# US 12,001,804 B2 · Using communicative discourse trees to detect distributed incompetence > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,001,804 B2 - Document ID: US12001804B2 - Status: Granted - Kind: B2 - Application number: 17/749,019 - Filed: 2022-05-19 - Published: 2022-09-08 - Granted: 2024-06-04 - Earliest claimed: 2017-05-10 - Assignees: Oracle International Corporation - Applicants: Oracle International Corporation - Inventors: Boris Galitsky - Web page: https://patentgrep.com/patent/US12001804B2 ## CPC - G06F 40/35: Discourse or dialogue representation - G06F 40/253: Grammatical analysis; Style critique - G06F 40/289: Phrasal analysis, e.g. finite state techniques or chunking - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Techniques are disclosed for detecting distributed incompetence in text of a conversation using communicative discourse trees and then inserting an automatic response from an autonomous agent (chatbot) or other entity. For example, a computing system generates a communicative discourse tree from utterances from multiple agents to a user. The computing system obtains a prediction of whether the text includes distributed incompetence by applying a trained predictive model to the communicative discourse tree. Based on the detection, the computing system generates an updated response to a user device. ## Claims ### Claim 1 A computer-implemented method for determining a presence of distributed incompetence by analyzing a communicative discourse tree, the method comprising: generating a communicative discourse tree from fragments of text, wherein the communicative discourse tree represents rhetorical relationships between the fragments of text, and wherein generating the communicative discourse tree comprises: creating, from the fragments of text, a discourse tree that comprises nodes, wherein each nonterminal node of the nodes represents a rhetorical relationship between two of the fragments of text, and each terminal node of the nodes is associated with one of the fragments of text; and matching each fragment that has a verb to a verb signature that comprises the verb of a respective fragment and one or more thematic roles that describe a relationship between the verb and related words in the fragment; detecting a presence of distributed incompetence in the fragments of text by applying, to the communicative discourse tree, a machine-learning model that is trained to detect distributed incompetence; generating a response based on the detection of the presence of distributed incompetence; and presenting the generated response to a user device. ### Claim 2 (depends on 1) The method of claim 1, further comprising: identifying, via the machine-learning model and in the communicative discourse tree, a first communicative action that identifies a first entity as a first actor and a second entity as a first recipient of the first communicative action; and identifying, via the machine-learning model and in the communicative discourse tree, a second communicative action that identifies the second entity as a second actor and the first entity as a second recipient of the second communicative action; and presenting information comprising the first actor and the second actor to the user device. ### Claim 3 (depends on 1) The method of claim 1, further comprising: presenting a message to a user device; receiving, from the user device, a response to the message; and extracting the fragments of text from the message and the response. ### Claim 4 (depends on 1) The method of claim 1, further comprising: identifying, via the machine-learning model and in the communicative discourse tree, a first communicative action that is of class “deny” and identifies a first actor; and presenting information comprising the first actor to the user device. ### Claim 5 (depends on 1) The method of claim 1, wherein the matching comprises: selecting a particular verb signature, comprising a verb of a respective fragment, from a plurality of verb signatures based on the particular verb signature having one or more roles of one or more words in the respective fragment; and associating the particular verb signature with the fragment. ### Claim 6 (depends on 5) The method of claim 5, wherein the associating further comprises: identifying each of a plurality of thematic roles in the particular verb signature; and matching, for each of the plurality of thematic roles in the particular verb signature, a corresponding word in the respective fragment to the thematic role. ### Claim 7 (depends on 1) The method of claim 1, wherein the verb is a communicative verb. ### Claim 8 A system for determining a presence of distributed incompetence by analyzing a communicative discourse tree comprising: one or more processors configured to perform operations comprising: generating a communicative discourse tree from fragments of text, wherein the communicative discourse tree represents rhetorical relationships between the fragments of text, and wherein generating the communicative discourse tree comprises: creating, from the fragments of text, a discourse tree that comprises nodes, wherein each nonterminal node of the nodes represents a rhetorical relationship between two of the fragments of text, and each terminal node of the nodes is associated with one of the fragments of text; and matching each fragment that has a verb to a verb signature that comprises the verb of a respective fragment and one or more thematic roles that describe a relationship between the verb and related words in the fragment; detecting a presence of distributed incompetence in the fragments of text by applying, to the communicative discourse tree, a machine-learning model that is trained to detect distributed incompetence; generating a response based on the detection of the presence of distributed