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 .
Claims 1-20 are pending, and claims 1, 12 and 20 are independent claims.
Response to Arguments
Applicant’s arguments, see Arguments pages 10-14, filed on 05/19/2026, with respect to 35 USC § 101 claim rejections have been fully considered and they are persuasive. Thus, the 35 USC § 101 rejections of claims 1-6, 8-16 and 18-20 are withdrawn.
Applicant's arguments, see Arguments page 14, filed on 05/19/2026, with respect to 35 USC § 103 claim rejections of claims 1-20 have been fully considered but they are not persuasive.
The Applicant argues that “with regard to the §103 rejection of claims 1, 5, 8, 10-12, 15, 19 and 20, Applicant respectfully traverses on the ground that the collective teachings of Farnan and Ferrucci fail to disclose or suggest each and every limitation of claims 1, 5, 8, 10-12, 15, 19 and 20, arranged as recited in those claims, and on the further ground that there is no suggestion or motivation to modify the collective teachings of Farnan and Ferrucci in a manner that would reach these limitations. The remaining cited references fail to overcome the deficiencies of Farnan and Ferrucci as applied to the independent claims. The remaining §103 rejections of claims 2, 6, 7, 9, 13 and 16- 18 are therefore also respectfully traversed” (Arguments, page 14).
The Examiner respectfully disagrees. The collective teachings of Farnan and Ferrucci disclose each of the different claims and the limitations against which those references are referred to. The proposed combination of references teaches or suggests all of the claimed features. The Applicant never provided a detailed argument against the 103 mapping of the claims and limitations with the prior arts provided. However, the Applicant’s argument suggests that the proposed combination of references fails to teach or suggest all of the claimed features. We strongly disagree. The combination or the collective teachings Farnan and Ferrucci disclose or suggest each and every limitation of claims 1, 5, 8, 10-12, 15, 19 and 20, as recited in those claims. Therefore, the 103 rejection is maintained.
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.
Claims 1, 5, 8, 10 - 12, 15 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Farnan et al. Pat App No. US 20250284729 A1 (Farnan) in view of Ferrucci et al. Pat App No. US 20250307321 A1 (Ferrucci) (Domestic Priority (Continuity Data): us-provisional-application US 63571408 20240328).
Regarding Claim 1, Farnan discloses a method comprising:
according to a first document, retrieving in a processor-based machine learning system multiple second documents associated with the first document (Farnan, para 0014, retrieve a related set of documents ), the processor-based machine learning system comprising at least a first encoder implementing a transformer-based machine learning model and a second encoder implementing a graph-based machine learning model, wherein the first document and the multiple second documents are each encoded by the first encoder into a first dimensional space having a first dimensionality, and the first document and the multiple second documents are each encoded by the second encoder into a second dimensional space having a second dimensionality different than the first dimensionality, the retrieving being based on first encodings generated by the first encoder and second encodings generated by the second encoder (Farnan, para 097-0104, determining a candidate category based on the first text using a categorization machine learning model… when executed by a set of processors, cause the set of processors to effectuate operations; Farnan, para 0039, a database query based on an initial phrase “machine learning” and determine, based on a knowledge graph, that the initial phrase “machine learning” is related to the phrase “neural network.” Some embodiments may then retrieve at least one document based on the phrase “neural network”; Farnan, para 0022, The encoder model 230 may then provide a set of summarization embeddings 214, where the summarization embeddings may be represented as a set of multidimensional vectors within an embedding space; Farnan, para 0081, generating (i) summarization embeddings based on the initial text summarization by providing, as input, the initial text summarization to a first encoder model and (ii) corpora-derived embeddings based on the related set of documents by providing, as input, text of the related set of documents to a second encoder model; determining a first region in an embedding space based on the summarization embeddings and a second region in the embedding space based on the corpora-derived embeddings; [i.e., First and second decoders and the corresponding embeddings and a first and second region in different embedding space disclosed]);
determining, in the processor-based machine learning system, duplication between the first document and the multiple second documents through contextual analysis of the first document and the multiple second documents (Farnan, para 0093-0104, obtaining initial text based on the set of documents comprising a first document and a second document, wherein a first text portion of the initial text is obtained based on the first document, and wherein a second text portion of the initial text is obtained based on the second document, and wherein the first document and the second document are not identical; determining that the first text portion is a duplicate of the second document… determining a candidate category based on the first text using a categorization machine learning model… when executed by a set of processors, cause the set of processors to effectuate operations);
determining, in the processor-based machine learning system, gaps between the first document and the multiple second documents through causal analysis of the first document and the multiple second documents (Farnan, para 0092-0104, providing the text of the set of documents to the encoder model comprises determining the text based on the set of documents, and wherein the set of documents comprises a first document and a second document, further comprising: determining a result indicating that a candidate identifier matches with an identifier of the second document, wherein the candidate identifier is indicated in metadata of the first document; and determining a candidate text difference between the first document and the second document, wherein determining the text of the set of documents comprises determining the text of the set of documents based on the candidate text difference… determining a candidate category based on the first text using a categorization machine learning model… when executed by a set of processors, cause the set of processors to effectuate operations).
Farnan does not specifically disclose generating a response about the first document according to the duplication and the gaps using a language model.
