Oracle international corporation (20240126924). ENTITY FOCUSED NATURAL LANGUAGE GENERATION simplified abstract
Contents
- 1 ENTITY FOCUSED NATURAL LANGUAGE GENERATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ENTITY FOCUSED NATURAL LANGUAGE GENERATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
ENTITY FOCUSED NATURAL LANGUAGE GENERATION
Organization Name
oracle international corporation
Inventor(s)
Praneet Pabolu of Bangalore (IN)
Sriram Chaudhury of Bangalore (IN)
ENTITY FOCUSED NATURAL LANGUAGE GENERATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240126924 titled 'ENTITY FOCUSED NATURAL LANGUAGE GENERATION
Simplified Explanation
The method described in the patent application involves generating natural language sentences based on fake entity values, analyzing the sentences to identify missing fake values, summarizing the sentences using text summarization, and generating additional natural language sentences incorporating the missing fake values.
- Populating fake values for entities
- Generating string of fake entity values
- Inserting sentinel token between fake values
- Generating natural language sentences based on fake values
- Analyzing sentences to identify missing fake values
- Summarizing sentences using text summarization
- Concatenating text summary with missing fake value
- Generating additional natural language sentences incorporating missing fake value
Potential Applications
This technology could be applied in various fields such as natural language processing, data analysis, and artificial intelligence.
Problems Solved
This technology helps in automating the generation of natural language sentences and identifying missing information in text data.
Benefits
The benefits of this technology include improved data analysis, enhanced text summarization, and efficient natural language generation.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of automated content generation tools for businesses.
Possible Prior Art
Prior art in this field may include existing natural language generation models, text summarization techniques, and data analysis tools.
Unanswered Questions
How does this technology handle complex sentence structures in natural language generation?
The patent abstract does not provide specific details on how the technology deals with complex sentence structures or grammar rules in natural language generation.
What is the accuracy rate of identifying missing fake values in the analyzed natural language sentences?
The patent abstract does not mention the accuracy rate of identifying missing fake values in the analyzed sentences, which could be crucial for evaluating the effectiveness of the technology.
Original Abstract Submitted
method includes populating fake value for each of entities, to generate string of fake entity values that correspond to entities; inserting sentinel token between adjacent fake values included in the string to generate first input data; generating, by natural language generation model, natural language sentences based on first input data, natural language sentences including one or more fake values from the string; analyzing natural language sentences to determine whether any fake value from the string is missing; based on the fake value missing, summarizing, using text summarization model, natural language sentences to generate text summary; concatenating the text summary with the fake value, to generate second input data; and generating, by a next sentence generation model, additional natural language sentence using the second input data, the additional natural language sentence including the fake value. additional natural language sentence is combined with natural language sentences to generate final natural language sentences.
