Automated predictive response computing platform implementing adaptive data flow sets to exchange data via an omnichannel electronic communication channel independent of data source
Various embodiments relate generally to data science and data analysis, computer software and systems, and control systems to provide a platform to implement automated responses to data representing electronic messages, among other things, and, more specifically, to a computing and data platform that implements logic to facilitate implementation of an automated predictive response computing system independent of electronic communication channel or linguistic language of an electronic message payload, the automated predictive response computing system being configured to implement, for example, adaptive data flow sets to automatically adapt a flow of responses responsive to, for example, classification of an electronic message. In some examples, a method may include detecting data representing one or more electronic messages, extracting features, identifying data representing intent-related data and data representing one or more entity attributes, and generating data as a reply as a function of intent-related data and one or more entity attributes.
1. A method comprising:
detecting data representing one or more electronic messages associated with an electronic communication channel in a plurality of various electronic communication channels associated with multiple data sources, each of which is associated with a processor and memory;
extracting features from one or more portions of data of the one or more electronic messages including a subset of features derived from units of image data using an image recognition algorithm to perform one or more symbolic character recognition and facial recognition;
identifying data representing a first subset of intent-related data and data representing one or more entity attributes in the subset of features;
selecting data identifying a flow set including a first adaptive flow set of a subset of adaptive flow sets based on at least the first subset of intent-related data;
generating data as a reply as a function of the first subset of intent-related data and data representing one or more entity attributes;
identifying data representing a second subset of intent-related data and data representing one or more other entity attributes in the subset of features;
selecting data representing a second adaptive flow set from the subset of adaptive flow sets based on at least the second subset of intent-related data implementing a call deflection algorithm to deflect the reply via another electronic communication channel other than the electronic communication channel.
2. The method of claim 1 further comprising:
generating data as another reply as a function of the second subset of intent-related data.
3. The method of claim 1 wherein extracting the features comprises:
classifying the data representing the one or more electronic messages as being associated with an intent.
4. The method of claim 3 wherein classifying the data comprises:
applying one or more natural language processing (“NLP”) algorithms to predict the intent.
5. The method of claim 1 further comprising:
analyzing data values representing the first subset of intent-related data and the second subset of intent-related data to derive a context or the one or more electronic messages.
6. The method of claim 5 further comprising:
determining other subsets of intent-related data;
correlating the first subset, the second subset, and the other subsets of intent-related data to corresponding subsets of adaptive flow sets; and
selecting the corresponding subsets of adaptive flow sets in the flow set adaptively based to generate corresponding reply data.
7. The method of claim 1 further comprising:
extracting data representing a linguistic language as one of the entity attributes; and
implementing the flow set using the linguistic language.
8. The method of claim 7 wherein the one of the entity attributes is metadata received via the electronic communication channel.
9. The method of claim 1 further comprising:
analyzing the data representing the one or more electronic messages to predict a linguistic language;
generating the reply using the linguistic language.
10. The method of claim 9 further comprising:
implementing a natural language processing (“NLP”) algorithm to predict the linguistic language.
11. The method of claim 1 further comprising:
receiving an electronic message of the one or more electronic messages a data representing voice-based audio data.
12. The method of claim 11 further comprising:
analyzing the data representing the voice-based audio data to predict a linguistic language; and
generating the reply using the linguistic language.
13. The method of claim 12 wherein the reply is a text-based reply using the linguistic language.
14. The method of claim 1 further comprising:
archiving as a version of one or more of the flow set and the subset of adaptive flow sets;
detecting a modification to the one or more of the flow set and the subset of adaptive flow sets; and
implementing a modified flow set or a modified subset of adaptive flow sets response to the modification.
15. The method of claim 1 further comprising:
implementing the method using a single bot application.
16. A system comprising:
a data store configured to receive streams of data via a network into an application computing platform; and
a processor configured to execute instructions to implement an application configured to:
detect data representing one or more electronic messages associated with an electronic communication channel in a plurality of various electronic communication channels associated with multiple data sources, each of which is associated with a processor and memory;
extract features from one or more portions of data of the one or more electronic messages including a subset of features derived from units of image data using an image recognition algorithm to perform one or more symbolic character recognition and facial recognition;
identify data representing a first subset of intent-related data and data representing one or more entity attributes in the subset of features;
select data identifying a flow set including a first adaptive flow set of a subset of adaptive flow sets based on at least the first subset of intent-related data;
generate data as a reply as a function of the first subset of intent-related data and data representing one or more entity attributes;
identify data representing a second subset of intent-related data and data representing one or more other entity attributes in the subset of features;
select data representing a second adaptive flow set from the subset of adaptive flow sets based on at least the second subset of intent-related data; and
implementing a call deflection algorithm to deflect the reply via another electronic communication channel other than the electronic communication channel.
17. The system of claim 16 wherein the processor is further configured to:
generate data as another reply as a function of the second subset of intent-related data.
18. The system of claim 16 wherein the processor is further configured to:
classify the data representing the one or more electronic messages as being associated with an intent.
19. The system of claim 18 wherein the processor is further configured to:
apply one or more natural language processing (“NLP”) algorithms to predict the intent.
20. The system of claim 16 wherein the processor is further configured to:
analyze data values representing the first subset of intent-related data and the second subset of intent-related data to derive a context or the one or more electronic messages.