However, Ferrucci, in the same field of endeavor discloses generating in the processor-based machine learning system, a response about the first document according to the duplication and the gaps using a language model of the processor-based machine learning system (Ferrucci, para 0048-0050, The NLU engine may output interpreted query results. The interpreted query results may include interpreted relation results and/or interpreted concept results with evidence texts. The research assistant tool may configure the knowledge aggregation and synthesis engine for processing the interpreted query results with evidence texts. The knowledge aggregation and synthesis engine may apply clustering and similarity algorithms to aggregate information in the interpreted query results. The clustering and similarity algorithms may determine to group text in the interpreted relation results and/or interpreted concept results based on a high degree of similarity. In some examples, the clustering and similarity algorithms may determine to cluster semantic relations and their associated arguments based on the similarity between relations and/or concepts. The similarity may be determined based on using a thesaurus and/or word embeddings. The clustering and similarity algorithms may determine a set of relation occurrences and combine the set to a single relational instance to generate a cluster. In some examples, the clustering and similarity algorithms may output aggregate confidence associated with evidence texts that support the cluster. The aggregate confidence may be based on the relevance score of the evidence texts. The aggregated query results may include clusters with annotated evidence texts. The knowledge aggregation and synthesis engine may determine to perform analysis on the aggregated query results with processes including originality detection, saliency computation, and authorship analysis. The originality detection may determine a count for knowledge source, wherein a lower count value is associated with higher originality. The originality detection may determine that a piece of evidence has been duplicated and/or sourced from the same place as another evidence text. The saliency computation determines a prominence in corpus and may be based at least in part on as frequency of the source. The saliency computation may determine confidence in count and relevance and/or could be defined by the user. The authorship analysis may determine the credibility of the author. The knowledge aggregation and synthesis engine may output aggregated query results with annotated evidence passages Ferrucci, 0097, The natural language understanding (NLU) engine 116 and the knowledge aggregation and synthesis engine 118 may determine scores for features, including but not limited to aggregation confidence, saliency, relevance, originality, author credibility, and the like; Ferrucci, para 155, The saliency computation determines a prominence in corpus ... The saliency computation may determine confidence in count and relevance and/or could be defined by the user; Ferrucci, para 0183-0184, Training data may include any portion of the data in the data store 244 that is selected to be used to train one or more ML models. In additional and/or alternative examples, at least some of the data may be stored in a storage system or other data repository. FIG. 3 illustrates an example implementation 300 of select components, including a semantic search engine 212 and a structured query engine 214 that may be configured to perform a search based on a data structure of input query. The select components may include the semantic search engine 212, the structured query engine 214, a natural language understanding (NLU) engine 216).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Ferrucci in the method of Farnan because this would enable searching chains of evidence across multiple documents rather than a single document by making use of a research assistant system including a research assistant tool and associated components and a graphical user interface to guide user input to research, discover, and evidence answers for complex research questions (Ferrucci, paras 0002 and 0035).
Regarding Claim 5, Farnan in view of Ferrucci disclose the method according to claim 2, wherein filtering the multiple documents comprises:
applying a clustering algorithm to the filtered list to filter out duplicate documents in the filtered list to obtain the multiple second documents (Farnan, para 0049, some embodiments may determine a region using a clustering algorithm, such as a density-based algorithm, to determine, as a region, a cluster in the embedding space and assign the additional positions within the cluster to the region. Furthermore, some embodiments may combine the results of different types of region-defining algorithms when determining a region).
Furthermore, Ferrucci teaches:
applying a threshold to the similarity to filter out documents whose similarities are lower than the threshold among the multiple documents to obtain a filtered list (Ferrucci, para 0171, the static rule knowledge base may return a list of rules whose consequent unifies (matches) the goal, and the rules have “relevance-similarity,” which is determined using a similarity function, to the context greater than predetermined threshold confidence);
Regarding Claim 8, Farnan in view of Ferrucci disclose the method according to claim 1, wherein performing causal analysis on the first document comprises:
determining a graphical representation of a causal relationship between multiple parts of the first document (Farnan, para 0078, reference to “an element” or “a element” includes a combination of two or more elements, notwithstanding the use of other terms and phrases for one or more elements, such as “one or more.” The term “or” is non-exclusive (i.e., encompassing both “and” and “or”), unless the context clearly indicates otherwise. Terms describing conditional relationships (e.g., “in response to X, Y,” “upon X, Y,” “if X, Y,” “when X, Y,” and the like) encompass causal relationships in which the antecedent is a necessary causal condition, the antecedent is a sufficient causal condition, or the antecedent is a contributory causal condition of the consequent (e.g., “state X occurs upon condition Y obtaining” is generic to “X occurs solely upon Y” and “X occurs upon Y and Z”)); and
determining attention weights of the multiple parts of the first document in the graphical representation (Farnan, para 0051-0053, Some embodiments may then use this complement region in the embedding space to generate one or more portions of a structured document. For example, some embodiments may determine one or more phrases based on the complement region by selecting a set of vectors in the complement region, using a decoder model to generate a corresponding set of phrases, and providing the corresponding set of phrases to a generative language model to generate a set of text portions to be used in one or more structured documents…the generative language model may include a recurrent neural network, a long short-term memory (LSTM) network model, a gated recurrent unit (GRU) model, a transformer model or another type of attention-based neural network model, etc. Some embodiments may use a generative language model that includes multiple types of neural network models, such as by using an ensemble model… By using pre-provided tokens or newly generated tokens based on the complement region to generate text in a structured document, some embodiments may increase the weight that more unique data has when generating the text).
Regarding Claim 10, Farnan in view of Ferrucci disclose the method according to claim 1, wherein determining gaps between the first document and the multiple second documents (Farnan, para 0092, … determining a candidate text difference between the first document and the second document, wherein determining the text of the set of documents comprises determining the text of the set of documents based on the candidate text difference) comprises:
encoding a part of the first document as a first embedding (Farnan, para 0045, Some embodiments may generate a set of vectors representing embeddings in embedding space based on text by providing the text to an encoder model, where the encoder model outputs embedding vectors in the embedding space based on input text);
encoding multiple parts of the multiple second documents as multiple second embeddings (para 0025, Some embodiments may then provide the set of corpora-derived text 222 to the encoder model 230 to generate a set of corpora-derived embeddings 224, where the set of corpora-derived embeddings 224 are in the same embedding space as the set of summarization embeddings 214);
Furthermore, Ferrucci teaches:
mapping the first embedding to a semantic space (Ferrucci, para 0186, The query engine may receive the example NL query 306 and determine to use the semantic search engine 212 to process the input query and search for the concepts over a text corpus by performing the document search 302 and the embedding search 304);
mapping the multiple second embeddings to the semantic space (Ferrucci, para 0153, The grouped text based on the semantic relations and their associated arguments forms a propositional cluster. The similarity may be determined based on using a thesaurus and/or word embeddings); and
comparing the mapped first embedding with the mapped multiple second embeddings to determine the gaps between the first document and the multiple second documents (Ferrucci, para 0153, In some examples, the clustering and similarity algorithm 226 may determine to cluster semantic relations and their associated arguments based on the similarity between relations and/or concepts. The grouped text based on the semantic relations and their associated arguments forms a propositional cluster. The similarity may be determined based on using a thesaurus and/or word embeddings. The clustering and similarity algorithm 226 may generate result clusters, including concept clusters, relation clusters, and propositional clusters. Each cluster may be annotated with the related portion of evidence texts, including a link to a summarized evidence passage).
