Sequence Optimization Engine Using Self-Learning Algorithms
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Solution Overview
Problem
Manual creation of sequences, such as grocery lists or email orders, is not reproducible and relies on limited personal information, missing opportunities for optimization using available contextual data like location, temporal, and historical information.
Innovation Solution
An engine recognizes sequences and processes associated metadata to generate optimized sequences, utilizing self-learning algorithms and referencing user behavior, crowdsourced data, and sensor information to tailor the order based on specific criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual sequence creation is used, then user discretion and personal intuition are applied, but the sequence is not reproducible and relies on limited personal information
Solution Approach 1:
The system performs self-learning by automatically analyzing user behavior patterns, historical data, and contextual information to generate optimized sequences without requiring continuous manual input. The engine learns from past actions and autonomously creates reproducible sequences that adapt to user preferences.
Solution Approach 2:
The system incorporates feedback loops where user interactions with sequences are monitored and fed back into the learning engine. This continuous feedback enables the system to refine its algorithms and improve sequence generation accuracy, ensuring reproducible results that align with user preferences.
2Ease of operation
If manual sequence creation is used, then personal intuition is leveraged, but substantial additional contextual information remains unused
Solution Approach 1:
The system serves multiple functions by simultaneously analyzing various data types including location information, temporal data, historical actions, and crowdsourced information. This multi-functional approach ensures comprehensive utilization of available contextual information to generate optimized sequences.
Solution Approach 2:
The system transitions from one-dimensional manual decision-making to multi-dimensional analysis by incorporating numerous contextual factors. The engine processes information across multiple dimensions (spatial, temporal, historical, contextual) to create comprehensive and optimized sequences that manual methods cannot achieve.
3Reliability
If automated sequence optimization is implemented, then contextual information is leveraged for improved sequences, but system complexity increases
Solution Approach 1:
The system segments the optimization process into distinct functional modules: data collection, data processing, sequence generation, and feedback analysis. This segmentation allows each component to be developed and optimized independently, reducing overall system complexity while maintaining high optimization performance.
Data Source
AI summary
Embodiments relate to apparatuses and methods configured for automatic learning and/or optimizing an order of items appearing in a sequence. Particular embodiments employ an engine to recognize sequences (e.g., lists of items) repeatedly encountered by a user. Examples of such sequences can include grocery lists, and emails present in an in-box. The engine then references available metadata associated with the sequence and its items, in order to present the user with an optimized sequence tailored to one or more criteria. Examples of available metadata can include sensed location information (of the user and/or other entities), temporal information, contextual influences, historical actions by the user, and/or general population habits (e.g., as may be determined via crowdsourcing). Certain embodiments may further generate a modified sequence based upon suggestions afforded by metadata associated with the sequence. Embodiments may utilize a self-learning scoring algorithm to perform sequence recognition, optimization, and/or modification.