DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This non-final office action is in response to the response filed 9 March 2026.
Claims 1-20 are pending. Claims 1, 10, and 17 are independent claims.
Oath/Declaration
The declaration submitted under 37 CFR 1.130 has been considered and is sufficient to disqualify Chanpuriya et al. (Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs) as prior art (see MPEP 717.01).
Drawings
The examiner accepts the drawings filed 14 March 2023.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1:
According to Step 1 of the two Step analysis, claims 1-9 are directed toward a method (process). Claims 10-16 are directed toward a system (machine). Claims 17-20 are directed toward a non-transitory computer readable medium (manufacture). Therefore, each of these claims falls within one of the four statutory categories.
Claim 1:
Step 2A, Prong 1:
The claim recites:
generating a temporal graph network comprising a plurality of entity nodes and temporal interaction edges connecting the plurality of entity nodes (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines a “temporal graph network” as “a graph representation of interconnected nodes that include a representation of time (paragraph 0034).” Therefore, this limitation encompasses an observation of entities and temporal interactions to generate a temporal graph network, either mentally or with the aid of pencil and paper, comprising a plurality of entity nodes and temporal interaction edges)
constructing a time-decayed line graph from the temporal graph network by: (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines a “time-decayed line graph” as “a line graph derived from a temporal graph network (paragraph 0037).” Therefore, this limitation encompasses an evaluation of a temporal graph network to construct a time-decayed line graph either mentally or with the aid of pencil and paper)
generating interaction nodes from temporal interaction edges of the temporal graph network (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “interaction node” as “node of a time-decayed line graph… generated from a temporal interaction edge of the temporal graph network (paragraph 0038).” Therefore, this limitation encompasses an evaluation of an interaction edge temporal graph network to generate an interaction node of the time-decayed line graph either mentally or with the aid of pencil and paper)
generating time-decayed weighted edges between the interaction nodes based on times of the temporal interaction edges (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “time-decayed weighted edge” as “edges in the time-decayed line graph that connect the interaction nodes (paragraph 0038).” Therefore, this limitation encompasses an evaluation of an edge in the temporal interaction edges in the temporal graph network to generate a time-decayed weighted edge either mentally or with the aid of pencil and paper)
generating at least one of a predicted edge classification or a temporal link prediction utilizing the time-decayed line graph (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “a predicted edge classification” as “a label, category, or class of an edge… based on the patterns of interaction between nodes in the network (paragraph 0044).” Additionally, the applicant’s specification defines a “temporal link prediction” as “a prediction of a relationship or link between nodes in a network… based on the historical interactions and patterns observed in the network over time (paragraph 0045).” Therefore, this limitation encompasses an observation of patterns and a judgment/opinion to generate a classification or prediction)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 2:
With respect to claim 2, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
generating the temporal graph network comprises generating the temporal interaction edges reflecting continuous times corresponding to entity interactions (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines a “temporal graph network” as “a graph representation of interconnected nodes that include a representation of time (paragraph 0034).” Therefore, this limitation encompasses an observation of entities and temporal interactions to generate a temporal graph network, either mentally or with the aid of pencil and paper, comprising temporal interaction edges that represent times corresponding to each entity interaction)
constructing the time-decayed line graph comprises generating the interaction nodes and time-decayed weighted edges from the continuous times corresponding to the temporal interaction edges (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “time-decayed weighted edge” as “edges in the time-decayed line graph that connect the interaction nodes (paragraph 0038).” Therefore, this limitation encompasses an evaluation of the temporal interaction edges in the temporal graph network to generate a time-decayed weighted edge either mentally or with the aid of pencil and paper)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 3:
With respect to claim 3, the claim depends upon claim 2. The analysis of claim 2 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein generating the at least one of the predicted edge classification of the temporal prediction comprises (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “a predicted edge classification” as “a label, category, or class of an edge… based on the patterns of interaction between nodes in the network (paragraph 0044).” Additionally, the applicant’s specification defines a “temporal link prediction” as “a prediction of a relationship or link between nodes in a network… based on the historical interactions and patterns observed in the network over time (paragraph 0045).” Therefore, this limitation encompasses an observation of patterns and a judgment/opinion to generate a classification or prediction)
generating continuous-time edge embeddings from the time-decayed line graph (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines “continuous-time” as “a temporal representation in which events are recorded as points in a continuous time domain… events are recorded as timestamps indicating the precise time (or near precise time, such as within one second or one minute) at which the occurred (paragraph 0041). Therefore, this limitation encompasses an observation to generate a continuous-time edge embedding from the time-decayed line graph)
generating the at least one of the predicted edge classification or the temporal link prediction from the continuous-time edge embeddings (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “a predicted edge classification” as “a label, category, or class of an edge… based on the patterns of interaction between nodes in the network (paragraph 0044).” Additionally, the applicant’s specification defines a “temporal link prediction” as “a prediction of a relationship or link between nodes in a network… based on the historical interactions and patterns observed in the network over time (paragraph 0045).” Therefore, this limitation encompasses an observation of patterns and a judgment/opinion to generate a classification or prediction based on the continuous-time edge embeddings)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 4:
