DETAILED ACTION
This action is responsive to the application filed on 07/25/2023. Claims 1-20 are pending and have been examined.
This action is Non-final.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition
of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the
conditions and requirements of this title.
Claims 1-20 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: The claim is directed to a method, which is one of four statutory categories as a process. The claim satisfies step 1.
Step 2A Prong 1:(a) “performing, by a hypergraph neural network, a node hypergraph convolution based on the hypergraph to obtain an updated node embedding for a node of the plurality of nodes” -- This limitation is directed to node hypergraph convolution, which is a mathematical operation performed over hypergraph-structured data. The specification discloses the mathematical operations underlying node hypergraph convolution, for example in Eq. 19 and paragraph [0120]. Therefore, this limitation recites a mathematical concept (math).
Step 2A Prong 2 and Step 2B:(a) “obtaining, by a hypergraph component, a hypergraph that includes a plurality of nodes and a hyperedge, wherein the hyperedge connects the plurality of nodes” -- This limitation is directed to obtaining data for use in the recited mathematical concept. Such activity amounts to mere data gathering and insignificant extra-solution activity, and therefore cannot integrate the judicial exception into a practical application (see MPEP 2106.05(g)). Furthermore, under Step 2B, the act of obtaining a graph containing data and explaining its connectivity/relationship to the data is a well-understood, routine, and conventional activity (WURC) that cannot provide significantly more than the judicial exception (see MPEP 2106.05(d)(II)).
(b) “generating, by the hypergraph component, an augmented hypergraph based on the updated node embedding” -- This limitation is directed to outputting or generating data based on the result of the mathematical concept. Such post-solution activity amounts to insignificant extra-solution activity, and therefore cannot integrate the judicial exception into a practical application (see MPEP 2106.05(g)). Furthermore, under step 2B, the act of generating an augmented version of a hypergraph based on updated embeddings is a well-understood, routine, and conventional activity (WURC) that cannot provide significantly more than the judicial exception (see MPEP 2106.05(d)(II)).
Thus, claim 1 is non-patent eligible.
Regarding claim 2:
Step 1: The claim is directed to a method, which is one of four statutory categories as a process. The claim satisfies step 1.
Step 2A Prong 1:
“performing, by the hypergraph neural network, a hyperedge hypergraph convolution based on the hypergraph” -- The limitation is directed to performing convolution on a hyperedge/graph based on the original hypergraph, which is directed to a mathematical concept/operation. Thus, the limitation is directed to math.
Step 2A Prong 2 and Step 2B:
“to obtain an updated hyperedge embedding, wherein the augmented hypergraph is based on the updated hyperedge embedding.”-- The limitation recites obtaining an updated hyperedge embedding, wherein the augmented hypergraph is based on these updated embeddings. The limitation is directed to an insignificant, extra-solution activity that cannot be integrated to a practical application (see MPEP 2106.05(g)). Furthermore, under Step 2B, the act of obtaining updating embeddings and basing a hypergraph off of those is directed to a well-understood, routine, and conventional activity (WURC), and cannot provide significantly more than the judicial exception (see MPEP 2106.05(d)(II)).
Thus, claim 2 is non-patent eligible. Claim 19 is analogous to claim 2, aside from claim type, and thus faces the same rejection.
Regarding claim 3:
Step 1: The claim is directed to a method, which is one of four statutory categories. Therefore, the claim satisfies step 1.
Step 2A Prong 1:(a) “The method of claim 1, further comprising: encoding, by a node encoder, the node and the hyperedge to obtain a preliminary node embedding and a preliminary hyperedge embedding, respectively” -- This limitation is directed to a mathematical concept. In particular, encoding nodes and hyperedges into embeddings involves mathematical transformations of input data into numerical vector representations for subsequent processing.
Step 2A Prong 2 and Step 2B:
(c) “wherein the updated node embedding is based on the preliminary node embedding and the preliminary hyperedge embedding” -- This limitation merely further defines how the mathematical concept is performed using intermediate numerical representations. The limitation is directed to an insignificant, extra-solution activity that cannot be integrated to a practical application (see MPEP 2106.05(g)). Furthermore, under Step 2B, the act of updating node embeddings based on past embeddings (collected data) , is a well-understood, routine, and conventional activity (WURC), and it cannot provide significantly more than the judicial exception (see MPEP 2106.05(d)(II)).
Thus, claim 3 is non-patent eligible. Claim 13 is analogous to claim 3, aside from claim type, and thus faces the same rejection.
Regarding claim 4:
Step 1: The claim is directed to a method, which is one of four statutory categories. Therefore, the claim satisfies step 1.
There are no elements to be evaluated under Step 2A Prong 1.
Step 2A Prong 2 and Step 2B:
“The method of claim 1, further comprising: identifying a plurality of hyperedges of the hypergraph” -- This limitation is directed to obtaining or recognizing data for use in the recited judicial exception. The limitations amounts to mere data gathering and insignificant extra-solution activity, and therefore cannot integrate the judicial exception into a practical application (see MPEP 2106.05(g)). Furthermore, under Step 2B, identifying hyperedges of a graph is a well-understood, routine, and conventional activity, and cannot provide significantly more than the judicial exception (see MPEP 2106.05(d)(II)).
“generating, by the hypergraph neural network, a plurality of hyperedge-dependent node embeddings corresponding to the plurality of hyperedges, respectively” -- This limitation recites generating hyperedge-dependent node embeddings involves mathematical transformations of hypergraph-structured data into numerical vector representations for subsequent processing. The limitation amounts to mere instructions to apply onto a computer in a high-level general manner, and it does not integrate to a practical application, nor provides significantly more than the judicial exception (see MPEP 2106.05(f)).
Thus, claim 4 is non-patent eligible. Claim 14 is analogous to claim 4, aside from claim type, and thus faces the same rejection.
Regarding claim 5:
Step 1: The claim is directed to a method, which is one of four statutory categories. Therefore, the claim satisfies step 1.
