Personal Value Vector Generation from Multi-Source Data

The patent segments the individual value assessment into multiple independent data dimensions (financial data, self-identified data, self-generated data, social connection data, societal connection data, consumer data, and third-party feedback data). Each dimension is collected and processed separately, then integrated to form a comprehensive personal value profile. This segmentation allows the system to maintain simplicity in each data collection module while achieving high overall accuracy through multi-dimensional assessment.

The patent implements preliminary action by pre-collecting and storing various types of data (financial, social, consumer, etc.) in advance before they are needed for assessment. Data is aggregated and organized into structured formats ahead of time, so when a personal value assessment is required, the system can quickly retrieve and process pre-prepared data rather than collecting everything from scratch. This reduces real-time processing time while maintaining complete information.

The patent transforms the personal value assessment from traditional two-dimensional representations (such as simple credit scores) to multi-dimensional vectors that capture numerous aspects of an individual's profile simultaneously. Each data category becomes a dimension in the vector space, allowing for much more precise matching with opportunities that have corresponding multi-dimensional requirements. The system manages this complexity through structured data models and efficient retrieval algorithms.