Hierarchical Entity Data Model for Form Autocompletion
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Solution Overview
Problem
Existing form-filling services face accuracy issues due to lack of contextual understanding, leading to inefficient or unsatisfactory user experiences, especially in complex forms with related fields, as they fail to distinguish between similar fields without context.
Innovation Solution
An online form-filling service generates a hierarchical entity-data model that identifies associations between input categories and entity categories using semantic matching and natural language processing, enabling accurate autocompletion by determining contextual relationships between fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing form-filling services use simple data fragments without contextual understanding, then the system complexity remains low, but the accuracy of form completion deteriorates
Solution Approach 1:
The patent introduces an intermediary component (context analysis module or NLP engine) that sits between the raw form data and the completion logic. This intermediary processes and understands the contextual relationships between form fields, enabling accurate completion without requiring the entire system to become complex. The intermediary translates unstructured form context into structured relationships that can be efficiently processed.
Solution Approach 2:
The patent segments the form completion task into distinct components: context extraction, relationship identification, and completion decision-making. By dividing the problem into manageable segments, the system achieves high accuracy through specialized sub-components rather than requiring overall system complexity to increase uniformly.
2Measurement precision
If form-filling services process complex forms with multiple related fields, then the accuracy improves through contextual understanding, but the processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-processing form contexts and pre-identifying relationships between fields before the actual completion task. Contextual patterns and field relationships are established in advance, so when completion is needed, the system can quickly retrieve and apply pre-analyzed information rather than performing full analysis each time.
Solution Approach 2:
The patent changes parameters such as the depth of context analysis or the granularity of relationship matching based on the specific form being processed. For simple forms, less processing is applied; for complex forms requiring high accuracy, more sophisticated analysis is activated. This dynamic parameter adjustment balances accuracy requirements with processing time constraints.
Data Source
AI summary
In some embodiments, a computing system computes a hierarchical entity data model to facilitate autocompleting forms by generating an electronic schema extraction from an electronic form lacking data for one or more fields. The computing system generates an electronic schema including an input category and input field elements. The computing system accesses a hierarchical entity-data model including and entity category and entity-data elements. The computing system identifies associations between the entity category and input category based on semantic matching including text of an entity category label and an input field category label or matching a number of fields within an entity category to an input category. The computing system verifies the association by applying a natural language processing engine to the input field elements and the entity-data elements. The computing system autocompletes one or more input field elements with entity data from one or more of the entity-data elements.