Decisioning platform using multi-domain signal evaluation systems

1 . A method performed by a multi-domain signal evaluation system for processing alphanumeric signals, the method comprising:

receiving a first digital artifact comprising unstructured alphanumeric signal data indicating contextual information associated with a decision logic condition,

wherein the first digital artifact corresponds to a set of compliance parameters that define one or more acceptable content elements of the unstructured alphanumeric signal data;

responsive to identifying a set of non-compliant alphanumeric signals from the unstructured alphanumeric signal data that fail to satisfy the set of compliance parameters:

generating a set of masking elements comprising a mapping to the set of non-compliant alphanumeric signals, and

generating a second digital artifact comprising alphanumeric signal data that substitutes or supplements the identified non-compliant alphanumeric signals of the unstructured alphanumeric signal data with the set of masking elements;

generating and binding to the second digital artifact a set of signal domain categories for the second digital artifact, wherein each of the set of the signal domain categories comprises a query responsiveness indicator;

using a set of query responsiveness indicators that satisfy a set of decisioning criteria, generating an artifact set comprising at least one particular second digital artifact; and

generating and configuring for display, at a user interface, a set of guidance artifacts that correspond to the generated artifact set, each displayed guidance artifact comprising: (1) a human-readable narrative generated using at least a portion of the artifact set, (2) a human-readable summary generated using the at least a portion of the artifact set, or (3) the at least a portion of the artifact set, wherein the at least a portion is sufficient to generate a response to a user query received via the user interface.

2 . The method of claim 1 , the method further comprising:

using the set of query responsiveness indicators for generated artifact set, generating a composite alphanumeric signal comprising a risk rating associated with the artifact set, the risk rating comprising a textual value, an alphanumeric value, a score, a Boolean value, a categorical value, or a combination thereof,

wherein each query responsiveness indicator in the set of query responsiveness indicators meets or exceeds a predetermined threshold.

3 . The method of claim 1 , the method further comprising:

responsive to receiving the user query, performing, by an intent classifier, operations comprising:

parsing the user query to generate a set of natural-language tokens; and

applying an executable classification model to (i) the set of natural-language tokens and (ii) a case identifier to generate an output set comprising a intent classifier.

4 . The method of claim 3 , wherein the intent classifier is based on an inference that relates to a scope of the user query.

5 . The method of claim 4 , the method further comprising:

performing, by a query processor, operations comprising:

generating a subset of second artifacts, wherein each particular artifact in the subset of second artifacts is: (i) responsive to the scope of the user query and (ii) satisfies a condition determined based on the set of natural-language tokens, the case identifier, or both.

6 . The method of claim 5 , the method further comprising:

performing, by a reasoner, operations comprising:

causing a relevancy checker to process the subset of second artifacts to generate a set of condition satisfaction indicia indicative of a degree to which the condition is satisfied by each artifact in the subset of second artifacts; and

responsive to evaluating the set of condition satisfaction indicia,

updating the condition by generating a replacement set of natural-language tokens different at least in part from the set of natural-language tokens, and

causing the query processor to generate a replacement subset of second artifacts using the updated condition.

7 . The method of claim 6 , wherein at least one of the intent classifier, the query processor, the reasoner, and the relevancy checker is an autonomously executable agent comprising a compute resource, the compute resource including at least one particular memory, at least one particular processor, and a set of particular computer-executable instructions configured to invoke a particular artificial intelligence (AI) model.

8 . The method of claim 1 , wherein the set of compliance parameters comprises personally identifiable information, a prohibited content type, a user specified content restriction, a data usage restriction, a third-party regulatory restriction, or a combination thereof.

9 . The method of claim 1 , further comprising generating a set of decisioning criteria by:

accessing an input data set comprising at least one of historical underwriting case information and medical records information; and

classifying portions of the input data set into a set of decisioning criteria.

10 . One or more non-transitory, computer-readable storage media comprising instructions recorded thereon, wherein the instructions when executed by at least one data processor of a multi-domain signal evaluation system, cause the multi-domain signal evaluation system to perform operations comprising:

receiving a first digital artifact comprising unstructured alphanumeric signal data indicating contextual information associated with a decision logic condition,

wherein the first digital artifact corresponds to a set of compliance parameters that define one or more acceptable content elements of the unstructured alphanumeric signal data;

responsive to identifying a set of non-compliant alphanumeric signals from the unstructured alphanumeric signal data that fail to satisfy the set of compliance parameters:

generating a set of masking elements comprising a mapping to the set of non-compliant alphanumeric signals, and

generating a second digital artifact comprising alphanumeric signal data that substitutes or supplements the identified non-compliant alphanumeric signals of the unstructured alphanumeric signal data with the set of masking elements;

generating and binding to the second digital artifact a set of signal domain categories for the second digital artifact, wherein each of the set of the signal domain categories comprises a query responsiveness indicator;

using a set of query responsiveness indicators that satisfy a set of decisioning criteria, generating an artifact set comprising at least one particular second digital artifact; and

generating and configuring for display, at a user interface, a set of guidance artifacts that correspond to the generated artifact set, each displayed guidance artifact comprising: (1) a human-readable narrative generated using at least a portion of the artifact set, (2) a human-readable summary generated using the at least a portion of the artifact set, or (3) the at least a portion of the artifact set, wherein the at least a portion is sufficient to generate a response to a user query received via the user interface.

