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 .
DETAILED ACTION
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.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 1, 10, 17 is/are directed to a system, method, etc. for generating a coreference resolution model. The claims rely on well understood, routine, and conventional structures such as a processor, memory, data structure, etc. to instruct the system along methods by which input data in the form of plural documents is analyzed sentence wise to determine dependency relations among the language elements within the document, sentences, etc. and to thereby generate an embedding vector for a neural network trained on data determined from the documents. The claims are considered a manner by which data resolves more data, in this case a plural documents, elements therein or determined therefrom based on analysis are used to resolve data in the form of neural network embedding(s) and the neural network is used to generate subsequent data in the form of a model; the claims are also considered a stand in for human behavior as the claims steps are substantially similar to the manner in which a human being might parse sentences to resolve relationships therein. As such the claims cannot be considered to integrate the judicial exceptions of an abstract idea and are considered data per se or programs per se nor do the claims integrate the judicial exception of human activity and/or mental processes such as operations performed in the human mind, human activity, human behavior; etc. as the claims do not include substantially more than the performance of such exceptions upon a computer claimed at a high level of generality and based on models intended to mimic or replicate human cognitive processes. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception as they are claimed at a high level of generality and essentially comprise a computer. Dependent claims 2-9, 11-15, 18-20 do not remedy and are similarly rejected as the claims further address additional subject matter which may be seen as the generation of data from data; a stand in for human behavior, and/or human application of agency in concert with assistive instructions, mathematic concepts, AI models, etc.
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 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-20 rejected under 35 U.S.C. 103 as being unpatentable over Chen: 20240037339 hereinafter Che further in view of Fei: 20210240929.
Regarding claim 1
Che teaches:
An apparatus for learning-based coreference resolution (Che: Abstract: a dependency graph maps entity relations), comprising:
a data generation module that extracts one or more natural language sentences from a natural language paragraph (Che: Abstract; ¶ 15, 45, 48, 49: for each input document unstructured natural-language sentences are provided as input to a machine-learning model to generate contextual word embeddings by building a coreference relation graph based on all the words of received sentences in an input document and determining a sentence level dependency graph by parsing the document sentence wise) and
performs dependency parsing on the natural language sentences to generate dependency relation data of the natural language sentences (Che: ¶ 2, 34, 37: sentence level dependency relations or parse sentences operable to generate a dependency tree);
an embedding module that generates an integrated embedding vector for the natural language paragraph based on the natural language sentence and the dependency relation data (Che: Abstract; ¶ 45-49, 65; Fig 5: system generates a graphs based on sentences, words, etc. of input documents, generates a graph of dependencies a graph of coreferences in concert with determined embeddings and provides same to a graph neural network wherein the embedding layer of the system receives a matrix of word vectors, values etc. as input and at least the embedding and encoding layers of the system output vectors); and
a coreference resolution module that trains a graph learning neural network based on the integrated embedding vector (Che: Abstract; ¶ 45-49, 65; Fig 5: system determines global coreference relations and local dependency relation to generate embedding vectors and thereby train a fusion layer of the graph neural network) and a first coreference mention preset (Che: Abstract; ¶ 54, 63; Fig 5: such as a golden label or other labelled data for training) for the natural language paragraph to generate a named entity resolution model (Che: Abstract; ¶ 15, etc.; Fig 5, etc.).
Che does not explicitly teach the generate a coreference resolution model such as by using deep learning-based coreference resolution in concert with the employ of a training data generation module.