Regarding Claim 11, Farnan in view of Ferrucci disclose the method according to claim 10, wherein generating the response about the first document comprises:
Furthermore, Ferrucci teaches: performing a nearest neighbor search on the mapped first embedding to determine multiple novel clusters associated with the first embedding, wherein the response about the first document is generated based on the multiple novel clusters (Ferrucci, para 0181-0182, In some examples, the trained ML model(s) 242 may classify an input query with context as relevant to one of the inference rules and determine an associated confidence score. In various examples, if the trained ML model(s) 242 has low confidence (e.g., a confidence score is at or below a low threshold) in its proof for an explanation to an input query, this low confidence may return no rules found. An extremely high confidence score (e.g., a confidence score is at or exceeds a high threshold) may indicate the rule is proof for an input query… The ML model(s) 242 may represent a single model or an ensemble of base-level ML models and may be implemented as any type of model(s) 242. For example, suitable ML model(s) 242 for use with the techniques and systems described herein include, without limitation, tree-based models, k-Nearest Neighbors (kNN)).
Regarding Claim 12, Farnan discloses an electronic device, comprising:
at least one processor (Farnan, para 0071, one or more physical processors); and
a memory coupled to the at least one processor and having instructions stored therein, the instructions, when executed by the at least one processor, causing the electronic device to perform actions (Farnan, para 0071- 0074, one or more physical processors programmed with one or more computer program instructions, and/or other components… The processors may be programmed to execute computer program instructions by software; hardware; firmware; some combination of software, hardware, or firmware; and/or other mechanisms for configuring processing capabilities on the processors) comprising:
according to a first document, retrieving in a processor-based machine learning system multiple second documents associated with the first document (Farnan, para 0014, retrieve a related set of documents) , the processor-based machine learning system comprising at least a first encoder implementing a transformer-based machine learning model and a second encoder implementing a graph-based machine learning model, wherein the first document and the multiple second documents are each encoded by the first encoder into a first dimensional space having a first dimensionality, and the first document and the multiple second documents are each encoded by the second encoder into a second dimensional space having a second dimensionality different than the first dimensionality, the retrieving being based on first encodings generated by the first encoder and second encodings generated by the second encoder; determining, in the processor-based machine learning system (Farnan, para 097-0104, determining a candidate category based on the first text using a categorization machine learning model… when executed by a set of processors, cause the set of processors to effectuate operations; Farnan, para 0039, a database query based on an initial phrase “machine learning” and determine, based on a knowledge graph, that the initial phrase “machine learning” is related to the phrase “neural network.” Some embodiments may then retrieve at least one document based on the phrase “neural network”; Farnan, para 0022, The encoder model 230 may then provide a set of summarization embeddings 214, where the summarization embeddings may be represented as a set of multidimensional vectors within an embedding space);
determining, in the processor-based machine learning system, duplication between the first document and the multiple second documents through contextual analysis of the first document and the multiple second documents (Farnan, para 0093-0104, obtaining initial text based on the set of documents comprising a first document and a second document, wherein a first text portion of the initial text is obtained based on the first document, and wherein a second text portion of the initial text is obtained based on the second document, and wherein the first document and the second document are not identical; determining that the first text portion is a duplicate of the second document… determining a candidate category based on the first text using a categorization machine learning model… when executed by a set of processors, cause the set of processors to effectuate operations);
determining, in the processor-based machine learning system, gaps between the first document and the multiple second documents through causal analysis of the first document and the multiple second documents (Farnan, para 0092-0104, providing the text of the set of documents to the encoder model comprises determining the text based on the set of documents, and wherein the set of documents comprises a first document and a second document, further comprising: determining a result indicating that a candidate identifier matches with an identifier of the second document, wherein the candidate identifier is indicated in metadata of the first document; and determining a candidate text difference between the first document and the second document, wherein determining the text of the set of documents comprises determining the text of the set of documents based on the candidate text difference… determining a candidate category based on the first text using a categorization machine learning model… when executed by a set of processors, cause the set of processors to effectuate operations)
Farnan does not specifically disclose generating a response about the first document according to the duplication and the gaps using a language model.
