receiving of a question to identify a node which is a mental process implemented in the human mind.)
identify a path in the knowledge graph relevant to the one or more nodes in the query; and (this limitation describes determining a path in the knowledge graph that is relevant to the node in the query, which is a mental process implemented in the human mind)
providing an output of the path in the knowledge graph in response to the query. (this limitation describes providing an answer to the question with the paths of the knowledge graph, which is a mental process that can be implemented using pen and paper)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
using the probabilistic graphical model to (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Takahashi et al.(US 20230289618 A1) in view of Eksombatchai et al. (US 10762134 B1) and further in view of Taguchi et al. (US 20210166141 A1).
As to claim 1, Takahashi teaches a method comprising:
training a probabilistic graphical model using a knowledge graph and a node-path matrix; (see Takahashi paragraph [0021] “A prediction model is then constructed using the feature vector set for each triple…”)
Takahashi does not explicitly teaches "receiving a query that identifies a node of the knowledge graph", "using the probabilistic graphical model to identify a path in the knowledge graph relevant to the node in the query; and", and "providing an output of the path in the knowledge graph in response to the query."
However, Eksombatchai teaches
receiving a query that identifies a node of the knowledge graph; (see Eksombatchai Col [3] L [44] “The plurality of random walks may be initiated from one or more query nodes in the node graph.”)
Furthermore, Taguchi teaches
using the probabilistic graphical model to identify a path in the knowledge graph relevant to the node in the query; and (see Taguchi claim [4] “calculates an optimum path among paths in the integrated graph connecting the inference start node and the inference end node.”)
providing an output of the path in the knowledge graph in response to the query. (see Taguchi paragraph [0057] “The information output unit 17 outputs the desired information found by the graph search unit 16”)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Takahashi to include the query‑receiving feature of Eksombatchai and the path‑identification/output features of Taguchi. Takahashi’s prediction model is built to predict an unknown direct relation path using a feature vector set (paragraph [0021]). A PHOSITA would recognize that to make such a model practically useful, the system must receive a query identifying a node of interest (as taught by Eksombatchai’s query nodes) and then output the identified path (as taught by Taguchi’s output unit).
As to claim 2, Takahashi as modified by Eksombatchai-Taguchi teaches the method of claim 1, further comprising:
using the probabilistic graphical model to determine a probability distribution of other nodes in the knowledge graph conditioned on the node in the query; and (see Taguchi claim [1] “calculates an importance level of a node as a component of the integrated graph from a probability of arriving at the node in a stationary state”)
using the probability distribution to identify the path relevant to the node in the query. (See Taguchi claim [4] “calculates an optimum path among paths in the integrated graph”)
As to claim 3, Takahashi as modified by Eksombatchai-Taguchi teaches the method of claim 2,
wherein the path includes the node in the query and other nodes with a probability value over a threshold of being on the path. (See Eksombatchai Col [6] L [15] “a defined amount of nodes in the node graph have visit counts or proximity scores that meet a defined visit count or a defined proximity score.”)
As to claim 4, Takahashi as modified by Eksombatchai-Taguchi teaches the method of claim 1,
using the probabilistic graphical model to identify a plurality of paths in the knowledge graph relevant to the node in the query; and (see Taguchi paragraph [0301] “a plurality of optimum paths … may be calculated”)
providing the output of the plurality of paths in the knowledge graph in response to the query. (see Taguchi paragraph [0057] “The information output unit 17 outputs the desired information…”)
As to claim 5, Takahashi as modified by Eksombatchai-Taguchi teaches the method of claim 1
wherein the probabilistic graphical model learns a joint distribution over the knowledge graph and a marginal probability represents a distribution of node importance values. (see Taguchi paragraph [0055] “The importance level of each node is, for example, a probability of arriving at each node”)
As to claim 6, Takahashi as modified by Eksombatchai-Taguchi teaches the method of claim 1,
wherein the node-path matrix identifies different connections simulated between subsets of nodes during path-based simulations on the knowledge graph and nodes identified on the different connections simulated during the path-based simulations. (See Eksombatchai Col [6] L [40] “The nodes visited by each step through the node graph by a random walk may be tracked and a visit count indicating an amount of visits by random walks to each node may be maintained.”)