In a related field of endeavor Fei teaches a deep reinforcement learning system and method (Fei: Title; ¶ 27, 29) for coreference resolution (Fei: ¶ 29, 30) such as by iteratively generating training data such as co-reference graphs for input document used over a plurality of epochs (Fei: ¶ 29, 30; Fig 1, 2; claims 2, 16) and a first coreference mention preset (Fei: ¶ 5, 6, etc.; claim 1, 2: such as the recited ground truth coreference information for a document and operable for training a coreference resolution model) for the natural language paragraph to generate, train, improve, etc. a coreference resolution model (Fei: Abstract; ¶ 5; claim 1). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to utilize integrated embeddings and annotated labelled mentions such as those discussed by Che to train the Pei coreference model for at least the purpose of deterring mentions and performing coreference recognition based thereon; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 2
Che in view of Fei teaches or suggests:
The apparatus of claim 1, wherein the training data generation module includes:
a natural language sentence extraction unit that extracts the natural language sentence from the natural language paragraph (Che: ¶ 47-50; Fig 5: system performs sentence extraction from documents reliably consisting of paragraphs, sections, etc.);
a dependency parsing unit that performs the dependency parsing on the natural language sentence (Che: ¶ 47-50; Fig 5: system parses sentences to determine dependency arcs); and
a dependency relation extraction unit that generates the dependency relation data by extracting the dependency relation between words included in the natural language sentence based on a dependency parsing result of the dependency parsing unit (Che: ¶ 47-50; Fig 5: system parses sentences to determine dependency arcs thereby generating token-wise or word-wise graphs of dependency relations). The claim is considered obvious over Che as modified by Fei as addressed in the base claim as it would have been obvious to apply the further teaching of Che and/or Fei to the modified device of Che and Fei; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 3
Che in view of Fei teaches or suggests:
The apparatus of claim 1, wherein the embedding module includes:
a natural language embedding unit that embeds the words included in the natural language sentence to generate a natural language embedding vector (Che: ¶ 45-50: system uses bert to determine embedding layer data including embeddings, word vector data input thereto, vectors output thereby to an embedding layer which subsequently outputs vectors to an encoding layer for a GNN operative upon and outputting matrices of vectors; adjacency matrices, etc.); (Fei: ¶ 51-62; Fig 4, 6: embeddings of spans input to coreference module to generate conference scores ;
a dependency relation embedding unit that embeds the dependency relation data to generate a dependency relation embedding vector that integrates the natural language embedding vector and the dependency relation embedding vector to generate the integrated embedding vector (Che: 45-50, 53, 54; Fig 5; and an integrated embedding unit provides vectorized data to the GNN which propagates the information for combining with the vectorized embedding and encoding outputs to generate output recognition data); (Fei: ¶ 51-62; Fig 4, 6). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to utilize the dependency mention representations of Che as vectorized inputs representing span embeddings to the Fei taught coreference module for at least the purpose of determining coreference scores thereby; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 4
Che in view of Fei teaches or suggests:
The apparatus of claim 1, wherein the coreference resolution module includes:
a deep learning neural network calculation unit that inputs the integrated embedding vector (see claim 1 supra: Che: Abstract, Fig 5, etc. system generates an integrated embedding vector as input to a neural network) to the deep learning neural network (Fei: ¶ 5, 6, 23, 28, 31, 41; Fig 1, 4; Claim 4: a policy network inputs documents, spans thereof by generating character, word, etc. embeddings from the document and utilizes same as input to the deep learning framework to calculate scores, probabilities, etc. based thereon and with respect to the input embedding vector of such a network such as to detect mentions);
a mention detection unit that detects a mention in the natural language paragraph based on a calculation result of the deep learning neural network (Fei: ¶ 5, 6, 31: policy network generates first, second, etc. coreference mentions based on a set of antecedent mentions, scores thereof which are calculated by the deep learning system);
a coreference recognition unit that generates a second coreference mention of the natural language paragraph based on a result of the mention detection (Fei: ¶ 5, 6, 31: policy network generates first, second, etc. coreference mentions based on a set of antecedent mentions, scores thereof which are calculated by the deep learning system); and
