Adaptive signal-to-event conversion platform

1 . One or more non-transitory, computer-readable storage media having computer-executable instructions stored thereon, the instructions, when executed by at least one data processor of an adaptive signal-to-event conversion system, cause the system to:

monitor digital communications signal data transmitted between two or more users, wherein a first user is linked to a stored communications signal profile, and wherein the digital communications signal data is stored as discrete signal chunks of one or more signal modalities;

convert, upon detecting a first timestamp corresponding to completion of storing a first discrete signal chunk from the digital communications signal data, a first signal data subset within the first discrete signal chunk into a first alphanumeric signal set, the first signal data subset corresponding to at least one signal modality of the one or more signal modalities;

input the first alphanumeric signal set and the stored communications signal profile of the first user into a first machine learning model to generate a first scoring distribution that maps the first discrete signal chunk to a first signal event category set associated with the digital communications signal data;

transmit, to a second user of the two or more users, a first interventive user action that is selectively identified based on the first signal event category set, the first interventive user action enabling the second user to transform new signal data from the digital communications signal data;

convert, upon detecting a second timestamp corresponding to completion of storing a second discrete signal chunk from the digital communications signal data, a second signal data subset within the second discrete signal chunk into a second alphanumeric signal set, the second signal data subset corresponding to the at least one signal modality of the one or more signal modalities;

input the second alphanumeric signal set, the first alphanumeric signal set, and the stored communications signal profile of the first user into the first machine learning model to generate a second scoring distribution that maps the first and the second discrete signal chunks to a second signal event category set associated with the digital communications signal data;

input the first alphanumeric signal set and the stored communications signal profile of the first user into a second machine learning model to generate an affective scoring distribution that maps the first discrete signal chunk to an affective domain set for the first user;

input the first alphanumeric signal set, the affective scoring distribution, and the stored communications signal profile of the first user into the first machine learning model to generate a third scoring distribution that maps the first discrete signal chunk to a third signal event category set;

determine a signal event divergence score via comparing the first, the second, and the third scoring distributions of the first, the second, and the third signal event category sets respectively; and

responsive to the signal event divergence score failing to satisfy a tolerance threshold, transmit, to the second user, a second interventive user action that is selectively identified based on the second signal event category set, the second interventive user action enabling the second user to transform the digital communications signal data differently from the first interventive user action.

2 . The one or more non-transitory, computer-readable storage media of claim 1 , wherein the second timestamp is within a predetermined time interval from the first timestamp.

3 . The one or more non-transitory, computer-readable storage media of claim 1 , wherein the at least one signal modality is a first signal modality, and wherein the instructions further cause the system to:

convert, upon detecting the first timestamp, a third signal data subset within the first discrete signal chunk into a third alphanumeric signal set, the third signal data subset corresponding to a second signal modality of the one or more signal modalities; and

input the first alphanumeric signal set, the third alphanumeric signal set, and the stored communications signal profile of the first user into the machine learning model to generate a third scoring distribution that maps the first discrete signal chunk to a third signal event category set.

4 . The one or more non-transitory, computer-readable storage media of claim 1 , wherein the instructions further cause the system to:

generate, via a semantic encoder, an embedded content identifier for a natural-language request that is received from the second user, the natural-language request comprising a query for information associated with the digital communications signal data;

determine, via comparing the embedded content identifier of the natural-language request to embedded content identifiers of signal data for prior digital communications between the two or more users, a historical alphanumeric signal set that corresponds to prior digital communications comprising similar signal data to the digital communications signal data;

input the natural-language request and the historical alphanumeric signal set into a generative machine learning model to output a human-readable narrative that responds to the natural-language request; and

transmit for display, via a user interface of the second user, the human-readable narrative.

5 . The one or more non-transitory, computer-readable storage media of claim 1 , wherein the machine learning model is a first machine learning model, and wherein the instructions further cause the system to:

access, from a remote database, an entity attribute model that maps signal data attributes to one or more categories of distinct entities;

input the first alphanumeric signal set and the entity attribute model into a second machine learning model to generate an entity set that comprises distinct entities corresponding to identified alphanumeric signal subsets of the first alphanumeric signal set; and

transmit for display, via a user interface of the second user, a visual representation of the first alphanumeric signal set, the visual representation comprising graphical mappings between the identified alphanumeric signal subsets and the distinct entities of the entity set.

