Text generator with an automated decision tree for creating text based on changing input data

• US 20050039107A1
• Filed: 08/12/2003
• Published: 02/17/2005
• Est. Priority Date: 08/12/2003
• Status: Active Grant

• Current Assignee: Markit on Demand, Inc. (S&P Global Inc.)
• Original Assignee: Wall Street On Demand
• Inventors: De La Lama, Arlel, Tanner, James, Hander, William B., Levine, Fredrick E., Leslie, John W.
• Granted Patent: US 7,376,552 B2
• Time in Patent Office: Days
• US Class Current: 715/256
• CPC Class Codes: G06F 40/56 Natural language generation

Abstract

A method for generating text using a computer system. The method includes selecting a model for generating a text that is based on a decision tree with nodes having one buckets or elements that include a conditional element and a sentence element or a pointer to a child node. The method includes processing a set of raw data selected for the particular model and then determining values of model variables. Evaluation of the model is performed to identify a set of the sentence elements to include in the text. The evaluating includes processing the conditional element of the buckets and when the conditional element is satisfied including the bucket'"'"'s sentence element in the set of identified sentence elements. The method continues with combining the identified sentence elements to generate the text. The combining is completed based on concatenation rules provided in or linked to each node.

Claims

1. A computer-based method for generating text, comprising:

selecting a model of a text, the model being a decision tree with a node having one or more buckets including a conditional element and a sentence element;

processing a set of raw data selected based on the selected model to determine a model variable;

evaluating the model to identify a set of the sentence elements to include in the text, wherein the evaluating includes processing the conditional element of a bucket and when the conditional element is satisfied including the sentence element for the bucket in the identified set of the sentence elements; and

combining the sentence elements in the identified set to form the text.

2. The method of claim 1, wherein the model includes a plurality of the nodes each having one or more buckets pointing to another of the nodes or including one of the sentence elements.

3. The method of claim 1, further including a user input comprising a unique key and retrieving the set of raw data from a data source based on the unique key.

4. The method of claim 3, wherein the user input further includes a model selection and the selecting includes processing the model selection to identify the selected model from a set of available text models.

5. The method of claim 1, wherein each of the nodes of the model include a set of concatenation rules for the sentence elements of the particular node and wherein the combining of the sentence elements includes concatenating the sentence elements for each of the nodes based on the sets of concatenation rules.

6. The method of claim 5, wherein the concatenation rules include definitions of connectors to be inserted between the sentence elements.

7. The method of claim 6, wherein the processing of one of the conditional elements includes retrieving the model variable and comparing a value of the model variable with a predefined conditional value for the bucket.

8. The method of claim 1, further including transferring the formed text to a user node for display to a user.

9. The method of claim 1, wherein the set of raw data includes financial analysis information, the model variable is a financial analysis variable, and the sentence elements include text related to financial analysis.

10. A text generation system, comprising:

means for storing a set of text models, each of the text models being decision tree based and including a parent node and a plurality of child nodes each having one or more bucket elements, wherein at least some of the bucket elements include conditional elements and a sentence element;

means for storing a model database including retrieved raw data and model variables for a selected one of the text models;

a generator engine configured for identifying the selected one of the text models, for processing the retrieved raw data to determine the model variables, and for generating a text by processing the conditional elements to identify a set of the sentence elements and combining the identified sentence elements.

11. The system of claim 10, wherein the generator engine is further configured to receive a user input including a unique key and to select the retrieved raw data based on the unique key.

12. The system of claim 11, wherein the user input includes a model selection and the generator engine uses the model selection in performing the identifying of the selected one of the text models.

13. The system of claim 10, wherein at least some of the nodes include a set of concatenation rules and wherein the generator engine uses the concatenation rules during the combining of the identified sentence elements.

14. The system of claim 13, wherein the concatenation rules define connectors to be inserted by the generator engine between combined ones of the sentence element in the generated text.

15. A method of building a model for use in generating text with a computer, comprising:

first defining a parent node of a decision tree;

second defining a set of child nodes linked to the parent node, wherein each of the child nodes includes one or more bucket elements that include a conditional and a pointer to another child node or a sentence element and wherein each of the child nodes includes a set of concatenation rules defining a method of combining the sentence elements of the child node;

wherein at least some of the conditional elements or sentence elements include model variables that are defined by values in a set of raw data linked to the model.

16. The method of claim 15, wherein the concatenation rules define connectors for insertion between two adjacent ones of the sentence elements.

17. The method of claim 16, wherein the connectors comprise punctuation marks.

18. The method of claim 16, wherein the connectors comprise conjunctions.