As to claim 7, Takahashi as modified by Eksombatchai-Taguchi teaches the method of claim 1,
wherein the path-based simulations randomly select two nodes in the knowledge graph as a subset of nodes and records node occurrence of the nodes identified on multiple simulated connections between the two nodes in the node-path matrix. (see Takahashi paragraph [0041] “collecting engine 201 selects a node pair of the knowledge graph randomly”)
As to claim 8, Takahashi as modified by Eksombatchai-Taguchi teaches the method of claim 1,
wherein the query identifies a set of nodes of the knowledge graph, and the method further comprises: using the probabilistic graphical model to identify one or more paths in the knowledge graph relevant to the set of nodes in the query; and (See Eksombatchai Col [3] L [44] “The plurality of random walks may be initiated from one or more query nodes in the node graph. Each query node may correspond to a collection or representation in the node graph.”)
providing the output of the one or more paths in the knowledge graph in response to the query. (See Eksombatchai Col [3] L [15] “content corresponding to the nodes with the highest visit counts or proximity scores may be recommended to a user.”)
As to claim 9, Takahashi as modified by Eksombatchai-Taguchi teaches the method of claim 1
wherein the query excludes a node, and the method further comprises: using the probabilistic graphical model to identify one or more paths in the knowledge graph without the node; and (See Eksombatchai Col [4] L [12] “The identified portion of the data may be excluded from being incorporated into the node graph.”)
providing the output of the one or more paths in the knowledge graph in response to the query. (See Eksombatchai Col [5] L [49] “the recommendation service may determine that node A is to have a greater influence than node B on the content that is ultimately recommended.”)
As to claim 10, Eksombatchai teaches
receiving a selection of a knowledge graph; (See Eksombatchai Col [3] L [10] “the node graph may be constructed from data…”)
running a plurality of path-based simulations on the knowledge graph; (See Eksombatchai Col [4] L [4] “running a plurality of random walks through a node graph”)
generating a normalized node-path matrix in response to running the plurality of path-based simulations on the knowledge graph; and (See Eksombatchai Col [6] L [41] “visit count indicating an amount of visits by random walks to each node may be maintained”)
Eksombatchai does not explicitly teach "training a probabilistic graphical model using the normalized node-path matrix and the knowledge graph"
However, Takahashi teaches
training a probabilistic graphical model using the normalized node-path matrix and the knowledge graph. (see Takahashi paragraph [0021] “A prediction model is then constructed using the feature vector set for each triple to predict an unknown direct relation path between two target nodes in the knowledge graph by obtaining a feature vector set corresponding to the two target nodes”)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Takahashi to include Eksombatchai’s random walk simulation and visit count generation (which yields a normalized node‑path matrix) to obtain the node‑path matrix for training the prediction model. Takahashi explicitly states that the feature vector set (node‑path matrix) is used to construct the prediction model (paragraph [0021]). Eksombatchai provides the practical method to obtain that matrix via random walks.
As to claim 11, Eksombatchaias modified by Takahashi -Taguchi teaches the method of claim 10,
wherein the plurality of path-based simulations simulate different connections between a set of nodes in the knowledge graph and identify nodes in the knowledge graph on different connections in a node-path matrix. (See Eksombatchai Col [3] L [44] “The plurality of random walks may be initiated from one or more query nodes in the node graph.”, and see Eksombatchai Col [6] L [40] “The nodes visited by each step through the node graph by a random walk may be tracked and a visit count indicating an amount of visits by random walks to each node may be maintained.”)
