Recursive Neural Network Event Embeddings for Future Prediction
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Solution Overview
Problem
Current models using Recursive Neural Networks (RNNs) for predicting future events focus on textual order rather than semantic order and fail to account for events that will not occur due to previous events, lacking the ability to generalize new situations and take automatic action in hazardous scenarios.
Innovation Solution
A method for training RNNs to generate embedding vectors based on observations of subjects, actions, and objects, allowing for predictions of future events and initiating actions to mitigate harm by comparing these vectors, which includes a system and computer-readable medium for processing and executing these steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current models focus on predicting textual order of events, then the model can process sequence data, but the model fails to capture semantic order and cannot remove events that will not occur due to previous events
Solution Approach 1:
The patent transforms event representations from simple textual sequences to rich semantic embeddings that capture meaning. By changing the parameter representation from text to vector embeddings with semantic relationships, the model achieves both accurate prediction and proper generalization to new situations.
Solution Approach 2:
The patent introduces an intermediary layer of semantic event embeddings that mediate between raw textual observations and future event predictions. This intermediary representation captures the semantic order and relationships, allowing the model to predict future events accurately while generalizing to unseen situations.
2Reliability
If the model predicts all possible future events without filtering, then the model maintains completeness of possibilities, but the model cannot identify events that will not occur due to previous events
Solution Approach 1:
The patent implements feedback mechanisms where the model's predictions are continuously refined based on semantic relationships and observation patterns. The system learns from past predictions and adjusts future predictions by incorporating semantic constraints, improving reliability while preserving necessary information.
Solution Approach 2:
The patent performs preliminary filtering of impossible events based on semantic relationships before final prediction. By pre-processing observations to establish semantic constraints and relationships, the model eliminates events that cannot occur due to previous events, improving prediction reliability.
3Measurement precision
If the model is trained on observed events only, then the model can predict known event patterns, but the model cannot generalize to new events not observed before
Solution Approach 1:
The patent creates a universal event embedding space that captures fundamental semantic relationships applicable across different domains and event types. This universal representation allows the model to predict both observed and novel events by leveraging transferable semantic patterns rather than domain-specific memorization.
Solution Approach 2:
The patent transforms specific event instances into generalizable semantic embeddings that capture underlying patterns and relationships. By changing from instance-specific representations to abstract semantic parameters, the model achieves both accurate prediction of known events and generalization to new events.
4Loss of information
If the model focuses on detecting entailment relationships between sentences, then the model can understand logical connections, but the model fails to detect temporal relationships and predict future events
Solution Approach 1:
The patent adds a temporal dimension to the semantic event embeddings by incorporating time-aware features and sequential patterns. This dimensional extension allows the model to simultaneously capture logical entailment relationships and temporal relationships, enabling accurate future event prediction while preserving both types of information.
Solution Approach 2:
The patent merges logical relationship detection with temporal relationship detection into a unified event prediction framework. By combining semantic entailment analysis with temporal sequence modeling, the system recovers both logical and temporal information to predict future events accurately.
Data Source
AI summary
Systems and methods for training a recursive neural network (RNN) is provided. The method includes generating, by the processor using the RNN, a plurality of embedding vectors based on a plurality of observations, wherein the observations include (i) a subject, (ii) an action taken by the subject, and (iii) an object on which the subject is taking the action on, wherein the subject and object are constant. The method further includes generating, by the processor, predictions of one or more future events based on one or more comparisons of at least some of the plurality of embedding vectors. The method also includes initiating, by the processor, based on the predictions, an action to a hardware device to mitigate expected harm to at least one item selected from the group consisting of the hardware device, another hardware device related to the hardware device, and a person related to the hardware device.