However, Ferrucci, in the same field of endeavor discloses generating, in the processor-based machine learning system, a response about the first document according to the duplication and the gaps using a language model of the processor-based machine learning system (Ferrucci, para 0048-0050, The NLU engine may output interpreted query results. The interpreted query results may include interpreted relation results and/or interpreted concept results with evidence texts. The research assistant tool may configure the knowledge aggregation and synthesis engine for processing the interpreted query results with evidence texts. The knowledge aggregation and synthesis engine may apply clustering and similarity algorithms to aggregate information in the interpreted query results. The clustering and similarity algorithms may determine to group text in the interpreted relation results and/or interpreted concept results based on a high degree of similarity. In some examples, the clustering and similarity algorithms may determine to cluster semantic relations and their associated arguments based on the similarity between relations and/or concepts. The similarity may be determined based on using a thesaurus and/or word embeddings. The clustering and similarity algorithms may determine a set of relation occurrences and combine the set to a single relational instance to generate a cluster. In some examples, the clustering and similarity algorithms may output aggregate confidence associated with evidence texts that support the cluster. The aggregate confidence may be based on the relevance score of the evidence texts. The aggregated query results may include clusters with annotated evidence texts. The knowledge aggregation and synthesis engine may determine to perform analysis on the aggregated query results with processes including originality detection, saliency computation, and authorship analysis. The originality detection may determine a count for knowledge source, wherein a lower count value is associated with higher originality. The originality detection may determine that a piece of evidence has been duplicated and/or sourced from the same place as another evidence text. The saliency computation determines a prominence in corpus and may be based at least in part on as frequency of the source. The saliency computation may determine confidence in count and relevance and/or could be defined by the user. The authorship analysis may determine the credibility of the author. The knowledge aggregation and synthesis engine may output aggregated query results with annotated evidence passages Ferrucci, 0097, The natural language understanding (NLU) engine 116 and the knowledge aggregation and synthesis engine 118 may determine scores for features, including but not limited to aggregation confidence, saliency, relevance, originality, author credibility, and the like; Ferrucci, para 155, The saliency computation determines a prominence in corpus ... The saliency computation may determine confidence in count and relevance and/or could be defined by the user; Ferrucci, para 0183-0184, Training data may include any portion of the data in the data store 244 that is selected to be used to train one or more ML models. In additional and/or alternative examples, at least some of the data may be stored in a storage system or other data repository. FIG. 3 illustrates an example implementation 300 of select components, including a semantic search engine 212 and a structured query engine 214 that may be configured to perform a search based on a data structure of input query. The select components may include the semantic search engine 212, the structured query engine 214, a natural language understanding (NLU) engine 216).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Ferrucci in the method of Farnan because this would enable searching chains of evidence across multiple documents rather than a single document by making use of a research assistant system including a research assistant tool and associated components and a graphical user interface to guide user input to research, discover, and evidence answers for complex research questions (Ferrucci, paras 0002 and 0035).
Regarding Claim 15, Farnan in view of Ferrucci disclose the electronic device according to claim 13, wherein filtering the multiple documents comprises:
applying a clustering algorithm to the filtered list to filter out duplicate documents in the filtered list to obtain the multiple second documents (Farnan, para 0049, some embodiments may determine a region using a clustering algorithm, such as a density-based algorithm, to determine, as a region, a cluster in the embedding space and assign the additional positions within the cluster to the region. Furthermore, some embodiments may combine the results of different types of region-defining algorithms when determining a region).
Furthermore, Ferrucci teaches:
applying a threshold to the similarity to filter out documents whose similarities are lower than the threshold among the multiple documents to obtain a filtered list (Ferrucci, para 0171, the static rule knowledge base may return a list of rules whose consequent unifies (matches) the goal, and the rules have “relevance-similarity,” which is determined using a similarity function, to the context greater than predetermined threshold confidence).
Regarding Claim 19, Farnan in view of Ferrucci disclose the electronic device according to claim 12, wherein determining gaps between the first document and the multiple second documents (Farnan, para 0092, … determining a candidate text difference between the first document and the second document, wherein determining the text of the set of documents comprises determining the text of the set of documents based on the candidate text difference) comprises:
encoding a part of the first document as a first embedding (Farnan, para 0045, Some embodiments may generate a set of vectors representing embeddings in embedding space based on text by providing the text to an encoder model, where the encoder model outputs embedding vectors in the embedding space based on input text);
encoding multiple parts of the multiple second documents as multiple second embeddings (para 0025, Some embodiments may then provide the set of corpora-derived text 222 to the encoder model 230 to generate a set of corpora-derived embeddings 224, where the set of corpora-derived embeddings 224 are in the same embedding space as the set of summarization embeddings 214);
Furthermore, Ferrucci teaches:
mapping the first embedding to a semantic space (Ferrucci, para 0186, The query engine may receive the example NL query 306 and determine to use the semantic search engine 212 to process the input query and search for the concepts over a text corpus by performing the document search 302 and the embedding search 304);
mapping the multiple second embeddings to the semantic space (Ferrucci, para 0153, The grouped text based on the semantic relations and their associated arguments forms a propositional cluster. The similarity may be determined based on using a thesaurus and/or word embeddings); and
comparing the mapped first embedding with the mapped multiple second embeddings to determine the gaps between the first document and the multiple second documents (Ferrucci, para 0153, In some examples, the clustering and similarity algorithm 226 may determine to cluster semantic relations and their associated arguments based on the similarity between relations and/or concepts. The grouped text based on the semantic relations and their associated arguments forms a propositional cluster. The similarity may be determined based on using a thesaurus and/or word embeddings. The clustering and similarity algorithm 226 may generate result clusters, including concept clusters, relation clusters, and propositional clusters. Each cluster may be annotated with the related portion of evidence texts, including a link to a summarized evidence passage).
