Abstract: A floor operations automation system can include an intelligent video module, profiles, decision rules, and a decision module. The intelligent video module can be configured to generate video analytics data from video captured by video cameras within a retail location. The profiles can contain data that represents a business practice or policy of the retail location and/or user-specified preferences for decision-related variables. The decision rules can express actions to be performed in response to predefined conditions within the retail location. The decision module can be configured to decide upon action to be performed, based upon the video analytics data, the profiles, and the decision rules. The decision can be made without direct input from a human agent of the retail location. The actions can affect a business system and/or a human agent associated with the retail location.

Abstract: Systems and tools for sharing medical costs between prospective patients seeking treatment, and existing patients of a medical network. Prospective patients may actively query medical costs for specific conditions and treatments for the specific conditions previously experienced by existing patients. Existing patients contributing to the documents accessible by the medical network may upload anonymous versions of EOBs, medical bills and medical documents to the medical network in exchange for rewards. The prospective patients may communicate and query the desired information using natural language inputs to instruct a client computing system to search the medical network for relevant treatment information. The prospective patient may use natural language commands or other inputs to filter the query results by condition, location, cost, quality of service, service provider, insurance provider, or any other parameter that may be detailed by the medical documents accessible through the medical network.

Abstract: Contextualizing vehicle data and predicting real-time driver actions. By unsiloing collected data relating to a driver, the actions of the driver can be predicted and the reasons for variations from the predicted actions can be determined based on the contextualized data.

Abstract: A floor operations automation system can include an intelligent video module, profiles, decision rules, and a decision module. The intelligent video module can be configured to generate video analytics data from video captured by video cameras within a retail location. The profiles can contain data that represents a business practice or policy of the retail location and/or user-specified preferences for decision-related variables. The decision rules can express actions to be performed in response to predefined conditions within the retail location. The decision module can be configured to decide upon action to be performed, based upon the video analytics data, the profiles, and the decision rules. The decision can be made without direct input from a human agent of the retail location. The actions can affect a business system and/or a human agent associated with the retail location.

Abstract: Methods for sharing medical costs between prospective patients seeking treatment, and existing patients of a medical network. Prospective patients may actively query medical costs for specific conditions and treatments for the specific conditions previously experienced by existing patients. Existing patients contributing to the documents accessible by the medical network may upload anonymous versions of EOBs, medical bills and medical documents to the medical network in exchange for rewards. The prospective patients may communicate and query the desired information using natural language inputs to instruct a client computing system to search the medical network for relevant treatment information. The prospective patient may use natural language commands or other inputs to filter the query results by condition, location, cost, quality of service, service provider, insurance provider, or any other parameter that may be detailed by the medical documents accessible through the medical network.

Abstract: Systems and tools for sharing medical costs between prospective patients seeking treatment, and existing patients of a medical network. Prospective patients may actively query medical costs for specific conditions and treatments for the specific conditions previously experienced by existing patients. Existing patients contributing to the documents accessible by the medical network may upload anonymous versions of EOBs, medical bills and medical documents to the medical network in exchange for rewards. The prospective patients may communicate and query the desired information using natural language inputs to instruct a client computing system to search the medical network for relevant treatment information. The prospective patient may use natural language commands or other inputs to filter the query results by condition, location, cost, quality of service, service provider, insurance provider, or any other parameter that may be detailed by the medical documents accessible through the medical network.

Abstract: A floor operations automation system can include an intelligent video module, profiles, decision rules, and a decision module. The intelligent video module can be configured to generate video analytics data from video captured by video cameras within a retail location. The profiles can contain data that represents a business practice or policy of the retail location and/or user-specified preferences for decision-related variables. The decision rules can express actions to be performed in response to predefined conditions within the retail location. The decision module can be configured to decide upon action to be performed, based upon the video analytics data, the profiles, and the decision rules. The decision can be made without direct input from a human agent of the retail location. The actions can affect a business system and/or a human agent associated with the retail location.

Abstract: One or more processors receive recording data of a meeting between a professional and a client. One or more processors analyze the recording data to make one or more determinations. One or more processors identify one or more characteristics of the professional based on the one or more determinations. One or more processors match the one or more characteristics of the professional to one or more preferences of an individual seeking one or more professionals. One or more processors build a profile of the professional based on the one or more characteristics and store the profile in a database. One or more processors search the database for one or more profiles that provide a match of the one or more preferences of the individual seeking one or more professionals and display the one or more profiles.

Abstract: One or more processors receive recording data of a meeting between a professional and a client. One or more processors analyze the recording data to make one or more determinations. One or more processors identify one or more characteristics of the professional based on the one or more determinations. One or more processors match the one or more characteristics of the professional to one or more preferences of an individual seeking one or more professionals. One or more processors build a profile of the professional based on the one or more characteristics and store the profile in a database. One or more processors search the database for one or more profiles that provide a match of the one or more preferences of the individual seeking one or more professionals and display the one or more profiles.

