Duration-Identification Model for Dynamic Service Scheduling
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Solution Overview
Problem
Merchants face difficulties in integrating data between various services provided by a service provider, particularly in distinguishing between goods and services, and determining the periods of time required to perform services.
Innovation Solution
The use of a service-identification model and a duration-identification model, trained with data from existing merchants, to automatically classify items as services or goods and determine the required service durations, respectively, facilitating seamless data integration and automated scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification and scheduling methods are used, then merchants can control the process, but errors increase and accuracy decreases
Solution Approach 1:
The patent introduces machine learning models (service-identification model and duration-identification model) as intermediaries between the raw data and the scheduling system. These models automatically classify items as services or goods and determine service durations, eliminating manual classification errors while maintaining system manageability through automated decision-making processes.
Solution Approach 2:
The patent replaces manual mechanical classification and scheduling processes with automated machine learning-based systems. The service-identification model and duration-identification model automatically process transaction data to classify items and determine durations, substituting human judgment with algorithmic decision-making to improve accuracy and consistency.
2Measurement precision
If automated classification models are implemented, then accuracy improves, but data integration complexity increases
Solution Approach 1:
The patent creates a universal data integration framework that handles both goods and services through the same automated classification process. The service-identification model and duration-identification model serve multiple functions: classifying items, determining durations, and integrating data across different service providers, thereby managing complexity through standardized multi-functional processing.
Solution Approach 2:
The patent performs classification and duration determination as preliminary actions before scheduling takes place. By using machine learning models to pre-classify items and determine service durations from historical data, the system prepares structured information in advance, reducing the complexity of real-time data integration and scheduling operations.
3Adaptability or versatility
If multiple services are integrated, then merchant capabilities expand, but data integration difficulties increase
Solution Approach 1:
The patent segments the data integration process into distinct functional components: the service-identification model for classification, the duration-identification model for time determination, and the scheduling system for appointment management. This segmentation allows each component to handle specific tasks independently, reducing overall integration difficulty while enabling expansion to multiple services.
Solution Approach 2:
The patent uses machine learning models as intermediary layers between diverse service data sources and the unified scheduling system. These intermediaries standardize and normalize data from different services, enabling seamless integration across multiple service providers while maintaining system manageability through consistent data processing protocols.
Data Source
AI summary
Techniques are described for dynamic scheduling via a duration-identification model. The techniques may include a service provider receiving information associated with services performed by employees of a merchant, including a duration of the service and an employee performing the service. In examples, a duration-identification model may be trained using the information, wherein the model can output a duration for a particular service with a particular employee. The duration-identification model may be applied to service requests for services to be performed over a given time period. Employee information is received, and a schedule of appointments associated with the service requests is determined that is based on the duration-identification model and the employee information. The schedule includes, for an appointment, an assigned employee and a time.