Regarding Claim 20, Farnan discloses a computer program product comprising a non-transitory computer-readable medium having machine-executable instructions stored therein, wherein the machine-executable instructions, when executed by a machine, cause the machine to perform actions (Farnan, para 0011, include a set of non-transitory storage media storing program instructions to perform one or more operations of subsystems; Farnan, para 0069, The processing devices may include one or more devices executing some or all of the operations of the methods in response to instructions stored electronically on a set of non-transitory, machine-readable media, such as an electronic storage medium) comprising:
according to a first document, retrieving in a processor-based machine learning system multiple second documents associated with the first document (Farnan, para 0014, retrieve a related set of documents), the processor-based machine learning system comprising at least a first encoder implementing a transformer-based machine learning model and a second encoder implementing a graph-based machine learning model, wherein the first document and the multiple second documents are each encoded by the first encoder into a first dimensional space having a first dimensionality, and the first document and the multiple second documents are each encoded by the second encoder into a second dimensional space having a second dimensionality different than the first dimensionality, the retrieving being based on first encodings generated by the first encoder and second encodings generated by the second encoder (Farnan, para 097-0104, determining a candidate category based on the first text using a categorization machine learning model… when executed by a set of processors, cause the set of processors to effectuate operations; Farnan, para 0039, a database query based on an initial phrase “machine learning” and determine, based on a knowledge graph, that the initial phrase “machine learning” is related to the phrase “neural network.” Some embodiments may then retrieve at least one document based on the phrase “neural network”; Farnan, para 0022, The encoder model 230 may then provide a set of summarization embeddings 214, where the summarization embeddings may be represented as a set of multidimensional vectors within an embedding space);
determining, in the processor-based machine learning system,duplication between the first document and the multiple second documents through contextual analysis of the first document and the multiple second documents (Farnan, para 0093-0104, obtaining initial text based on the set of documents comprising a first document and a second document, wherein a first text portion of the initial text is obtained based on the first document, and wherein a second text portion of the initial text is obtained based on the second document, and wherein the first document and the second document are not identical; determining that the first text portion is a duplicate of the second document… determining a candidate category based on the first text using a categorization machine learning model… when executed by a set of processors, cause the set of processors to effectuate operations);
determining, in the processor-based machine learning system, gaps between the first document and the multiple second documents through causal analysis of the first document and the multiple second documents (Farnan, para 0092-0104, providing the text of the set of documents to the encoder model comprises determining the text based on the set of documents, and wherein the set of documents comprises a first document and a second document, further comprising: determining a result indicating that a candidate identifier matches with an identifier of the second document, wherein the candidate identifier is indicated in metadata of the first document; and determining a candidate text difference between the first document and the second document, wherein determining the text of the set of documents comprises determining the text of the set of documents based on the candidate text difference… determining a candidate category based on the first text using a categorization machine learning model… when executed by a set of processors, cause the set of processors to effectuate operations).
Farnan does not specifically disclose generating, in the processor-based machine learning system, a response about the first document according to the duplication and the gaps using a language model.
However, Ferrucci, in the same field of endeavor discloses generating, in the processor-based machine learning system, a response about the first document according to the duplication and the gaps using a language model (Ferrucci, para 0048-0050, The NLU engine may output interpreted query results. The interpreted query results may include interpreted relation results and/or interpreted concept results with evidence texts. The research assistant tool may configure the knowledge aggregation and synthesis engine for processing the interpreted query results with evidence texts. The knowledge aggregation and synthesis engine may apply clustering and similarity algorithms to aggregate information in the interpreted query results. The clustering and similarity algorithms may determine to group text in the interpreted relation results and/or interpreted concept results based on a high degree of similarity. In some examples, the clustering and similarity algorithms may determine to cluster semantic relations and their associated arguments based on the similarity between relations and/or concepts. The similarity may be determined based on using a thesaurus and/or word embeddings. The clustering and similarity algorithms may determine a set of relation occurrences and combine the set to a single relational instance to generate a cluster. In some examples, the clustering and similarity algorithms may output aggregate confidence associated with evidence texts that support the cluster. The aggregate confidence may be based on the relevance score of the evidence texts. The aggregated query results may include clusters with annotated evidence texts. The knowledge aggregation and synthesis engine may determine to perform analysis on the aggregated query results with processes including originality detection, saliency computation, and authorship analysis. The originality detection may determine a count for knowledge source, wherein a lower count value is associated with higher originality. The originality detection may determine that a piece of evidence has been duplicated and/or sourced from the same place as another evidence text. The saliency computation determines a prominence in corpus and may be based at least in part on as frequency of the source. The saliency computation may determine confidence in count and relevance and/or could be defined by the user. The authorship analysis may determine the credibility of the author. The knowledge aggregation and synthesis engine may output aggregated query results with annotated evidence passages Ferrucci, 0097, The natural language understanding (NLU) engine 116 and the knowledge aggregation and synthesis engine 118 may determine scores for features, including but not limited to aggregation confidence, saliency, relevance, originality, author credibility, and the like; Ferrucci, para 155, The saliency computation determines a prominence in corpus ... The saliency computation may determine confidence in count and relevance and/or could be defined by the user; Ferrucci, para 0183-0184, Training data may include any portion of the data in the data store 244 that is selected to be used to train one or more ML models. In additional and/or alternative examples, at least some of the data may be stored in a storage system or other data repository. FIG. 3 illustrates an example implementation 300 of select components, including a semantic search engine 212 and a structured query engine 214 that may be configured to perform a search based on a data structure of input query. The select components may include the semantic search engine 212, the structured query engine 214, a natural language understanding (NLU) engine 216).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Ferrucci in the method of Farnan because this would enable searching chains of evidence across multiple documents rather than a single document by making use of a research assistant system including a research assistant tool and associated components and a graphical user interface to guide user input to research, discover, and evidence answers for complex research questions (Ferrucci, paras 0002 and 0035).
Claims 2 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Farnan in view of Ferrucci, and further in view of Wu Pat App No. CN 113094519 A (Wu).
Regarding Claim 2, Farnan in view of Ferrucci disclose the method according to claim 1, wherein retrieving multiple second documents associated with the first document (Farnan, para 0014, retrieve a related set of documents) comprises:
encoding the first document and multiple documents in a document library (Farnan, para 0025, the encoder model 230 to generate a set of corpora-derived embeddings 224, where the set of corpora-derived embeddings 224 are in the same embedding space as the set of summarization embeddings 214; [“the same embedding space” as “document library”]);
determining a similarity between the first document and each of the multiple documents (Farnan, para 0093, obtaining initial text based on the set of documents comprising a first document and a second document, wherein a first text portion of the initial text is obtained based on the first document, and wherein a second text portion of the initial text is obtained based on the second document, and wherein the first document and the second document are not identical; determining that the first text portion is a duplicate of the second document);
Furthermore, Ferrucci teaches:
filtering the multiple documents to obtain the multiple second documents associated with the first document (Ferrucci, para 0078, the search parameters may include search terms, search filters, search conditions, search process, and the like. The search terms may include keywords used for a document search engine).