Abstract: One or more processors receive recording data of a meeting between a professional and a client. One or more processors analyze the recording data to make one or more determinations. One or more processors identify one or more characteristics of the professional based on the one or more determinations. One or more processors match the one or more characteristics of the professional to one or more preferences of an individual seeking one or more professionals. One or more processors build a profile of the professional based on the one or more characteristics and store the profile in a database. One or more processors search the database for one or more profiles that provide a match of the one or more preferences of the individual seeking one or more professionals and display the one or more profiles.

Abstract: A method for electronically providing targeted messages can begin when a source-concealing targeted messaging system receives data items provided by a source user that define a targeted message that is to be presented within a social media context. The data items can include a set of recipients, a message body, and a set of trigger conditions indicating when the targeted message is to be presented to the recipients. The recipients can be members of a contact list of the source user within the social media system providing the social media context. Satisfaction of the trigger conditions can be monitored and the social media system can be monitored recipients. When the trigger conditions are satisfied and a recipient is currently using the social media system, the targeted message can be presented within a user interface of the social media system, such that the source user appears unaffiliated with the targeted message.

Abstract: One or more processors receive recording data of a meeting between a first individual and a second individual. One or more processors analyze the recording data to identify one or more characteristics of the first individual. One or more processors match the one or more characteristics of the first individual to one or more preferences of a third individual.

Abstract: One or more processors receive recording data of a meeting between a professional and a client. One or more processors analyze the recording data to make one or more determinations. One or more processors identify one or more characteristics of the professional based on the one or more determinations. One or more processors match the one or more characteristics of the professional to one or more preferences of an individual seeking one or more professionals. One or more processors build a profile of the professional based on the one or more characteristics and store the profile in a database. One or more processors search the database for one or more profiles that provide a match of the one or more preferences of the individual seeking one or more professionals and display the one or more profiles.

Abstract: One or more processors receive recording data of a meeting between a professional and a client. One or more processors analyze the recording data to make one or more determinations. One or more processors identify one or more characteristics of the professional based on the one or more determinations. One or more processors match the one or more characteristics of the professional to one or more preferences of an individual seeking one or more professionals. One or more processors build a profile of the professional based on the one or more characteristics and store the profile in a database. One or more processors search the database for one or more profiles that provide a match of the one or more preferences of the individual seeking one or more professionals and display the one or more profiles.

Abstract: A user input pertaining to a question is received. Based on the question a new message is generated and posted in at least one web based forum. Responses to the new message are monitored and analyzed to determine whether, for each response, the response pertains to a subject of the question. Responsive to determining that at least one response pertains to the subject of the question, information contained in the response is presented to the user.

Abstract: Managing reservations for limited resources such as meeting rooms within a network computing system may include, responsive to receiving a reservation for a meeting room from a requestor, determining, using a processor, availability of meeting rooms for the reservation and determining, using the processor, eligibility of the requestor according to a courtesy score for the requestor. The courtesy score may be determined according to historical usage of the meeting rooms by the requestor. The reservation may be selectively confirmed according to the courtesy score and the availability of the meeting rooms. Further, a particular meeting room may be assigned to the reservation subsequent to the confirming.

Abstract: Identifying mentors may include, responsive to receiving a mentorship request, generating search parameters, using a central request processor, from the mentorship request, and distributing the search parameters to a mentor identification processor. Identifying mentors may include selecting, from private data of the organization, a candidate mentor set of at least one candidate mentor that meets the search parameters and that has agreed to serve as a mentor, matching a candidate mentor with the mentee according to publicly accessible information retrieved for each candidate mentor, and determining whether a matched candidate mentor accepts selection for mentorship for the mentee. Identifying mentors may also include sending a notification specifying the matched candidate mentor to a device of the mentee only responsive to determining that the matched candidate mentor accepts the mentorship.

Abstract: Contextualizing vehicle data and predicting real-time driver actions. By unsiloing collected data relating to a driver, the actions of the driver can be predicted and the reasons for variations from the predicted actions can be determined based on the contextualized data.

Abstract: One or more processors receive recording data of a meeting between a first individual and a second individual. One or more processors analyze the recording data to identify one or more characteristics of the first individual. One or more processors match the one or more characteristics of the first individual to one or more preferences of a third individual.

Abstract: One or more processors receive recording data of a meeting between a professional and a client. One or more processors analyze the recording data to make one or more determinations. One or more processors identify one or more characteristics of the professional based on the one or more determinations. One or more processors match the one or more characteristics of the professional to one or more preferences of an individual seeking one or more professionals. One or more processors build a profile of the professional based on the one or more characteristics and store the profile in a database. One or more processors search the database for one or more profiles that provide a match of the one or more preferences of the individual seeking one or more professionals and display the one or more profiles.