# US 2013/0159219 A1 · Predicting the Likelihood of Digital Communication Responses

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- Number: US 2013/0159219 A1
- Document ID: US20130159219A1
- Status: Published application
- Kind: A1
- Application number: 13/325,386
- Filed: 2011-12-14
- Published: 2013-06-20
- Earliest claimed: 2011-12-14
- Assignees: Microsoft Corporation
- Inventors: Patrick Pantel; Michael Gamon; Yoav Y. Artzi
- Web page: https://patentgrep.com/patent/US20130159219A1

## CPC

- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- G06N 20/00: Machine learning
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

Different advantageous embodiments provide for response prediction. A social element is received by a prediction mechanism. A feature set is generated for the social element. A prediction is generated using the feature set and a prediction model.

## Claims

### Claim 1

A method comprising:
receiving, by a prediction mechanism, a social element;
generating a feature set for the social element; and
generating a prediction using the feature set and a prediction model.

### Claim 2 (depends on 1)

The method of claim 1 wherein generating the feature set comprises generating a feature vector using one or more feature values extracted from the social element.

### Claim 3 (depends on 1)

The method of claim 1 wherein generating the feature set comprises generating one or more feature values using a feature value extractor, and wherein the feature value extractor includes a number of feature modules for processing the social element to generate the one or more feature values.

### Claim 4 (depends on 1)

The method of claim 1 wherein receiving the social element comprises receiving a microblog post.

### Claim 5 (depends on 1)

The method of claim 1 wherein the steps are performed in an online environment.

### Claim 6 (depends on 1)

The method of claim 1 wherein generating the prediction comprises outputting at least one of a definitive answer or a probability.

### Claim 7 (depends on 1)

The method of claim 1, further comprising:
receiving, by the prediction mechanism, a plurality of social elements;
generating a plurality of features sets for the plurality of social elements, wherein a feature set is generated for each social element in the plurality of social elements; and
generating a prediction for the each social element in the plurality of social elements using the plurality of feature sets and a prediction model, wherein generating the prediction is performed in an offline environment.

### Claim 8

An apparatus for response prediction, the apparatus comprising:
a prediction mechanism configured to analyze a social element and generate a prediction associated with the social element using a prediction model.

### Claim 9 (depends on 8)

The apparatus of claim 8 wherein the prediction mechanism further comprises a trainer configured to receive a plurality of social elements from a social graph and train the prediction model using the plurality of social elements and training information.

### Claim 10 (depends on 9)

The apparatus of claim 9 wherein the training information comprises at least one of a sentiment lexicon, a stop word list, hashtag salience scores, or word salience scores.

### Claim 11 (depends on 8)

The apparatus of claim 8 wherein the prediction mechanism further comprises:
a feature value extractor configured to extract one or more feature values from the social element.

### Claim 12 (depends on 11)

The apparatus of claim 11 wherein the prediction mechanism further comprises:
a feature vector generator configured to process the one or more feature values to generate a feature vector for the social element; and
a decoder configured to process the feature vector and generate the prediction using the prediction model.

### Claim 13

A system comprising:
a feature value extractor configured to extract one or more feature values from one or more social elements;
a feature vector generator configured to generate one or more feature vectors for the one or more social elements using the one or more feature values extracted by the feature value extractor; and
a prediction model configured to generate a prediction using the one or more feature vectors generated.

### Claim 14 (depends on 13)

The system of claim 13 wherein the prediction model is trained using training information, and wherein the training information includes at least one of a subset of social elements from a plurality of social elements provided by a social graph, a sentiment lexicon, a stop word list, hashtag salience scores, or word salience scores.

### Claim 15 (depends on 13)

The system of claim 13 wherein each feature vector in the one or more feature vectors is associated with a corresponding social element in the one or more social elements.

### Claim 16 (depends on 13)

The method of claim 13 wherein the feature value extractor includes a number of feature modules configured to process the one or more social elements and generate the one or more feature values.

### Claim 17 (depends on 13)

The system of claim 13, further comprising:
a decoder configured to generate a prediction using the prediction model and the one or more feature vectors.

### Claim 18 (depends on 13)

The system of claim 13 wherein the one or more social elements comprises one or more microblog posts.

### Claim 19 (depends on 13)

The system of claim 13, wherein the one or more social elements is provided by a social graph.

### Claim 20 (depends on 19)

The system of claim 19 wherein the social graph is based on a social networking service.