DETAILED ACTION
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 .
Information Disclosure Statement
The information disclosure statement filed 15 April 2024 fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. It has been placed in the application file, but the information referred to therein has not been considered.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-6 and 8-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-5 and 7-17 of U.S. Patent No. 12,032,916. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the issued patent are narrower in scope than that of the instant application. Therefore, the claims of the issued patent anticipate the claims of the instant application. Please see the below mapping with respect to the claims below.
Instant Application
US Patent: 12,032,916
Claim 1: A method of dialogue parsing, executable by a processor, comprising:
Claim 1: A method of dialogue parsing, executable by a processor, comprising:
receiving dialogue data
receiving dialogue data having one or more elementary discourse units;
; initializing nodes and edges of a structural self-aware graph neural network (SSA-GNN) based on the dialogue data
initializing nodes and edges of a structural self-aware graph neural network (SSA-GNN) based on the one or more elementary discourse units; {elementary discourse units are dialogue data}
; determining a local representation, by multiple bidirectional gated recurrent units (GRUs) consuming utterances represented by the dialogue data
determining a local representation, by multiple bidirectional gated recurrent units (GRUs) consuming each utterance represented by the one or more elementary discourse units
and then concatenating a last hidden state in the multiple GRUs
and then concatenating a last hidden state in the multiple GRUs
, and a global representation, by applying another bidirectional GRU on the local representations
, and a global representation, by applying another bidirectional GRU on the local representations, for each of the elementary discourse units;
generating, in a neural network, at least one edge-specific vector representing an edge between a pair of elementary discourse units
, the at least one edge-specific vector being generated based on the determined local and global representations
, the at least one edge-specific vector capturing relation information for the pair of elementary discourse units;
; identifying relationships between the elementary discourse units
identifying relationships between the elementary discourse units
based on the at least one edge-specific vector generated in the neural network
based on structure-aware scaled dot-product attention of the SSA-GNN
and based on structure-aware scaled dot-product attention of the SSA-GNN
and a layer-wise classifier on edge hidden states
and a layer-wise classifier on edge hidden states of each SSA-GNN layer; and
; and predicting a contextual link between non-adjacent elementary discourse units
predicting a contextual link between non-adjacent elementary discourse units
based on the identified relationships
based on the identified relationships
Claim 2: The method of claim 1,
Claim 2: The method of claim 1,
wherein the determining of the local representations comprises: processing each elementary discourse unit of the dialogue data through a first bidirectional gated recurrence unit
wherein the determining of the local representations comprises: processing each elementary discourse unit through a first bidirectional gated recurrence unit
; and concatenating a hidden state generated by the first bidirectional gated recurrence unit in two directions for each elementary discourse unit.
; and concatenating a hidden state generated by the first bidirectional gated recurrence unit in two directions for each elementary discourse unit
Claim 3: The method of claim 2
Claim 3: The method of claim 2
, further comprising updating the hidden state
, further comprising updating the hidden state
based on structure-aware scaled dot-product attention.
based on structure-aware scaled dot-product attention.
Claim 4: The method of claim 2,
Claim 4: The method of claim 2,
wherein determining the global representations comprises: processing each local representation through a second bidirectional gated recurrence unit.
wherein determining the global representations comprises: processing each local representation through a second bidirectional gated recurrence unit.
Claim 5: The method of claim 1,
Claim 5: The method of claim 1,
wherein the relationships between elementary discourse units of the dialogue data are identified
wherein the relationships between the elementary discourse units are identified
based on capturing implicit structural information corresponding to: a node-specific vector for each elementary discourse unit
based on capturing implicit structural information corresponding to: a node-specific vector for each elementary discourse unit
, and an edge-specific vector, of the at least one edge-specific vector generated in the neural network, for each pair of elementary discourse units.
, and an edge-specific vector, of the at least one edge-specific vector generated in the neural network, for each pair of elementary discourse units.
Claim 6: The method of claim 1
Claim 7: The method of claim 1
, further comprising training the neural network based on a layer-wise relation classification on edge hidden states of each layer of the neural network for each pair of elementary discourse units.
, further comprising training the neural network based on a layer-wise relation classification on edge hidden states of each layer of the neural network for each pair of elementary discourse units.