incompetence; and presenting the generated response to an user device. ### Claim 9 (depends on 8) The system of claim 8, wherein the operations further comprise: identifying, via the machine-learning model and in the communicative discourse tree, a first communicative action that identifies a first entity as a first actor and a second entity as a first recipient of the first communicative action; and identifying, via the machine-learning model and in the communicative discourse tree, a second communicative action that identifies the second entity as a second actor and the first entity as a second recipient of the second communicative action; and presenting information comprising the first actor and the second actor to the user device. ### Claim 10 (depends on 8) The system of claim 8, wherein the operations further comprise: presenting a message to a user device; receiving, from the user device, a response to the message; and extracting the fragments of text from the message and the response. ### Claim 11 (depends on 8) The system of claim 8, wherein the operations further comprise: identifying, via the machine-learning model and in the communicative discourse tree, a first communicative action that is of class deny and identifies a first actor; and presenting information comprising the first actor to the user device. ### Claim 12 (depends on 8) The system of claim 8, wherein the matching comprises: selecting a particular verb signature, comprising a verb of a respective fragment, from a plurality of verb signatures based on the particular verb signature having one or more roles of one or more words in the respective fragment; and associating the particular verb signature with the fragment. ### Claim 13 (depends on 12) The system of claim 12, wherein the associating further comprises: identifying each of a plurality of thematic roles in the particular verb signature; and matching, for each of the plurality of thematic roles in the particular verb signature, a corresponding word in the respective fragment to the thematic role. ### Claim 14 (depends on 8) The system of claim 8, wherein each verb signature of the verb signatures comprises one of (i) an adverb, (ii) a noun phrase, or (iii) a noun. ### Claim 15 A non-transitory computer-readable medium storing a set of instructions for determining a presence of distributed incompetence by analyzing a communicative discourse tree, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to perform operations comprising: generating a communicative discourse tree from fragments of text, wherein the communicative discourse tree represents rhetorical relationships between the fragments of text, and wherein generating the communicative discourse tree comprises: creating, from the fragments of text, a discourse tree that comprises nodes, wherein each nonterminal node of the nodes represents a rhetorical relationship between two of the fragments of text, and each terminal node of the nodes is associated with one of the fragments of text; and matching each fragment that has a verb to a verb signature that comprises the verb of a respective fragment and one or more thematic roles that describe a relationship between the verb and related words in the fragment; detecting a presence of distributed incompetence in the fragments of text by applying, to the communicative discourse tree, a machine-learning model that is trained to detect distributed incompetence; generating a response based on the detection of the presence of distributed incompetence; and presenting the generated response to an user device. ### Claim 16 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the one or more instructions, when executed by the one or more processors, cause the processors to perform operations further comprising: identifying, via the machine-learning model and in the communicative discourse tree, a first communicative action that identifies a first entity as a first actor and a second entity as a first recipient of the first communicative action; and identifying, via the machine-learning model and in the communicative discourse tree, a second communicative action that identifies the second entity as a second actor and the first entity as a second recipient of the second communicative action; and presenting information comprising the first actor and the second actor to the user device. ### Claim 17 (depends on 15) The non-transitory computer readable medium of claim 15, wherein the operations further comprise: presenting a message to a user device; receiving, from the user device, a response to the message; and extracting the fragments of text from the message and the response. ### Claim 18 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the one or more instructions, when executed by the one or more processors, cause the processors to perform operations: identifying, via the machine-learning model and in the communicative discourse tree, a first communicative action that is of class deny and identifies a first actor; and presenting information comprising the first actor to the user device. ### Claim 19 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the matching comprises: selecting a particular verb signature, comprising a verb of a respective fragment, from a plurality of verb signatures based on the particular verb having one or more roles of one or more words in the respective fragment; and associating the particular verb signature with the fragment. ### Claim 20 (depends on 19) The non-transitory computer-readable medium of claim 19, wherein the associating further comprises: identifying each of a plurality of thematic roles in the particular verb signature; and matching, for each of the plurality of thematic roles in the particular verb signature, a corresponding word in the respective fragment to the thematic role.