Farnan in view of Ferrucci do not specifically disclose sorting the multiple documents according to the similarities.
However, Wu, in the same field of endeavor, discloses sorting the multiple documents according to the similarities (Wu, 9th page, 3rd para, based on the similarity value of the candidate retrieval document and the document to be retrieved, ordering the plurality of candidate retrieval documents ).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Wu in the method of Farnan in view of Ferrucci because this would enable ordering/sorting documents based on the similarity value to improve search efficiency (Wu, Abstract).
Regarding Claim 13, Farnan in view of Ferrucci disclose the electronic device according to claim 12, wherein retrieving multiple second documents associated with the first document (Farnan, para 0014, retrieve a related set of documents) comprises:
encoding the first document and multiple documents in a document library (Farnan, para 0025, the encoder model 230 to generate a set of corpora-derived embeddings 224, where the set of corpora-derived embeddings 224 are in the same embedding space as the set of summarization embeddings 214; [“the same embedding space” as “document library”]);
determining a similarity between the first document and each of the multiple documents (Farnan, para 0093, obtaining initial text based on the set of documents comprising a first document and a second document, wherein a first text portion of the initial text is obtained based on the first document, and wherein a second text portion of the initial text is obtained based on the second document, and wherein the first document and the second document are not identical; determining that the first text portion is a duplicate of the second document);
Furthermore, Ferrucci teaches:
filtering the multiple documents to obtain the multiple second documents associated with the first document (Ferrucci, para 0078, the search parameters may include search terms, search filters, search conditions, search process, and the like. The search terms may include keywords used for a document search engine).
Farnan in view of Ferrucci do not specifically disclose sorting the multiple documents according to the similarities.
However, Wu, in the same field of endeavor, discloses sorting the multiple documents according to the similarities (Wu, 9th page, 3rd para, based on the similarity value of the candidate retrieval document and the document to be retrieved, ordering the plurality of candidate retrieval documents ).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Wu in the method of Farnan in view of Ferrucci because this would enable ordering/sorting documents based on the similarity value to improve search efficiency (Wu, Abstract).
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Farnan in view of Ferrucci, further in view of Sun et al., "Overview of Capsule Neural Networks," Journal of Internet Technology, vol. 23, no. 1, pp. 33-44, Jan. 2022, and further in view of Tim Rapley, "Doing Conversation, Discourse and Document Analysis," Sage Research Methods, Publisher: SAGE Publications Ltd, City: 55 City Road, Online ISBN: 9781529771152, 2021 (Rapley).
Regarding Claim 6, Farnan in view of Ferrucci disclose the method according to claim 1, wherein performing contextual analysis on the first document.
Farnan in view of Ferrucci do not specifically disclose determining a hierarchical relationship between multiple parts of the first document, the hierarchical relationship being determined by using a capsule network and a parse tree.
However, Sun, in the same field of endeavor, discloses determining a hierarchical relationship between multiple parts of the first document, the hierarchical relationship being determined by using a capsule network and a parse tree (Sun, page 35, left col, 2nd - 3rd para, hierarchical relationship and other relationships between the low-level features detected by the low-level capsule and the high-level feature of the high-level capsule are encoded… The capsule model adopts a parse tree structure, and each active capsule corresponds to each node on the parse tree one-to-one. The low-level capsules (child nodes) are used to detect the low-level feature information, and high-level capsules (parent nodes) are used to receive output information from child nodes; Sun, page 35, right col, 6th para, The capsule model can be considered as a parse tree, because each active capsule selects the next layer of the capsule as the parent capsule in the parse tree; [i.e., “The capsule model’ and “the parse tree” can be used interchangeably]).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Sun in the method of Farnan in view of Ferrucci because this would enable not only summarize the shortcomings of convolutional neural networks and introduce the basic
concept of capsule network, but also analyze and summarize the improvements in the dynamic routing mechanism and network structure of the capsule network in recent years and the combination of the capsule network with other network structures (Sun, Abstract).
Farnan in view of Ferrucci and Sun do not specifically disclose determining a substantial relationship between the multiple parts of the first document, the substantial relationship comprising a discourse relationship and a sentiment relationship between the multiple parts.
However, Rapley, in the same field of endeavor, discloses determining a substantial relationship between the multiple parts of the first document, the substantial relationship comprising a discourse relationship and a sentiment relationship between the multiple parts (Rapley, 22nd page, 4th para, This often draws on elements of conversation analysis, discourse analysis and rhetoric to focus on how apparently ‘internal’ psychological events, like ‘memory’, ‘emotion’ or ‘attitudes’, are produced, negotiated and accomplished in and through social actions, interactions and texts ).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Rapley in the method of Farnan in view of Ferrucci and Sun because this would enable introducing ‘discourse analysis’ and show that for those analyzing discourse the primary interest is how language is used in certain contexts, whether the discourses happens in interview transcripts, a range of newspaper editorials, audiotape of conversations or scientific research articles (Rapley, 15th page, 3rd para).
Regarding Claim 16, Farnan in view of Ferrucci disclose the electronic device according to claim 12, wherein performing contextual analysis on the first document.
Farnan in view of Ferrucci do not specifically disclose determining a hierarchical relationship between multiple parts of the first document, the hierarchical relationship being determined by using a capsule network and a parse tree.