Claim 8: The method of claim 1,
Claim 1 (cont): (Limitations of claim 1 are incorporated by reference)
wherein the dialogue data comprises one or more elementary discourse units,
receiving dialogue data having one or more elementary discourse units;
wherein initializing the nodes and the edges of the SSA-GNN based on the dialogue data comprises initializing the nodes and the edges of the SSA-GNN based on the one or more elementary discourse units of the dialogue data
initializing nodes and edges of a structural self-aware graph neural network (SSA-GNN) based on the one or more elementary discourse units;
, and wherein determining the local representation, by the multiple GRUs consuming the utterances represented by the dialogue data and then concatenating the last hidden state in the multiple GRUs , and the global representation, by applying the other bidirectional GRU on the local representations comprises determining the local representation, by the multiple GRUs consuming each utterance represented by the one or more elementary discourse units of the dialogue data
determining a local representation, by multiple bidirectional gated recurrent units (GRUs) consuming each utterance represented by the one or more elementary discourse units
and then concatenating the last hidden state in the multiple GRUs
and then concatenating a last hidden state in the multiple GRUs
, and the global representation, by applying the other bidirectional GRU on the local representations
, and a global representation, by applying another bidirectional GRU on the local representations
for each of the elementary discourse units.
for each of the elementary discourse units.
Claim 9: The method of claim 8
Claim 1 (cont): (Limitations of claim 1 are incorporated by reference)
, further comprising generating, in a neural network, at least one edge-specific vector representing an edge between a pair of elementary discourse units
generating, in a neural network, at least one edge-specific vector representing an edge between a pair of elementary discourse units
, the at least one edge-specific vector being generated based on the determined local and global representations
, the at least one edge-specific vector being generated based on the determined local and global representations
, the at least one edge-specific vector capturing relation information for the pair of elementary discourse units.
, the at least one edge-specific vector capturing relation information for the pair of elementary discourse units;
Claim 10: The method of claim 9,
Claim 1 (cont): (Limitations of claim 1 are incorporated by reference)
wherein identifying the relationships between the elementary discourse units based on the structure-aware scaled dot-product attention of the SSA-GNN and the layer-wise classifier on the edge hidden states comprises identifying the relationships between the elementary discourse units
identifying relationships between the elementary discourse units
based on the at least one edge specific vector generated in the neural network and
based on the at least one edge-specific vector generated in the neural network
based on the structure-aware scaled dot-product attention of the SSA-GNN
and based on structure-aware scaled dot-product attention of the SSA-GNN
and the layer-wise classifier on the edge hidden states of each SSA GNN layer.
and a layer-wise classifier on edge hidden states of each SSA-GNN layer…
Claim 11: A computer system for dialogue parsing
Claim 8: A computer system for dialogue parsing,
, the computer system comprising: one or more computer-readable non-transitory storage media configured to store computer program code
the computer system comprising: one or more computer-readable non-transitory storage media configured to store computer program code; and
; and one or more computer processors configured to access said computer program code and operate as instructed by said computer program code
one or more computer processors configured to access said computer program code and operate as instructed by said computer program code
, said computer program code including: receiving code configured to cause the one or more computer processors to receive dialogue data
, said computer program code including: receiving code configured to cause the one or more computer processors to receive dialogue data having one or more elementary discourse units;
; initializing code configured to cause the one or more computer processors to initialize nodes and edges of a structural self-aware graph neural network (SSA-GNN) based on the dialogue data
initializing code configured to cause the one or more computer processors to initialize nodes and edges of a structural self-aware graph neural network (SSA-GNN) based on the one or more elementary discourse units; {elementary discourse units are dialogue data}
; determining code configured to cause the one or more computer processors to determine a local representation, by multiple bidirectional gated recurrent units (GRUs) consuming utterances represented by the dialogue data and then concatenating a last hidden state in the multiple GRUs
determining code configured to cause the one or more computer processors to determine a local representation, by multiple bidirectional gated recurrent units (GRUs) consuming each utterance represented by the one or more elementary discourse units {elementary discourse units are dialogue data} and then concatenating a last hidden state in the multiple GRUs
, and a global representation, by applying another bidirectional GRU on the local representations
, and a global representation, by applying another bidirectional GRU on the local representations, for each of the elementary discourse units;
network code configured to cause the one or more computer processors to generate, in a neural network, at least one edge-specific vector representing an edge between a pair of elementary discourse units, the at least one edge-specific vector being generated based on the determined local and global representations, the at least one edge-specific vector capturing relation information for the pair of elementary discourse units;
; identifying code configured to cause the one or more computer processors to identify relationships between the elementary discourse units
identifying code configured to cause the one or more computer processors to identify relationships between the elementary discourse units
based on the at least one edge-specific vector generated in the neural network
based on structure-aware scaled dot-product attention of the SSA-GNN
and based on structure-aware scaled dot-product attention of the SSA-GNN
and a layer-wise classifier on edge hidden states
and a layer-wise classifier on edge hidden states of each SSA-GNN layer; and
; and predicting code configured to cause the one or more computer processors to predict a contextual link between non-adjacent elementary discourse units
predicting code configured to cause the one or more computer processors to predict a contextual link between non-adjacent elementary discourse units
based on the identified relationships.