Step 2A Prong 1:(a) “The method of claim 1, further comprising: generating a hyperedge transition matrix, wherein the node hypergraph convolution is based on the hyperedge transition matrix.” -- The limitation is directed to generating a matrix representing transition relationships within a hypergraph and using a transition matrix as the basis for convolution over hypergraph data, which involves mathematical operations performed on numerical matrix representations. Thus, the limitation is directed to math.
There are no elements to be evaluated under Step 2A Prong 2 and Step 2B.
Thus, claim 5 is non-patent eligible. Claim 15 is analogous to claim 5, aside from claim type, and thus faces the same rejection.
Regarding claim 6:
Step 1: The claim is directed to a method, which is one of four statutory categories. Therefore, the claim satisfies step 1.
Step 2A Prong 1:(a) “The method of claim 1, further comprising: generating a node transition matrix, wherein the node hypergraph convolution is based on the node transition matrix.” -- The limitation is directed to generating a matrix representing transition relationships within a hypergraph and using a transition matrix as the basis for convolution over hypergraph data, which involves mathematical operations performed on numerical matrix representations. Thus, the limitation is directed to math.
There are no elements to be evaluated under Step 2A Prong 2 and Step 2B.
Thus, claim 6 is non-patent eligible. Claim 16 is analogous to claim 6, aside from claim type, and thus faces the same rejection.
Regarding claim 7:
Step 1: The claim is directed to a method, which is one of four statutory categories. Therefore, the claim satisfies step 1.
Step 2A Prong 1:There are no elements to be evaluated under step 2A Prong 1.
Step 2A Prong 2 and Step 2B:
“generating, by the hypergraph component, an additional hyperedge based on the updated node embedding,” -- This limitation recites outputting or generating additional data based on the result of the judicial exception. Such post-solution activity amounts to insignificant extra-solution activity, and therefore cannot integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception (see MPEP 2106.05(g)).
“wherein the augmented hypergraph includes the additional hyperedge” -- The limitation recites that the augmented hypergraph will include the additional hyperedge. The limitation amounts to no more than mere further limiting to a field of use/environment, and thus it cannot be integrated to a practical application, nor provide significantly more than the judicial exception (see MPEP 2106.05(h)).
Thus, claim 7 is non-patent eligible.
Regarding claim 8:
Step 1: The claim is directed to a method, which is one of four statutory categories. Therefore, the claim satisfies step 1.
Step 2A Prong 1:(a) “The method of claim 1, further comprising: generating a hyper-incidence matrix based on the hypergraph, wherein a preliminary node embedding for a node of the plurality of nodes and a preliminary hyperedge embedding for the hyperedge are based on the hyper-incidence matrix” -- The limitation is directed to generating an incidence matrix representing relationships between nodes and hyperedges and basing embeddings on a matrix representation, which involves mathematical transformations of data into numerical vector representations. This limitation is directed to a mathematical concept.
There are no elements to be evaluated under Step 2A Prong 2 and Step 2B.
Thus, claim 8 is non-patent eligible.
Regarding claim 9:
Step 1: The claim is directed to a method, which is one of four statutory categories. Therefore, the claim satisfies step 1.
Step 2A Prong 1:(a) “The method of claim 1, further comprising: generating a node diagonal degree matrix based on the hypergraph, wherein a preliminary node embedding for a node of the plurality of nodes and a preliminary hyperedge embedding for the hyperedge are based on the node diagonal degree matrix.” -- This limitation is directed to generating a diagonal degree matrix representing node degree relationships within a hypergraph and basing embeddings on a matrix representation involves mathematical transformations of data into numerical vector representations, which involves mathematical structures and operations. This limitation is directed to a mathematical concept.
Thus, claim 9 is non-patent eligible.
Regarding claim 10:
Step 1: The claim is directed to a method, which is one of four statutory categories. Therefore, the claim satisfies step 1.
Step 2A Prong 1:(a) “and generating a predicted document element based on the augmented hypergraph” -- This limitation is directed to generating a prediction based on analyzed gathered information. The limitation can be performed using evaluation, observation, and judgement, with aid of pen and paper, and thus the limitation is directed to a mental process.
Step 2A Prong 2 and Step 2B:
(a) “The method of claim 1, further comprising: obtaining a plurality of documents including a plurality of document elements” -- This limitation is directed to obtaining data for use in the recited judicial exception. Such activity amounts to mere data gathering and insignificant extra-solution activity, and therefore cannot integrate the judicial exception into a practical application (see MPEP 2106.05(g)). Furthermore, under Step 2B, the act of obtaining document/document elements (gathering data) is a well-understood, routine, and conventional activity (WURC) that cannot provide significantly more than the judicial exception (see MPEP 2106.05(d)(II)).
(b) “generating the hypergraph based on the plurality of document elements” -- This limitation is directed to preparing data/data representation based on gathered data (document elements). Such activity amounts to an insignificant extra-solution activity and therefore cannot integrate the judicial exception into a practical application (see MPEP 2106.05(g)). Furthermore, under step 2B, generating a hypergraph based on a plurality of gathered data is a well-understood, routine, and conventional activity (WURC), which cannot provide significantly more than the judicial exception (see MPEP 2106.05(d)(II)).
Thus, claim 10 is non-patent eligible.
Regarding claim 11:
Step 1: The claim is directed to a method, which is one of four statutory categories. Therefore, claim 11 satisfies step 1.There are no elements to be evaluated under step 2A Prong 1.
Step 2A Prong 2 and Step 2B:
“The method of claim 1, further comprising: providing a content item to a user based on the augmented hypergraph, wherein the user and the content item are represented by the plurality of nodes” -- This limitation is directed to outputting the result of the judicial exception. Such activity amounts to insignificant post-solution activity and therefore cannot integrate the judicial exception into a practical application (see MPEP 2106.05(g)). Furthermore, under Step 2B, the act of sending/receiving data over a network is a well-understood, routine, and conventional activity (WURC), and it cannot provide significantly more than the judicial exception (see MPEP 106.05(d)(II)).
Thus, claim 11 is non-patent eligible.
Regarding claim 12:
Step 1: The claim is directed to a method, which is one of four statutory categories. Therefore, the claim satisfies step 1.