With respect to claim 4, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein constructing the time-decayed line graph comprises (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines a “time-decayed line graph” as “a line graph derived from a temporal graph network (paragraph 0037).” Therefore, this limitation encompasses an evaluation of a temporal graph network to construct a time-decayed line graph either mentally or with the aid of pencil and paper):
generating a first interaction node from a first temporal interaction edge of the temporal graph network and corresponding to a first interaction time (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “interaction node” as “node of a time-decayed line graph… generated from a temporal interaction edge of the temporal graph network (paragraph 0038).” Therefore, this limitation encompasses an evaluation of an interaction edge temporal graph network at a first interaction time to generate a first interaction node of the time-decayed line graph either mentally or with the aid of pencil and paper)
generating a second interaction node from a second temporal interaction edge of the temporal graph network and corresponding to a second interaction time (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “interaction node” as “node of a time-decayed line graph… generated from a temporal interaction edge of the temporal graph network (paragraph 0038).” Therefore, this limitation encompasses an evaluation of an interaction edge temporal graph network at a second interaction time to generate a second interaction node of the time-decayed line graph either mentally or with the aid of pencil and paper)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 5:
With respect to claim 5, the claim depends upon claim 4. The analysis of claim 4 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein constructing the time-decayed line graph comprises based on determining that the first temporal interaction edge and the second temporal interaction edge are connected to a shared entity node of the temporal graph network, generating a time-decayed weighted edge between the first interaction node and the second interaction node (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “time-decayed weighted edge” as “edges in the time-decayed line graph that connect the interaction nodes (paragraph 0038).” Therefore, this limitation encompasses a judgement to determine that the first temporal interaction edge and the second temporal interaction edge are connected to a shared entity node of the temporal graph network, and responsive to this judgement, generating a time-decayed weighted edge between the first interaction node and the second interaction node either mentally or with the aid of pencil and paper)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 6:
With respect to claim 6, the claim depends upon claim 5. The analysis of claim 5 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein generating the time-decayed weighted edge comprises determining a time-decayed weight for the time-decayed weighted edge based on the first interaction time and the second interaction time (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “time-decayed weighted edge” as “edges in the time-decayed line graph that connect the interaction nodes (paragraph 0038).” Therefore, this limitation encompasses an evaluation to determine a time-decayed weight for the time-decayed weighted edge based on the first interaction time and the second interaction time and generating a time-decayed weighted edge either mentally or with the aid of pencil and paper)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 7:
With respect to claim 7, the claim depends upon claim 6. The analysis of claim 6 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein determining the time-decayed weight comprises generating the time-decayed weight inversely proportional to a difference between the first interaction time and the second interaction time (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The limitation encompasses an evaluation to determine a time-decayed weight that is inversely proportional to the difference between the first interaction time and the second interaction time)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 8:
With respect to claim 8, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein generating the predicted edge classification comprises (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “a predicted edge classification” as “a label, category, or class of an edge… based on the patterns of interaction between nodes in the network (paragraph 0044).” Additionally, the applicant’s specification defines a “temporal link prediction” as “a prediction of a relationship or link between nodes in a network… based on the historical interactions and patterns observed in the network over time (paragraph 0045).” Therefore, this limitation encompasses an observation of patterns and a judgment/opinion to generate a classification or prediction):
generating a vector representation from the time-decayed weighted edge (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Therefore, this limitation encompasses an evaluation of time-decayed weighted edges to generate a vector representation)
generating… the predicted edge classification from the vector representation from the time-decayed weighted edges (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “a predicted edge classification” as “a label, category, or class of an edge… based on the patterns of interaction between nodes in the network (paragraph 0044).” Therefore, this limitation encompasses an observation of patterns in a vector representation and a judgment/opinion to generate a classification based on the vector representation)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
utilizing a classification machine learning model trained on a set of vector representations and ground truth labels
The applicant’s specification defines the “machine learning model” as “a computer algorithm or collection of computer algorithms that can be trained and/or tuned based on inputs to approximate unknown functions (paragraph 0046).”
Therefore, the machine learning model is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional element:
utilizing a classification machine learning model trained on a set of vector representations and ground truth labels
The applicant’s specification defines the “machine learning model” as “a computer algorithm or collection of computer algorithms that can be trained and/or tuned based on inputs to approximate unknown functions (paragraph 0046).”