based on the identified relationships
Claim 12: The computer system of claim 11,
Claim 9: The computer system of claim 8,
wherein the determining code comprises
wherein the determining code comprises
: processing code configured to cause the one or more computer processors to process each elementary discourse unit of the dialogue data through a first bidirectional gated recurrence unit
: processing code configured to cause the one or more computer processors to process each elementary discourse unit through a first bidirectional gated recurrence unit
; and concatenating code configured to cause the one or more computer processors to concatenate a hidden state generated by the first bidirectional gated recurrence unit in two directions for each elementary discourse unit.
; and concatenating code configured to cause the one or more computer processors to concatenate a hidden state generated by the first bidirectional gated recurrence unit in two directions for each elementary discourse unit.
Claim 13: The computer system of claim 12
Claim 10: The computer system of claim 9
, further comprising updating code configured to cause the one or more computer processors to update the hidden state
, further comprising updating code configured to cause the one or more computer processors to update the hidden state
based on structure-aware scaled dot-product attention.
based on structure-aware scaled dot-product attention.
Claim 14: The computer system of claim 12,
Claim 11: The computer system of claim 9,
wherein the determining code further comprises
wherein the determining code further comprises
: second processing code configured to cause the one or more computer processors to process each local representation through a second bidirectional gated recurrence unit.
: second processing code configured to cause the one or more computer processors to process each local representation through a second bidirectional gated recurrence unit.
Claim 15: The computer system of claim 11,
Claim 12: The computer system of claim 8,
wherein the relationships between the elementary discourse units of the dialogue data are identified
wherein the relationships between the elementary discourse units {elementary discourse units are dialogue data} are identified
based on capturing implicit structural information corresponding to
based on capturing implicit structural information corresponding to
: a node-specific vector for each elementary discourse unit
: a node-specific vector for each elementary discourse unit
, and an edge-specific vector, of the at least one edge-specific vector generated in the neural network, for each pair of elementary discourse units.
, and an edge-specific vector, of the at least one edge-specific vector generated in the neural network, for each pair of elementary discourse units.
Claim 16: The computer system of claim 11
Claim 13: The computer system of claim 8
, further comprising training code configured to cause the one or more computer processors to train the neural network
, further comprising training code configured to cause the one or more computer processors to train the neural network
based on a layer-wise relation classification on edge hidden states of each layer of the neural network for each pair of elementary discourse units.
based on a layer-wise relation classification on edge hidden states of each layer of the neural network for each pair of elementary discourse units.
Claim 17: The computer system of claim 16,
Claim 14: The computer system of claim 13,
wherein the layer-wise relation classification is based on at least one interim edge-specific vector
wherein the layer-wise relation classification is based on at least one interim edge-specific vector
at each layer of the neural network.
at each layer of the neural network.