Step 2A Prong 1:
“performing, by a hypergraph neural network, a node hypergraph convolution based on the hypergraph to obtain a predicted node embedding for a node of the plurality of nodes;” -- The limitation is directed to performing convolution on a hypergraph node based on the hypergraph to obtain a predicted node embedding for a group of nodes. The limitation is directed to the use of mathematical operations/concept, and thus the limitation is directed to math (see SPEC [0120]).
Step 2A Prong 2 and Step 2B:
“obtaining, by a training component, training data that includes a hypergraph including a plurality of nodes and a hyperedge, wherein the hyperedge connects the plurality of nodes;” -- The limitation recites obtaining training data that includes a hypergraph with nodes and a hyperedge, and that the hyperedge will be connected to the nodes. The limitation amounts to insignificant, extra-solution activity that cannot be integrated to a practical application (see MPEP 2106.05(g)). Furthermore, under Step 2B, the act of obtaining data and mere data relationships is a well-understood, routine, and conventional activity (WURC), and it cannot provide significantly more than the judicial exception (see MPEP 2106.05(d)(II)).
“training, by the training component, the hypergraph neural network based on the training data and the predicted node embedding.” -- The limitation recites training the network based on training data and the prediction node embeddings using the training component. The limitation is no more than mere instructions to apply onto a computer in high-level, generic manner, and it does not integrate to a practical application, nor provides significantly more than the judicial exception (see MPEP 2106.05(f)).
Thus, claim 12 is non-patent eligible.
Regarding claim 17:
Step 1: The claim is directed to an apparatus, which is one of four statutory categories. Therefore, the claim satisfies step 1.
Step 2A Prong 1:(a) “a hypergraph neural network including a node hypergraph convolution layer configured to perform a node hypergraph convolution based on the hypergraph to obtain an updated node embedding for a node of the plurality of nodes” -- This limitation is directed to a node hypergraph convolution performed over hypergraph-structured data. The specification discloses the mathematical operations underlying node hypergraph convolution, (for example in Eq. 19 and paragraph [0120]). Therefore, this limitation recites a mathematical concept.
Step 2A Prong 2 and Step 2B:(a) “An apparatus comprising: at least one processor; at least one memory comprising instructions executable by the at least one processor” -- This limitation is directed to generic computer components performing their ordinary functions. These elements are recited at a high level of generality and amount to no more than mere instructions to apply the judicial exception using generic computer components, which cannot integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception (see MPEP 2106.05(f)).
(b) “a hypergraph component configured to obtain a hypergraph that includes a plurality of nodes and a hyperedge, wherein the hyperedge connects the plurality of nodes” -- This limitation is directed to obtaining data for use in the recited mathematical concept. Such activity amounts to mere data gathering and insignificant extra-solution activity, and therefore cannot integrate the judicial exception into a practical application (see MPEP 2106.05(g)). Furthermore, under Step 2B, the act of configuring a component to obtain a hypergraph that includes a group of its data and how it connects to another is a well-understood, routine, and conventional activity (WURC), which cannot provide significantly more than the judicial exception (see MPEP 2106.05(d)(II)).
Thus, claim 17 is non-patent eligible.
Regarding claim 18:
Step 1: The claim is directed to an apparatus, which is one of four statutory categories. Therefore, the claim satisfies step 1.There are no elements to be evaluated under step 2A Prong 1.
Step 2A Prong 2 and Step 2B:(a) “The apparatus of claim 17, wherein: the hypergraph component generates an augmented hypergraph based on the updated node embedding” -- This limitation is directed to outputting or generating data based on the result of the judicial exception. Such post-solution activity amounts to insignificant extra-solution activity, and therefore cannot integrate the judicial exception into a practical application (see MPEP 2106.05(g)). Furthermore, under Step 2B, the act of generating new data based on updated gathered information is a well-understood, routine, and conventional activity (WURC), and it cannot provide significantly more than the judicial exception (see MPEP 2106.05(d)(II)).
Thus, claim 18 is non-patent eligible.
Regarding claim 20:
Step 1: The claim is directed to an apparatus, which is one of four statutory categories. Therefore, claim 20 satisfies step 1.
There are no elements to be evaluated under Step 2A Prong 1.
Step 2A Prong 2 and Step 2B:
(b) “The apparatus of claim 17, further comprising: a training component configured to update parameters of the hypergraph neural network based on training data” -- This limitation recites a training component that will update parameters of a hypergraph based on gathered training data. The limitation is directed to an insignificant, extra-solution activity that cannot be integrated to a practical application (see MPEP 2106.05(g)). Furthermore, under Step 2B, the act of updating parameters based on gathered data is a well-understood, routine, and conventional activity (WURC), and it does not provide significantly more than the judicial exception (see MPEP 2106.05(d)(II)).
Thus, claim 20 is non-patent eligible.
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise
available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in 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.
Claim(s) 1,3-6, 8-9, and 13-17 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US20230037388A1, by Sakhinana et. al. (referred herein as Sakhinana).
Regarding claim 1, Sakhinana teaches:
obtaining, by a hypergraph component, a hypergraph that includes a plurality of nodes and a hyperedge, wherein the hyperedge connects the plurality of nodes, ([Sakhinana, [0010]] ”each of the molecular graph as an undirected hypergraph, wherein the undirected hypergraph comprises a plurality of nodes and a plurality of hyperedges connecting the plurality of nodes”, wherein the examiner interprets ”a plurality of hyperedges connecting the plurality of nodes” to be the same as a hyperedge, wherein the hyperedge connects the plurality of nodes because they are both directed to a hypergraph structure in which nodes are connected by a hyperedge).
performing, by a hypergraph neural network, a node hypergraph convolution based on the hypergraph, ([Sakhinana, [0044] ”Hypergraph attention driven convolution, on molecular hypergraph results in learning efficient embeddings on the high-order molecular graph-structured data”, wherein the examiner interprets ”Hypergraph attention driven convolution, on molecular hypergraph” to be the same as node hypergraph convolution based on the hypergraph because they are both directed to convolutional processing performed over a hypergraph structure).