Therefore, the machine learning model is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 9:
With respect to claim 9, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein generating the temporal link prediction comprises (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “a predicted edge classification” as “a label, category, or class of an edge… based on the patterns of interaction between nodes in the network (paragraph 0044).” Additionally, the applicant’s specification defines a “temporal link prediction” as “a prediction of a relationship or link between nodes in a network… based on the historical interactions and patterns observed in the network over time (paragraph 0045).” Therefore, this limitation encompasses an observation of patterns and a judgment/opinion to generate a classification or prediction):
generating a vector representation from the time-decayed weighted edge (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Therefore, this limitation encompasses an evaluation of time-decayed weighted edges to generate a vector representation)
generating… a binary temporal link prediction (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Additionally, the applicant’s specification defines a “temporal link prediction” as “a prediction of a relationship or link between nodes in a network… based on the historical interactions and patterns observed in the network over time (paragraph 0045).” Therefore, this limitation encompasses an observation based on historical interactions and patterns observed in the network over time and a judgment/opinion to generate a classification based on the vector representation)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
utilizing a binary temporal link classification machine learning model trained on positive vector representations and negative vector representations of historical time-decayed weighted edges
The applicant’s specification defines the “machine learning model” as “a computer algorithm or collection of computer algorithms that can be trained and/or tuned based on inputs to approximate unknown functions (paragraph 0046).”
Therefore, the machine learning model is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional element:
utilizing a binary temporal link classification machine learning model trained on positive vector representations and negative vector representations of historical time-decayed weighted edges
The applicant’s specification defines the “machine learning model” as “a computer algorithm or collection of computer algorithms that can be trained and/or tuned based on inputs to approximate unknown functions (paragraph 0046).”
Therefore, the machine learning model is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 10:
Step 2A, Prong 1:
The claim recites:
generate a time-decayed line graph by (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines a “time-decayed line graph” as “a line graph derived from a temporal graph network (paragraph 0037).” Therefore, this limitation encompasses an evaluation of a temporal graph network to construct a time-decayed line graph either mentally or with the aid of pencil and paper):
generating a first interaction node from a first temporal interaction edge of the temporal graph network and corresponding to a first interaction time (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “interaction node” as “node of a time-decayed line graph… generated from a temporal interaction edge of the temporal graph network (paragraph 0038).” Therefore, this limitation encompasses an evaluation of an interaction edge temporal graph network at a first interaction time to generate a first interaction node of the time-decayed line graph either mentally or with the aid of pencil and paper)
generating a second interaction node from a second temporal interaction edge of the temporal graph network and corresponding to a second interaction time (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “interaction node” as “node of a time-decayed line graph… generated from a temporal interaction edge of the temporal graph network (paragraph 0038).” Therefore, this limitation encompasses an evaluation of an interaction edge temporal graph network at a second interaction time to generate a second interaction node of the time-decayed line graph either mentally or with the aid of pencil and paper)
based on determining that the first temporal interaction edge and the second temporal interaction edge are connected to a shared entity node of the temporal graph network, generating a time-decayed weighted edge between the first interaction node and the second interaction node (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “time-decayed weighted edge” as “edges in the time-decayed line graph that connect the interaction nodes (paragraph 0038).” Therefore, this limitation encompasses a judgement to determine that the first temporal interaction edge and the second temporal interaction edge are connected to a shared entity node of the temporal graph network, and responsive to this judgement, generating a time-decayed weighted edge between the first interaction node and the second interaction node either mentally or with the aid of pencil and paper)
determining a time-decayed weight for the time-decayed weighted edge based on the first interaction time and the second interaction time (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “time-decayed weighted edge” as “edges in the time-decayed line graph that connect the interaction nodes (paragraph 0038).” Therefore, this limitation encompasses an evaluation to determine a time-decayed weight for the time-decayed weighted edge based on the first interaction time and the second interaction time and generating a time-decayed weighted edge either mentally or with the aid of pencil and paper)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional elements:
one or more memory devices comprising a temporal graph network comprising a plurality of entity nodes and temporal interaction edges connecting the plurality of entity nodes
one or more servers configured to cause the system to
The elements of one or more memory device and one or more servers are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional elements:
one or more memory devices comprising a temporal graph network comprising a plurality of entity nodes and temporal interaction edges connecting the plurality of entity nodes
one or more servers configured to cause the system to
The elements of one or more memory device and one or more servers are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 11:
With respect to claim 11, the claim depends upon claim 10. The analysis of claim 10 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein the first temporal interaction edge reflects a first continuous time and the second temporal interaction edge reflects a second continuous time and wherein… generate the first time-decayed weighted edge based on the first continuous time and the second continuous time (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines “continuous-time” as “a temporal representation in which events are recorded as points in a continuous time domain… events are recorded as timestamps indicating the precise time (or near precise time, such as within one second or one minute) at which the occurred (paragraph 0041). Additionally, the applicant’s specification defines an “time-decayed weighted edge” as “edges in the time-decayed line graph that connect the interaction nodes (paragraph 0038).” Therefore, this limitation encompasses an observation to generate a first temporal edge based on a first continuous time and an observation to generate a second temporal edge based on a second continuous time and an evaluation to determine a time-decayed weight for the time-decayed weighted edge based on the first continuous time and the second continuous time and generating a time-decayed weighted edge either mentally or with the aid of pencil and paper)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
