Social Media Data Usefulness Scoring for Business Logic
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Solution Overview
Problem
Current data aggregation tools from social media services lack sophisticated processing rules and business logic, making it difficult to transform raw social media data into useful business information for better business results and customer satisfaction.
Innovation Solution
The Social Media Advisor for Retention and Treatment (SMART) system processes social media data by retrieving information from multiple sources, filtering, validating, and scoring it to provide actionable insights for business applications, using a processor-based system with a presentation and application component that includes filtering, validation, and usefulness scoring modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data aggregation tools are used to collect social media data, then the quantity of data is increased, but the quality and usefulness of the data remains low due to lack of processing rules and business logic
Solution Approach 1:
The patent introduces an intermediary processing layer between data collection and business application. This layer includes data normalization modules, validation rules, and business logic processors that transform raw social media data into structured, usable information. The intermediary processes the data through multiple stages: initial filtering, normalization against business rules, validation, and enrichment with contextual information, thereby improving data quality without losing the volume advantage.
Solution Approach 2:
The system applies parameter changes by transforming raw data through multiple processing parameters including normalization standards, validation thresholds, and scoring criteria. Data undergoes parameter transformations such as converting unstructured text to structured fields, applying weightings to different data sources, and adjusting quality metrics based on predefined business rules, thereby enhancing data usefulness while maintaining quantity.
2Measurement precision
If sophisticated processing rules and business logic are applied to social media data, then the usefulness of data is improved, but the device complexity increases
Solution Approach 1:
The patent segments the data processing system into distinct modular components: data collection modules, normalization modules, validation modules, scoring modules, and application interfaces. Each module performs a specific function with defined input and output parameters. This segmentation allows complex processing rules to be implemented in manageable, independently configurable units, reducing overall system complexity while maintaining high data usefulness.
Solution Approach 2:
The system employs universal processing frameworks and standardized data models that can handle multiple types of social media data through common processing rules. The business logic layer is designed to be application-agnostic, serving multiple business functions (analytics, customer service, marketing) through a single unified processing pipeline, thereby reducing complexity through reuse rather than requiring separate processing systems for each function.
3Productivity
If raw social media data is collected without processing, then the data gathering process is simple and fast, but the data cannot be effectively used for business applications
Solution Approach 1:
The system performs preliminary processing actions during the data collection phase itself. Data is normalized, validated, and pre-processed at the point of collection rather than being stored in raw form for later processing. This preliminary action includes initial filtering of irrelevant data, basic normalization to standard formats, and preliminary scoring based on data source reliability, enabling faster subsequent processing while ensuring data usability from the outset.
Data Source
AI summary
A method and apparatus are implemented in one or more processors for processing various social media data received over a network for collection, analysis, and application to business logic and/or business applications. Based on personally identifying information of an account holder, social media data regarding the account holder is retrieved from one or more social media sources or a clearing house over the network. The retrieved social media data is processed (i.e., parsed and/or filtered, and validated) via certain criteria. A usefulness score for the social media data is computed based on various factors including at least one of an identity match value, a truth confidence value, and a context data relevance value. The social media data and its computed usefulness score can be presented to a user and business applications for further processing and treatment of the account holder.