to obtain an updated node embedding for a node of the plurality of nodes, ([Sakhinana, [0031]] “The message passing phase generates neural messages and update node representations by aggregating encoded information of node's embeddings from confined graph neighborhood.” AND [0032] “ node-embedding update neural network function, Vf. Mf and Vf might take possession of diverse in specific to be at variance with function settings. During the span of the message passing phase, the node-level embedding hi t of every unique vertex in the molecular graph as given by its computational graph” , wherein the examiner interprets ”update node representations by aggregating encoded information of node's embeddings…node-level embedding hi t of every unique vertex in the molecular graph as given by its computational graph” to be the same as to obtain an updated node embedding for a node of the plurality of nodes because they are both directed to generating an updated learned representation for a node after neural processing).
generating, by the hypergraph component, an augmented hypergraph based on the updated node embedding. ([Sakhinana, [0031, 0086]] “The message passing phase generates neural messages and update node representations by aggregating encoded information of node's embeddings from confined graph neighborhood…the effectiveness of learning in the depiction of the task pertinent chemical hypergraph substructures into augmented expressiveness of the node embeddings on the graph/hypergraph topology leads to improvements in property prediction tasks accuracies.” , wherein the examiner interprets “update node representations by aggregating encoded information of node's embeddings from confined graph neighborhood…hypergraph substructures into augmented expressiveness of the node embeddings on the graph/hypergraph topology” to be the same as generating an augmented hypergraph based on the updated node embeddings, because they are both directed to using updated node-level representations that reflect hypergraph-topology information to produce an enhanced hypergraph-related representation for subsequent processing.). This limitation can also apply to claim 18, as it is analogous, aside from claim type, and thus the mapping applies to both.
Regarding claim 3, Sakhinana teaches The method of claim 1, (see rejection of claim 1).
Sakhinana further teaches:
further comprising: encoding, by a node encoder, the node and the hyperedge to obtain a preliminary node embedding and a preliminary hyperedge embedding, respectively, ([Sakhinana. [0031] “The message passing phase generates neural messages and update node representations by aggregating encoded information of node's embeddings from confined graph neighborhood. AND [0041] “Hypergraph convolutions are designed to take G^H = (V^H(|V^H|)) nodes and N^H(|E^H|) hyperedges as input…F_k^{(L)} is the abstract representation of the k-th node in the (L)-th layer”, wherein the examiner interprets the hypergraph convolution taking “nodes and hyperedges as input” and producing “F_k^{(L)}” as the preliminary node representation along with the corresponding hyperedge-level aggregation via H^{HT} as the preliminary hyperedge representation to be the same as encoding, by a node encoder, the node and the hyperedge to obtain a preliminary node embedding and a preliminary hyperedge embedding, respectively because they are both directed to a processing component that jointly encodes both node and hyperedge information to produce initial vector representations for each.)
wherein the updated node embedding is based on the preliminary node embedding and the preliminary hyperedge embedding. ([Sakhinana, [0032] “and the received messages are perceived by the target node by performing mathematical computations to update its hidden representation.” AND [0059] “are the input of the (L)-th and (L+1)-th layer respectively. A symmetric normalization is put in to avoid exploding/vanishing gradient and thus, F (L+1)=σ(D H -1/2 H H W H B H -1 H H T D H -1/2 F (L) P) (Equation 10) and F(L+1) is differentiable with respect to F(L) and P.”, wherein the examiner interprets the updated node embedding “F^{(L+1)}” computed from both the preliminary node embedding “F^{(L)}” and the preliminary hyperedge embedding “H^{HT} F^{(L)}” through the incidence matrix H^H and message passing/node representation updating by aggregating encoded information to be the same as the updated node embedding is based on the preliminary node embedding and the preliminary hyperedge embedding because they are both directed to a formulation in which the updated node-level vector representation is computed by jointly aggregating and transforming both the preliminary node features and the preliminary hyperedge-level results.) Claim 13 is analogous to claim 3, and thus the same rejection can be set forth as above.
Regarding claim 4, Sakhinana teaches The method of claim 1, (see rejection of claim 1).
Sakhinana further teaches further comprising: identifying a plurality of hyperedges of the hypergraph; ([Sakhinana, [0009]] “the undirected hypergraph comprises a plurality of nodes and a plurality of hyperedges connecting the plurality of nodes”, wherein the examiner interprets “the undirected hypergraph comprises a plurality of hyperedges connecting the plurality of nodes” to be the same as identifying a plurality of hyperedges of the hypergraph because they are both directed to recognizing the presence of multiple distinct hyperedges within the hypergraph structure.)
and generating, by the hypergraph neural network, a plurality of hyperedge-dependent node embeddings corresponding to the plurality of hyperedges, respectively. ([Sakhinana, [0011]] “performing attention over each node of a set of nodes from amongst the plurality of nodes with a first set of feature vectors associated with a hyperedge within a local-intra neighborhood of the node to compute a plurality of intra-hyperedge neural-message aggregations…updating a set of hidden state vectors for each node of the set of nodes in the hyperedge by utilizing the plurality of intra-hyperedge neural-message aggregations”, wherein the examiner interprets “a plurality of intra-hyperedge neural-message aggregations” used to update “a set of hidden state vectors for each node of the set of nodes in the hyperedge” to be the same as a plurality of hyperedge-dependent node embeddings corresponding to the plurality of hyperedges, respectively because they are both directed to computing multiple node-level vector representations, each produced within the context of a specific hyperedge, such that each resulting node representation is conditioned upon and uniquely associated with the particular hyperedge in which it was computed.) Claim 14 is analogous to claim 4, and thus the same rejection can be set forth as above.
Regarding claim 5, Sakhinana teaches The method of claim 1, (see rejection of claim 1).
Sakhinana further teaches further comprising: generating a hyperedge transition matrix, wherein the node hypergraph convolution is based on the hyperedge transition matrix. ([Sakhinana, [0011]] “Furthermore, the method includes, learning, in a plurality of iterations, a dynamic transient incidence matrix through a hypergraph-attention mechanism between a node and a set of hyperedges associated with the node of the hypergraph to perform a hyper-graph convolution using the HMPNN”, wherein the examiner interprets “a dynamic transient incidence matrix” to be the same as a hyperedge transition matrix because they are both directed to a learned matrix structure that encodes the connectivity and weighting relationships between nodes and their associated hyperedges in the hypergraph. The examiner further interprets “to perform a hyper-graph convolution” using the dynamic transient incidence matrix to be the same as the node hypergraph convolution is based on the hyperedge transition matrix because they are both directed to a node-level convolution operation that uses the hyperedge-derived transition matrix as its foundational propagation mechanism.) Claim 15 is analogous to claim 5, and thus the same rejection can be set forth as above.