the one or more servers are further configured to cause the system to
The one or more servers are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional element:
the one or more servers are further configured to cause the system to
The one or more servers are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 12:
With respect to claim 12, the claim depends upon claim 11. The analysis of claim 11 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
generate a continuous-time edge embeddings from the time-decayed weighted edge (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines “continuous-time” as “a temporal representation in which events are recorded as points in a continuous time domain… events are recorded as timestamps indicating the precise time (or near precise time, such as within one second or one minute) at which the occurred (paragraph 0041). Therefore, this limitation encompasses an observation to generate a continuous-time edge embedding from the time-decayed weighted edge)
generate at least one predicted edge classification or the temporal link prediction utilizing the time-decayed weighted edge (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “a predicted edge classification” as “a label, category, or class of an edge… based on the patterns of interaction between nodes in the network (paragraph 0044).” Additionally, the applicant’s specification defines a “temporal link prediction” as “a prediction of a relationship or link between nodes in a network… based on the historical interactions and patterns observed in the network over time (paragraph 0045).” Therefore, this limitation encompasses an observation of patterns and a judgment/opinion to generate a classification or prediction based on the time-decayed weighted edge)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 13:
With respect to claim 13, the claim depends upon claim 11. The analysis of claim 11 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
generating a third interaction node from a third temporal interaction edge of the temporal graph network and corresponding to a third interaction time (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “interaction node” as “node of a time-decayed line graph… generated from a temporal interaction edge of the temporal graph network (paragraph 0038).” Therefore, this limitation encompasses an evaluation of an interaction edge temporal graph network at a third interaction time to generate a third interaction node of the time-decayed line graph either mentally or with the aid of pencil and paper)
based on determining that the third temporal interaction edge is connected to the shared entity node of the temporal graph network, generating a second time-decayed weighted edge between the first interaction node and the third interaction node (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “time-decayed weighted edge” as “edges in the time-decayed line graph that connect the interaction nodes (paragraph 0038).” Therefore, this limitation encompasses a judgement to determine that the first temporal interaction edge and the third temporal interaction edge are connected to a shared entity node of the temporal graph network, and responsive to this judgement, generating a second time-decayed weighted edge between the first interaction node and the third interaction node either mentally or with the aid of pencil and paper)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 14:
With respect to claim 14, the claim depends upon claim 13. The analysis of claim 13 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
generate a continuous-time edge embedding from the time-decayed weighted edge and a second continuous-time edge embedding from the second time-decayed weighted edge (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines “continuous-time” as “a temporal representation in which events are recorded as points in a continuous time domain… events are recorded as timestamps indicating the precise time (or near precise time, such as within one second or one minute) at which the occurred (paragraph 0041). Therefore, this limitation encompasses an observation to generate a continuous-time edge embedding from the time-decayed weighted edge and an observation to generate a second continuous-time edge embedding from the second time-decayed weighted edge)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 15:
With respect to claim 15, the claim depends upon claim 10. The analysis of claim 10 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
determine a time difference between the first interaction time and the second interaction time and determine the time-decay weight for the time-decayed weighted edge inversely proportional to the time difference (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The limitation encompasses an evaluation to determine a time-decayed weight that is inversely proportional to the difference between the first interaction time and the second interaction time)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 16:
With respect to claim 16, the claim depends upon claim 15. The analysis of claim 15 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
generate a vector representation from the time-decayed weighted edge (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Therefore, this limitation encompasses an evaluation of time-decayed weighted edges to generate a vector representation)
generate… at least one of a predicted edge classification or a temporal link prediction (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “a predicted edge classification” as “a label, category, or class of an edge… based on the patterns of interaction between nodes in the network (paragraph 0044).” Additionally, the applicant’s specification defines a “temporal link prediction” as “a prediction of a relationship or link between nodes in a network… based on the historical interactions and patterns observed in the network over time (paragraph 0045).” Therefore, this limitation encompasses an observation of patterns in a vector representation and a judgment/opinion to generate a classification based on the vector representation)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
utilizing a machine learning model trained on the vector representation
The applicant’s specification defines the “machine learning model” as “a computer algorithm or collection of computer algorithms that can be trained and/or tuned based on inputs to approximate unknown functions (paragraph 0046).”
Therefore, the machine learning model is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional element:
utilizing a machine learning model trained on the vector representation
The applicant’s specification defines the “machine learning model” as “a computer algorithm or collection of computer algorithms that can be trained and/or tuned based on inputs to approximate unknown functions (paragraph 0046).”