6 . The one or more non-transitory, computer-readable storage media of claim 1 , wherein the instructions further cause the system to:

responsive to detecting, upon detecting a third timestamp, a termination event preventing storage of new discrete signal chunks of the digital communications signal data:

determine a discrete signal chunk set that comprises discrete signal chunks corresponding to timestamps between the first timestamp and the third timestamp;

convert a third signal data subset within the discrete signal chunk set into a third alphanumeric signal set, the third signal data subset corresponding to the at least one signal modality of the one or more signal modalities;

input the third alphanumeric signal set into a generative machine learning to output a human-readable narrative that summarizes contents of the digital communications signal data between the first and the third timestamps; and

transmit for display, via a user interface of the second user, the human-readable narrative.

7 . The one or more non-transitory, computer-readable storage media of claim 1 , wherein the instructions further cause the system to:

selectively identify, based on the first signal event category set, at least one delayed communication action for processing the stored discrete signal chunks after termination of the digital communications signal data; and

responsive to detecting a termination event preventing storage of new discrete signal chunks of the digital communications signal data, automatically execute the at least one delayed communication action.

8 . The one or more non-transitory, computer-readable storage media of claim 1 , wherein the instructions further cause the system to:

access, from a remote database, an event compliance schema comprising one or more required signal event categories for the digital communications signal data;

determine, from the first signal event category set, a signal event category subset comprising signal event categories that satisfy the one or more required signal event categories of the event compliance schema; and

responsive to failure to detect at least one required signal event category of the event compliance schema in the signal event category subset, transmit, to the second user, an alert indicating deviation of the digital communications signal data from the event compliance schema.

9 . The one or more non-transitory, computer-readable storage media of claim 1 , wherein the one or more signal modalities can comprise alphanumeric characters, audio signals, visual images, tactile motion information, compressed files, other transmissible signal representations that are interpretable via machine code, or a combination thereof.

10 . The one or more non-transitory, computer-readable storage media of claim 1 , wherein the digital communications signal data is monitored via a communicative interface accessible between the two or more users, the communicative interface facilitating an audio call, a video conference, an email exchange, a text chat, or a combination thereof.

11 . A computing system comprising:

at least one hardware processor; and

at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the computing system to:

monitor digital communications signal data transmitted among a user set, the digital communications signal data comprising discrete signal chunks of one or more signal modalities;

convert, upon detecting a first timestamp, a first discrete signal chunk of the digital communications signal data into a first alphanumeric signal set, the first discrete signal chunk comprising signal data that corresponds to at least one signal modality of the one or more signal modalities;

input the first alphanumeric signal set into a machine learning model to generate a first scoring g distribution that maps the digital communications signal data to a first signal event category set;

transmit, to an operating user of the user set, a first interventive action that is selected based on the first signal event category set and, when executed by the at least one user, causes a transformation of new signal data from the digital communications signal data;

convert, upon detecting a second timestamp, a second discrete signal chunk of the digital communications signal data into a second alphanumeric signal set, the second discrete signal chunk comprising signal data that corresponds to the at least one signal modality of the one or more signal modalities;

input the second alphanumeric signal set and the first alphanumeric signal set into the machine learning model to generate a second scoring distribution that maps the digital communications signal data to a second signal event category set;

determine a signal event divergence score via comparing the first and the second scoring distributions of the first and the second signal event category sets respectively;

responsive to the signal event divergence score failing to satisfy a tolerance threshold, transmit, to the operating user, a second interventive action that is selected based on the second signal event category set and, when executed by the operating user, causes a different transformation of the digital communications signal data;

selectively identify, based on the first signal event category set, at least one delayed communication action for processing the stored discrete signal chunks after termination of the digital communications signal data; and

responsive to detecting a termination event preventing storage of new discrete signal chunks of the digital communications signal data, automatically execute the at least one delayed communication action.

12 . The computing system of claim 11 , wherein the at least one signal modality is a first signal modality, and wherein the computing system is further caused to:

convert, upon detecting the first timestamp, a signal data subset of the first discrete signal chunk into a third alphanumeric signal set, the signal data subset corresponding to a second signal modality of the one or more signal modalities; and

input the first alphanumeric signal set and the third alphanumeric signal set into the machine learning model to generate a third scoring distribution that maps the first discrete signal chunk to a third signal event category set.

13 . The computing system of claim 11 further caused to:

generate, via a semantic encoder, an embedded content identifier for a natural-language request that is received from the operating user, the natural-language request comprising a query for information associated with the digital communications signal data;

determine, via comparing the embedded content identifier of the natural-language request to embedded content identifiers of signal data for prior digital communications transmitted among the user set, a historical alphanumeric signal set that corresponds to prior digital communications comprising similar signal data to the digital communications signal data;

input the natural-language request and the historical alphanumeric signal set into a generative machine learning model to output a human-readable narrative that responds to the natural-language request; and

transmit for display, via a user interface of the operating user, the human-readable narrative.