Regarding claim 6, Sakhinana teaches The method of claim 1, (see rejection of claim 1).
Sakhinana further teaches further comprising: generating a node transition matrix, ([Sakhinana, [0067]] “Performing the attention within the local-intra neighborhood of the node includes evaluating a first transient incidence matrix. The first transient incidence matrix fill refers to the transient matrix learnt by computing pairwise attention coefficients between the node and its associated hyperedge.”, wherein the examiner interprets “a first transient incidence matrix” learnt by “computing pairwise attention coefficients between the node and its associated hyperedge” to be the same as a node transition matrix because they are both directed to a node-centric matrix that encodes the weighted transition relationships between each node and its directly connected hyperedge elements.)
wherein the node hypergraph convolution is based on the node transition matrix. ([Sakhinana, [0009]] “updating a set of hidden state vectors for each node of the set of nodes in the hyperedge by utilizing the plurality of intra-hyperedge neural-message aggregations”, wherein the examiner interprets “updating a set of hidden state vectors for each node of the set of nodes in the hyperedge by utilizing the plurality of intra-hyperedge neural-message aggregations” to be the same as the node hypergraph convolution is based on the node transition matrix because they are both directed to performing a node-level convolution operation that uses a node-derived transition matrix, namely the first transient incidence matrix, as the core propagation mechanism by which each node's hidden state representation is aggregated and updated through the hyperedge structure.) Claim 16 is analogous to claim 6, and thus the same rejection can be set forth as above.
Regarding claim 8, Sakhinana teaches The method of claim 1, (see rejection of claim 1).
Sakhinana further teaches further comprising: generating a hyper-incidence matrix based on the hypergraph, ([Sakhinana, [0010]] “graph cached in a scaling diagonal matrix. Furthermore learn, in a plurality of iterations, a dynamic transient incidence matrix through a hypergraph-attention mechanism between a node and a set of hyperedges associated with the node of the hypergraph”, wherein the examiner interprets “a dynamic transient incidence matrix” and “a set of hyperedges attached with the node of the hypergraph” to be the same as generating a hyper-incidence matrix based on the hypergraph because they are both directed to generating an incidence matrix from node-hyperedge relationships of the hypergraph.)
wherein a preliminary node embedding for a node of the plurality of nodes and a preliminary hyperedge embedding for the hyperedge are based on the hyper-incidence matrix. ([Sakhinana, [0009]] “performing attention over each node of a set of nodes from amongst the plurality of nodes with a first set of feature vectors associated with a hyperedge within a local-intra neighborhood of the node to compute a plurality of intra-hyperedge neural-message aggregations. Performing the attention within the local-intra neighborhood of the node comprises evaluating a first transient incidence matrix … perform attention over each node from amongst the set of nodes with a second set feature vectors associated with a set of inter-hyperedges within a global-inter neighborhood of the node to compute a plurality of inter-hyperedge neural-message aggregations, wherein performing the attention within the global-inter neighborhood of the node comprises evaluating a second transient incidence matrix;” wherein the examiner interprets “a first set of feature vectors” used while “evaluating a first transient incidence matrix” to be the same as a preliminary node embedding for a node of the plurality of nodes ... based on the hyper-incidence matrix because they are both directed to node-associated feature representations that are produced or used on the basis of an incidence matrix derived from the hypergraph. The examiner further interprets “a second set feature vectors associated with a set of inter-hyperedges” used while “evaluating a second transient incidence matrix” to be the same as a preliminary hyperedge embedding for the hyperedge ... based on the hyper-incidence matrix because they are both directed to hyperedge-associated feature representations that are produced or used on the basis of an incidence matrix derived from the hypergraph).
Regarding claim 9, Sakhinana teaches The method of claim 1, (see rejection of claim 1).
Sakinana further teaches further comprising: generating a node diagonal degree matrix based on the hypergraph, ([Sakhinana, [0056]] “The node degree of the hypergraph G^H is described as D_{kk}^H = Σ_{j=1}^{M^H} H_{kj}^H. Here, D^H ∈ R^{M^H×M^H} and B^H ∈ R^{N^H×N^ H} are both diagonal matrices”, wherein the examiner interprets “D^H ∈ R^{M^H×M^H}” with entries “D_{kk}^H = Σ_{j=1}^{M^H} H_{kj}^H” derived from the hypergraph G^H to be the same as a node diagonal degree matrix based on the hypergraph because they are both directed to computing a diagonal matrix whose entries encode the degree of each node in the hypergraph, where the node degree is determined by counting the number of hyperedges each node belongs to, yielding a square diagonal matrix derived directly from the hypergraph structure.)
wherein a preliminary node embedding for a node of the plurality of nodes ([Sakhinana, [0057]] “Here, Fk (L) is the abstract representation of the k-th node in the (L)-th layer.”, wherein the examiner interprets “the abstract representation of the k-th node” to be the same as a preliminary node embedding for a node of the plurality of nodes because they are both directed to a node-level representation used in the hypergraph neural processing.)
and a preliminary hyperedge embedding for the hyperedge are based on the node diagonal degree matrix. ([Sakhinana, [0058-0059]] “The hypergraph convolution can be expressed in a matrix form as :.. A symmetric normalization is put in to avoid exploding/vanishing gradient” and [Sakhinana, [0064]] “Here, F^(ε H) denotes the static-hyperedge feature matrix, and a is a weight vector used to output a scalar attention value.”, wherein the examiner interprets “the static-hyperedge feature matrix” to be the same as a preliminary hyperedge embedding for the hyperedge because they are both directed to a hyperedge-associated representation used in the hypergraph neural processing. The examiner further interprets the hypergraph convolution expressed in matrix form using “Dv” to be the same as are based on the node diagonal degree matrix because they are both directed to using the node diagonal degree matrix within the matrix-form hypergraph convolution that operates on the node representation and the hyperedge representation.)
