Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendments filed 08/21/2025 have been entered. Claims 1-4, 6-7, 9-17 remain pending in the application.
Applicant’s amendments and arguments, with respect to claim rejections of claims 1 under 35 U.S.C 101 filed 05/21/2025 have been considered and are not persuasive. Therefore, the previous rejections as set forth in the previous office action will be maintained.
The applicant argues that the amended claims reflect improvements to the technology of training of a learning network for hypergraph-based-multi-agent battlefield situation awareness. The claim recites a cost function which introduces specific technical improvements over the conventional art, resulting in better training of a learning network for hypergraph-based-multi-agent battlefield situation awareness. The identified problem in conventional technology comprises of limitations in speed and accuracy in determining similarity between agents and inferring battlefield situations awareness and process information collected by numerous agents. Thus, the claimed invention provides the solution by introducing the cost function as recited in the amended claim. Accordingly, the claims integrate the alleged judicial exception into a practical application and thus impose a meaningful limit on the judicial exception.
The examiner respectfully disagrees. The alleged improvements relate solely to the accuracy or quality of the information produced by the claimed mathematical cost function and do not reflect any improvement to computer technology or to the functioning of a computer, processor memory structure or machine learning network architecture. The recited cost function is a mathematical formula comprising dot products, summation, sigmoid functions, and vector operations, which constitutes a mathematical concept and a mental process under MPEP 2106.04 Step 2A, Prong 1 eligibility analysis. The mathematical operation of the recited cost function merely describes numerical relationships between number representation and does not recite any improvement. Performing a mathematical computation, regardless of its complexity, does not improve the functioning of the computer itself or any technological component and therefore cannot constitute a technological improvement. The amended claim does not modify how the computer performs computations; it merely specifies which mathematical formula is applied, which does not improve any technological process. Although the applicant argues that the cost function allegedly improves training performance, the alleged improvement lies solely in the numerical relationships expressed by the mathematical formula, which does not improve the function of any computer component or machine learning architecture.
Furthermore, the claim does not recite how any computer hardware, data structure, graph-processing mechanism or network operation is improved. Instead, the claims apply a mathematical operation on data using generic units, which does not integrate the judicial exception into a practical application. Accordingly, the amended claims remain directed to an abstract idea and do not amount to significantly more. Therefore, the recited cost function remains a mathematical concept and does not constitute a technological improvement under 35. U.S.C 101.
Applicant’s amendments and arguments, with respect to claim rejections of claims 1 under 35 U.S.C 103 filed 05/21/2025 have been considered and are not persuasive. Therefore, the previous rejections as set forth in the previous office action will be maintained.
The applicant argues that the claim is amended to include the subject matter of claim 8 which recites the cost function “wherein the cost function is defined for the (agent, adjacent agent) pair (I,j) according to the following Equation:
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(here, in the above Equation, i, j, and v denote the agents,
k, l, and m denote nodes constructing the agent knowledge graph,
ei, ej, and ev denote agent embedding vectors,
uk, ul, and um denote relation aware vectors assigned to the nodes,
c denotes a dot product of the relation aware vectors of each of the nodes written in a subscript,
Ѱa denotes a set of agents, and Ѱf denotes a set of the nodes constructing the agent knowledge graph,
σ denotes a sigmoid function, and
f denotes a vector function that returns a sum of the relation aware vector of a node written in the subscript and the agent embedding vector as an argument)”
The examiner respectfully disagrees that the cite reference does not teach the amended claim which recite the cost function. For instances, Wang teaches the underlying conceptual content represented by the symbols within a part of the limitation which recites the cost function “... i, j, and v denote the agents, k, l, and m denote nodes constructing the agent knowledge graph, ei, ej, and ev denote agent embedding vectors, uk, ul, and um denote relation aware vectors assigned to the nodes, c denotes a dot product of the relation aware vectors of each of the nodes written in a subscript, Ѱa denotes a set of agents, and Ѱf denotes a set of the nodes constructing the agent knowledge graph, ...” at paragraph 21 “edges 104 represent relationships between such entities.”, paragraph 31 “FIG. 3 illustrates a multi-source data fusion and knowledge graph construction system environment 300 according to an illustrative embodiment. As shown, multi-source data 302 comprises data from a plurality of data sources 302-1, 302-2, 302-3, . . . , 302-N ... and knowledge graph (KG) construction engine 310 so as to generate a knowledge graph 330”, paragraph 37 “To measure the similarity between nodes in different sub-graphs, an embedding calculation process 700 illustrated in FIG. 7 is provided in accordance with an illustrative embodiment ... node embedding computes low-dimensional vector representations of nodes in a graph. These vectors, also called embeddings, can be used for machine learning purposes. A main goal of node embedding is to encode nodes of a graph so that similarity in the embedding space (e.g., dot product) approximates similarity in the original data represented by the graph”, and paragraph 40 “Step 906 forms a plurality of sub-graph structures comprising a sub-graph structure for each of the data sources” Wang discloses the plurality of data source which is analogous to “set of agents” and “the agents” within the claim. Wang discloses the constructing of a knowledge subgraph of each data source, wherein each subgraph comprises of nodes and edges representing relationship between nodes, which are analogous to the “nodes constructing the agent knowledge graph”, and the “set of nodes constructing the agent knowledge graph” within the claim. Furthermore, Wang discloses measure the similarity between nodes in different sub-graphs using an embedding calculation process to calculate node embedding vector and a similarity determination based on embedding vectors, which corresponds to the “agent embedding vector” and “relation aware vectors” within the claim. Finally, Wang discloses a dot product calculation to capture the similarity relationship between embedding vectors, which is analogous to the claimed process of the “dot product of the relation aware vectors of each of the nodes” within the claim.
