Temporal Prediction Model for Startup Event Timing
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Solution Overview
Problem
Conventional models for making venture capital decisions are largely manual and subjective, relying on intuition to predict a startup's success timeline.
Innovation Solution
The development of systems and methods that use machine-learning to train a temporal prediction model, which predicts the next likely event and the time of that event for a startup based on historical financial data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual and subjective models are used for venture capital decisions, then decision-making process is simple and intuitive, but prediction accuracy and reliability of success timeline estimates deteriorate
Solution Approach 1:
The patent replaces manual, intuitive decision-making processes with an automated machine learning system. The temporal prediction model uses historical financial data and company features to objectively predict startup success timelines, replacing subjective human judgment with data-driven algorithms that process information systematically and consistently.
Solution Approach 2:
The patent introduces a temporal prediction model as an intermediary between historical data and investment decisions. This model acts as a mediator that transforms raw historical financial data into actionable predictions about future startup performance, enabling more informed decision-making without requiring direct subjective assessment of each startup.
2Productivity
If manual decision-making processes are used, then system complexity is low, but time consumption and productivity in analyzing multiple startups deteriorate
Solution Approach 1:
The patent implements a self-service system where the temporal prediction model automatically processes historical financial data, extracts company features, and generates predictions without requiring manual intervention for each startup analysis. The system serves itself by continuously learning from data and providing predictions that can be directly used for investment decisions.
Solution Approach 2:
The patent transforms the decision-making process by changing key parameters from subjective qualitative assessments to objective quantitative predictions. The model outputs specific time predictions and probability estimates that can be directly compared and evaluated, enabling faster and more consistent decision-making across multiple startup opportunities.
3Reliability
If intuition-based methods are used to predict startup success timelines, then ease of operation is maintained, but reliability and consistency of predictions deteriorate
Solution Approach 1:
The patent segments the prediction process into distinct components: data collection, feature extraction, model processing, and prediction output. This segmentation allows each component to be optimized independently while maintaining overall system reliability. The temporal prediction model consistently applies the same processing steps to different startups, ensuring uniform and reliable predictions.
Solution Approach 2:
The patent incorporates feedback mechanisms where the temporal prediction model continuously learns from historical outcomes and adjusts its predictions accordingly. This feedback loop improves prediction consistency over time by refining the model based on actual startup performance data, making the system progressively more reliable while maintaining automated operation.
Data Source
AI summary
Systems and methods are directed to predicting temporal startup measurements using a machine-trained model. The system determines a training dataset of features associated with different funding, exit, and closure events and corresponding times of the funding, exit, and closure events from historical financial data. A temporal prediction model is trained using the training dataset. The temporal prediction model can comprise a recurrent neural network (e.g., gated recurrent unit). During runtime, the system accesses new data associated with potential future investment opportunities with startups and determines (e.g., compute) company features based, in part, on the new data. The system applies the company features to the temporal prediction model to simultaneously predict a next event and a time of the next event for each startup. A user interface can then be presented that shows the predicted next event and the predicted time of the predicted next event for each startup.