a training unit that trains the deep learning neural network based on the first coreference mention and the second coreference mention to generate the coreference resolution model (Fei: ¶ 31, 58; claim 2: a ground truth first coreference mention integrated with coreference mentions and antecedents thereof to thereby compute, resolve, etc. a reward, gradient based thereon and to update, train, etc. the policy network thereby). The claim is considered obvious over Che as modified by Fei as addressed in the base claim as it would have been obvious to apply the further teaching of Che and/or Fei to the modified device of Che and Fei; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 5
Che in view of Fei teaches or suggests:
The apparatus of claim 1, wherein the deep learning neural network is a neural network based on a long short-term memory (LSTM) (Che: ¶ 38, 40; Fig 5: GNN comprises, based on, etc. LSTM layer); (Fei: ¶ 6, 42, etc.: neural system integrated with LSTM layer). The claim is considered obvious over Che as modified by Fei as addressed in the base claim as it would have been obvious to apply the further teaching of Che and/or Fei to the modified device of Che and Fei; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 6
Che in view of Fei teaches or suggests:
The apparatus of claim 1, wherein the deep learning neural network is a neural network based on bidirectional encoder representations from transformers (BERT) (Che: ¶ 45, 46, etc.: system based on BERT). The claim is considered obvious over Che as modified by Fei as addressed in the base claim as it would have been obvious to apply the further teaching of Che and/or Fei to the modified device of Che and Fei; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 7
Che in view of Fei teaches or suggests:
The apparatus of claim 4, wherein the training unit calculates a coreference recognition error between the first coreference mention and the second coreference mention, and trains the deep learning neural network based on the error (Fei: ¶ 31, 58; claim 2:a ground truth first coreference mention integrated with coreference mentions and antecedents thereof to thereby compute, resolve, etc. a reward, gradient based thereon and to update, train, etc. the policy network thereby, such as with respect to optimizing epochs, error level, iterations, etc.). The claim is considered obvious over Che as modified by Fei as addressed in the base claim as it would have been obvious to apply the further teaching of Che and/or Fei to the modified device of Che and Fei; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 8
Che in view of Fei teaches or suggests:
The apparatus of claim 4, wherein the training unit calculates a coreference recognition error between the first coreference mention and the second coreference mention, and trains a mention detection parameter of the mention detection unit based on the error (Fei: ¶ 31, 58; claim 2:a ground truth first coreference mention integrated with coreference mentions and antecedents thereof to thereby compute, resolve, etc. a reward, gradient based thereon and to update, train, etc. the policy network thereby, such as with respect to optimizing epochs, error level, iterations, etc.). The claim is considered obvious over Che as modified by Fei as addressed in the base claim as it would have been obvious to apply the further teaching of Che and/or Fei to the modified device of Che and Fei; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 9
Che in view of Fei teaches or suggests:
The apparatus of claim 4, wherein the training unit calculates a coreference recognition error between the first coreference mention and the second coreference mention, and trains a coreference recognition parameter of the coreference recognition unit based on the error (Fei: ¶ 31, 58; claim 2:a ground truth first coreference mention integrated with coreference mentions and antecedents thereof to thereby compute, resolve, etc. a reward, gradient based thereon and to update, train, etc. the policy network thereby, such as with respect to optimizing epochs, error level, iterations, etc.). The claim is considered obvious over Che as modified by Fei as addressed in the base claim as it would have been obvious to apply the further teaching of Che and/or Fei to the modified device of Che and Fei; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 10
Che teaches:
An apparatus for learning-based coreference resolution (Che: Abstract: a dependency graph maps entity relations), comprising:
a first data storage that stores a natural language paragraph and a first coreference mention preset for the natural language paragraph (Che: ¶ 23, 24, 27, 45, 46, 54, 63; Fig 2, 3, 5: first storage devices 16 provide data to dialog computer; dialog computer processes input documents and stores same in second memory 32, 34 and/or in cache memory associated with processor 30; dialog computer stores received text and encodes same for processing and buffering by a graph learning architecture as depicted in figure 5; golden labels and structured, labelled, etc. data are additionally stored and retrieved for training of the figure 5 graph learning architecture); a second data storage (id.: the memory of the dialog computer);
