# US 2021/0082425 A1 · Reduced training for dialog systems using a database

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2021/0082425 A1
- Document ID: US20210082425A1
- Status: Published application
- Kind: A1
- Application number: 16/983,950
- Filed: 2020-08-03
- Published: 2021-03-18
- Earliest claimed: 2019-09-12
- Assignees: Oracle International Corporation
- Applicants: Oracle International Corporation
- Inventors: Mark Edward Johnson; Michael Rye Kennewick
- Web page: https://patentgrep.com/patent/US20210082425A1

## CPC

- G06F 40/35: Discourse or dialogue representation
- G10L 15/22: Procedures used during a speech recognition process, e.g. man-machine dialogue
- G06F 16/221: {Column-oriented storage; Management thereof}
- G06F 16/2455: Query execution
- G06F 16/3329: Natural language query formulation
- G06F 40/295: Named entity recognition
- G06N 3/0895: Weakly supervised learning, e.g. semi-supervised or self-supervised learning
- G06N 3/09: Supervised learning
- G06N 3/096: Transfer learning
- G06N 5/04: Inference or reasoning models
- G06N 20/00: Machine learning
- G10L 13/00: Speech synthesis; Text to speech systems
- G10L 15/063: {Training}
- G10L 15/1815: {Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning}
- G10L 15/1822: {Parsing for meaning understanding}
- G10L 15/30: Distributed recognition, e.g. in client-server systems, for mobile phones or network applications
- G06N 3/08: Learning methods
- G10L 2015/223: {Execution procedure of a spoken command}
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

Techniques are described for training and executing a machine learning model using data derived from a database. A dialog system uses data from the database to generate related training data for natural language understanding applications. The generated training data is then used to train a machine learning model. This enables the dialog system to leverage a large amount of available data to speed up the training process as compared to conventional labeling techniques. The dialog system uses the trained machine learning model to identify a named entity from a received spoken utterance and generate and output a speech response based upon the identified named entity.

## Claims

### Claim 1

A method comprising:
receiving, by a dialog system, a spoken utterance;
identifying, by the dialog system, a named entity from the spoken utterance using a machine learning model, wherein the machine learning model has been trained on data extracted from a database that maps a plurality of named entities to respective named entity types based upon columns in the database;
generating, by the dialog system, a speech response based upon the identified named entity; and
providing, by the dialog system, the speech response as output.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
extracting raw data from the database;
generating training data from the extracted raw data; and
training the machine learning model on the generated training data.

### Claim 3 (depends on 2)

The method of claim 2, wherein generating the training data comprises:
identifying metadata associated with the columns of the database; and
using the metadata and corresponding entries of the columns as seed data to generate the training data.

### Claim 4 (depends on 1)

The method of claim 1, wherein:
the machine learning model is a first machine learning model and the named entity is a first named entity; and
the method further comprises identifying a second named entity using a second machine learning model.

### Claim 5 (depends on 1)

The method of claim 1, wherein:
the database further comprises a plurality of requestable values; and
the method further comprises:
identifying, by the dialog system using the database, a requestable value, of the plurality of requestable values, that maps to the identified named entity,
wherein the speech response includes the requestable value or a derivative thereof.

### Claim 6 (depends on 5)

The method of claim 5, wherein:
the database comprises a plurality of tables; and
the method further comprises selecting a particular table from the plurality of tables based upon the identified named entity,
wherein the selected table is used to identify the requestable value.

### Claim 7 (depends on 6)

The method of claim 6, wherein identifying the requestable value comprises executing a query on the selected table to retrieve the requestable value mapped to the identified named entity.

### Claim 8

A non-transitory computer-readable memory storing a plurality of instructions executable by one or more processors, the plurality of instructions comprising instructions that when executed by the one or more processors cause the one or more processors to perform processing comprising:
receiving a spoken utterance;
identifying a named entity from the spoken utterance using a machine learning model, wherein the machine learning model has been trained on data extracted from a database that maps a plurality of named entities to respective named entity types based upon columns in the database;
generating a speech response based upon the identified named entity; and
providing the speech response as output.

### Claim 9 (depends on 8)

The non-transitory computer-readable memory of claim 8, the processing further comprising:
extracting raw data from the database;
generating training data from the extracted raw data; and
training the machine learning model on the generated training data.

### Claim 10 (depends on 9)

The non-transitory computer-readable memory of claim 9, wherein generating the training data comprises:
identifying metadata associated with the columns of the database; and
using the metadata and corresponding entries of the columns as seed data to generate the training data.

### Claim 11 (depends on 8)

The non-transitory computer-readable memory of claim 8, wherein:
the machine learning model is a first machine learning model and the named entity is a first named entity; and
the processing further comprises identifying a second named entity using a second machine learning model.

### Claim 12 (depends on 8)

The non-transitory computer-readable memory of claim 8, wherein:
the database further comprises a plurality of requestable values; and
the processing further comprises:
identifying, using the database, a requestable value, of the plurality of requestable values, that maps to the identified named entity,
wherein the speech response includes the requestable value or a derivative thereof.

### Claim 13 (depends on 12)

The non-transitory computer-readable memory of claim 12, wherein:
the database comprises a plurality of tables; and
the processing further comprises selecting a particular table from the plurality of tables based upon the identified named entity,
wherein the selected table is used to identify the requestable value.

### Claim 14 (depends on 13)

The non-transitory computer-readable memory of claim 13, wherein identifying the requestable value comprises executing a query on the selected table to retrieve the requestable value mapped to the identified named entity.

### Claim 15

A dialog system comprising:
one or more processors;
a memory coupled to the one or more processors, the memory storing a plurality of instructions executable by the one or more processors, the plurality of instructions comprising instructions that when executed by the one or more processors cause the one or more processors to perform processing comprising:
receiving a spoken utterance;
identifying a named entity from the spoken utterance using a machine learning model, wherein the machine learning model has been trained on data extracted from a database that maps a plurality of named entities to respective named entity types based upon columns in the database;
generating a speech response based upon the identified named entity; and
providing the speech response as output.

### Claim 16 (depends on 15)

The dialog system of claim 15, the processing further comprising:
extracting raw data from the database;
generating training data from the extracted raw data; and
training the machine learning model on the generated training data.

### Claim 17 (depends on 16)

The dialog system of claim 16, wherein generating the training data comprises:
identifying metadata associated with the columns of the database; and
using the metadata and corresponding entries of the columns as seed data to generate the training data.

### Claim 18 (depends on 15)

The dialog system of claim 15, wherein:
the machine learning model is a first machine learning model and the named entity is a first named entity; and
the processing further comprises identifying a second named entity using a second machine learning model.

### Claim 19 (depends on 15)

The dialog system of claim 15, wherein:
the database further comprises a plurality of requestable values; and
the processing further comprises:
identifying, using the database, a requestable value, of the plurality of requestable values, that maps to the identified named entity,
wherein the speech response includes the requestable value or a derivative thereof.

### Claim 20 (depends on 19)

The dialog system of claim 19, wherein:
the database comprises a plurality of tables; and
the processing further comprises selecting a particular table from the plurality of tables based upon the identified named entity,
wherein the selected table is used to identify the requestable value.