Then, Tsatsin teaches the underlying conceptual content of the cost equation, the sigmoid function and the summation operation at paragraph 44 “Referring to FIG. 2, the distance between embeddings y and y+ is identified ... After calculating the distances between the embeddings, a loss L (or error) can be calculated ... As the loss L becomes closer to zero, the lower the error. A low error rate indicates that distances between the embeddings output from the neural network satisfy that y+ is closer to y ... Back propagation simply means that a gradient of the loss L is fed back into the neural network Net so that the weights can be adjusted to minimize the loss L as desired by the user”, paragraph 90 “Typically, if the network Net implements, for example, a sigmoid feed-forward function, then the back propagation is also performed using the same sigmoid function”, and paragraph 99 “ the back propagation repeatedly adjusts parameters of the neural network until a sum of differences calculated from (i) a distance between the vector yt and the vector y ... satisfies a predetermined criteria”. Tsatsin discloses the underlying conceptual content of the claimed cost function equation because Tsatsin discloses computing a distance between two embedding vectors and use the distance to calculate a loss value L that becomes smaller as the embeddings of related items move closer, and applies backpropagation to minimize that loss. Tsatsin further discloses using a sigmoid function when the neural network implements a sigmoid feedforward architecture and additionally discloses a summation operation over embeddings-based differences as part of the training objective. These disclosures correspond to the components of the claimed cost function – namely the sigmoid, the summation over relational terms, and the minimization of the cost derived from the embedding vectors of the (agent, adjacent agent) pair within the claim – thereby teach the conceptual elements embodied in the annotated equation of the limitation.
Additionally, Wang provides the semantic interpretation of the terms used within the cost function (e.g., embedding vectors, relation aware vectors, set of agents, node relations), thereby supplying the structural meaning for each variable while Tsatsin supplies the underlying cost function mechanics. The motivation to combine the teachings can be found below and in the previous Office Action. Together. Wang and Tsatsin supply all components embodied in the amended equation. Accordingly, the amendment does not overcome the prior art, and the rejection of claim 1 is maintained.
However, upon further consideration, new ground(s) of rejections have been raised (See Below.)
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a knowledge graph generation unit” in claim 1, 2, 3
“a knowledge graph concatenation unit” in claim 1, 4
“a battlefield situation awareness unit” in claim 1, 11
“a hypergraph-based random sample module” in claim 1, 6, 7
“an agent embedding learning module” in claim 1
“a similarity inference and concatenation” in claim 1
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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 1-4, 6-7, 9-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
Step 1:
Claim 1 recites a system, one of the four statutory categories of patentable subject matter.
Step 2A, Prong I:
Claim 1 further recites the limitations of:
“... generate agent knowledge graphs including nodes, relations, and node embedding vectors for each agent” The process of generating a knowledge graph with nodes, relations and node embedding vectors for each agent is a mental process. A person can manually generate a knowledge graph by drawing the graph using a pen and paper, wherein the graph comprises of nodes representing knowledge, relations between nodes and node embedding vectors.
“... analyze a similarity between the respective agents and a similarity between pieces of data and concatenate the plurality of agent knowledge graphs to generate a global knowledge graph”. The process of analyzing for similarity between agents and data pieces, then concatenate a plurality of graphs to generate on global graph is considered to be a mental process. A person ordinary skilled in the art can mentally analyze for similarity between agents and data, they can then further manually concatenate multiple graphs by drawing multiple graphs linked together using a pen and paper to create a global graph.
