Sentence Model Data Structure for Predictive Analytics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing predictive analytic methods fail to efficiently collect, store, and analyze data from disparate sources due to lack of universal data representation, inefficient use of native function calls, high memory utilization, and limited utilization of hierarchical data structures for decision-making and recommendation generation.
Innovation Solution
The technology employs a sentence model to format data, allowing for efficient collection and storage of data from various sources, optimized native function calls for quick querying, and reduced memory usage by operating within volatile memory, enabling predictive analytics and recommendations for improving website utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data from disparate sources is collected and stored using traditional methods, then data can be gathered from multiple sources, but the system lacks a universal way to store data uniformly and requires complex data structures
Solution Approach 1:
The patent applies a universal sentence model data structure that can uniformly represent data from any source type (structured, unstructured, semi-structured). The sentence model with subject-predicate-object components serves as a universal container that adapts to diverse data formats without requiring source-specific storage mechanisms, thereby resolving the contradiction between data collection versatility and storage structure complexity.
Solution Approach 2:
The patent transforms heterogeneous data from various sources into a standardized parameterized sentence model format. By changing the representation parameters of diverse data types into a common sentence structure with defined components (subject, predicate, object, modifiers), the system achieves uniform storage while maintaining the ability to handle disparate source data.
2Measurement precision
If hierarchical data structures are used to predict categorization, then classification can be achieved, but the system fails to expose decisions/attributes leading to outcomes and generate recommendations
Solution Approach 1:
The patent segments the hierarchical prediction process into distinct sentence model components (subject, predicate, object, modifiers) that can be individually analyzed and traced. This segmentation allows the system to not only predict outcomes but also expose the specific attributes and decision paths that lead to predictions, as each component represents a discrete element of the decision-making process.
Solution Approach 2:
The sentence model acts as an intermediary structure between raw data and prediction outcomes. It preserves and structures the decision path information through its hierarchical components, allowing the system to trace back from predictions to the specific attributes and decisions that led to them, thereby preventing information loss while maintaining prediction accuracy.
3Productivity
If native function calls and optimized frameworks are used, then processing speed increases, but data must be parsed through multiple information contents in legacy systems
Solution Approach 1:
The patent applies preliminary action by pre-processing and converting legacy data into the sentence model format before analysis. This upfront transformation creates a standardized, query-optimized structure that enables the use of native function calls and optimized frameworks without requiring repeated parsing of complex legacy data formats during processing, thereby resolving the contradiction between processing speed and parsing time.
4Quantity of substance
If data is stored in traditional formats, then data can be retained, but memory utilization is high and data cannot be entirely stored in volatile memory
Solution Approach 1:
The patent changes the structural parameters of data representation by converting traditional hierarchical or tabular formats into a compact sentence model. This parameter change reduces redundancy and optimizes memory layout, allowing data to be stored more efficiently in volatile memory while maintaining full data capacity, thereby resolving the contradiction between storage capacity and memory utilization.
Data Source
AI summary
Systems and methods are described herein. In one embodiment, the method includes receiving a goal associated with a predicate-object pair; receiving utilization data including a plurality of predicate-object pairs including the predicate-object pair associated with the goal; determining a prediction model comprising a plurality of nodes that form a hierarchical structure including a root node and two or more leaf nodes and organized based on one or more of an information gain and a business gain, the two or more leaf nodes including a leaf node associated with the predicate-object pair of the goal; identifying nodes in the hierarchical structure that trace a path from the root node to the node associated with the goal; and causing a recommendation for at least partial completion of the goal to be presented to a user, the recommendation based on the one or more nodes that trace the path.