Claim 18: A non-transitory computer readable medium having stored thereon a computer program for dialogue parsing
Claim 15: A non-transitory computer readable medium having stored thereon a computer program for dialogue parsing
, the computer program configured to cause one or more computer processors to
, the computer program configured to cause one or more computer processors to:
: receive dialogue data
receive dialogue data having one or more elementary discourse units;
; initialize nodes and edges of a structural self-aware graph neural network (SSA-GNN) based on the dialogue data
initialize nodes and edges of a structural self-aware graph neural network (SSA-GNN) based on the one or more elementary discourse units; {elementary discourse units are part of the dialogue data}
; determine a local representation, by multiple bidirectional gated recurrent units (GRUs) consuming utterances represented by the dialogue data and then concatenating a last hidden state in the multiple GRUs
determine a local representation, by multiple bidirectional gated recurrent units (GRUs) consuming each utterance represented by the one or more elementary discourse units {elementary discourse units are part of the dialogue data} and then concatenating a last hidden state in the multiple GRUs
, and a global representation, by applying another bidirectional GRU on the local representations
, and a global representation, by applying another bidirectional GRU on the local representations, for each of the elementary discourse units;
generate, in a neural network, at least one edge-specific vector representing an edge between a pair of elementary discourse units, the at least one edge-specific vector being generated based on the determined local and global representations, the at least one edge-specific vector capturing relation information for the pair of elementary discourse units;
; identify relationships between the elementary discourse units
identify relationships between the elementary discourse units
based on the at least one edge-specific vector generated in the neural network and
based on structure-aware scaled dot-product attention of the SSA-GNN and a layer-wise classifier on edge hidden states
based on structure-aware scaled dot-product attention of the SSA-GNN and a layer-wise classifier on edge hidden states of each SSA-GNN layer; and
; and predict a contextual link between non-adjacent elementary discourse units
predict a contextual link between non-adjacent elementary discourse units
based on the identified relationships.
based on the identified relationships.
Claim 19: The computer readable medium of claim 15,
Claim 16: The computer readable medium of claim 15,
wherein the computer program is further configured to cause one or more computer processors to
wherein the computer program is further configured to cause one or more computer processors to
: process each elementary discourse unit through a first bidirectional gated recurrence unit
: process each elementary discourse unit through a first bidirectional gated recurrence unit
; and concatenate a hidden state generated by the first bidirectional gated recurrence unit in two directions for each elementary discourse unit.
; and concatenate a hidden state generated by the first bidirectional gated recurrence unit in two directions for each elementary discourse unit.
Claim 20: The computer readable medium of claim 16,
Claim 17: The computer readable medium of claim 16,
wherein the computer program is further configured to cause one or more computer processors to update the hidden state
wherein the computer program is further configured to cause one or more computer processors to update the hidden state
based on structure-aware scaled dot-product attention.
based on structure-aware scaled dot-product attention.
Claim 7 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 6 of U.S. Patent No. 12,032,916 in view of Mao (CN110751038A, hereinafter Mao). The claims of the issued patent match that of the instant application but does not teach “wherein the layer-wise relation classification is based on at least one interim edge-specific vector at each layer of the neural network”. The layer-wise classification described in Mao relies on the edge vectors produced by the preceding layer to perform the transformation for the current layer. The output of layer i-1 is an "edge feature matrix". The rows in this matrix are the interim edge-specific vectors for that layer. These interim vectors are the direct input to the attention components of layer i, and the classification/transformation performed at layer i is based on said vectors. (Mao, pg. 10, paras. 1-2). It would have been obvious to one or ordinary skilled in the art to have modified the issued patent with the graph attention mechanisms of Mao, as the proposed recognition method “achieves the best results in both table structure recognition data sets” such as those used in dialogue systems, “especially in complex table structure recognition, the effect is obviously improved,” as recognized by Mao. (Mao, pg. 1, para. 1, pg. 3, para. 2). Please see the below mapping with respect to the claims below.
Instant Application
US Patent: 12,032,916
Claim 1: A method of dialogue parsing, executable by a processor, comprising:
Claim 1: A method of dialogue parsing, executable by a processor, comprising:
receiving dialogue data
receiving dialogue data having one or more elementary discourse units;
; initializing nodes and edges of a structural self-aware graph neural network (SSA-GNN) based on the dialogue data
initializing nodes and edges of a structural self-aware graph neural network (SSA-GNN) based on the one or more elementary discourse units; {elementary discourse units are dialogue data}
; determining a local representation, by multiple bidirectional gated recurrent units (GRUs) consuming utterances represented by the dialogue data
determining a local representation, by multiple bidirectional gated recurrent units (GRUs) consuming each utterance represented by the one or more elementary discourse units
and then concatenating a last hidden state in the multiple GRUs
and then concatenating a last hidden state in the multiple GRUs
, and a global representation, by applying another bidirectional GRU on the local representations
, and a global representation, by applying another bidirectional GRU on the local representations, for each of the elementary discourse units;
generating, in a neural network, at least one edge-specific vector representing an edge between a pair of elementary discourse units
, the at least one edge-specific vector being generated based on the determined local and global representations
, the at least one edge-specific vector capturing relation information for the pair of elementary discourse units;
; identifying relationships between the elementary discourse units
identifying relationships between the elementary discourse units
based on the at least one edge-specific vector generated in the neural network
based on structure-aware scaled dot-product attention of the SSA-GNN
and based on structure-aware scaled dot-product attention of the SSA-GNN
and a layer-wise classifier on edge hidden states
and a layer-wise classifier on edge hidden states of each SSA-GNN layer; and
; and predicting a contextual link between non-adjacent elementary discourse units
predicting a contextual link between non-adjacent elementary discourse units
based on the identified relationships
based on the identified relationships
Claim 6: The method of claim 1
Claim 7: The method of claim 1
, further comprising training the neural network based on a layer-wise relation classification on edge hidden states of each layer of the neural network for each pair of elementary discourse units.
