Abstract: A fracture pattern characterization method, system, and computer program product, include classifying fracture patterns of reservoir layers using a correlation of reservoir information associated with target images and the target images extracted from a database and determining a fracture pattern characterization of a new reservoir layer input based on the classified fracture patterns.

Abstract: A method for answering a natural-language user query includes receiving a query from a user by a central agent, parsing the query, and determining if an answer is known to the central agent as being within a local database. If the answer is known, the query is answered with a known natural-language answer using the local database. If not, artificial conversational entity (ACE) invocation is performed. The ACE invocation includes determining a topical field for the query, determining an ACE, of a plurality of ACEs, to send the query to, based on the topical field, and sending the query to the ACE. If the ACE is capable of answering, a natural-language answer is received from the ACE and presented to the user. If not, a natural-language answer is created using social knowledge and presented to the user. Social knowledge includes data crawled from subscriber-provided content of social media services.

Abstract: A system for coordinating multiple chatbots in a group conversation using natural dialog systems, including a creation unit that enables a user to create a group chat with chatbots; a response unit that allows the user to reply to any utterance extracted from a message received from a member of the group chat; a transmission unit that sends messages to every member of the group chat; a development unit that develops chatbots that understand natural language and interact in a group chat using natural dialogue; a network connection; a first database that stores a knowledge base extracted from all the utterances exchanged by members of the group chat; a second database that stores all interactions between the user and the group chat, and a third database that stores all interaction protocols used by the members of the group chat.

Abstract: Embodiments focus on the real-time processing and analysis of event-related images posts on social media. By computing the most posted images and grouping them in accordance to their similarity, these images can be organized in a visual interface to provide the users an image-driven social media topic detection system. The groups of images with the highest frequency represent the most prominent topics. By keeping track of the temporal occurrence of the images, it is possible to visualize the temporal evolution of the topics.

Abstract: Embodiments focus on the real-time processing and analysis of event-related images posts on social media. By computing the most posted images and grouping them in accordance to their similarity, these images can be organized in a visual interface to provide the users an image-driven social media topic detection system. The groups of images with the highest frequency represent the most prominent topics. By keeping track of the temporal occurrence of the images, it is possible to visualize the temporal evolution of the topics.

Abstract: A photographing method including: displaying, at an image acquisition device, a scene that includes a first person; displaying, at the image acquisition device, a first score in the scene, wherein the first score is indicative of the first person's contentment with the way he or she looks in the scene; and acquiring, at the image acquisition device, a photograph of the scene when the first score is equal to or greater than a threshold value.

Abstract: A method for determining significant words or phrases within social media data includes receiving a stream of data from at least one social media source. The stream includes one or more words or phrases along with corresponding time stamps indicating when the word/phrase was used. One or more words/phrases to be analyzed is determined from the stream. A time period of interest is identified. The time period is divided into a plurality of non-overlapping time windows. The stream is analyzed within the time period of interest to determine how many instances of each words/phrases have timestamps within each time window. One or more of the words/phrases are identified as significant based on a level of co-occurrence of the words/phrases related to the determination as to how many instances of each words/phrases have timestamps within each window.