Dynamic Questionnaire for Personalized Recipe Selection
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Solution Overview
Problem
Existing online food distribution services lack flexibility in understanding customer preferences, leading to inefficient recipe selection and increased food waste, as they typically require customers to know what they want and do not adapt to individual tastes and histories.
Innovation Solution
A dynamic individualized customer questionnaire system that selects and optimizes recipes based on customer preferences and history by presenting questions dynamically, using a data structure generated from user feedback, to predict and optimize recipe orders, reducing errors and waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static questionnaire is used to collect customer preferences, then the system implementation is simple, but the system cannot adapt to individual customer tastes and preferences
Solution Approach 1:
The patent implements a dynamic questionnaire system that adapts its questions and flow based on customer responses in real-time. The system transitions from a static, predetermined questionnaire to a dynamic one that modifies its path according to customer preferences, thereby improving adaptability while managing complexity through algorithmic control.
Solution Approach 2:
The system incorporates feedback mechanisms where customer responses to questionnaire questions influence subsequent questions and recipe recommendations. The dynamic questionnaire uses feedback from each answer to adjust the questioning path, enabling the system to learn and adapt to individual customer preferences iteratively.
2Measurement precision
If the system presents many questions to understand customer preferences, then the accuracy of preference identification improves, but the time required for ordering increases
Solution Approach 1:
The dynamic questionnaire adjusts its length and depth based on the information already gathered. As the system accumulates preference data from customer responses, it dynamically determines when sufficient information has been collected to make accurate recipe recommendations, thereby reducing unnecessary questions and time loss.
Solution Approach 2:
The system uses a threshold-based approach where it stops asking questions once a sufficient level of preference accuracy is achieved, rather than exhaustively asking all possible questions. This partial action principle allows the system to achieve adequate measurement precision without the excessive time cost of comprehensive questioning.
3Adaptability or versatility
If the system provides fixed recipe options based on limited preferences, then the system operation is efficient, but the system cannot provide personalized recipe recommendations
Solution Approach 1:
The patent implements dynamic recipe recommendation that adapts to customer preferences in real-time. The system generates personalized recipe recommendations by dynamically filtering and ranking recipes based on the preference data collected during the interactive questionnaire, thereby achieving personalization without sacrificing operational efficiency through automated processing.
Solution Approach 2:
The system changes the parameters of recipe selection based on customer responses. As customers answer questionnaire questions, the system dynamically adjusts the weighting and filtering parameters of recipe matching algorithms, transforming fixed recipe options into personalized recommendations while maintaining computational efficiency through parameter-based filtering.
4Measurement precision
If the system collects detailed customer preference data, then the quality of recipe recommendations improves, but the data processing complexity increases
Solution Approach 1:
The system uses feedback loops to process preference data efficiently. As customer responses are collected during the questionnaire, the system immediately processes this feedback to update preference profiles and filter recipe options in real-time, rather than accumulating all data and processing it afterward. This reduces data processing complexity by distributing the computational load throughout the interaction.
Solution Approach 2:
The system performs preliminary data processing and filtering during the questionnaire itself, rather than waiting until the end. As each preference is collected, the system preliminarily filters the recipe database and updates recommendations, reducing the complexity of final data processing by pre-processing information incrementally throughout the customer interaction.
Data Source
AI summary
Systems and methods to select recipes for a customer. A request for at least one recipe is received for a customer. A plurality of questions are dynamically presented to the customer. This dynamic presentation of questions includes: selecting a next question from a dynamic-question-selection data structure based on a previous question presented to the customer and a previous answer received from the customer; presenting the next question to the customer; and receiving a next answer to the next question from the customer. A plurality of recipe attributes associated with the plurality of questions presented to the customer are identified. A group of initial recipes are selected from a plurality of recipes for the customer based on the plurality of recipe attributes. The at least one recipe for the customer is selected from the group of initial recipes and provided to the customer.