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Order processing during pandemics for offline mode

Summary

This code pattern explains how to build an AI-powered back-end system that can take your daily essential orders through an offline mode.

Description

To contend with panic purchases and hoarding during crises, how can you maintain social distancing while getting the essentials that you need?

Because many people do not know how to use or have access to available online portals, there must be a different option for them to receive their essential items such as food and medicine. For example, a toll-free phone number-based approach to contacting a control tower might be a better option. In this option, the user can call the toll free number and order their essentials. However, with large numbers of people using this toll-free number-based approach, the traffic at the back end will be high, and to process these calls manually is tedious.

In this code pattern, we build an AI-powered back-end system that can take the daily essentials orders through an offline mode. The system processes the audio request by converting it to a formatted order list. Then, this system can be connected to the inventory database for optimizing supply chain management. This solution can be applied to various domains, such as ordering medicine and ordering groceries.

Flow

flow

  1. Feed the audio to the Speech to Text service.
  2. Convert the text into English using Watson Language Translator.
  3. Feed the English text to the Watson Knowledge Studio model, which is deployed on Watson Natural Language Understanding.
  4. The model deployed on Watson Natural Language Understanding identifies all of the required attributes from the text.
  5. These extracted attributes get stored on Db2 on IBM Cloud.
  6. Visualize the order and customer details from the recordings on a dashboard.

Instructions

Find the detailed steps for this pattern in the readme file. The steps show you how to:

  1. Clone the GitHub repository.
  2. Set up the Watson Speech to Text service.
  3. Set up the Watson Language Translator service.
  4. Set up Watson Knowledge Studio and Watson Natural Language Understanding.
  5. Set up IBM Db2.
  6. Add the credentials to the application.
  7. Deploy the application to Cloud Foundry.
  8. Analyze the results.