Therefore, the machine learning model is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 17:
Step 2A, Prong 1:
The claim recites:
generating a temporal graph network comprising a plurality of entity nodes and temporal interaction edges connecting the plurality of entity nodes (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines a “temporal graph network” as “a graph representation of interconnected nodes that include a representation of time (paragraph 0034).” Therefore, this limitation encompasses an observation of entities and temporal interactions to generate a temporal graph network, either mentally or with the aid of pencil and paper, comprising a plurality of entity nodes and temporal interaction edges)
constructing a time-decayed line graph from the temporal graph network by: (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines a “time-decayed line graph” as “a line graph derived from a temporal graph network (paragraph 0037).” Therefore, this limitation encompasses an evaluation of a temporal graph network to construct a time-decayed line graph either mentally or with the aid of pencil and paper)
generating a first interaction node from a first temporal interaction edge connected to an initial entity node and corresponding to a first interaction time (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “interaction node” as “node of a time-decayed line graph… generated from a temporal interaction edge of the temporal graph network (paragraph 0038).” Therefore, this limitation encompasses an evaluation of an interaction edge temporal graph network at a first interaction time to generate a first interaction node of the time-decayed line graph either mentally or with the aid of pencil and paper)
generating a second interaction node from a second temporal interaction edge connected to the initial entity node and corresponding to a second interaction time (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “interaction node” as “node of a time-decayed line graph… generated from a temporal interaction edge of the temporal graph network (paragraph 0038).” Therefore, this limitation encompasses an evaluation of an interaction edge temporal graph network at a second interaction time to generate a second interaction node of the time-decayed line graph either mentally or with the aid of pencil and paper)
generating a time-decayed weighted edge between the first interaction node and the second interaction node based on the first interaction time and the second interaction time (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “time-decayed weighted edge” as “edges in the time-decayed line graph that connect the interaction nodes (paragraph 0038).” Therefore, this limitation encompasses a judgement to determine that the first temporal interaction edge and the second temporal interaction edge are connected to a shared entity node of the temporal graph network, and responsive to this judgement, generating a time-decayed weighted edge between the first interaction node and the second interaction node either mentally or with the aid of pencil and paper based on the first and second interaction times)
generating at least one of a predicted edge classification or a predicted temporal link utilizing the time-decayed line graph (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “a predicted edge classification” as “a label, category, or class of an edge… based on the patterns of interaction between nodes in the network (paragraph 0044).” Additionally, the applicant’s specification defines a “temporal link prediction” as “a prediction of a relationship or link between nodes in a network… based on the historical interactions and patterns observed in the network over time (paragraph 0045).” Therefore, this limitation encompasses an observation of patterns and a judgment/opinion to generate a classification or prediction)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
a non-transitory computer readable medium storing executable instructions which, when executed by a processing device, cause the processing device to perform operations
The element of a non-transitory computer readable medium is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional elements:
a non-transitory computer readable medium storing executable instructions which, when executed by a processing device, cause the processing device to perform operations
The element of a non-transitory computer readable medium is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 18:
With respect to claim 18, the claim depends upon claim 17. The analysis of claim 17 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
generating the first temporal interaction edge comprising a first continuous time corresponding to a first interaction; generating a second temporal interaction edge to reflect a second continuous time corresponding to a second interaction; and generating the time-decayed weighted edge based on the first continuous time of the first temporal interaction edge and the second continuous time of the second temporal interaction edge (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines “continuous-time” as “a temporal representation in which events are recorded as points in a continuous time domain… events are recorded as timestamps indicating the precise time (or near precise time, such as within one second or one minute) at which the occurred (paragraph 0041). Additionally, the applicant’s specification defines an “time-decayed weighted edge” as “edges in the time-decayed line graph that connect the interaction nodes (paragraph 0038).” Therefore, this limitation encompasses an observation to generate a first temporal edge based on a first continuous time and an observation to generate a second temporal edge based on a second continuous time and an evaluation to determine a time-decayed weight for the time-decayed weighted edge based on the first continuous time and the second continuous time and generating a time-decayed weighted edge either mentally or with the aid of pencil and paper)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 19:
With respect to claim 19, the claim depends upon claim 17. The analysis of claim 17 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
generate a vector representation from the time-decayed weighted edge (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Therefore, this limitation encompasses an evaluation of time-decayed weighted edges to generate a vector representation)
generate… a predicted edge classification or a binary temporal link prediction from the vector representation from the time-decayed weighted edge (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The applicant’s specification defines an “a predicted edge classification” as “a label, category, or class of an edge… based on the patterns of interaction between nodes in the network (paragraph 0044).” Additionally, the applicant’s specification defines a “temporal link prediction” as “a prediction of a relationship or link between nodes in a network… based on the historical interactions and patterns observed in the network over time (paragraph 0045).” Therefore, this limitation encompasses an observation of patterns in a vector representation and a judgment/opinion to generate a classification based on the vector representation)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
utilizing a classification machine learning model
The applicant’s specification defines the “machine learning model” as “a computer algorithm or collection of computer algorithms that can be trained and/or tuned based on inputs to approximate unknown functions (paragraph 0046).”