“... generate a hypergraph including hypernodes corresponding to each of the agents and hyperedges representing a set of adjacent agents of each of the nodes, and generate an agent list based on the hypergraph” The limitation recites an abstract idea of a mental process. A person can manually generate a hypergraph by drawing a hypergraph with hypernodes and hyperedges and generate a list of agents corresponding to each node using pen and paper.
“... derive an (agent, adjacent agent) pair ...” The limitation recites an abstract idea of a mental process. A person can mentally determine an agent pair by select two agents connected via an edge as drawn on the graph.
“... generate the global knowledge graph by concatenating each agent knowledge graph through the analysis of the similarity between the agents using the agent embedding vector and an analysis of a similarity between data of a specific agent and data collected by an agent adjacent to the specific agent using the agent embedding vector” ...” The limitation recites an abstract idea of a mental process. A person can manually generate the global knowledge graph by concatenating two or more knowledge graph representing each agent together using pen and paper to draw a combination of graph. The person can further mentally analyze each agent knowledge graph for their similarity using the agent embedding vector to concatenate the similar graph together, wherein the agent embedding vector is just a numerical representation and a human’s mind is capable of performing similarity analysis using numerical representation.
“wherein the cost function is defined for the (agent, adjacent agent) pair (I,j) according to the following Equation:
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(here, in the above Equation, i, j, and v denote the agents,
k, l, and m denote nodes constructing the agent knowledge graph,
ei, ej, and ev denote agent embedding vectors,
uk, ul, and um denote relation aware vectors assigned to the nodes,
c denotes a dot product of the relation aware vectors of each of the nodes written in a subscript,
Ѱa denotes a set of agents, and Ѱf denotes a set of the nodes constructing the agent knowledge graph,
σ denotes a sigmoid function, and
f denotes a vector function that returns a sum of the relation aware vector of a node written in the subscript and the agent embedding vector as an argument)” This limitation recites an abstract idea of a mathematical concept which is the cost function defined for the agent pair. The cost function represents a mathematical equation within the category of mathematical concept. Furthermore, the calculation of such mathematical equation can be performed mentally by a person, thus the limitation further represents a mental process.
Step 2A, Prong I:
Claim 1 further recites the limitations of:
“a knowledge graph generation unit ...”, “a knowledge graph concatenation unit ...”, “a battlefield situation awareness unit ...”, “wherein the knowledge graph concatenation unit includes: a hypergraph-based random sample module ...”, “a similarity inference and concatenation module ...” these limitations are additional element of a high-level recitation of generic computer components used as a tool, and does not provide integration into a practical application. These limitations simply recite named units and modules along with their corresponding features without explaining how these named units performed the claimed features, thus they are just high-level recitation of generic computer components used as a tool. Furthermore, the claimed features of these generic computer components can be performed as abstract ideas of mental processes as explained above, thus they do not provide any improvement to computer hardware component or element.
“an agent embedding learning module configured to ... applying a context window to the agent list, and train an agent embedding vector so that a value of a cost function calculated from the (agent, adjacent agent) pair is minimized” This additional element recites a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not provide integration into a practical application. The limitation recites the application of the context window to the agent list without providing any technical details on how the context window is applied or improvement to the technique of context window to display information of the agent. The limitation further recites the application of conventional machine learning practice, by training a machine learning model with the minimization of the cost function without providing any improvement toward the machine learning training technique or algorithm or improvement to computer hardware element.
“... receive pieces of data collected by agents as an input ...” This additional element recites additional element of an insignificant extra-solution of a well-known technique of mere data gathering as identified in MPEP 2106.05(g), and does not provide integration into a practical application.
“infer a battlefield situation using a battlefield situation hierarchical structure and the global knowledge graph” This additional element recites a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not provide integration into a practical application. The limitation recites an application of the battlefield situation hierarchical structure and the global knowledge graph to infer a battlefield situation without provide the technical detail on how battlefield situation is inferred using the hierarchical structure and the global knowledge graph. The limitation simply recites the application of conventional machine learning technique of knowledge graph to represent information paths without providing any technical improvement toward the knowledge graph technique or improvement toward computer hardware elements.
Step 2B:
When considered individually or in combination, the additional limitations and elements of claim 1 does not amount to significantly more than the judicial exception for the same reasons discussed above as to why the additional limitations do not integrate the abstract idea into a practical application. The additional elements of outlined in Step 2A performing functions as designed simply accomplishes execution of the abstract ideas.
