Personalized Software Flow Generation via User Clustering
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Solution Overview
Problem
Conventional software application delivery models present static and non-personalized flows to users, leading to irrelevant sections being skipped or misunderstood, particularly in applications like tax return preparation and filing, where users may not be aware of legitimate deductions due to numerous irrelevant or missing expense categories.
Innovation Solution
A method and system that generates personalized software application flows by clustering users based on their input information using word embedding techniques, identifying personalized information clusters, and modifying default flows to include relevant nodes such as interview screens or chat sessions, ensuring users are presented with pertinent expense categories tailored to their specific business or profession.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static and non-personalized flow is presented to all users, then the software application delivery model is simple to implement, but the relevance and usefulness of the flow nodes decrease for users with unique requirements
Solution Approach 1:
The patent implements dynamic flow generation by transforming static, pre-coded flow nodes into dynamic, personalized flows. The system analyzes user inputs (e.g., business type, industry sector) and dynamically selects appropriate flow nodes from a pool of possible nodes, creating a customized navigation path for each user rather than using a one-size-fits-all static flow.
Solution Approach 2:
The patent segments the flow into multiple nodes representing different sections or screens, each with associated flow actions. These segmented nodes can be independently selected and combined based on user characteristics, allowing the system to create personalized flows by assembling relevant segments rather than presenting all possible sections.
2Ease of operation
If default flow nodes are presented to all users, then the software application delivery model is easy to operate, but users may skip or misunderstand irrelevant sections due to lack of personalization
Solution Approach 1:
The patent applies local quality by tailoring specific flow nodes to individual user needs rather than applying a uniform flow to all users. The system identifies relevant flow nodes based on user-specific characteristics (e.g., business type, industry) and presents only those locally relevant sections, ensuring each user sees information pertinent to their situation rather than a generic overview.
3Adaptability or versatility
If numerous expense categories are presented to all users, then the software application delivery model is comprehensive, but the complexity and irrelevance of certain categories increase for specific user segments
Solution Approach 1:
The patent changes the parameter of flow node selection based on user inputs such as business type, industry sector, and other characteristics. The system uses these parameters to filter and select appropriate expense categories and flow nodes, transforming a static comprehensive list into a dynamic filtered list that adapts to user-specific parameters, thereby reducing complexity while maintaining comprehensiveness.
Data Source
AI summary
Disclosed are techniques for generating a personalized flow for a software delivery model. These techniques identify user information expressed in natural language for a specific user. One or more user clusters may be determined for the specific user based on at least one user vector representation of a form of the user information. One or more personalized information clusters may be identified for a user cluster of the one or more user clusters based at least in part on the at least one user vector representation. A personalized software application flow may be generated and presented to the specific user by using at least the one or more personalized information clusters for the specific user.