Methods and systems for implementing an intelligent digital assistant in a multi-application network
Disclosed are methods and apparatuses for implementing a digital assistant computing operation in a multi-application network. The methods include: receiving an input command from a computing device; analyzing the input command; determining based on the analysis of the input command, a digital request data object associated with the input command; determining, based on the digital request data object, intent data, and generating based on the intent data, a first set of operation recommendations comprising a first operation recommendation. The methods also include: determining a first application function associated with the first operation recommendation; determining or accessing an application programming interface (API) associated with the first application function; connecting the API to the first application function; executing the first operation recommendation to generate a computing operation result; and rendering the computing operation result on a first graphical interface.
1 . A method for implementing a digital assistant computing operation in a multi-application network, the method comprising:
receiving, by one or more processors, an input command from a computing device;
resolving, by the one or more processors, the input command into at least partially resolved data;
determining, by the one or more processors, a digital request data object associated with the input command and intent data associated with the digital request data object;
generating, by the one or more processors, a first set of operation recommendations based on the intent data;
receiving, via the one or more processors, an indication of a first operation recommendation among the first set of operation recommendations;
determining, by the one or more processors, a first application function associated with the first operation recommendation and a first application programming interface associated with the first application function;
executing, by the one or more processors, the first operation recommendation by accessing the first application function via the first application programming interface, to form a first computing operation result;
transmitting the first computing operation result to the computing device;
receiving, by the one or more processors, an indication of a second operation recommendation among the first set of operation recommendations;
determining, by the one or more processors, a second application function associated with the second operation recommendation and an interface associated with the second application function, the interface comprising one of the first application programming interface and a second application programming interface;
executing, by the one or more processors, the second operation recommendation by accessing the second application function via the interface, to form a second computing operation result;
transmitting the second computing operation result to the computing device;
determining, by the one or more processors, an exception event associated with the digital request data object based on context data;
generating, by the one or more processors, a second set of operation recommendations for resolving the exception event;
executing, by the one or more processors, a third operation recommendation among the second set of operation recommendations associated with resolving the exception event;
receiving, by the one or more processors, a user input comprising user feedback associated with the second set of operation recommendations;
identifying, by the one or more processors, one or more keywords within the user feedback;
modifying, by the one or more processors, the second set of operation recommendations based on the one or more keywords to generate a modified second set of operation recommendations; and
storing, by the one or more processors, the modified second set of operation recommendations.
2 . The method of claim 1 , further comprising:
generating the context data, the context data being associated with the digital request data object, the context data indicating one or more of one or more exception events and a processing stage associated with the digital request data object; and
initiating display of the context data on a graphical user interface.
3 . The method of claim 2 , wherein:
the graphical user interface is a single graphical user interface configured to display a plurality of analysis data including the first computing operation result,
the plurality of analysis data is generated from analysis operations associated with one or more digital request data objects including the digital request data object,
the analysis operations are executed by one or more native or non-native application functions associated with the multi-application network, and
the plurality of analysis data are displayed on the single graphical user interface.
4 . The method of claim 1 , further comprising one or more of automatically formatting and automatically arranging, by the one or more processors, one or more of the first computing operation result, the first set of operation recommendations, the second computing operation result and the second set of operation recommendations for display on at least one graphical user interface.
5 . The method of claim 1 , wherein at least one of:
the first application function is associated with or comprised in a first application, and
the second application function is associated with or comprised in the first application or a second application.
6 . The method of claim 1 , wherein the input command comprises a natural language input.
7 . The method of claim 1 , further comprising improving, based on a learning computing operation, at least one of the first operation recommendation and the second operation recommendation.
8 . The method of claim 1 , wherein the multi-application network is configured for inter-domain or multi-application function data processing associated with the digital request data object using the input command or a further input command.
9 . The method of claim 1 , wherein the first application function comprises at least one of:
a native application function that is native to the multi-application network, and
a non-native application function that is not native to the multi-application network.
10 . The method of claim 1 , wherein the digital request data object comprises one or more of a document and a file.
11 . The method of claim 10 , wherein the digital request data object includes at least one of:
account data associated with the digital request data object, and
parametric data associated with the digital request data object.
12 . The method of claim 11 , wherein the parametric data comprises at least one of:
identifier data associated with the digital request data object,
quantitative data associated with the digital request data object,
first exception event data associated with the digital request data object, and
resolution data associated with second exception event data associated with the digital request data object.
13 . The method of claim 1 , wherein a data model associated with the digital assistant computing operation is configured to:
track or assimilate a trajectory of a plurality of input commands including the input command leading to a selection of specific operation recommendations including the first operation recommendation; and
recommend the second set of operation recommendations for display on a graphical user interface associated with the computing device.
14 . The method of claim 1 , wherein a data model associated with the digital assistant computing operation comprises a large language model.
15 . The method of claim 1 , further comprising automatically formatting one or more of:
the first computing operation result for display on at least one graphical user interface based on the context data, and
the second set of operation recommendations based on one or more of the first operation recommendation and the context data.
16 . The method of claim 1 , wherein a data model associated with the digital assistant computing operation is one of an artificial intelligence language model or a machine learning language model.
17 . The method of claim 1 , wherein at least one of: the input command is analyzed using a data model, the second set of operation recommendations comprises a first progression of operations for resolving the exception event, the second set of operation recommendations comprises a stage-wise progression of operations for resolving the exception event.
18 . The method of claim 1 , further comprising:
determining, using further context data, a further exception event associated with a further digital request data object; and
transmitting, based on at least a partial similarity of the further exception event and the exception event, the modified second set of operation recommendations.
19 . The method of claim 1 , further comprising:
receiving, via the one or more processors, an indication of further user feedback associated with at least one of: the first set of operation recommendations, the first computing operation result, the second computing operation result, the second set of operation recommendations, and a third computing operation result associated with executing the third operation recommendation; and
modifying, based on the further user feedback, at least one of: the first set of operation recommendations, the first computing operation result, the second computing operation result, the second set of operation recommendations, and the third computing operation result.