| CPC G06F 16/35 (2019.01) [G06F 16/34 (2019.01); G06F 40/284 (2020.01); G06F 40/30 (2020.01)] | 28 Claims |
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24. A computer-implemented system comprising:
one or more processors;
a memory; and
a computer-readable medium operably coupled to the one or more processors, the computer-readable medium having computer-readable instructions stored thereon that, when executed by the one or more processors, cause a computing device to perform operations comprising:
obtaining a text document that includes text describing an action performable in the real world by an author associated with the text;
extracting, via a text processing model, one or more action tokens that represent the action described in the text document, wherein extracting the one or more action tokens from the text document includes identifying a modal verb from a respective sentence within the text document;
executing, via the text processing model, a plurality of linguistic pattern searches that search the text document for one or more likelihood tokens associated with the one or more action tokens, wherein the plurality of linguistic pattern searches includes a temporal-based hypothetical conditional pattern search, wherein searching the text document for the one or more likelihood tokens includes searching for one or more tokens within the respective sentence of the text document that satisfies token likelihood search criteria of the temporal-based hypothetical conditional pattern search, wherein the token likelihood search criteria is satisfied when:
a token within the respective sentence of the text document matches to a set of conditional words, and
the token that matched to the set of conditional words is detected in a position relative to the modal verb satisfying one or more concept rules of the temporal-based hypothetical conditional search pattern; and
wherein the text processing model groups the plurality of linguistic pattern searches into a plurality of likelihood categories, including:
a first likelihood category of the plurality of likelihood categories that includes a first subgroup of linguistic pattern searches of the plurality of linguistic pattern searches,
a second likelihood category of the plurality of likelihood categories that includes a second subgroup of linguistic pattern searches of the plurality of linguistic pattern searches, and
a third likelihood category of the plurality of likelihood categories that includes a third subgroup of linguistic pattern searches of the plurality of linguistic pattern searches, wherein the first likelihood category, the second likelihood category, and the third likelihood category are distinct from each other, where each of the plurality of likelihood categories level of likelihood is associated with a different level of likelihood from one another, and wherein the first subgroup, the second subgroup, and the third subgroup of linguistic pattern searches are distinct from each other;
classifying, via the text processing model, the action to the first likelihood category based on a respective linguistic pattern search of the plurality of linguistic pattern searches that identified the one or more likelihood tokens belonging to the first subgroup of linguistic pattern searches, wherein the likelihood category indicates a likelihood of the action taking place in the real world, wherein the likelihood of the action represents a probability of the action being performed by the author in the real world, and
classifying, via a domain classification model, the text document to a respective domain;
computing, via an activity prioritization model, a priority value of the action described in the text document based on an input of the likelihood category and the respective domain; and
generating a priority summary artifact that visually prioritizes the text document over one or more other text documents when the priority value of the action satisfies a pre-defined maximum priority threshold value.
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