US 10,373,618 B2 · Natural language recommendation feedback

Abstract

Systems parse natural language expressions to extract items and values of their attributes and store them in a database. Systems also parse natural language expressions to extract values of attributes of user preferences and store them in a database. Recommendation engines use the databases to make recommendations. Parsing is of speech or text and uses conversation state, discussion context, synonym recognition, and speaker profile. Database pointers represent relative attribute values. Recommendations use machine learning to crowdsource from databases of many user preferences and to overcome the cold start problem. Parsing and recommendations use current or stored values of environmental parameters. Databases store different values of the same user preference attributes for different activities. Systems add unrecognized attributes and legal values when encountered in natural language expressions.

Claims19

Dependent claims

A non-transitory computer readable medium storing code that, if executed by a computer system, would cause the computer system to:

parse a first person's first natural language expression to identify an item and determine an item attribute value;
store, in an item database, the value in association with the attribute of the item;
parse a second person's second natural language expression from an identified user to determine a value preference for the attribute;
store, in a user database, the preference for the attribute in association with the identified user;
and
use a recommendation engine on the item attribute value and the preference associated with the identified user to produce a recommendation.

3 more elements

Dependencies are inferred from the claim text.

Natural language recommendation feedback

Life of this patent

Natural language recommendation feedback

Life of this patent

Abstract

Claims19