Additional elements “a knowledge graph generation unit ...”, “a knowledge graph concatenation unit ...”, “a battlefield situation awareness unit ...”, “wherein the knowledge graph concatenation unit includes: a hypergraph-based random sample module”, “a similarity inference and concatenation module” are all high-level recitation of generic computer components used as a tool, and does not amount to significantly more than the judicial exception for the same reason discussed above.
The additional element “an agent embedding learning module configured to ... applying a context window to the agent list, and train an agent embedding vector so that a value of a cost function calculated from the (agent, adjacent agent) pair is minimized” recites a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not amount to significantly more than the judicial exception for the same reason discussed above.
The additional element “... receive pieces of data collected by agents as an input ...” further recites a well-understood, routine, conventional activity as identified in MPEP 2106.05(d)(II)(i), which indicate that receiving data is a well-understood, routine, conventional activity when it is claimed in a generic manner (as it is here). Accordingly, a conclusion that the providing step is well-understood, routine, conventional activity is supported under Berkheimer option II.
The additional element “infer a battlefield situation using a battlefield situation hierarchical structure and the global knowledge graph” recites a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not amount to significantly more than the judicial exception for the same reason discussed above.
In conclusions from above for the elements considered as a mental process, elements reciting additional element of instruction to apply an exception as identified in MPEP 2106.05(f), elements reciting a well-known technique of mere data gathering as identified in MPEP 2106.05(g) and a well-understood, routine, conventional activity as identified in MPEP 2106.05(d) are carried over and do not provide significantly more than the abstract idea. Looking at the limitations in combination and the claims as a whole does not change this conclusion and the claim is ineligible.
Therefore, additional limitations of claim 1 do not amount to significantly more than the judicial exception.
Thus, claim 1 recites abstract ideas with additional elements rendered at a high level of generality resulting in claims that do not integrate the abstract idea into a practical application or amount to significantly more than the judicial exception.
Therefore, claim 1 is not patent eligible.
Regarding claim 2 depends on claim 1 thus the rejection of claim 1 is incorporated.
Claim 2 recites the limitation:
“The system of claim 1, wherein the knowledge graph generation unit ...” this limitation is an additional element of a high-level recitation of generic computer components used as a tool, and does not provide integration into a practical application or amount to significantly more than the judicial exception.
“... defines an object, which is recognized from the data collected by the agent, as the node to generate a node list, defines features of the respective nodes as a property to generate a property list, and defines a predetermined relation between the nodes to generate a relation list” This limitation recites a mental process. A person can mentally recognize and define an object from data collected by the agent as a node of the graph, mentally generate a list of nodes, mentally define features of nodes as a property to generate a property list and defines relation between the nodes to generate a list of relation.
Claim 2 recites abstract ideas with additional elements rendered at a high level of generality resulting in claims that do not integrate the abstract idea into a practical application or amount to significantly more than the judicial exception. Therefore, claim 2 is not patent eligible.
Regarding claim 3 depends on claim 1 thus the rejection of claim 1 is incorporated.
Claim 3 recites the limitations:
“The system of claim 1, wherein the knowledge graph generation unit...” this limitation is an additional element of a high-level recitation of generic computer components used as a tool, and does not provide integration into a practical application or amount to significantly more than the judicial exception.
“...generates a node embedding vector by concatenating an embedding vector (vec1) related to a property of the node, an embedding vector (vec2) in which a node name is defined, and an embedding vector (vec3) related to the relation between the nodes” This limitation recites a mental process. A person can mentally concatenate or link various embedding vectors relating to information within a node to generate an embedding vector.
“trains the embedding vector (vec1) so that the embedding vector (vec1) is similar to a property vector directly related to each of the nodes and dissimilar to a property vector not related to each of the nodes” This limitation recites a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not provide integration into a practical application or significantly more than the abstract idea. The limitation recites the training of embedding vector so that a similar and dissimilar condition is satisfied without reciting how the training is performed to obtain these similar and dissimilar conditions.
“trains the embedding vector (vec3) so that embedding vectors (vec3) of the nodes having the relation to each other become similar.” This limitation recites a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not provide integration into a practical application or significantly more than the abstract idea. The limitation recites the training of embedding vector so that a similar condition is satisfied without reciting how the training is performed to obtain this similar condition.
Claim 3 recites abstract ideas with additional elements rendered at a high level of generality resulting in claims that do not integrate the abstract idea into a practical application or amount to significantly more than the judicial exception. Therefore, claim 3 is not patent eligible.
Regarding claim 4 depends on claim 1 thus the rejection of claim 1 is incorporated.