However, Sun, in the same field of endeavor, discloses determining a hierarchical relationship between multiple parts of the first document, the hierarchical relationship being determined by using a and a parse tree (Sun, page 35, left col, 2nd - 3rd para, hierarchical relationship and other relationships between the low-level features detected by the low-level capsule and the high-level feature of the high-level capsule are encoded… The capsule model adopts a parse tree structure, and each active capsule corresponds to each node on the parse tree one-to-one. The low-level capsules (child nodes) are used to detect the low-level feature information, and high-level capsules (parent nodes) are used to receive output information from child nodes; Sun, page 35, right col, 6th para, The capsule model can be considered as a parse tree, because each active capsule selects the next layer of the capsule as the parent capsule in the parse tree; [i.e., “The capsule model’ and “the parse tree” can be used interchangeably]); and
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Sun in the method of Farnan in view of Ferrucci because this would enable not only summarize the shortcomings of convolutional neural networks and introduce the basic concept of capsule network, but also analyze and summarize the improvements in the dynamic routing mechanism and network structure of the capsule network in recent years and the combination of the capsule network with other network structures (Sun, Abstract).
Farnan in view of Ferrucci and Sun do not specifically disclose determining a substantial relationship between the multiple parts of the first document, the substantial relationship comprising a discourse relationship and a sentiment relationship between the multiple parts.
However, Rapley, in the same field of endeavor, discloses determining a substantial relationship between the multiple parts of the first document, the substantial relationship comprising a discourse relationship and a sentiment relationship between the multiple parts (Rapley, 22nd page, 4th para, This often draws on elements of conversation analysis, discourse analysis and rhetoric to focus on how apparently ‘internal’ psychological events, like ‘memory’, ‘emotion’ or ‘attitudes’, are produced, negotiated and accomplished in and through social actions, interactions and texts ).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Rapley in the method of Farnan in view of Ferrucci and Sun because this would enable introducing ‘discourse analysis’ and show that for those analyzing discourse the primary interest is how language is used in certain contexts, whether the discourses happens in interview transcripts, a range of newspaper editorials, audiotape of conversations or scientific research articles (Rapley, 15th page, 3rd para).
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Farnan in view of Ferrucci, and further in view of Takacs Pat App No. US 20190164245 A1 (Takacs).
Regarding Claim 7, Farnan in view of Ferrucci disclose the method according to claim 1, wherein determining duplication between the first document and the second documents (Farnan, para 0093, obtaining initial text based on the set of documents comprising a first document and a second document, wherein a first text portion of the initial text is obtained based on the first document, and wherein a second text portion of the initial text is obtained based on the second document, and wherein the first document and the second document are not identical; determining that the first text portion is a duplicate of the second document).
Farnan in view of Ferrucci do not specifically disclose training a Siamese network using multiple documents in a document library, and comparing the first document contextually analyzed and the multiple second documents contextually analyzed by using the trained Siamese network to determine the duplication between the first document and the second documents.
However, Takacs, in the same field of endeavor discloses:
training a Siamese network using multiple documents in a document library (Takacs, para 0087, create a Siamese deep learning model for each pair of texts, where the model trains on a binary training set where TRUE LINKS=1 and NON_LINKS=0; creating a dense vector representation by each side of the Siamese model; The final layer of the deep learning model, immediately connected to the Siamese layers, will be a weighted sum of the sum of the absolute values of the element-wise subtraction between the 2 vector representations from the Siamese neural networks, plus the element-wise addition between the 2 vector representations from the Siamese neural networks); and
comparing the first document contextually analyzed and the multiple second documents contextually analyzed by using the trained Siamese network to determine the duplication between the first document and the second documents (Takacs, para 0040, cosine similarity gives a 0-to-1 “similarity score” between the raw text of incident reports, depending on how many words in each raw text overlap… In many embodiments, cosine similarity is replaced with a pair of neural networks which are Siamese-trained, very similar to a Siamese LSTM-type system trained to recognize duplicate texts, but with the additional ability to learn relevant concepts and recognize similarity amongst detected concepts in the pairs, such as vehicle description and suspect description. Preferably, this pair of neural networks learns words and phrases that are relevant to crime linking and are capable of computing the similarities between these words and phrases to offer improved incident linking above what pure cosine similarity can offer).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Takacs in the method of Farnan in view of Ferrucci because this would create a better text comparison tool as an augmented cosine similarity that will learn to magnify or lessen certain dimensions of the dense vectors which correspond to concepts present in the text that contain descriptions relevant to crime linking (Takacs, para 0087).
Regarding Claim 17, Farnan in view of Ferrucci disclose the electronic device according to claim 12, wherein determining duplication between the first document and the second documents (Farnan, para 0093, obtaining initial text based on the set of documents comprising a first document and a second document, wherein a first text portion of the initial text is obtained based on the first document, and wherein a second text portion of the initial text is obtained based on the second document, and wherein the first document and the second document are not identical; determining that the first text portion is a duplicate of the second document).
Farnan in view of Ferrucci do not specifically disclose training a Siamese network using multiple documents in a document library, and comparing the first document contextually analyzed and the multiple second documents contextually analyzed by using the trained Siamese network to determine the duplication between the first document and the second documents.
However, Takacs, in the same field of endeavor discloses:
training a Siamese network using multiple documents in a document library (Takacs, para 0087, create a Siamese deep learning model for each pair of texts, where the model trains on a binary training set where TRUE LINKS=1 and NON_LINKS=0; creating a dense vector representation by each side of the Siamese model; The final layer of the deep learning model, immediately connected to the Siamese layers, will be a weighted sum of the sum of the absolute values of the element-wise subtraction between the 2 vector representations from the Siamese neural networks, plus the element-wise addition between the 2 vector representations from the Siamese neural networks); and
comparing the first document contextually analyzed and the multiple second documents contextually analyzed by using the trained Siamese network to determine the duplication between the first document and the second documents (Takacs, para 0040, cosine similarity gives a 0-to-1 “similarity score” between the raw text of incident reports, depending on how many words in each raw text overlap… In many embodiments, cosine similarity is replaced with a pair of neural networks which are Siamese-trained, very similar to a Siamese LSTM-type system trained to recognize duplicate texts, but with the additional ability to learn relevant concepts and recognize similarity amongst detected concepts in the pairs, such as vehicle description and suspect description. Preferably, this pair of neural networks learns words and phrases that are relevant to crime linking and are capable of computing the similarities between these words and phrases to offer improved incident linking above what pure cosine similarity can offer).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Takacs in the method of Farnan in view of Ferrucci because this would create a better text comparison tool as an augmented cosine similarity that will learn to magnify or lessen certain dimensions of the dense vectors which correspond to concepts present in the text that contain descriptions relevant to crime linking (Takacs, para 0087).
