Event Text Generation From Object Data and Adjective Selection
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Solution Overview
Problem
Manual generation of high-quality textual content reporting real-world events and objects is time-consuming and complex, especially with increasing frequency and number of events and objects, necessitating automation.
Innovation Solution
Systems and methods for analyzing audio and image data to identify objects and events, selecting adjectives, and generating descriptive textual content automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual generation of textual content is used to report real-world events and objects, then the quality and relevance of the reported information can be maintained through expert judgment, but the time consumption and complexity increase significantly as the number of events and objects increases
Solution Approach 1:
The patent replaces the manual mechanical process of expert judgment and text writing with an automated system using machine learning models and natural language generation algorithms. The system automatically analyzes sensor data, identifies objects and events, selects appropriate adjectives, and generates descriptive textual content without human intervention, thereby eliminating time consumption while maintaining quality through algorithmic precision
Solution Approach 2:
The system enables self-service by allowing the automated textual content generation system to independently process sensor data, make decisions about object and event identification, select descriptive adjectives, and generate reports without requiring manual expert intervention. The system serves itself by autonomously completing the entire workflow from data input to text output
2Reliability
If manual generation of textual content is used to report a large number of real-world objects and events, then accurate reporting can be achieved through careful observation, but the task becomes too complex and burdensome
Solution Approach 1:
The patent segments the complex reporting task into distinct modular components: sensor data acquisition, object identification, event detection, adjective selection, and text generation. Each component is handled by specialized algorithms or models, breaking down the overall complexity into manageable segments that can be processed independently and systematically
Solution Approach 2:
The system replaces the complex manual cognitive process of observing, analyzing, and reporting multiple objects and events with an automated computational system. The machine learning models and natural language generation algorithms handle the complexity of processing large numbers of objects and events, maintaining accuracy through systematic algorithmic approaches rather than human observation
3Productivity
If automated systems are used to generate textual content reporting events and objects, then time consumption and complexity are reduced, but the system requires sophisticated data analysis and adjective selection capabilities
Solution Approach 1:
The patent implements a universal system that handles multiple functions: detecting objects, identifying events, selecting adjectives, and generating text. The system is designed to be multi-functional, capable of processing different types of sensor data and generating appropriate textual descriptions for various objects and events using the same underlying architecture and algorithms
Solution Approach 2:
The system introduces intermediary components such as machine learning models and natural language processing algorithms that mediate between raw sensor data and final textual output. These intermediaries bridge the gap between data acquisition and text generation, handling the complex transformations and decisions required while allowing the overall system to maintain high productivity
Data Source
AI summary
Systems, methods and non-transitory computer readable media for analyzing objects data to generate a textual content reporting events are provided. An indication of an event may be received. An indication of a group of one or more objects associated with the event may be received. For each object of the group of one or more objects, data associated with the object may be received. The data associated with the group of one or more objects may be analyzed to select an adjective. A particular description of the event may be generated. The particular description may be based on the group of one or more objects. The particular description may include the selected adjective. A textual content may be generated. The textual content may include the particular description. The generated textual content may be provided.