11 . The one or more non-transitory, computer-readable storage media of claim 10 , the operations further comprising:

using the set of query responsiveness indicators for generated artifact set, generating a composite alphanumeric signal comprising a risk rating associated with the artifact set, the risk rating comprising a textual value, an alphanumeric value, a score, a Boolean value, a categorical value, or a combination thereof,

wherein each query responsiveness indicator in the set of query responsiveness indicators meets or exceeds a predetermined threshold.

12 . The one or more non-transitory, computer-readable storage media of claim 10 , the operations further comprising:

responsive to receiving the user query, performing, by an intent classifier, operations comprising:

parsing the user query to generate a set of natural-language tokens; and

applying an executable classification model to (i) the set of natural-language tokens and (ii) a case identifier to generate an output set comprising a intent classifier.

13 . The one or more non-transitory, computer-readable storage media of of claim 12 , wherein the intent classifier is based on an inference that relates to a scope of the user query.

14 . The one or more non-transitory, computer-readable storage media of claim 13 , the operations further comprising:

by a query processor,

generating a subset of second artifacts, wherein each particular artifact in the subset of second artifacts is: (i) responsive to the scope of the user query and (ii) satisfies a condition determined based on the set of natural-language tokens, the case identifier, or both.

15 . The one or more non-transitory, computer-readable storage media of claim 14 , the operations further comprising:

by a reasoner,

causing a relevancy checker to process the subset of second artifacts to generate a set of condition satisfaction indicia indicative of a degree to which the condition is satisfied by each artifact in the subset of second artifacts; and

responsive to evaluating the set of condition satisfaction indicia,

updating the condition by generating a replacement set of natural-language tokens different at least in part from the set of natural-language tokens, and

causing the query processor to generate a replacement subset of second artifacts using the updated condition.

16 . The one or more non-transitory, computer-readable storage media of claim 15 , wherein at least one of the intent classifier, the query processor, the reasoner, and the relevancy checker is an autonomously executable agent comprising a compute resource, the compute resource including at least one particular memory, at least one particular processor, and a set of particular computer-executable instructions configured to invoke a particular artificial intelligence (AI) model.

17 . The one or more non-transitory, computer-readable storage media of claim 10 , wherein the set of compliance parameters comprises personally identifiable information, a prohibited content type, a user specified content restriction, a data usage restriction, a third-party regulatory restriction, or a combination thereof.

18 . The one or more non-transitory, computer-readable storage media of claim 10 , the operations further comprising generating a set of decisioning criteria by:

accessing an input data set comprising at least one of historical underwriting case information and medical records information; and

classifying portions of the input data set into a set of decisioning criteria.

19 . A multi-domain signal evaluation system comprising at least one processor and at least one memory having computer-executable instructions recorded thereon, wherein the instructions when executed by the at least one processor, cause the multi-domain signal evaluation system to:

receive a first digital artifact comprising unstructured alphanumeric signal data indicating contextual information associated with a decision logic condition,

wherein the first digital artifact corresponds to a set of compliance parameters that define one or more acceptable content elements of the unstructured alphanumeric signal data;

responsive to identifying a set of non-compliant alphanumeric signals from the unstructured alphanumeric signal data that fail to satisfy the set of compliance parameters:

generate a set of masking elements comprising a mapping to the set of non-compliant alphanumeric signals, and

generate a second digital artifact comprising alphanumeric signal data that substitutes or supplements the identified non-compliant alphanumeric signals of the unstructured alphanumeric signal data with the set of masking elements;

generate and bind to the second digital artifact a set of signal domain categories for the second digital artifact, wherein each of the set of the signal domain categories comprises a query responsiveness indicator;

using a set of query responsiveness indicators that satisfy a set of decisioning criteria, generate an artifact set comprising at least one particular second digital artifact; and

generate and configure for display, at a user interface, a set of guidance artifacts that correspond to the generated artifact set, each displayed guidance artifact comprising: (1) a human-readable narrative generated using at least a portion of the artifact set, (2) a human-readable summary generated using the at least a portion of the artifact set, or (3) the at least a portion of the artifact set, wherein the at least a portion is sufficient to generate a response to a user query received via the user interface.

20 . The system of claim 19 , wherein the instructions when executed by the at least one processor, cause the multi-domain signal evaluation system to generate a set of decisioning criteria by performing operations to:

access an input data set comprising at least one of historical underwriting case information, medical records information, and claim history information; and

classify portions of the input data set into a set of decisioning criteria.