Information Processing Model for Non-Text Input Conversion
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Solution Overview
Problem
Existing models are limited in their ability to input information other than sentences, hindering their effectiveness in various applications and tasks.
Innovation Solution
An information processing method that obtains learning data to generate a model capable of inputting information other than sentence text, by converting such information into a format that can be processed by the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a model is generated to treat sentence text as input, then the model can accurately interpret industry-specific expressions, but the model cannot handle information other than sentences
Solution Approach 1:
The patent applies universality by designing a model that can process multiple types of input information (sentences, numbers, dates, booleans, categories) through a unified architecture. The model converts diverse input formats into a standardized representation that can be processed by the same neural network, enabling one model to handle various input types appropriately without sacrificing processing accuracy for any specific type.
2Adaptability or versatility
If the model is designed to process only sentence text, then the model structure remains simple, but the model's applicability to diverse tasks is limited
Solution Approach 1:
The patent introduces an intermediary conversion layer that transforms diverse input information into a standardized text representation before it enters the neural network. This mediator component (the conversion mechanism) handles the complexity of processing multiple input types separately, while keeping the core neural network structure relatively simple and unified. The intermediary layer absorbs the variability in input formats, protecting the main model architecture from becoming overly complex.
3Adaptability or versatility
If learning data is obtained for converting information other than sentences, then the model can handle diverse input types, but the learning process becomes more complex
Solution Approach 1:
The patent applies preliminary action by pre-processing and organizing diverse input information into structured learning data before training the model. The learning data is prepared in advance with proper conversions and transformations, so that during model training, the system only needs to learn the mapping from these pre-processed representations to outputs. This preliminary organization of learning data simplifies the actual training process by reducing the complexity of handling diverse input formats during learning.
Data Source
AI summary
An information processing method includes: obtaining the learning data which is to be used in the learning of a model that treats, as the input, a plurality of sets of input information containing post-conversion information obtained by conversion of the information other than a sentence text representing a sentence; and performing learning using the learning data and generating the model to which the information other than the sentence text can be input upon conversion.