Recipient Notification Recommendations for Scalable Document Status Tracking
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Solution Overview
Problem
Current document management systems do not provide reliable and scalable methods for sending notifications to receiving entities about the status of inbound documents, leading to inefficiencies for entities receiving large volumes of documents from various sending entities.
Innovation Solution
A document management system that generates and sends notifications to receiving entities based on predefined recipient notification definitions, which include event criteria and data types, and utilizes a machine-learned model to recommend optimal notification settings for sending entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If receiving entities make API calls to determine document statuses, then they can obtain document status information, but the method is not reliable and scalable for large volumes of documents
Solution Approach 1:
Instead of having receiving entities actively query the document management system via API calls, the system inverts the approach by pushing notifications to receiving entities when document events occur. This inversion transforms a pull-based query system into a push-based notification system, making the receiving entities passive recipients of status updates rather than active query initiators.
Solution Approach 2:
The document management system provides self-service by automatically monitoring document events and generating notifications without requiring receiving entities to initiate any actions. The system autonomously tracks document statuses and proactively informs receiving entities of relevant events, eliminating the need for manual or automated querying by the receiving entities.
2Loss of information
If receiving entities receive all document notifications, then they have complete information, but the burden increases for managing large volumes of documents
Solution Approach 1:
The notification system applies local quality by customizing notifications based on specific receiving entities' needs and characteristics. Instead of a uniform notification approach, the system allows different receiving entities to receive different types of notifications based on their roles, preferences, and relevance criteria. This ensures each entity receives appropriate information without being overwhelmed by irrelevant notifications.
Solution Approach 2:
The system performs preliminary action by allowing receiving entities to configure their notification preferences in advance. Entities can pre-specify which types of document events they want to be notified about, filtering out irrelevant information before it reaches them. This preliminary configuration reduces the volume of notifications received while ensuring completeness of relevant information.
3Adaptability or versatility
If sending entities manually configure notification settings, then they have control over notifications, but the process is time-consuming and complex
Solution Approach 1:
The system incorporates feedback by using machine learning models that analyze historical notification data and entity interactions. The model learns from past configurations and outcomes, then provides intelligent recommendations for notification settings. This feedback loop enables the system to suggest optimal configurations based on patterns observed in similar scenarios, reducing the complexity of manual setup while maintaining flexibility.
Solution Approach 2:
The machine learning model acts as an intermediary between the sending entities and the notification system. Instead of entities directly configuring complex notification parameters, they interact with the ML model which translates their needs into appropriate notification configurations. This intermediary layer simplifies the user interface and configuration process while preserving the underlying flexibility and control options.
Data Source
AI summary
A system and a method are disclosed for generating recipient notification recommendations using a machine-learned model for a sending entity sending a set of documents to an acting entity and a subset of the set of documents to a receiving entity. The receiving user is subscribed to a notification service of a document management system to receive push notifications regarding statuses of inbound documents. The notifications for the receiving entity are generated based on recipient notification definition provided by the sending entity. The document management system trains a machine-learned model to generate recipient notification recommendations for the sending entity selecting event criteria that indicate when to generate the notifications and types of data to include in the notifications. The machine-learned model is trained based on data associated with historical notification definitions provided by historical sending entities.