Abstract: A method, system, and computer program product for determining selection parameters for filtering algorithms using environmental data of images. The method may include receiving an image. The method may also include analyzing the image using at least image processing. The method may also include identifying, based on the analyzing, image data and environmental data of the image. The method may also include inputting the image data and the environmental data into a machine learning algorithm, where the machine learning algorithm includes mapped relationships between at least the environmental data and selection parameters. The method may also include predicting, using the machine learning algorithm, optimal selection parameters for the image. The method may also include applying the optimal selection parameters to a filtering algorithm for the image. The system and computer program product may include similar steps.

Abstract: A method, system, and computer program product for determining selection parameters for filtering algorithms using environmental data of images. The method may include receiving an image. The method may also include analyzing the image using at least image processing. The method may also include identifying, based on the analyzing, image data and environmental data of the image. The method may also include inputting the image data and the environmental data into a machine learning algorithm, where the machine learning algorithm includes mapped relationships between at least the environmental data and selection parameters. The method may also include predicting, using the machine learning algorithm, optimal selection parameters for the image. The method may also include applying the optimal selection parameters to a filtering algorithm for the image. The system and computer program product may include similar steps.

Abstract: A method, computer program product, and a system where a processor(s), obtains, via a conversational agent executing on a client, a conversation thread and generates topic distributions. The processors(s) identifies topics and pre-fetches web content, based on utilizing the topics as search keywords. The processor(s) parses the pre-fetched web content to identify, in the pre-fetched web content, a first content type and a second content type. The processor(s) filters the parsed pre-fetched web content, to separate the first content type from the second content type and extracts a signature of a REST call associated with the pre-fetched web content from the first type of content and tokenizes the second type of content to supply context for invoking the REST call. The processor(s) generates a package that includes signature and the context and injects the package into the client so it is invocable by the conversational agent, based on the context.

Abstract: A method, computer program product, and a system where a processor(s), obtains, via a conversational agent executing on a client, a conversation thread and generates topic distributions. The processors(s) identifies topics and pre-fetches web content, based on utilizing the topics as search keywords. The processor(s) parses the pre-fetched web content to identify, in the pre-fetched web content, a first content type and a second content type. The processor(s) filters the parsed pre-fetched web content, to separate the first content type from the second content type and extracts a signature of a REST call associated with the pre-fetched web content from the first type of content and tokenizes the second type of content to supply context for invoking the REST call. The processor(s) generates a package that includes signature and the context and injects the package into the client so it is invocable by the conversational agent, based on the context.

Abstract: Root cause analysis of issues relating to a vehicle of interest using user reviews from media and service centers of vehicles to link sensor data to the last time that class of vehicles came in for service to determine the issue and the resolution. The root cause analysis includes identifying potential user reviews from a source regarding the vehicle of interest; identifying issues regarding the vehicle of interest in the user reviews; identifying a set of sensors of the vehicle of interest which relate to the identified issues from the user reviews; comparing and correlating sensor values from the set of sensors of the vehicle of interest belonging to users posting a same issue to identify a common set of sensors observing the same issue in the vehicle of interest; analyzing a root cause for each identified issue; and sending the root cause for the identified issue to a user.