Therefore, the machine learning model is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional element:
utilizing a classification machine learning model
The applicant’s specification defines the “machine learning model” as “a computer algorithm or collection of computer algorithms that can be trained and/or tuned based on inputs to approximate unknown functions (paragraph 0046).”
Therefore, the machine learning model is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 20:
With respect to claim 20, the claim depends upon claim 17. The analysis of claim 17 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein generating the time-decayed weighted edge comprises generating a time-decayed weight for the time-decayed weighted edge based on the first interaction time and the second interaction time wherein the time-decayed weight is inversely proportional to a difference between the first interaction time and the second interaction time (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. The limitation encompasses an evaluation to determine a time-decayed weight that is inversely proportional to the difference between the first interaction time and the second interaction time)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 9-14, and 17-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sharma et al. (US 2023/0232285, filed 12 October 2022, hereafter Sharma).
As per independent claim 1, Sharma discloses a computer-implemented method comprising:
generating a temporal graph network comprising a plurality of entity nodes and temporal interaction edges connecting the plurality of entity nodes (paragraph 0002: Here, a graph is generating comprising nodes representing each entity and edges representing interactions between entities)
constructing a time-decayed line graph from the temporal graph network by:
generating interaction nodes from temporal interaction edges of the temporal graph network (paragraph 0022: Here, a node neighborhood of the node is generated (interaction nodes) for each node using a graph neural network that receives as input the state (temporal interaction edges) of the graph neural network)
generating time-decayed weighted edges between the interaction nodes based on times of the temporal interaction edges (paragraph 0022: Here, an edge spatial layer is generated (time-decayed weighted edges) for each edge that receives the edge of the at least one edges as input)
generating at least one of a predicted edge classification or a temporal link prediction utilizing the time-decayed line graph (paragraph 0022: Here, a fully connected layer of the graph neural network combines the output data from the node spatial layer and the edge spatial layer with an input temporal state of the network to predict the state of the graph network at the one or more future time steps)
As per dependent claim 2, Sharma discloses wherein:
generating the temporal graph network comprises generating the temporal interaction edges reflecting continuous times corresponding to entity interactions (paragraphs 0034-0036: Here, a node neighborhood of a node includes other nodes connected to the node at a specific time (paragraph 0033) by an edge (paragraph 0034))
constructing the time-decayed line graph comprises generating the interaction nodes and time-decayed weighted edges from the continuous times corresponding to the temporal interaction edges (Figure 4; paragraphs 0033-0038: Here, spatiotemporal changes are modeled in the graph network by depicting the node layers and edge layers to encode spatiotemporal interactions between nodes and edges)
As per dependent claim 3, Sharma discloses wherein generating the at least one of the predicted edge classification or the temporal link prediction comprises:
generating continuous-time edge embeddings from the time-decayed line graph Figure 4; paragraphs 0033-0038: Here, the spatial layer is configured to receive as input the state of the graph network at a time step t. From this spatial features and edge features are determined for the graph (paragraph 0040))
generating the at least one of the predicted edge classification or the temporal link prediction from the continuous-time edge embeddings (Figure 4; paragraphs 0033-0038: Here, spatiotemporal changes are modeled in the graph network by depicting the node layers and edge layers to encode spatiotemporal interactions between nodes and edges)
As per dependent claim 4, Sharma discloses wherein constructing the time-decayed line graph comprises:
generating a first interaction node from a first temporal interaction edge of the temporal graph network and corresponding to the first interaction time (Figure 4; paragraphs 0033- 0034: Here, a plurality of interaction nodes are generated. Specifically, each node of the plurality of nodes, for each time, is represented in the network)
generating a second interaction node from a second temporal interaction edge of the temporal graph network and corresponding to a second interaction time (Figure 4; paragraphs 0033- 0034: Here, a plurality of interaction nodes are generated. Specifically, each node of the plurality of nodes, for each time, is represented in the network)
As per dependent claim 5, Sharma discloses wherein constructing the time-decayed line graph comprises based on determining that the first temporal interaction edge and the second temporal interaction edge are connected to a shared entity node of the temporal graph network, generating a time-decayed weighted edge between the first interaction node and the second interaction node (Figure 4; paragraphs 0033-0034: Here, for each node adjacency information for each node is identified for each state of the node. This adjacency information by indicating pairs of nodes joined by an edge. This adjacency information and edge neighborhood information (paragraph 0037) is used to generate a graph neural network that encodes the spatial and temporal interactions for nodes and edges).
As per dependent claim 6, Sharma discloses wherein generating the time-decayed weighted edge comprises determining a time-decayed weight for the time-decayed weighted edge based on the first interaction time and the second interaction time (paragraphs 0082-0083).