, further comprising training the neural network based on a layer-wise relation classification on edge hidden states of each layer of the neural network for each pair of elementary discourse units.
Claim 7: The method of claim 6,
Claims 1 and 6 as mapped above
wherein the layer-wise relation classification is based on at least one interim edge-specific vector at each layer of the neural network.
See Mao
Claim Objections
Claims 19 and 20 are objected to because of the following informalities:
Regarding claim 19, the preamble of claim 19 recites “The computer readable medium of claim 15” which is understood to be a clerical error, as claim 15 is directed to a computer system, not a computer readable medium, and claim 19 appears to more appropriately depend from claim 18.
In light of the above, the following proposed claim amendment to claim 19, if accepted by the applicant, would overcome the objection: “The computer readable medium of claim [[15]]18”
Regarding claim 20, the preamble of claim 20 recites “The computer readable medium of claim 16” which is understood to be a clerical error, as claim 16 is directed to a computer system, not a computer readable medium, and claim 20 appears to more appropriately depend from claim 19.
In light of the above, the following proposed claim amendment to claim 20, if accepted by the applicant, would overcome the objection: “The computer readable medium of claim [[16]]19”.
For purposes of compact prosecution, claims 19 and 20 will be analyzed as if dependent from claims 18 and 19, respectively
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, and mutatis mutandis claims 11 and 18, recite the limitation "the elementary discourse units" in line 9. There is insufficient antecedent basis for this limitation in the claim.
Claims 2-10, 12-17, and 19-20 depend from claims 1, 11, and 18, and incorporate all limitations therefrom. Therefore, claims 2-10, 12-17, and 19-20 are rejected under 35 U.S.C. 112(b) for at least the same reasons as claims 1, 11, and 18.
Appropriate correction is required.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Non-Patent Literature to Shi (Shi, Zhouxing, and Minlie Huang. “A deep sequential model for discourse parsing on multi-party dialogues.” Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33. No. 01. 2019, hereinafter Shi) in view of Non-Patent Literature to Lin (Lin, Xiang, Shafiq Joty, Prathyusha Jwalapuram, and M. Saiful Bari. "A unified linear-time framework for sentence-level discourse parsing." arXiv preprint arXiv:1905.05682 (2019), hereinafter Lin) and Mao (CN110751038A, hereinafter Mao).
Regarding claim 1, Shi discloses A method of dialogue parsing, executable by a processor (systems and methods described with reference to the "deep sequential model for discourse parsing on multi-party dialogues"; Shi, ¶ p. 7008, Col. 1, lines 12-13), comprising: receiving dialogue data ("proposed model... makes a sequential scan of the Elementary Discourse Units (EDUs) in a dialogue."; Shi, ¶ p. 7008, Col. 1, lines 16-17); determining a local representation, by... [a bidirectional gated recurrent unit (GRUs)] consuming utterances represented by the dialogue data ("In our model, we use two categories of discourse representations" including "local representations {determining a local representation}... [which] are non-structured and encode the local information of EDUs individually" where "Our model first computes the non-structured representations of the EDUs with hierarchical Gated Recurrent Unit (GRU) (Cho et al. 2014) encoders" where "the model makes a sequential scan of the EDUs {consuming utterances represented by the dialogue data}"; Shi, ¶ p. 7009, col. 1, lines 15-18; col. 2, lines 25-33) and then concatenating a last hidden state in the multiple GRUs ("For each EDU ui, a bidirectional GRU (bi-GRU) encoder is applied on the word sequence, and the last hidden states in two directions are concatenated as the local representation of ui,"; Shi, ¶ p. 7009, Col. 2, lines 34-37), and a global representation, by applying another bidirectional GRU on the local representations (The second of the "two categories of discourse representations" is "global representations {...and a global representation...