Personalized Content Suggestions for Document Creation
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Solution Overview
Problem
Users face inefficiencies and frustration during document creation due to starting from scratch or using pre-canned templates that require extensive processing to personalize, and they struggle with unfamiliar application interfaces across different devices.
Innovation Solution
Implementing machine learning modeling to analyze signal data and provide personalized content suggestions, adapting to user intent and context for improved document creation efficiency and user experience, allowing for real-time tailored suggestions across various applications and devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users start document creation from scratch, then users have complete freedom to create custom content, but computing resources are heavily consumed and document creation latency increases
Solution Approach 1:
The system pre-generates multiple template options with different content structures, styles, and layouts before the user needs them. When a user initiates document creation, these pre-prepared templates are immediately available for selection, eliminating the need to generate content from scratch and significantly reducing creation latency while maintaining customization freedom.
Solution Approach 2:
The system generates templates by varying multiple parameters including content structure, formatting styles, layout configurations, and thematic elements. By changing these parameters to create diverse template variations, the system provides users with customized content options without requiring complete content generation, thus reducing computing resource consumption and time.
2Productivity
If users use pre-canned templates for document creation, then document creation speed increases, but templates contain irrelevant content that requires extensive processing to personalize
Solution Approach 1:
Instead of providing generic templates with uniform content, the system generates templates with locally optimized content sections tailored to specific user needs, document types, and contextual information. Each template contains relevant content in appropriate sections while leaving other areas customizable, reducing the need for extensive content deletion and personalization work.
Solution Approach 2:
The system pre-personalizes templates by pre-populating them with relevant content based on user profiles, past document history, and contextual data before the user receives them. This preliminary personalization action ensures templates arrive ready-to-use with minimal irrelevant content, allowing users to quickly customize without extensive processing.
3Ease of manufacture
If applications provide basic templates with pre-canned content, then users have a starting structure for document creation, but users feel locked into the template structure requiring numerous processing operations to modify
Solution Approach 1:
The system creates dynamically adaptable templates where the structure, content, and formatting can be easily modified at any point during document creation. Templates are designed with flexible, reconfigurable elements that allow users to add, remove, or restructure sections without being locked into the original template layout, making modification as easy as the initial structure provision.
Solution Approach 2:
The system divides templates into independent, modular sections that can be individually selected, modified, or removed. This segmentation allows users to keep only the relevant portions of a template and discard or replace unwanted sections without affecting the overall document structure, significantly reducing the processing operations needed for template personalization.
4Adaptability or versatility
If different applications present different user interfaces across devices, then applications can be optimized for specific devices, but users struggle with unfamiliar interfaces and features
Solution Approach 1:
The system implements a universal interface framework that provides consistent core functionality and document creation capabilities across all devices and applications. This universal layer ensures users encounter familiar interface elements and workflows regardless of which device or application they use, while still allowing device-specific optimizations for enhanced performance and features.
Data Source
AI summary
The present disclosure relates to processing operations that generate and present personalized content suggestions to assist a user with document creation. Machine learning modeling may be trained and implemented to evolve pre-canned suggestions for document creation into highly personalized content suggestions, thereby improving the document creation process and user interface experience for users of applications/services that are utilized to create digital documents. As an example, signal data may be detected and analyzed, identifying a specific user's intent to create a digital document. Machine learning modeling may be implemented to evaluate different aspects of collected signal data and identify content from previously created documents, associated with a user account, that may be most relevant to the real-time document creation experience of the user. Personalized contextual suggestions may be presented to a user through a user interface. Examples described herein may be extensible across any type of application/service configured for document creation.