14 . The computing system of claim 11 , wherein the machine learning model is a first machine learning model, and wherein the computing system is further caused to:

input the first alphanumeric signal set into a second machine learning model to generate an affective scoring distribution that maps the first discrete signal chunk to an affective domain set for the user set; and

input the first alphanumeric signal set and the affective scoring distribution into the first machine learning model to generate a third scoring distribution that maps the first discrete signal chunk to a third signal event category set.

15 . The computing system of claim 11 , wherein the machine learning model is a first machine learning model, and wherein the computing system is further caused to:

access, from a remote database, an entity attribute model that maps signal data attributes to one or more categories of distinct entities;

input the first alphanumeric signal set and the entity attribute model into a second machine learning model to generate an entity set that comprises distinct entities corresponding to identified alphanumeric signal subsets of the first alphanumeric signal set; and

transmit for display, via a user interface of the operating user, a visual representation of the first alphanumeric signal set, the visual representation comprising graphical mappings between the identified alphanumeric signal subsets and the distinct entities of the entity set.

16 . The computing system of claim 11 further caused to:

responsive to detecting, upon detecting a third timestamp, a termination event preventing storage of new discrete signal chunks of the digital communications signal data:

determine a discrete signal chunk set that comprises discrete signal chunks corresponding to timestamps between the first timestamp and the third timestamp;

convert a third discrete signal chunk from the discrete signal chunk set into a third alphanumeric signal set, the third discrete signal chunk comprising signal data that corresponds to the at least one signal modality of the one or more signal modalities;

input the third alphanumeric signal set into a generative machine learning to output a human-readable narrative that summarizes contents of the digital communications signal data between the first and the third timestamps; and

transmit for display, via a user interface of the operating user, the human-readable narrative.

17 . The system of claim 11 further caused to:

access, from a remote database, an event compliance schema comprising one or more required signal event categories for the digital communications signal data;

determine, from the first signal event category set, a signal event category subset comprising signal event categories that satisfy the one or more required signal event categories of the event compliance schema; and

responsive to failure to detect at least one required signal event category of the event compliance schema in the signal event category subset, transmit, to the operating user, an alert indicating deviation of the digital communications signal data from the event compliance schema.

18 . A computer-implemented method for a signal-to-event conversion platform, the method comprising:

monitoring digital communications signal data transmitted among a user set, the digital communications signal data comprising a discrete signal chunk set of one or more signal modalities; and

responsive to detecting a first discrete signal chunk not found within the discrete signal chunk set of the monitored digital communications signal data:

converting signal data of the first discrete signal chunk into a first alphanumeric signal set, the signal data corresponding to at least one signal modality of the one or more signal modalities;

inputting the first alphanumeric signal set into a machine learning model to generate a first scoring distribution that maps the digital communications signal data to a first signal event category set;

retrieving, for a second discrete signal chunk within the discrete signal chunk set, a second alphanumeric signal set and a second scoring distribution that maps the digital communications signal data to a second signal event category set;

determining a signal event divergence score via comparing the first and the second scoring distributions of the first and the second signal event category sets respectively;

when the signal event divergence score fails to satisfy a tolerance threshold, transmitting, to at least one user of the user set, an interventive action that is selected based on the first signal event category set and, when executed by the at least one user, causes a transformation of the digital communications signal data;

selectively identifying, based on the first signal event category set, at least one delayed communication action for processing the discrete signal chunks after termination of the digital communications signal data; and

responsive to detecting a termination event preventing storage of new discrete signal chunks of the digital communications signal data, automatically executing the at least one delayed communication action.

19 . The computer-implemented method of claim 18 , wherein the machine learning model is a first machine learning model, and wherein the computer-implemented method further comprises:

inputting the first alphanumeric signal set into a second machine learning model to generate an affective scoring distribution that maps the first discrete signal chunk to an affective domain set for the user set; and

inputting the first alphanumeric signal set and the affective scoring distribution into the first machine learning model to generate a third scoring distribution that maps the first discrete signal chunk to a third signal event category set.

20 . The computer-implemented method of claim 18 further comprising:

selectively identifying, based on the first signal event category set, at least one delayed communication action for processing the discrete signal chunks after termination of the digital communications signal data; and

responsive to detecting a termination event preventing storage of new discrete signal chunks of the digital communications signal data, automatically executing the at least one delayed communication action.