System and method for intelligent and automatic electronic communication support and routing

Patent metadata

Field	Value
Publication number	US-2019108486-A1
Application number	US-201715725983-A
Country	US
Kind code	A1
Filing date	Oct 5, 2017
Priority date	Oct 5, 2017
Publication date	Apr 11, 2019
Grant date	—

Abstract

Methods for automatic and intelligent electronic communication support, including using machine learning, are performed by systems and apparatuses. The methods intelligently and automatically route electronic communication support requests and intelligently and automatically provide senders with information related to their support requests. The methods generate feature vectors from cleaned request information via featurization techniques, and utilize machine-learning algorithms/models and algorithm/model outputs based on the input feature vectors. Based on the algorithm/model outputs and personalized to the specific sender, relevant support information is automatically provided to the sender. The methods also determine a set of prior communications related to the support request based on a similarity measure, and provide prior communication information to the sender. The methods also include routing support requests to correct feature owner recipients based on the algorithm/model outputs.

First claim

What is claimed is:

1 . A system comprising:
at least one memory configured to store program logic; and at least one processor configured to access the memory and to execute the program logic, the program logic comprising: featurization logic configured to:
apply featurization to first information to generate a feature vector, the first information being received in a first electronic communication from a sender;
selector logic configured to:
provide the feature vector as an input to a machine-learning model that automatically determines a model output based on the feature vector; and
based at least in part on the model output, automatically select one or more of second information from a plurality of support information or a recipient from a plurality of possible recipients; and
transmitter logic configured to perform at least one of:
provide a second electronic communication that includes the second information to one or more of the sender or the recipient; or
provide the first electronic communication to the recipient.

2 . The system of claim 1 , wherein the machine-learning model is a classifier, a regression model, a clustering model, or a comparison model.

3 . The system of claim 1 , wherein featurization includes performing at least one featurization operation that transforms at least a portion of the first information into one or more representations that describe characteristics of the at least a portion of the first information, the featurization logic being configured to perform the at least one featurization operation comprising one or more of:
a K-means clustering featurization; a keyword featurization; a content-based featurization; a semantic-based featurization; an n-gram featurization; a skip-gram featurization; a bag of words featurization; a char-gram featurization; or a feature selection featurization; or wherein the first electronic communication comprises one or more of: an electronically mailed (emailed) support request; a technical support request; a posting on messaging thread or forum; a social media posting; an instant message; a conversation with automated mechanism; a billing request; feedback; or a notification.

4 . The system of claim 3 , wherein the keyword featurization comprises a representation for one or more of keywords or keyphrases;
wherein the content-based featurization comprises at least one electronic message attribute of a character count, a byte count, or a ratio of numeric to alphabetic characters; or wherein the semantic-based featurization comprises one or more triplet sets that each include an entity, and action, and a qualifier.

5 . The system of claim 1 , wherein the featurization logic is further configured to determine the feature vector based on support reference information accessible through a network;
or wherein the selector logic is further configured to determine an indication of urgency based on the feature vector, and wherein the program logic further comprises responder logic configured to include the indication of urgency in the first electronic communication provided to the recipient.

6 . The system of claim 1 , wherein the second information comprises at least one communication-based portion, determined based on the feature vector, comprising:
a previously-determined resolution, one or more previously-received electronic communications, or a selectable link to a proposed resolution, the selectable link being automatically generated based on a determination of the proposed resolution from the plurality of support information; and wherein the selector logic is further configured to determine a ranking for portions of the second information, and wherein the program logic further comprises responder logic configured to provide the portions of the second information in the second communication in an order according to the ranking.

7 . The system of claim 1 , wherein at least one of the model output or the second communication is personalized to the sender based on one or more of:
a prior response sent to the sender; an effectiveness for resolution of a prior response sent to a different sender; a team membership or a service membership of the sender; a setting or preference of the sender; or an attribute of the sender.

8 . The system of claim 1 , wherein the selector logic is configured to utilize an updated machine-learning model that is updated as an incremental update or as a full update based on feedback associated with the second electronic communication, the feedback being one or more of:
an efficacy rating for the second information from the sender, a number of communications including the first communication and the second communication that have been exchanged between the sender and the recipient for a resolution, a lack of a response from the sender to the second electronic communication, an amount of time elapsed between the provision of the first electronic communication to the recipient and when the recipient takes an action in response to the first electronic communication, or the feature vector and the model output.

9 . A system comprising:
at least one memory configured to store program logic; and at least one processor configured to access the memory and to execute the program logic, the program logic comprising: featurization logic configured to:
apply featurization to first information to generate a feature vector, the first information being received in a first electronic communication from a sender;
locator logic configured to:
automatically determine a set of prior communications based on a measure of similarity between the feature vector and feature vectors associated with the set of prior communications, and
automatically select second information from the set of prior communications;
responder logic configured to:
generate a second electronic communication that includes the second information; and
transmitter logic configured to:
provide the second electronic communication to the sender.

10 . The system claim 9 , wherein featurization includes performing at least one featurization operation that transforms at least a portion of the first information into one or more representations that describe characteristics of the at least a portion of the first information, the featurization logic being configured to perform the at least one featurization operation comprising one or more of:
a K-means clustering featurization; a keyword featurization; a content-based featurization; a semantic-based featurization; an n-gram featurization; a skip-gram featurization; a bag of words featurization; a char-gram featurization; or a feature selection featurization; wherein the first electronic communication comprises one or more of: an electronically mailed (emailed) support request; a technical support request; a posting on messaging thread or forum; a social media posting; an instant message; a conversation with automated mechanism; a billing request; feedback; or a notification; or wherein the measure of similarity is determined by a machine-learning comparison model.

11 . The system of claim 9 , wherein the second information comprises at least one of:
a previously-determined resolution, one or more previously-received electronic communications, or a selectable link to a proposed resolution, the selectable link being automatically generated based on a determination of the proposed resolution; and wherein the responder logic is configured to provide portions of the second information in the second communication in an order according to the measure of similarity.

Assignees

• Microsoft Technology Licensing Llc

Inventors

• Jain Navendu
• Hu Shane

Classifications

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• H04L63/1433

  Vulnerability analysis · CPC title

• H04L51/02

  using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages · CPC title

• G06Q10/107 Primary

  Computer-aided management of electronic mailing [e-mailing] · CPC title