Claim 4 recites the limitations:
“The system of claim 1, wherein the knowledge graph concatenation unit...” this limitation is an additional element of a high-level recitation of generic computer components used as a tool, and does not provide integration into a practical application or amount to significantly more than the judicial exception.
“...constructs an incidence matrix in a way to select an agent adjacent to a node of a specific agent knowledge graph” This limitation recites a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not provide integration into a practical application or significantly more than the abstract idea. The limitation recites constructing an incidence matrix to perform a selection function without reciting how the matrix is constructed or configured to perform the selection of adjacent agent.
“the adjacent agent includes a node having a node embedding vector similar to the node embedding vector of the node as a component of the agent knowledge graph” This limitation recites a mental process. A person can mentally identify the similarity between the node embedding vector at the node of an agent with another adjacent agent also having a node embedding vector at the node.
Claim 4 recites abstract ideas with additional elements rendered at a high level of generality resulting in claims that do not integrate the abstract idea into a practical application or amount to significantly more than the judicial exception. Therefore, claim 4 is not patent eligible.
Regarding claim 6 depends on claim 1 thus the rejection of claim 1 is incorporated.
Claim 6 recites the limitations:
“The system of claim 1, wherein the hypergraph-based random sample module...” this limitation is an additional element of a high-level recitation of generic computer components used as a tool, and does not provide integration into a practical application or amount to significantly more than the judicial exception.
“... generates a hyperedge including a plurality of agents based on a similarity between the node embedding vectors assigned to each node of the agent knowledge graph and a node embedding vector of another agent knowledge graph” This limitation recites a mental process. A person ordinary skilled in the art can manually generate a hyperedge using a pen and paper based on mentally analyzing the similarity between the node embedding vectors of each agent graph.
“generates a hypergraph including the hyperedge and a hypernode corresponding to an agent included in the hyperedge” this limitation recites a mental process. A person ordinary skilled in the art can manually create a hypergraph with hypernodes and hyperedges representing each of the agents by drawing them using a pen and paper.
Claim 6 recites abstract ideas with additional elements rendered at a high level of generality resulting in claims that do not integrate the abstract idea into a practical application or amount to significantly more than the judicial exception. Therefore, claim 6 is not patent eligible.
Regarding claim 7 depends on claim 1 thus the rejection of claim 1 is incorporated.
Claim 7 recites the limitations:
“The system of claim 5, wherein the hypergraph-based random sample module...” this limitation is an additional element of a high-level recitation of generic computer components used as a tool, and does not provide integration into a practical application or amount to significantly more than the judicial exception.
“... generates the agent list in a way to move an arbitrary agent according to a random walk rule based on the hypergraph” This limitation recites a mental process. A person ordinary skilled in the art can mentally generate the agent list in a way to move an agent according to a random walk rule of the hypergraph.
“the random walk rule defines a movement probability from a current agent to a next agent and includes a rule for determining the movement probability according to the number of hyperedges included in the hypergraph shared by an agent adjacent to the current agent and a previously visited agent” This limitation recites a mental process. A person ordinary skilled in the art can mentally configure the random walk rule with a movement probability from an agent to a next agent according to the number of hyperedges included in the hypergraph.
Claim 7 recites abstract ideas with additional elements rendered at a high level of generality resulting in claims that do not integrate the abstract idea into a practical application or amount to significantly more than the judicial exception. Therefore, claim 7 is not patent eligible.
Regarding claim 9 depends on claim 1 thus the rejection of claim 1 is incorporated.
Claim 9 recites the limitations of a relation aware vector of a node k, and an arbitrary factor with mathematical equations which recites an abstract idea of a mathematical concept.
Claim 9 recites abstract ideas resulting in claims that do not integrate the abstract idea into a practical application or amount to significantly more than the judicial exception. Therefore, claim 9 is not patent eligible.
Regarding claim 10 depends on claim 8 thus the rejection of claim 8 is incorporated.
Claim 10 recites the limitations of a relation aware vector of the node k with a mathematical equation which recites an abstract idea of a mathematical concept.
Claim 10 recites abstract ideas resulting in claims that do not integrate the abstract idea into a practical application or amount to significantly more than the judicial exception. Therefore, claim 10 is not patent eligible.
Regarding claim 11 depends on claim 1 thus the rejection of claim 1 is incorporated.
Claim 11 recites the limitations:
“The system of claim 1, wherein the battlefield situation awareness unit...” this limitation is an additional element of a high-level recitation of generic computer components used as a tool, and does not provide integration into a practical application or amount to significantly more than the judicial exception.
“... defines the battlefield situatio