} [which] encode the global information of the EDU sequence or the predicted discourse structure {... for each of the elementary discourse units}" where the system uses the "local representations of the EDUs...as input to a GRU encoder and the hidden states are viewed as the non-structured global representations of the EDUs"; Shi, ¶ p. 7009, Col. 2, lines 25-33); identifying relationships between the elementary discourse units… ("These non-structured representations are used for predicting dependency relations and encoding structured representations."; Shi, ¶ p. 7009, col. 1, lines 15-20) ; and predicting a contextual link between non-adjacent elementary discourse units (The system "compute[s] the structured representation of ui once its parent and the corresponding relation type are decided" for "predicting a dependency relation {predicting a contextual link} linking from uj to ui {between... elementary discourse units}"; Shi, ¶ p. 7010, Col. 1, lines 24-25; Col. 1 lines 33-34) based on the identified relationships (The system computes the structured representations using "the embedding vector of relation type rji {based on the identified relationships}"; Shi, ¶ p. 7010, Col. 1, line 42-Col. 2, line 5). However, Shi fails to expressly recite initializing nodes and edges of a structural self-aware graph neural network (SSA-GNN) based on the dialogue data; determining a local representation, by multiple bidirectional gated recurrent units (GRUs) consuming utterances represented by the dialogue data; identifying relationships between the elementary discourse units based on structure-aware scaled dot-product attention of the SSA-GNN and a layer-wise classifier on edge hidden states.
Lin teaches a “neural framework for sentence-level discourse analysis.” (Lin, Abstract). Regarding claim 1, Lin teaches determining a local representation, by multiple bidirectional gated recurrent units (GRUs) consuming utterances represented by the dialogue data ("our encoder uses six (6) recurrent layers of BiGRU cells, and generates hidden statesH = (h1;:::;hn) by composing the word representations {local representations} sequentially from left-to-right and from right-to-left."; Lin, ¶ pg. 4, col. 1, para. 3).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the discourse parsing model of Shi to incorporate the teachings of Lin to include determining a local representation, by multiple bidirectional gated recurrent units (GRUs) consuming utterances represented by the dialogue data. The “recurrent neural network (RNN) based on bidirectional Gated Recurrent Units or BiGRU” of Lin is based on the same original model relied on by Shi (Both authors cite the same original author Cho, and same paper, as the source for the bidirectional GRUs applied in their respective systems), and can “capture long range dependencies” while using “fewer parameters” than prior art cells, thus reducing processing overhead without sacrificing quality of results in an attention mechanism, as recognized by Lin. (Lin, p. 4, col. 1, para. 3). However, Shi and Lin fail to expressly recite initializing nodes and edges of a structural self-aware graph neural network (SSA-GNN) based on the dialogue data; identifying relationships between the elementary discourse units based on structure-aware scaled dot-product attention of the SSA-GNN and a layer-wise classifier on edge hidden states.
Mao teaches systems and methods for “table structure recognition” as applied to “identified machine-understandable tables” for “dialogue systems”. (Mao, pg. 3, para. 2). Regarding claim 1, Mao teaches initializing nodes and edges of a structural self-aware graph neural network (SSA-GNN) based on the dialogue data (Discloses creating a graph where the table cells are nodes and their relationships are edges, and where the table and cells can correspond to "machine-understandable tables" as used in for dialogue in "dialogue systems"; Mao, ¶ pg. 5, para. 1 (step 2. and 2.); pg. 3, para. 2); identifying relationships between the elementary discourse units based on structure-aware scaled dot-product attention of the SSA-GNN (Discloses a "graph attention layer" that employs a scaled dot-product attention (as understood from the explicit use of the associated formula). Further, the attention is structure aware in that it is masked by the adjacency matrix B, forcing it to consider existing connections in the graph.; Mao, ¶ pg. 14, para. 4; pg. 15, Formula at top of page. ([0060] in the original document)) and a layer-wise classifier on edge hidden states (Discloses an "edge classification model" that uses 2N graph attention components" to process and refine an "edge feature matrix {edge hidden states}," where each of the N components is a learnable layer-wise function which implicitly classifies or transforms the edge states to make them more separable for later application {a layer-wise classifier}; Mao, ¶ pg. 10, paras. 1-2).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the discourse parsing model of Shi , as modified by the discourse analysis of Lin, to incorporate the teachings of Mao to include initializing nodes and edges of a structural self-aware graph neural network (SSA-GNN) based on the dialogue data; identifying relationships between the elementary discourse units based on structure-aware scaled dot-product attention of the SSA-GNN and a layer-wise classifier on edge hidden states. The graph attention mechanisms of Mao “achieves the best results in both table structure recognition data sets” such as those used in dialogue systems, “especially in complex table structure recognition, the effect is obviously improved,” as recognized by Mao. (Mao, pg. 1, para. 1, pg. 3, para. 2).