As to claim 12, Eksombatchaias modified by Takahashi -Taguchi teaches the method of claim 10,
wherein the plurality of path-based simulations randomly select two nodes in the knowledge graph and records nodes identified on simulated connections the two nodes in a node-path matrix. (See Takahashi paragraph [0041] “In one embodiment, collecting engine 201 selects a node pair of the knowledge graph randomly.”)
As to claim 13, Eksombatchaias modified by Takahashi -Taguchi teaches the method of claim 12,
wherein the plurality of path-based simulations continue to randomly select two nodes until each node pair in the knowledge graph is selected or a compute limit is reached. (See Eksombatchai Col [6] L [6] “the plurality of random walks through the node graph may terminate once a defined amount of individual random walks have been initiated and terminated.”)
As to claim 14, Eksombatchaias modified by Takahashi -Taguchi teaches the method of claim 10,
wherein the plurality of path-based simulations include a single-exclusion shortest path, a personalized page-rank, and a random walk based traversal. (see Taguchi claim [1] “performing a random walk on the integrated graph or by using an algorithm of PageRank”, and see Taguchi claim [4] “calculates an optimum path among paths in the integrated graph connecting the inference start node and the inference end node.”)
As to claim 15, Eksombatchaias modified by Takahashi -Taguchi teaches the method of claim 10,
wherein the normalized node-path matrix identifies different connections simulated between nodes in the knowledge graph during the plurality of path-based simulations on the knowledge graph and a node occurrence of nodes identified on the different connections during the plurality of path-based simulations. (See Eksombatchai Col [6] L [40] “The nodes visited by each step through the node graph by a random walk may be tracked and a visit count indicating an amount of visits by random walks to each node may be maintained.”)
As to claim 16, Eksombatchaias modified by Takahashi -Taguchi teaches the method of claim 15,
wherein the normalized node-path matrix combines values of the node occurrence of the nodes on the different connections simulated during the plurality of path-based simulations on the knowledge graph. (see Takahashi claim [1] “feature vector set comprises a set of occurrences of each relation path for a node pair along with a corresponding direct relation path; and constructing a prediction model by using said feature vector set for each triple to predict a direct relation path between two target nodes in said knowledge graph by obtaining a feature vector set corresponding to said two target nodes.”)
As to claim 17, Eksombatchaias modified by Takahashi -Taguchi teaches the method of claim 10,
wherein the knowledge graph merges a plurality of knowledge graphs selected into a single knowledge graph. (see Taguchi paragraph [0054] “The graph combination unit 14 generates the integrated graph, in which a plurality of items of information have been integrated, by dynamically combining graphs”)
As to claim 18, Eksombatchaias modified by Takahashi -Taguchi teaches the method of claim 17,
wherein the plurality of knowledge graphs are from different domains. (see Taguchi paragraph [0049] “integrates items of information respectively belonging to multiple domains different from each other”)
As to claim 19, Eksombatchaias modified by Takahashi -Taguchi teaches the method of claim 17,
wherein the plurality of knowledge graphs include different content or data types. (see Taguchi paragraph [0053] “The external information is acquired from, for example, an application 41, a user interface 42, a GPS (Global Positioning System) 43, a camera 44, the Internet 45, an external database 46, a sensor 47 or the like.”)
As to claim 20, Eksombatchaias modified by Takahashi -Taguchi teaches the method of claim 10,
receiving a query that identifies one or more nodes of the knowledge graph; (See Eksombatchai Col [3] L [7] “The plurality of random walks may be initiated from one or more query nodes in the node graph.”)
using the probabilistic graphical model to identify a path in the knowledge graph relevant to the one or more nodes in the query; and (see Taguchi claim [4] “calculates an optimum path among paths in the integrated graph connecting the inference start node and the inference end node.”
providing an output of the path in the knowledge graph in response to the query. (see Taguchi paragraph [0057] “The information output unit 17 outputs the desired information found by the graph search unit 16”)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDULLAH K ABOUD whose telephone number is (571)272-0025. The examiner can normally be reached Mon-Fri 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li B Zhen, can be reached at (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ABDULLAH KHALED ABOUD/ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121
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