DAIP Platform Consolidating Enterprise Channel Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Enterprise systems face challenges in providing relevant assistance to users across different channels due to disparate computer systems and resources, making it difficult to offer personalized and omni-channel experiences.
Innovation Solution
Implementing Data, AI, and Intents Personalization (DAIP) technology, which involves a computing platform that receives unstructured activity data from various channels, identifies user intents using machine learning models, generates status tracking information, and stores it in a consolidated repository, enabling personalized content and alerts across all channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If disparate computer systems and resources are used to support applications on different enterprise channels, then each channel can operate independently with its own system configuration, but it becomes difficult to provide relevant assistance to users who need help across multiple channels
Solution Approach 1:
The patent consolidates user activity data from multiple disparate enterprise channels into a unified data structure. The computing platform receives unstructured activity data from various channels (mobile banking, online banking, ATM, IVR, etc.), processes it through a unified pipeline, and stores it in a standardized format that can be accessed across all channels, enabling seamless omnichannel user assistance
Solution Approach 2:
The computing platform acts as an intermediary layer between disparate enterprise channel systems and the user assistance functions. It receives unstructured activity data from source systems without native intent mapping functionality, applies intent discovery models to extract user intents, and provides structured intent information to channels that need it, thereby bridging the information gap across channels
2Productivity
If unstructured activity data is received from source systems without native intent mapping functionality, then data can be captured from all channels, but the data cannot be directly used for intent recognition and personalized assistance
Solution Approach 1:
The system performs preliminary processing of unstructured activity data by applying intent discovery models to extract user intents before the data reaches channels that need structured intent information. The computing platform proactively transforms raw activity events into meaningful intent classifications, making the data ready for personalized assistance applications
Solution Approach 2:
The patent replaces manual or rule-based intent mapping mechanisms with machine learning-based intent discovery models. These models automatically analyze unstructured activity data patterns to identify user intents, substituting complex mechanical intent mapping processes with intelligent algorithms that can handle diverse unstructured data formats across channels
3Reliability
If a consolidated event repository is implemented to store status tracking information, then omnichannel intent tracking becomes possible, but the system complexity increases
Solution Approach 1:
The consolidated event repository serves multiple functions: it stores raw activity data, stores processed intent information, provides data for personalization, and enables cross-channel tracking. This single unified repository replaces multiple separate data storage systems across channels, reducing overall system complexity while improving reliability through centralized data management
Solution Approach 2:
The system implements homogeneous data structures and schemas for storing activity data and intent information across all channels. By standardizing data formats, validation rules, and access protocols in the consolidated repository, the system reduces complexity through uniformity while ensuring consistent and reliable omnichannel tracking
Data Source
AI summary
Aspects of the disclosure relate to generating and providing enhanced user interfaces by implementing data, AI, intents and personalization (DAIP) technology. In some embodiments, a computing platform may receive, from enterprise computing infrastructure, first unstructured activity data associated with a first enterprise channel. Subsequently, the computing platform may identify one or more user intents by applying an intent discovery model to the first unstructured activity data associated with the first enterprise channel. Then, the computing platform may generate status tracking information based on identifying the one or more user intents. Thereafter, the computing platform may store, in a consolidated event repository, the status tracking information generated based on identifying the one or more user intents, and storing the status tracking information in the consolidated event repository may cause the status tracking information to be exposed to multiple computer systems associated with different enterprise channels.