Regarding claim 2, the rejection of claim 1 is incorporated. Shi further discloses wherein the determining of the local representations comprises: processing each elementary discourse unit of the dialogue data through a first bidirectional gated recurrence unit ("For each EDU ui, a bidirectional GRU (bi-GRU) encoder is applied on the word sequence"; Shi, ¶ p. 7009, Col. 2, lines 34-35); and concatenating a hidden state generated by the first bidirectional gated recurrence unit in two directions for each elementary discourse unit ("the last hidden states in two directions are concatenated as the local representation of ui, denoted as hi."; Shi, ¶ p. 7009, Col. 2, lines 36-37).
Regarding claim 3, the rejection of claim 2 is incorporated. Shi disclose all of the elements of the current invention as stated above. However, Shi fail(s) to expressly recite further comprising updating the hidden state based on structure-aware scaled dot-product attention.
The relevance of Mao is described above with relation to claim 1. Regarding claim 3, Mao teaches further comprising updating the hidden state based on structure-aware scaled dot-product attention (The purpose of the N layers of the "graph attention components" is to update the hidden states (the "point feature matrix" and the "edge feature matrix"), which the system performs by using the scaled dot-product attention formula. As previously indicated, the attention is structure aware because it’s masked by the adjacency matrix, which encodes the graphs structure.; Mao, ¶ pg. 3, para. 15-16).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the discourse parsing model of Shi, as modified by the discourse analysis of Lin, as modified by the graph attention mechanisms of Mao, to further incorporate the teachings of Mao to include further comprising updating the hidden state based on structure-aware scaled dot-product attention. The graph attention mechanisms of Mao “achieves the best results in both table structure recognition data sets” such as those used in dialogue systems, “especially in complex table structure recognition, the effect is obviously improved,” as recognized by Mao. (Mao, pg. 1, para. 1, pg. 3, para. 2).
Regarding claim 4, the rejection of claim 2 is incorporated. Shi further discloses wherein determining the global representations comprises: processing each local representation through a second bidirectional gated recurrence unit ("The local representations of the EDUs... are taken as input to a GRU encoder and the hidden states are viewed as the non-structured global representations of the EDUs" where the GRUs are described as bidirectional GRUs.; Shi, ¶ p. 7010, col. 1, lines 1-5).
Regarding claim 5, the rejection of claim 1 is incorporated. Shi disclose all of the elements of the current invention as stated above. However, Shi fail(s) to expressly recite wherein the relationships between elementary discourse units of the dialogue data are identified based on capturing implicit structural information corresponding to: a node-specific vector for each elementary discourse unit, and an edge-specific vector, of the at least one edge-specific vector generated in the neural network, for each pair of elementary discourse units.
The relevance of Mao is described above with relation to claim 1. Regarding claim 5, Mao teaches wherein the relationships between elementary discourse units of the dialogue data are identified (Mao identifies relationships between the units by using the neural network model to predict the adjacency relationship between points (cells) and edges, where a cell is analogous to an elementary discourse unit.; Mao, ¶ pg. 6, para. 1) based on capturing implicit structural information (the model uses adjacency matrix B to "Record the structural information of the undirected graph"; Mao, ¶ pg. 10, para. 1) corresponding to: a node-specific vector for each elementary discourse unit (The model creates and uses a feature vector for each node, which it calls a "point" or "cell". The method includes extracting "the feature information of each cell (node)" which it stores in a "point feature matrix"; Mao, ¶ pg. 5, para. 8 (step 3), para. 10), and an edge-specific vector (The method further includes extracting "the feature information of… each edge" which is stored in an edge feature matrix" by taking the "eigenvector of each edge as a row"; Mao, ¶ pg. 5, para. 8 (step 3); pg. 6, para. 1), of the at least one edge-specific vector generated in the neural network, for each pair of elementary discourse units (The model’s neural network generates new, updates edge vectors at each of the n layers where "The point to edge attention component is responsible for integrating the feature information" where the "latent representation of the edge feature matrix… is represented by H’E" and which is performed on "all edges"; Mao, ¶ pg. 10, para. 2; pg. 15, para. 1).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the discourse parsing model of Shi, as modified by the discourse analysis of Lin, as modified by the graph attention mechanisms of Mao, to further incorporate the teachings of Mao to include wherein the relationships between elementary discourse units of the dialogue data are identified based on capturing implicit structural information corresponding to: a node-specific vector for each elementary discourse unit, and an edge-specific vector, of the at least one edge-specific vector generated in the neural network, for each pair of elementary discourse units. The graph attention mechanisms of Mao “achieves the best results in both table structure recognition data sets” such as those used in dialogue systems, “especially in complex table structure recognition, the effect is obviously improved,” as recognized by Mao. (Mao, pg. 1, para. 1, pg. 3, para. 2).
