Abstract: Various apparatuses are disclosed (e.g., system, device, method, or the like) for guiding the capture and assessing the quality of an image, including dental images. The apparatuses may use trained neural networks to examine images and provide users feedback regarding image quality. The neural networks may be trained based on images within image groups that have been ranked based on perceived image quality.

Abstract: The technology disclosed herein enables ambient noise level determination at an endpoint using a secondary audio capture system for the endpoint. In a particular embodiment, a method includes during a communication session, receiving an audio signal representing sound captured by a secondary audio capture system for an endpoint on the communication session. A primary audio capture system for the endpoint captures voice communications from a user of the endpoint for the communication session. The method further determining an ambient noise level for the endpoint from the audio signal and, in response to determining that the ambient noise level satisfies a loudness criterion, presenting a notification to the user about the ambient noise level.

Abstract: The technology disclosed herein enables ambient noise level determination at an endpoint using a secondary audio capture system for the endpoint. In a particular embodiment, a method includes during a communication session, receiving an audio signal representing sound captured by a secondary audio capture system for an endpoint on the communication session. A primary audio capture system for the endpoint captures voice communications from a user of the endpoint for the communication session. The method further determining an ambient noise level for the endpoint from the audio signal and, in response to determining that the ambient noise level satisfies a loudness criterion, presenting a notification to the user about the ambient noise level.

Abstract: A server receives a request from a client to provide a price and/or time quote and transmits initial diagnostic questions. The server receives a response to the initial questions and generates a initial cost range and/or time range estimate quote based on weighting pre-assigned in a knowledge base to information included in the response. The weightings assigned to possible responses are based on analysis of historical data. The server transmits the initial cost and/or time estimate quote to the client. Further, the server may refine the initial cost range and/or time range estimate quote in response to a request. Refining may includes generating additional diagnostic questions, receiving a responses, generating a refined quote based on a weighting pre-associated in the knowledge base with information included in the responses, and transmitting the refined quote to the client. Refining the quote may be an iterative process.

Abstract: Users access a deployment environment utilizing a browser. The deployment environment automatically creates an application based on user input and then deploys the created application in response to an indication from a user. Deployment may include providing an automatically generated reference. When an application is accessed via a browser utilizing such a reference, the deployment environment may execute and deliver access to the referenced application. In some implementations, the deployment environment may allow the user to choose between a wizard and a development tool. The wizard may present a series of prompts that guide the user through a default template for designing an application, which is then automatically created based on the user's responses.