Executable Data Structure Classification for Life Insurance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
There is a lack of efficient and tailored executable data structures that meet the unique needs of individuals, particularly in the placement of dividend-paying participating whole life plans.
Innovation Solution
An apparatus and method for classifying an entity to an executable data structure, which involves receiving entity input through a graphical user interface, determining the degree of interaction by comparing low-level and high-level entity data, identifying protocol metrics, determining protocol objects, and establishing an executable data structure based on these objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional executable data structures are used, then general data management is possible, but they cannot meet unique individual needs particularly for dividend-paying participating whole life plans
Solution Approach 1:
The patent segments the executable data structure into multiple hierarchical levels (high-level entity data, low-level entity data, protocol objects, protocol metrics). This segmentation allows the system to tailor data structures to individual needs by selectively assembling relevant segments while maintaining manageability through modular organization.
Solution Approach 2:
The patent implements dynamic classification that adapts the executable data structure based on the entity's interaction degree. The system dynamically adjusts the level of detail and complexity of the data structure depending on the specific entity being classified, allowing high adaptability while controlling complexity through conditional instantiation.
2Productivity
If classified entity data is used to generate executable data structures, then tailored solutions can be created, but the process requires multiple data collection stages and interaction analysis
Solution Approach 1:
The patent performs preliminary classification of entities into standardized categories (such as life insurance policy types) before generating specific executable data structures. This preliminary action organizes entity data in advance, enabling faster retrieval and assembly of appropriate data structure templates, thereby improving productivity while reducing the time required for custom data structure generation.
Solution Approach 2:
The patent changes parameters of the executable data structure based on the classified entity characteristics. By adjusting parameters such as protocol objects and metrics according to the entity class, the system efficiently generates tailored data structures without requiring complete redesign, thus improving productivity while minimizing additional processing time.
3Measurement precision
If detailed entity data is collected and analyzed, then accurate classification can be achieved, but the system requires comparing low-level and high-level data across multiple time points
Solution Approach 1:
The patent adds a temporal dimension to data collection by gathering entity data at multiple time points (initial low-level data, subsequent high-level data). This multi-dimensional approach enables accurate classification by observing changes over time while managing complexity through structured temporal progression of data collection stages.
Solution Approach 2:
The patent introduces protocol objects and protocol metrics as intermediary elements between raw entity data and the final executable data structure. These intermediaries simplify the processing complexity by providing standardized representation layers that bridge detailed low-level data and high-level classifications, enabling accurate classification without direct complex comparison of all raw data elements.
Data Source
AI summary
An apparatus for classifying an entity to an executable data structure, the apparatus including a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the processor to receive entity input from an entity using a graphical user interface, wherein receiving the entity data includes receiving a low-level entity data at a first time and receiving a high-level entity data at a subsequent time determine a degree of interaction pertaining to the entity by comparing the low-level entity data to the high-level entity data, identify at least a protocol metric as a function of the degree of interaction, determine a protocol object as a function of the high-level entity data and the at least a protocol metric, establish an executable data structure for the entity as a function of the protocol object and display the executable data structure using the graphical user interface.