a data generation module that extracts the natural language paragraph (Che: Abstract; ¶ 15, 45, 48, 49: for each input document unstructured natural-language sentences are provided as input to a machine-learning model to generate contextual word embeddings by building a coreference relation graph based on all the words of received sentences in an input document and determining a sentence level dependency graph by parsing the document sentence wise); and the first coreference mention from the first data storage (Che: ¶ 54, 63, etc.: such as the stored labelled data or golden data), extracts one or more natural language sentences from the natural language paragraph Che: Abstract; ¶ 15, 45, 48, 49), performs dependency parsing on the natural language sentence to generate dependency relation data of the natural language sentence (Che: ¶ 2, 34, 37: sentence level dependency relations or parse sentences operable to generate a dependency tree), and stores the natural language paragraph, the first coreference mention, the natural language sentence, and the dependency relation data in the second data storage (Che: Abstract; ¶ 45-49, 65; Fig 5: such as by buffering in memory 32, 34 and/or memory associated with processor 30);
an embedding module that extracts the natural language sentence and the dependency relation data from the second data storage (Che: Abstract; ¶ 45-49, 65; Fig 5: system generates a graphs based on sentences, words, etc. of input documents, generates a graph of dependencies a graph of coreferences in concert with determined embeddings and provides same to a graph neural network wherein the embedding layer of the system receives a matrix of word vectors, values etc. as input and at least the embedding and encoding layers of the system output vectors); and generates an integrated embedding vector for the natural language paragraph based on the natural language sentence and the dependency relation data (id.); and
a coreference resolution module that trains a graph learning neural network based on the integrated embedding vector and the first coreference mention to generate a coreference resolution model (Che: Abstract; ¶ 45-49, 65; Fig 5: system determines global coreference relations and local dependency relation to generate embedding vectors and thereby train a fusion layer of the graph neural network) and a first coreference mention preset (Che: Abstract; ¶ 54, 63; Fig 5: such as a golden label or other labelled data for training) for the natural language paragraph to generate a named entity resolution model (Che: Abstract; ¶ 15, etc.; Fig 5, etc.).
In a related field of endeavor Fei teaches a deep reinforcement learning system and method (Fei: Title; ¶ 27, 29) for coreference resolution (Fei: ¶ 29, 30) such as by iteratively generating training data such as co-reference graphs for input document used over a plurality of epochs (Fei: ¶ 29, 30; Fig 1, 2; claims 2, 16). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to utilize integrated embeddings and annotated labelled mentions such as those discussed by Che to train the Pei coreference model for at least the purpose of deterring mentions and performing coreference recognition based thereon; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 11, 18—the claims are considered to recite substantially similar subject matter to that of claim 2 and are similarly rejected.
Regarding claim 12—the claim is considered to recite substantially similar subject matter to that of claim 3 and is similarly rejected.
Regarding claim 13, 19—the claims are considered to recite substantially similar subject matter to that of claim 4 and are similarly rejected.
Regarding claim 14—the claim is considered to recite substantially similar subject matter to that of claim 5 and is similarly rejected.
Regarding claim 15—the claim is considered to recite substantially similar subject matter to that of claim 6 and is similarly rejected.
Regarding claim 16—the claim is considered to recite substantially similar subject matter to that of claim 7 and is similarly rejected.
Regarding claim 17—the claim is considered to recite substantially similar subject matter to that of claims 1, 10 and is similarly rejected.
Regarding claim 20
Che in view of Fei teaches or suggests:
The method of claim 17, further comprising inferring a third coreference mention for the natural language paragraph input by a user using the coreference resolution model Fei: ¶ 31, 58; claim 2: a ground truth first coreference mention integrated with coreference mentions and antecedents thereof to thereby compute, resolve, etc. a reward, gradient based thereon and to update, train, etc. the policy network thereby which when employed by a user operates to detect or resolve coreferences, scores thereof). The claim is considered obvious over Che as modified by Fei as addressed in the base claim as it would have been obvious to apply the further teaching of Che and/or Fei to the modified device of Che and Fei; one of ordinary skill in the art would have expected only predictable results therefrom.
Conclusion
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/PAUL C MCCORD/ Primary Examiner, Art Unit 2692
/CAROLYN R EDWARDS/ Supervisory Patent Examiner, Art Unit 2692