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Farnan in view of Ferrucci, and further in view of Shaib et al Pat App No. US 20210183484 A1 (Shaib).
Regarding Claim 9, Farnan in view of Ferrucci disclose the method according to claim 8, wherein performing causal analysis on the first document further comprises:
Farnan in view of Ferrucci do not specifically disclose sorting the attention weights of the multiple parts of the first document, applying a first threshold to the attention weights of the multiple parts, and selecting the part of the first document with an attention weight higher than the first threshold as the first document causally analyzed.
However, Shaib, in the same field of endeavor discloses:
sorting the attention weights of the multiple parts of the first document (Shaib, para 0015, The units of explanation are sorted based on their corresponding attention weights; Shaib, para 0229, sorted the words based on their attention weights and take the top n words as the prediction rationale );
applying a first threshold to the attention weights of the multiple parts (Shaib, para 0204-0205, the system can be configured to determine an attention threshold score
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); and
selecting the part of the first document with an attention weight higher than the first threshold as the first document causally analyzed ( Shaib, para 0204-0205, the system can be configured to determine an attention threshold score
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where α denotes attention scores, n.sub.s is the number of sentences, and i=min(20, n.sub.s/10). This ensures that selected sentences have higher attention scores than uniform attention;
).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Shaib in the method of Farnan in view of Ferrucci because this would create a multi-task clinical language framework making use of voluminous clinical documentation, esp. text notes (e.g., free-text notes) that contain valuable information which are impractical for human to review due to the extremely large amount of time and effort required to manually process the notes, and thus introducing a hierarchical convolutional neural network (CNN) transformer-based machine learning with an explicit attention mechanism as an interpretable and multi-task clinical language model (Abstract, para 0002-0009).
Regarding Claim 18, Farnan in view of Ferrucci disclose the electronic device according to claim 12, wherein performing causal analysis on the first document comprises:
determining a graphical representation of a causal relationship between multiple parts of the first document (Farnan, para 0078, reference to “an element” or “a element” includes a combination of two or more elements, notwithstanding the use of other terms and phrases for one or more elements, such as “one or more.” The term “or” is non-exclusive (i.e., encompassing both “and” and “or”), unless the context clearly indicates otherwise. Terms describing conditional relationships (e.g., “in response to X, Y,” “upon X, Y,” “if X, Y,” “when X, Y,” and the like) encompass causal relationships in which the antecedent is a necessary causal condition, the antecedent is a sufficient causal condition, or the antecedent is a contributory causal condition of the consequent (e.g., “state X occurs upon condition Y obtaining” is generic to “X occurs solely upon Y” and “X occurs upon Y and Z”));
determining attention weights of the multiple parts of the first document in the graphical representation (Farnan, para 0051-0053, Some embodiments may then use this complement region in the embedding space to generate one or more portions of a structured document. For example, some embodiments may determine one or more phrases based on the complement region by selecting a set of vectors in the complement region, using a decoder model to generate a corresponding set of phrases, and providing the corresponding set of phrases to a generative language model to generate a set of text portions to be used in one or more structured documents…the generative language model may include a recurrent neural network, a long short-term memory (LSTM) network model, a gated recurrent unit (GRU) model, a transformer model or another type of attention-based neural network model, etc. Some embodiments may use a generative language model that includes multiple types of neural network models, such as by using an ensemble model… By using pre-provided tokens or newly generated tokens based on the complement region to generate text in a structured document, some embodiments may increase the weight that more unique data has when generating the text);
Farnan in view of Ferrucci do not specifically disclose sorting the attention weights of the multiple parts of the first document, applying a first threshold to the attention weights of the multiple parts, and selecting the part of the first document with an attention weight higher than the first threshold as the first document causally analyzed.
However, Shaib, in the same field of endeavor discloses:
sorting the attention weights of the multiple parts of the first document (Shaib, para 0015, The units of explanation are sorted based on their corresponding attention weights; Shaib, para 0229, sorted the words based on their attention weights and take the top n words as the prediction rationale);
applying a first threshold to the attention weights of the multiple parts (Shaib, para 0204-0205, the system can be configured to determine an attention threshold score
PNG
media_image1.png
46
220
media_image1.png
Greyscale
); and
selecting the part of the first document with an attention weight higher than the first threshold as the first document causally analyzed (Shaib, para 0204-0205, the system can be configured to determine an attention threshold score
PNG
media_image1.png
46
220
media_image1.png
Greyscale
where α denotes attention scores, n.sub.s is the number of sentences, and i=min(20, n.sub.s/10). This ensures that selected sentences have higher attention scores than uniform attention).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Shaib in the method of Farnan in view of Ferrucci because this would create a multi-task clinical language framework making use of voluminous clinical documentation, esp. text notes (e.g., free-text notes) that contain valuable information which are impractical for human to review due to the extremely large amount of time and effort required to manually process the notes, and thus introducing a hierarchical convolutional neural network (CNN) transformer-based machine learning with an explicit attention mechanism as an interpretable and multi-task clinical language model (Abstract, para 0002-0009).
Allowable Subject Matter
Claims 3, 4 and 14 are objected to as being dependent upon rejected base claims, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The reasons for allowance are that the prior art of record do not specifically teach the limitations as recited in the mentioned claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MULUGETA TUJI DUGDA/Examiner, Art Unit 2653
/DOUGLAS GODBOLD/Primary Examiner, Art Unit 2655
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