Watson IoT Receptionist BOT: The “typical” tasks of a receptionist are answering phones and greeting people who walk through the door. Of course, receptionists have always done many other things to keep offices running smoothly, but these are the tasks that most people think of first. In some companies, virtual receptionists and visitor management systems have replaced front-desk staff altogether. So the idea is to find a first step towards a visitor management system using the IoT devices and visual recognition to propose a visitor registration management solution. A Bluemix cloud based solution to register, track and manage the visitors. Features include mail notifications as well as a real-time dashboard on which you can check the activities.
The Tone Analyzer to calculate the scores for various emotions like anger, joy, sadness, etc. The service request priority can be high or low, for example a service request can be classified as priority 1 if the description include an anger tone or can be classified as priority 4 if the description include a joy tone. My guess is that if an user is quite his request can be managed with calm, instead if the user is angry his request have be handled withhighest priority. My solution is able to leverage the power of IBM Bluemix during the IBM Control Desk service request handling, in details my POC uses the Watson Tone Analyzer service. The Watson Tone Analyzer Service uses linguistic analysis to detect three types of tones from written text: emotion, social tendencies, and language style.
IBM Watson Retrieve and Rank service helps users find the most relevant information for their query by using a combination of search and machine learning algorithms to detect “signals” in the data. You can load data into the service, train a machine learning model based on known relevant results, then leverage this model to provide improved results to their end users based on their question or query
Create a work order based on data sent from IoT device : for example a company needs to monitor a storeroom’s temperature and if the detected temperature is more than x° a work order is opened against an asset management system, for example Maximo. The work order may consist in an location inspections for maintenance or in asset inspection, for example a conditional unit.
Real-time meter data from IoT device associated to an asset: for example a company needs to display in real time meters data feeds by IoT devices. A typical scenario using the sensor data related to a location can update the meters, moreover the data are shown in real time by a web dashboard.
visual recognition for quality control in production lines: based on Watson Visual Recognition service a pattern recognition solutions can be used for automatic quality control in production processes. The Watson feature extraction and machine learning techniques are used to design classification systems for a variety of image-based inspection tasks. My idea is to use the visual recognition for quality control vision system for the automatic inspection of mechanical parts containing defects.
asset model detection using visual recognition: the idea is to use the visual recognition to identify an asset type. The visual recognition service is based on a machine learning, in training phase the service learns to recognize the asset class by very different type of images for that class, so an particular asset can be recognize although the image is very different from matching model.