Regarding claim 17,
Sakhinana teaches:
An apparatus comprising: at least one processor; at least one memory comprising instructions executable by the at least one processor; ([Sakhinana, [0010]] “a system for molecular property prediction using a hypergraph message passing neural network (HMPNN) is provided. The system includes a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to”, wherein the examiner interprets “one or more hardware processors” to be the same as at least one processor because they are both directed to processor hardware that executes the claimed operations, and wherein the examiner interprets “a memory storing instructions” and “the one or more hardware processors are configured by the instructions” to be the same as at least one memory comprising instructions executable by the at least one processor because they are both directed to memory that stores instructions for execution by processor hardware.)
a hypergraph component configured to obtain a hypergraph that includes a plurality of nodes and a hyperedge, wherein the hyperedge connects the plurality of nodes; ([Sakhinana, [0010]] “represent each of the molecular graph as an undirected hypergraph, wherein the undirected hypergraph comprises a plurality of nodes and a plurality of hyperedges connecting the plurality of nodes”, wherein the examiner interprets “an undirected hypergraph” to be the same as a hypergraph because they are both directed to a hypergraph data structure, and wherein the examiner interprets “a plurality of hyperedges connecting the plurality of nodes” to be the same as a hyperedge, wherein the hyperedge connects the plurality of nodes because they are both directed to hyperedges that connect nodes in the hypergraph.)
and a hypergraph neural network including a node hypergraph convolution layer configured to perform a node hypergraph convolution based on the hypergraph (Sakhinana, [0011]] “Furthermore, the method includes learning, in a plurality of iterations, a dynamic transient incidence matrix through a hypergraph-attention mechanism between a node and a set of hyperedges associated with the node of the hypergraph to perform a hyper-graph convolution using the HMPNN, via the one or more hardware processors.”, wherein the examiner interprets “the HMPNN” to be the same as a hypergraph neural network because they are both directed to a neural network that operates on a hypergraph. The examiner further interprets “a hypergraph-attention mechanism between a node and a set of hyperedges associated with the node of the hypergraph to perform a hyper-graph convolution” to be the same as a node hypergraph convolution layer configured to perform a node hypergraph convolution based on the hypergraph because they are both directed to node-centered convolutional processing over a hypergraph using node and hyperedge relationships.)
to obtain an updated node embedding for a node of the plurality of nodes. ([Sakhinana, [0011]] “updating a set of hidden state vectors for each node of the set of nodes in the hyperedge by utilizing the plurality of intra-hyperedge neural-message aggregations”, wherein the examiner interprets “a set of hidden state vectors for each node” to be the same as an updated node embedding for a node of the plurality of nodes because they are both directed to updated learned representations associated with nodes after hypergraph neural processing.)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this
Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not
identically disclosed as set forth in section 102, if the differences between the claimed invention and the
prior art are such that the claimed invention as a whole would have been obvious before the effective filing
date of the claimed invention to a person having ordinary skill in the art to which the claimed invention
pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are
summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2 is rejected under 35 U.S.C. 103 as being unpatentable over Sakhinana in view of US20230342918A1, by Wang et. al. (referred herein as Wang).
Regarding claim 2, Sakhinana teaches The method of claim 1, (see rejection of claim 1).
Sakhinana further teaches wherein performing, by the hypergraph neural network, a hyperedge hypergraph convolution based on the hypergraph, ([Sakhinana, [0031]] “The message passing phase generates neural messages and update node representations by aggregating encoded information of node's embeddings from confined graph neighborhood”, AND [0086] “performing attention over each node from amongst the set of nodes with a second set feature vectors associated with a set of inter-hyperedges within a global-inter neighborhood of the node to compute a plurality of inter-hyperedge neural-message aggregations”, wherein the examiner interprets “a second set feature vectors associated with a set of inter-hyperedges” and “compute a plurality of inter-hyperedge neural-message aggregations” to be the same as performing, by the hypergraph neural network, a hyperedge hypergraph convolution based on the hypergraph because they are both directed to hypergraph neural processing performed with respect to hyperedges of the hypergraph).
Sakhinana does not teach to obtain an updated hyperedge embedding…wherein the augmented hypergraph is based on the updated hyperedge embedding.
Wang teaches:
to obtain an updated hyperedge embedding, ([Wang, [0008]] “performing vertex convolution calculation on the first hypergraph matrix and the second hypergraph matrix respectively to acquire a first hyperedge feature and a second hyperedge feature; performing hyperedge convolution calculation on the first hyperedge feature and the second hyperedge feature to acquire a fused feature;”, wherein the examiner interprets “acquire a first hyperedge feature and a second hyperedge feature” and “acquire a fused feature” to be the same as to obtain an updated hyperedge embedding because they are both directed to generating an updated learned representation associated with hyperedges after hyperedge-side convolution processing).
wherein the augmented hypergraph is based on the updated hyperedge embedding, ([Wang, [0008]] “performing hypergraph fusion on the non-Euclidean spacial feature and the Euclidean spacial feature to acquire the feature parameters…performing hypergraph data transformation on the non-Euclidean spacial feature and the Euclidean spacial feature respectively to acquire a first hypergraph matrix and a second hypergraph matrix; performing vertex convolution calculation on the first hypergraph matrix and the second hypergraph matrix respectively to acquire a first hyperedge feature and a second hyperedge feature; performing hyperedge convolution calculation on the first hyperedge feature and the second hyperedge feature to acquire a fused feature”, wherein the examiner interprets “performing hypergraph data transformation”, “acquire a first hypergraph matrix and a second hypergraph matrix”, and “performing hyperedge convolution calculation on the first hyperedge feature and the second hyperedge feature to acquire a fused feature” to be the same as wherein the augmented hypergraph is based on the updated hyperedge embedding because they are both directed to generating or revising a hypergraph-level representation based on updated hyperedge-associated features produced by hyperedge convolution).