Regarding claim 6, the rejection of claim 1 is incorporated. Shi disclose all of the elements of the current invention as stated above. However, Shi fail(s) to expressly recite further comprising training the neural network based on a layer-wise relation classification on edge hidden states of each layer of the neural network for each pair of elementary discourse units.
The relevance of Mao is described above with relation to claim 1. Regarding claim 6, Mao teaches further comprising training the neural network (Discloses training a neural network in section "(A) model training"; Mao, ¶ pg. 18, para. 3) based on a layer-wise relation classification (Discloses an "edge classification model" that uses 2N graph attention components" to process and refine an "edge feature matrix {edge hidden states}," where each of the N layers are performing one step in a distributed layer-wise classification process., where each layer’s function is an implicit classification or transformation which makes the final classification possible. {a layer-wise relation classifier}; Mao, ¶ pg. 10, paras. 1-2) on edge hidden states of each layer of the neural network for each pair of elementary discourse units. (The model takes an initial "edge feature matrix" as input, where, for the i-th layer the input is the "edge feature matrix obtained by the previous layer" and "output gets the current edge feature matrix". These matrices are the edge hidden states for each layer, which, in light of Shi, is performed for each pair of elementary discourse units.; Mao, ¶ pg. 17 (inclusive)-pg. 18, para. 1; pg. 14, para. 1).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the discourse parsing model of Shi, as modified by the discourse analysis of Lin, as modified by the graph attention mechanisms of Mao, to further incorporate the teachings of Mao to include further comprising training the neural network based on a layer-wise relation classification on edge hidden states of each layer of the neural network for each pair of elementary discourse units. The graph attention mechanisms of Mao “achieves the best results in both table structure recognition data sets” such as those used in dialogue systems, “especially in complex table structure recognition, the effect is obviously improved,” as recognized by Mao. (Mao, pg. 1, para. 1, pg. 3, para. 2).
Regarding claim 7, the rejection of claim 6 is incorporated. Shi disclose all of the elements of the current invention as stated above. However, Shi fail(s) to expressly recite wherein the layer-wise relation classification is based on at least one interim edge-specific vector at each layer of the neural network.
The relevance of Mao is described above with relation to claim 1. Regarding claim 7, Mao teaches wherein the layer-wise relation classification is based on at least one interim edge-specific vector at each layer of the neural network (The layer-wise classification described in Mao relies on the edge vectors produced by the preceding layer to perform the transformation for the current layer. The output of layer i-1 is an "edge feature matrix". The rows in this matrix are the interim edge-specific vectors for that layer. These interim vectors are the direct input to the attention components of layer i, and the classification/ transformation performed at layer i is based on said vectors.; Mao, ¶ pg. 10, paras. 1-2).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the discourse parsing model of Shi, as modified by the discourse analysis of Lin, as modified by the graph attention mechanisms of Mao, to further incorporate the teachings of Mao to include wherein the layer-wise relation classification is based on at least one interim edge-specific vector at each layer of the neural network. The graph attention mechanisms of Mao “achieves the best results in both table structure recognition data sets” such as those used in dialogue systems, “especially in complex table structure recognition, the effect is obviously improved,” as recognized by Mao. (Mao, pg. 1, para. 1, pg. 3, para. 2).
Regarding claim 8, the rejection of claim 1 is incorporated. Shi disclose all of the elements of the current invention a