As per dependent claim 9, Sharma discloses wherein generating the temporal link prediction comprises:
generating a vector representation from the time-decayed weighted edges (paragraph 0021: Here, the temporal graph network is trained on a plurality of temporal state vectors)
generating, utilizing a binary temporal link classification machine learning model trained on positive vector representations and negative vector representations of historical time-decayed weighted edges, a binary temporal link prediction (paragraphs 0032-0039: Here, it is determined, using trained historical state data, whether two nodes are linked. The examiner interprets this as a binary classification because nodes can be either “linked” or “not linked”)
As per independent claim 10, Sharma discloses a system comprising:
one or more memory devices (Figure 9) comprising a temporal graph network comprising a plurality of entity nodes and temporal interaction edges connecting the plurality of nodes (paragraph 0002: Here, a graph is generating comprising nodes representing each entity and edges representing interactions between entities)
one or more servers (paragraph 0086) configured to cause the system to generate a time-decayed line graph by:
generating a first interaction node from a first temporal interaction edge of the temporal graph network and corresponding to the first interaction time (Figure 4; paragraphs 0033- 0034: Here, a plurality of interaction nodes are generated. Specifically, each node of the plurality of nodes, for each time, is represented in the network)
generating a second interaction node from a second temporal interaction edge of the temporal graph network and corresponding to a second interaction time (Figure 4; paragraphs 0033- 0034: Here, a plurality of interaction nodes are generated. Specifically, each node of the plurality of nodes, for each time, is represented in the network)
based on determining that the first temporal interaction edge and the second temporal interaction edge are connected to a shared entity node of the temporal graph network, generating a time-decayed weighted edge between the first interaction node and the second interaction node (Figure 4; paragraphs 0033-0034: Here, for each node adjacency information for each node is identified for each state of the node. This adjacency information by indicating pairs of nodes joined by an edge. This adjacency information and edge neighborhood information (paragraph 0037) is used to generate a graph neural network that encodes the spatial and temporal interactions for nodes and edges)
determining a time-decayed weight for the time-decayed weighted edge based on the first interaction time and the second interaction time (paragraphs 0082-0083)
As per dependent claim 11, Sharma discloses wherein the first temporal interaction edge reflects a first continuous time and the second temporal interaction edge reflects a second continuous time and wherein the one or more servers are further configured to cause the system to generate the time-decayed weighted edge based on the first continuous time and the second continuous time (Figure 4; paragraphs 0033-0034: Here, for each node adjacency information for each node is identified for each state of the node. This adjacency information by indicating pairs of nodes joined by an edge. This adjacency information and edge neighborhood information (paragraph 0037) is used to generate a graph neural network that encodes the spatial and temporal interactions for nodes and edges)
As per dependent claim 12, Sharma discloses where the one or more servers are further configure to cause the system to:
generate a continuous-time edge embedding from the time-decayed weighted edge Figure 4; paragraphs 0033-0038: Here, the spatial layer is configured to receive as input the state of the graph network at a time step t. From this spatial features and edge features are determined for the graph (paragraph 0040))
generating at least one of a predicted edge classification or a temporal link prediction utilizing the time-decayed weighted edge (Figure 4; paragraphs 0033-0038: Here, spatiotemporal changes are modeled in the graph network by depicting the node layers and edge layers to encode spatiotemporal interactions between nodes and edges)
As per dependent claim 13, Sharma discloses where the one or more servers are further configured to cause the system to generate the time-decayed line graph by:
generating a third interaction node from a third temporal interaction edge of the temporal graph network and corresponding to a third interaction time (Figure 4; paragraphs 0033- 0034: Here, a plurality of interaction nodes are generated. Specifically, each node of the plurality of nodes, including a third interaction node, for each time, is represented in the network)
based on determining that the third temporal interaction edge is connected to the shared entity node of the temporal graph network, generating a second time-decayed weighted edge between the first interaction node and the third interaction node (Figure 4; paragraphs 0033-0034: Here, for each node adjacency information for each node is identified for each state of the node. This adjacency information by indicating pairs of nodes joined by an edge. This adjacency information and edge neighborhood information (paragraph 0037) is used to generate a graph neural network that encodes the spatial and temporal interactions for each of the nodes and edges. This includes connections between a first interaction node and a third interaction node)
As per dependent claim 14, Sharma discloses wherein the one or more servers are further configured to cause the system to generate a continuous-time edge embedding from the time-decayed weighted edge and a second continuous-time edge embedding from the second time-decayed weighted edge (paragraphs 0082-0083).