Sakhinana, Wang, and the instant application are analogous art because they are all directed to hypergraph-based neural processing using node-related information, hyperedge-related information, and hypergraph structure.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the method of claim 1 disclosed by Sakhinana to include the “performing hyperedge convolution calculation on the first hyperedge feature and the second hyperedge feature to acquire a fused feature” disclosed by Wang. One would be motivated to do so to effectively generate updated hyperedge-associated representations and use those representations in hypergraph-level processing, as suggested by Wang ([Wang, [0008]] “performing hyperedge convolution calculation on the first hyperedge feature and the second hyperedge feature to acquire a fused feature.”). Claim 19 is highly similar to claim 2, and thus the rejection can be applied to both claims.
Claims 7, and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Sakhinana in view of US11816618B1, by Cheek et. al. (referred herein as Cheek).
Regarding claim 7, Sakhinana teaches The method of claim 1, (see rejection of claim 1).
Sakhinana does not teach further comprising: generating, by the hypergraph component, an additional hyperedge based on the updated node embedding, wherein the augmented hypergraph includes the additional hyperedge.
Cheek teaches further comprising: generating, by the hypergraph component, an additional hyperedge ([Cheek col 2, lines 17-20] “When the category corresponds to a node, the system may identify a timestamp for the electronic object, and it will update the hypergraph by assigning the electronic object to an edge of the corresponding node”, wherein the examiner interprets “assigning the electronic object to an edge of the corresponding node” to be the same as generating, by the hypergraph component, an additional hyperedge because they are both directed to adding edge structure to the hypergraph in association with a node.)
based on the updated node embedding ([Cheek, col. 2, lines 53-58] “the workflow management system may associate one or more compressed context representations with one or more of the electronic objects that are assigned to hypergraph data, and therefore assign one or more compressed context representations to one or more nodes, edges or pins of the graph”, wherein the examiner interprets “update a set of hidden state vectors for each node of the set of nodes” and “compressed context representations” assigned to “one or more nodes” to be the same as updated node embedding because they are both directed to updated node-associated representations used in subsequent hypergraph processing.)
wherein the augmented hypergraph includes the additional hyperedge. ([Cheek, col. 2, lines 18-20] “it will update the hypergraph by assigning the electronic object to an edge of the corresponding node”, wherein the examiner interprets “update the hypergraph” and “edge of the corresponding node” to be the same as wherein the augmented hypergraph includes the additional hyperedge because they are both directed to a modified hypergraph that includes an added edge structure.)
Sakhinana, Cheek, and the instant application are analogous art because they are all directed to hypergraph-based processing that includes modifying hypergraph structure.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the undirected hypergraph technique disclosed by Sakhinana to include the hypergraph updating technique disclosed by Cheek. One would be motivated to do so to effectively update and expand the hypergraph structure by adding edge connections associated with nodes, as suggested by Cheek ([Cheek, col. 2, lines 18-20] “it will update the hypergraph by assigning the electronic object to an edge of the corresponding node.”)
Regarding claim 11, Sakhinana teaches The method of claim 1, (see rejection of claim 1).
Sakhinana does not teach further comprising: providing a content item to a user based on the augmented hypergraph, wherein the user and the content item are represented by the plurality of nodes.
Cheek teaches:
further comprising: providing a content item to a user ([Cheek, col 2, lines 38-41] “when selected, will cause an electronic device of which the user interface is a component to display the object via an application that generated the electronic object to which the edge associated with the pin is assigned”, wherein the examiner interprets “display the object” to be the same as providing a content item to a user because they are both directed to presenting an item of electronic content to a user through a user interface.)
based on the augmented hypergraph, wherein the user and the content item are represented by the plurality of nodes. ([Cheek, col 2, Lines, 9-24] “The hypergraph data also includes edges that are associated with one or more of the nodes and that correspond to one or more of the electronic objects. The workflow management system saves the hypergraph data to a memory. When the workflow management system receives a new electronic object, it will assign a category to the new electronic object, and it will determine whether the category corresponds to one or more nodes of the hypergraph data. When the category corresponds to a node, the system may identify a timestamp for the electronic object, and it will update the hypergraph by assigning the electronic object to an edge of the corresponding node, optionally with a chronological location that corresponds to the timestamp. A workflow presentation system will cause a display device to output a graphical user interface that includes a hypergraph constructed from the hypergraph data.”, wherein the examiner interprets “update the hypergraph” and “a hypergraph constructed from the hypergraph data” to be the same as based on the augmented hypergraph because they are both directed to presenting information using a hypergraph that has been updated and then used as the basis for display. The examiner further interprets “people” to be the same as the user and “electronic objects” to be the same as the content item because they are both directed to entities represented as nodes in the hypergraph).
Sakhinana, Cheek, and the instant application are analogous art because they are all directed to hypergraph-based processing that includes modifying hypergraph structure.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the undirected hypergraph technique disclosed by Sakhinana to include the object display technique disclosed by Cheek. One would be motivated to do so to effectively present hypergraph-associated content to users through a user interface based on relationships encoded in the hypergraph, as suggested by Cheek ([Cheek, col 2, lines 38-41] “display the object via an application that generated the electronic object”).
Regarding claim 12, Sakhinana teaches:
A method comprising: obtaining, by a training component, training data that includes a hypergraph including a plurality of nodes and a hyperedge, wherein the hyperedge connects the plurality of nodes; ([Sakhinana, [0011]] “one or more hardware processors cause accessing a database comprising information associated with a plurality of molecules, via one or more hardware processors. Each molecule of the plurality of molecules represented as a molecular graph. Further the method includes representing each of the molecular graph as an undirected hypergraph, via the one or more hardware processors, wherein the undirected hypergraph comprises a plurality of nodes and a plurality of hyperedges connecting the plurality of nodes”, wherein the examiner interprets “one or more hardware processors cause accessing a database comprising information associated with a plurality of molecules” to be the same as obtaining, by a training component, training data because they are both directed to acquiring data by a processing component for subsequent machine-learning operations. The examiner further interprets “a plurality of nodes and a plurality of hyperedges connecting the plurality of nodes” to be the same as a hyperedge, wherein the hyperedge connects the plurality of nodes because they are both directed to hyperedges that connect multiple nodes in a hypergraph structure.)
