Immersive Search Interface for Multi-Tenant Database Query Customization
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Solution Overview
Problem
Traditional search interfaces are not efficient or intuitive, lacking a natural and immersive experience for users, particularly in environments like customer relationship management (CRM), where users need to navigate complex data sets.
Innovation Solution
Implementing an immersive full-page search flow interface that allows natural language queries, machine learning-driven query customization based on user behavior, and providing a 'starter pack' of common queries, with features like auto-complete, favorite items, trending topics, and object type filtering, supported by multi-tenant database systems for data management and access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional search interfaces with fixed fields and options are used, then the system structure is simple and easy to implement, but the user experience is not efficient or intuitive
Solution Approach 1:
The search interface dynamically adapts to user input by automatically suggesting query refinements, object types, and data ranges. The system transitions from a static form-based interface to a dynamic conversational interface that evolves based on user interactions and search context.
Solution Approach 2:
The system performs self-service by automatically analyzing user input, suggesting relevant object types (accounts, contacts, opportunities), and proposing query refinements without requiring users to manually configure search parameters or navigate complex filters.
2Adaptability or versatility
If natural language processing and machine learning are implemented, then query understanding and personalization improve, but system complexity and computational resources increase
Solution Approach 1:
The system incorporates feedback loops where machine learning models analyze user search behavior, query patterns, and interaction data to continuously improve query understanding and personalization. User interactions feed back into the system to refine suggestions and adapt to individual preferences.
Solution Approach 2:
The patent replaces traditional mechanical search interface elements (dropdown menus, checkboxes, form fields) with intelligent natural language processing and machine learning-based suggestions, allowing the system to understand and adapt to user intent without complex manual configuration.
3Productivity
If comprehensive search options and filters are provided, then search capability and coverage improve, but interface complexity and user cognitive load increase
Solution Approach 1:
The system performs preliminary actions by proactively suggesting object types, query refinements, and data ranges before the user completes their search. This allows users to access comprehensive search capabilities without having to manually configure all parameters, as the system has already prepared relevant suggestions based on initial input.
Data Source
AI summary
Improved integrated search techniques. A request for performance of a search for objects is received within a multi-tenant database environment having a plurality of tenants each having individual tenant information. A query is generated in response to the request. The query is specialized based on tenant information corresponding to a tenant from which the request originates. The tenant information is retrieved from the multi-tenant database environment. The query is performed on information stored in the multi-tenant database environment. Results of the query are presented to a user in a graphical user interface.