Neural Time Entry Generation With Feedback-Based Retraining
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Solution Overview
Problem
Traditional timekeeping methods are prone to errors, inefficiencies, and require excessive memory and processing resources, lacking real-time data integration with productivity and collaborative software.
Innovation Solution
A neural network is trained using user data to generate time entries, with continuous retraining based on user feedback, integrating with productivity and collaborative software like Microsoft 365 and Epoch, and utilizing smart watches for time recording.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual timekeeping methods are used, then users can record time entries, but the process is prone to errors and inefficiencies
Solution Approach 1:
The system enables self-service by automatically generating time entries using a neural network that processes user data from productivity and collaborative software. The neural network autonomously creates time entries with narratives, task descriptions, and duration estimates based on detected patterns in user activity data, eliminating the need for manual time entry creation while maintaining accuracy.
Solution Approach 2:
The patent replaces the mechanical manual process of time entry creation with an intelligent automated system. The neural network substitutes human manual input with algorithmic generation, using machine learning models trained on historical time entry data and user behavior patterns to automatically produce accurate time entries without manual intervention.
2Reliability
If traditional timekeeping systems are used, then time entries can be recorded, but excessive memory and processing resources are required
Solution Approach 1:
The system extracts only the essential features and patterns from user data that are relevant for time entry generation. The neural network is trained to identify and process only key parameters such as task types, duration patterns, and project associations, discarding redundant information. This selective extraction reduces memory requirements while maintaining entry accuracy.
Solution Approach 2:
The patent transforms raw user data into optimized parameters suitable for neural network processing. By converting unstructured activity data into structured features (task categories, time patterns, project identifiers), the system reduces data dimensionality and memory footprint while preserving the information necessary for accurate time entry generation.
3Adaptability or versatility
If traditional timekeeping methods are used, then time entries can be created, but real-time data integration with productivity software is lacking
Solution Approach 1:
The system merges timekeeping functionality with productivity and collaborative software by establishing real-time data integration. The neural network directly accesses and processes data from multiple software sources (email, calendar, project management tools) simultaneously, combining information from these integrated sources to generate comprehensive time entries without manual data collection or reconciliation.
Solution Approach 2:
The patent introduces an intermediary layer (the neural network system) that bridges productivity software and timekeeping functions. This intermediary automatically retrieves, processes, and synthesizes data from various software sources, translating raw data from different formats into standardized time entries, thereby enabling seamless integration without requiring manual intervention.
4Productivity
If manual time entry methods are used, then users can record time, but the process requires excessive user involvement and is inefficient
Solution Approach 1:
The system enables self-service by automatically generating time entries using a neural network that processes user data from productivity and collaborative software. The neural network autonomously creates time entries with narratives, task descriptions, and duration estimates based on detected patterns in user activity data, eliminating the need for manual time entry creation while maintaining accuracy.
Solution Approach 2:
The system implements feedback mechanisms where user corrections and approvals of generated time entries are fed back into the neural network for continuous learning and improvement. This feedback loop allows the system to adapt to user preferences and patterns over time, reducing the need for manual intervention while maintaining high accuracy and user satisfaction.
Data Source
AI summary
Systems and methods for generating intelligent time entries are disclosed herein. In an embodiment, a computer-implemented method of training a neural network to create time entries includes retrieving user data related to a task performed by a user, creating a first training set comprising the user data as an input and an approved time entry as an output, training the neural network in a first stage using the first training set, creating a second training set comprising the user data as an input and a revised time entry as an output, and training the neural network in a second stage using the second training set.