performing, by a hypergraph neural network, a node hypergraph convolution based on the hypergraph ([Sakhinana, [0056]] “At step 406, the method 400 includes learning, in a plurality of iterations, a dynamic transient incidence matrix through hypergraph-attention mechanism between a node and a set of hyperedges associated with the node of the hypergraph to perform a hyper-graph convolution using the HMPNN.”, wherein the examiner interprets “to perform a hyper-graph convolution using the HMPNN.” to be the same as performing, by a hypergraph neural network, a node hypergraph convolution based on the hypergraph because they are both directed to carrying out hypergraph convolution by a hypergraph neural network over the hypergraph.)
to obtain a predicted node embedding for a node of the plurality of nodes; … and the predicted node embedding; ([Sakhinana, [0010]] “update a set of hidden state vectors for each node of the set of nodes in the hyperedge”, wherein the examiner interprets “update a set of hidden state vectors for each node of the set of nodes” to be the same as to obtain a predicted node embedding for a node of the plurality of nodes because they are both directed to generating a node-associated learned representation for each node after hypergraph neural processing. The examiner further interprets “hidden state vectors for each node” to be the same as the predicted node embedding because they are both directed to node-level learned representations output by the hypergraph neural network and used in later processing.)
Sakhinana does not teach and training, by the training component, the hypergraph neural network based on the training data and the predicted node embedding.
Cheek teaches and training, by the training component, the hypergraph neural network based on the training data ([Cheek, col 19, lines 40-45] “If the system receives such a 40 reassignment, it may provide the new object-workstream association to the machine learning model (step 504) so that the model may learn from the correction and use the result of the reassignment to make better workstream assignment decisions on future objects.” and [Cheek, Fig 5] “RECEIVE USER REASSIGNMENT OF OBJECТ 503 … TRAIN MODEL USING REASSIGNMENT OF OBJECТ 504”, wherein the examiner interprets “REASSIGNMENT OF OBJECT” used in “TRAIN MODEL USING REASSIGNMENT OF OBJECT” to be the same as training, by the training component, the hypergraph neural network and “based on the training data” because they are all directed to training a machine-learning model by a processing component.) (Claim 20 is analogous to this portion of the claim, therefore the same rejection applies.)
Sakhinana, Cheek, and the instant application are analogous art because they are all directed to hypergraph-based machine learning and training of neural network models.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the undirected hypergraph technique disclosed by Sakhinana to include the correction and reassignment approach disclosed by Cheek. One would be motivated to do so to effectively improve training of the hypergraph neural network using updated training signals, as suggested by Cheek ([Cheek, col 19, lines 42-45] “so that the model may learn from the correction and use the result of the reassignment to make better workstream assignment decisions.”).
Claims 10 is rejected under 35 U.S.C. 103 as being unpatentable over Sakhinana in view of Cheek further in view of US20240355350A1, by Chen et. al. (referred herein as Chen).
Regarding claim 10, Sakhinana teaches The method of claim 1, (see rejection of claim 1).
Sakhinana does not teach obtaining a plurality of documents including a plurality of document elements; generating the hypergraph based on the plurality of document elements; and generating a predicted document element based on the augmented hypergraph.
Cheek teaches obtaining a plurality of documents including a plurality of document elements; generating the hypergraph based on the plurality of document elements; ([Cheek, col 2, lines 1-7] “In this method, a workflow management system analyzes electronic objects that are managed by various external applications during a time period. The workflow management system extracts metadata from each of the electronic objects, and it computes hypergraph data from the metadata.”, and [Cheek, col 3, lines 30-33] “In various embodiments, at least some of the electronic objects may be messages transferred via a messaging application, audio files or video files.”, wherein the examiner interprets “electronic objects” to be the same as a plurality of documents because they are both directed to multiple digital content items, and interprets “metadata from each of the electronic objects” to be the same as a plurality of document elements because they are both directed to constituent descriptive information associated with each content item. The examiner further interprets “computes hypergraph data from the metadata” to be the same as generating the hypergraph based on the plurality of document elements because they are both directed to forming hypergraph information from descriptive elements associated with multiple content items.)
Sakhinana and Cheek does not teach and generating a predicted document element based on the augmented hypergraph.
Chen teaches and generating a predicted document element based on the augmented hypergraph. (Chen, [0081] “A main idea of this step is to explore the cross-modal and cross-context multivariate and high-order information. In the present disclosure, hyper-graph H is first constructed from the sequentially encoded utterances”, and [Chen [0116]] “S5. A prediction label is acquired for each utterance in the input conversation based on the emotional representation, and the prediction label is output as a multimodal ERC result”, wherein the examiner interprets “hyper-graph H is first constructed from the sequentially encoded utterances” to be the same as the augmented hypergraph because they are both directed to a hypergraph structure generated from constituent elements of a larger content item. The examiner further interprets “A prediction label is acquired for each utterance in the input conversation” to be the same as generating a predicted document element because they are both directed to generating a predicted element for an individual constituent item, and interprets “based on the emotional representation” after the hypergraph-based processing to be the same as based on the augmented hypergraph because they are both directed to producing the predicted element from a representation derived through hypergraph processing).
Sakhinana, Cheek, Chen, and the instant application are analogous art because they are all directed to hypergraph-based processing of document elements and generating hypergraph structures from content data.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the undirected hypergraph technique disclosed by Sakhinana to include the metadata utilization approach disclosed by Cheek. One would be motivated to do so to effectively generate hypergraph structures from document-derived elements for subsequent neural processing, as suggested by Cheek ([Cheek, col 2, lines 5-7] “it computes hypergraph data from the metadata.”). It would have also been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the undirected hypergraph technique disclosed by Sakhinana to include the prediction label acquisition approach disclosed by Chen. One would be motivated to do so to effectively generate predicted elements for individual components of structured content after hypergraph-based representation learning, as suggested by Chen ([Chen, [0116]] “A prediction label is acquired for each utterance in the input conversation.”).
Conclusion
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/DEVAN KAPOOR/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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