As per independent claim 17, Sharma discloses a non-transitory computer readable medium storing executed instructions (Figure 9, item 904) which, when executed by a processing device (Figure 9, item 902), cause the processing device to perform operations comprising:
generating a temporal graph network comprising a plurality of entity nodes and temporal interaction edges connecting the plurality of entity nodes (Figure 4; paragraphs 0033- 0034: Here, a plurality of interaction nodes are generated. Specifically, each node of the plurality of nodes, for each time, is represented in the network)
constructing a time-decayed line graph from the temporal graph network by:
generating a first interaction node from a first temporal interaction edge connected to the first entity node and corresponding to a first interaction time (Figure 4; paragraphs 0033- 0034: Here, a plurality of interaction nodes are generated. Specifically, each node of the plurality of nodes, for each time, is represented in the network)
generating a second interaction node from a second temporal interaction edge connected to the initial entity node and corresponding to a second interaction time (Figure 4; paragraphs 0033- 0034: Here, a plurality of interaction nodes are generated. Specifically, each node of the plurality of nodes, for each time, is represented in the network)
generating a time-decayed weighted edge between the first interaction node and the second interaction node based on the first interaction time and the second interaction time (Figure 4; paragraphs 0033-0034: Here, for each node adjacency information for each node is identified for each state of the node. This adjacency information by indicating pairs of nodes joined by an edge. This adjacency information and edge neighborhood information (paragraph 0037) is used to generate a graph neural network that encodes the spatial and temporal interactions for nodes and edges)
generating at least one of a predicted edge classification or a predicted temporal link utilizing the time-decayed line graph (paragraph 0022: Here, a fully connected layer of the graph neural network combines the output data from the node spatial layer and the edge spatial layer with an input temporal state of the network to predict the state of the graph network at the one or more future time steps)
As per dependent claim 18, Sharma discloses wherein generating the temporal graph network comprises: generating the first temporal interaction edge comprising a first continuous time corresponding to a first interaction, generating a second temporal interaction edge to reflect a second continuous time corresponding to a second interaction, and generating the time-decayed weighted edge based on the first continuous time of the first temporal interaction edge and the second continuous time of the second temporal interaction edge (Figure 4; paragraphs 0033-0034: Here, for each node adjacency information for each node is identified for each state of the node. This adjacency information by indicating pairs of nodes joined by an edge. This adjacency information and edge neighborhood information (paragraph 0037) is used to generate a graph neural network that encodes the spatial and temporal interactions for nodes and edges)
As per dependent claim 19, Sharma discloses wherein generating the temporal link prediction comprises:
generating a vector representation from the time-decayed weighted edges (paragraph 0021: Here, the temporal graph network is trained on a plurality of temporal state vectors)
generating, utilizing a classification machine learning model, a predicted edge classification or a binary temporal link prediction from the vector representation from the time-decayed weighted edge (paragraphs 0032-0039: Here, it is determined, using trained historical state data, whether two nodes are linked. The examiner interprets this as a binary classification because nodes can be either “linked” or “not linked”)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 7, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma and further in view of Ulasen et al. (US 12694335, filed 11 January 2023, hereafter Ulasen).
As per dependent claim 7, Sharma discloses the limitations similar to those in claim 6, and the same rejection is incorporated herein. Sharma fails to specifically disclose generating the time-decayed weight inversely proportional to a difference between the first interaction time and the second interaction time.
However, Ulasen, which is analogous to the claimed invention because it is directed toward training neural networks using weights, discloses weights inversely proportional to a difference between the first interaction and the second interaction (column 5, lines 24-44). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Ulasen with Sharma, with a reasonable expectation of success, as it would have allowed for the weight to increase as the difference lessens/similarity increases (Ulasen: column 5, lines 24-44).
With respect to claim 15, the claim recites the limitations substantially similar to those in claim 7. Claim 15 is rejected under similar rationale.
With respect to claim 20, the claim recites the limitations substantially similar to those in claim 7. Claim 20 is rejected under similar rationale.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma and further in view of Wu et al. (US 12664589, filed 31 October 2022, hereafter Wu).
As per dependent claim 8, Sharma discloses the limitations similar to those in claim 1, and the same rejection is incorporated herein. Sharma discloses generating a vector representation from the time-decayed weighted edges (paragraph 0021: Here, the temporal graph network is trained on a plurality of temporal state vectors). But, Sharma fails to specifically disclose:
generating, utilizing a classification machine learning model trained on a set of vector representations and ground truth labels, the predicted edge classification from the vector representation from the time-decayed weighted edges
However, Wu, which is analogous to the claimed invention because it is directed toward training a classification model, discloses:
generating, utilizing a classification machine learning model trained on a set of vector representations and ground truth labels, the predicted classification from the vector representation from the weighted data (column 13, line 49- column 14, line 15: Here, a training engine uses a loss function to measure error compared to the ground truth dataset, the ground truth dataset used to train the neural network (column 3, line 64- column 4, line 22). Based upon this difference, a classification is applied to the data to provide a recommendation to the user (column 3, lines 19-49))
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Wu with Sharma, with a reasonable expectation of success, as it would have allowed for training a classification engine based upon a set of ground truths and classifying data based upon weights (Wu: column 13, line 49- column 14, line 15).
With respect to claim 16, the claim recites the limitations substantially similar to those in claim 8. Claim 16 is rejected under similar rationale.
Response to Arguments
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
She et al. (2023/0088676): Discloses a graph neural network training using neighbor sampling and constraint learning (Abstract)
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/KYLE R STORK/Primary Examiner, Art Unit 2128
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