# US 2018/0322188 A1 · Automatic conversation creator for news

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- Number: US 2018/0322188 A1
- Document ID: US20180322188A1
- Status: Published application
- Kind: A1
- Application number: 15/772,472
- Filed: 2016-10-26
- Published: 2018-11-08
- Earliest claimed: 2015-10-30
- Assignees: Microsoft Technology Licensing, LLC
- Applicants: Microsoft Technology Licensing, LLC
- Inventors: Ming ZHOU; Yu-Ting KUO; Furu WEI; Lei CUI; Shaohan HUANG; Kati LONDON; Wei-Ying MA; Haoyong ZHANG
- Web page: https://patentgrep.com/patent/US20180322188A1

## CPC

- G06Q 10/40: Business processes related to social networking or social networking services
- G06F 16/3329: Natural language query formulation
- G06F 16/3347: {using vector based model}
- G06F 16/335: Filtering based on additional data, e.g. user or group profiles (filtering in web context G06F16/9535, G06F16/9536)
- G06F 16/9535: Search customisation based on user profiles and personalisation
- G06F 18/2411: based on the proximity to a decision surface, e.g. support vector machines
- G06F 40/284: Lexical analysis, e.g. tokenisation or collocates
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

An example environment can automatically generate content for a conversation in response to a presented content item. An example computing device receives information associated with a content item produced by a source system, the content item being accessible to other the computing devices via a network. In response to receiving the information, the computing device identifies a start comment. In response to identifying the start comment, the computing device identifies a reply to the start comment using at least a ranking model.

## Claims

### Claim 1

A computing device comprising:
a processor;
a display device; and
a computer-readable storage medium in communication with the processor, the computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by the processor, cause the computing device to:
receive information associated with a content item produced by a source system, the content item being accessible to other computing devices via a network;
in response to receiving the information, identify a start comment;
in response to identifying the start comment, create a reply to the start comment using at least a ranking model; and
output the start comment and the reply.

### Claim 2 (depends on 1)

The computing device of claim 1, wherein the identifying the start comment comprises:
determining from a plurality of comments an amount of likes for the comments of the plurality of the comments; and
identifying the start comment as the comment of the plurality of comments having the greatest amount of likes.

### Claim 3 (depends on 1)

The computing device according to claim 1, wherein the creating the reply comprises:
identifying a plurality of pairs, the plurality of pairs comprises the start comment and one of a plurality of replies;
ranking the plurality of pairs in response to execution of the ranking model; and
identifying a reply in the pair with the highest rank as the reply to the start comment.

### Claim 4 (depends on 1)

The computing device according to claim 1, wherein the computer-readable storage medium has further computer-executable instructions stored thereon that cause the computer to:
identify lexical-level features for a plurality of pairs of comments and replies previously posted to one or more content item webpages;
identify entity-level features for the plurality of pairs;
identify topic vectors for the plurality of pairs;
identify one or more quality features for the plurality of pairs;
create feature vectors for the plurality of pairs based at least in part on the lexical-level features, entity-level features, topic vectors and one or more quality features; and create the ranking model based at least in part on the feature vectors.

### Claim 5 (depends on 1)

The computing device according to claim 1, wherein the computer-readable storage medium has further computer-executable instructions stored thereon that cause the computer to create the classifier using a support vector machine.

### Claim 6 (depends on 1)

The computing device according to claim 1, wherein the computer-readable storage medium has further computer-executable instructions stored thereon that cause the computer to:
receive a comment from the computing device;
in response to receiving the comment from the computing device, identify a reply to the comment from the computing device based at least in part on the ranking model; and
output the reply identified by the ranking model.

### Claim 7

A computer-implemented method comprising:
receiving, at a computing device, information associated with a content item produced by a source system, the content item being accessible to other computing devices via a network;
outputting at least a portion of the content item;
in response to receiving the information, generating a start comment and identifying a reply to the start comment based at least in part on a ranking model; and outputting the start comment and the reply concurrently with the content item.

### Claim 8 (depends on 7)

The computer-implemented method of claim 7, wherein the identifying the start comment comprises:
determining from a plurality of comments an amount of likes for the comments of the plurality of the comments; and
identifying the start comment as the comment of the plurality of comments having the greatest amount of likes.

### Claim 9 (depends on 7)

The computer-implemented method according to claim 7, wherein the identifying the reply comprises:
identifying a plurality of pairs, the plurality of pairs comprises the start comment and one of a plurality of replies;
ranking the plurality of pairs in response to execution of the ranking model; and
identifying a reply in the pair with the highest rank as the reply to the start comment.

### Claim 10 (depends on 7)

The computer-implemented method according to claim 7, further comprising training the ranking model by:
identifying lexical-level features for a plurality of pairs of comments and replies previously posted to one or more content item webpages;
identifying entity-level features for the plurality of pairs;
identifying topic vectors for the plurality of pairs;
identifying one or more quality features for the plurality of pairs;
creating feature vectors for the plurality of pairs based at least in part on the identified lexical-level features, entity-level features, topic vectors and one or more quality features; and
creating the ranking model based at least in part on the created feature vectors.

### Claim 11 (depends on 7)

The computer-implemented method according to claim 7, wherein creating the ranking model comprises:
applying weight values to the plurality of pairs; and
creating a classifier based on the weight values.

### Claim 12 (depends on 7)

The computer-implemented method according to claim 7, further comprising creating the classifier using a support vector machine.

### Claim 13 (depends on 7)

The computer-implemented method according to claim 7, wherein the content item comprises a news article webpage or a posting webpage.

### Claim 14 (depends on 7)

The computer-implemented method according to claim 7, further comprising:
receiving a comment from the computing device;
in response to receiving the comment from the computing device, identifying a reply to the comment from the computing device based at least in part on the ranking model; and
outputting the reply identified by the ranking model.

### Claim 15 (depends on 7)

A computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by a computer, cause the computer to perform the method according to claim 7.