Systems, methods, and devices for automated provisioning are disclosed herein. The system can include a memory including a user profile database having n-dimension attributes of a user. The system can include a user device and a source device. The system can include a server that can: generate and store a user profile in the user profile database and generate and store a characterization vector from the user profile. The server can identify a service for provisioning, receive updates to at least some of the attributes of the first user, and trigger regeneration of the characterization vector from the received inputs. The server can: regenerate the characterization vector, determine an efficacy of the provisioned services, and automatically identify a second service for provisioning for a second user based on the efficacy of the provisioned services to the first user.

A method and apparatus for determining gaze direction information, includes a light source for forming illuminating light to an eye region of a user, and optical element(s) configured to guide the illuminating light from the light source to the eye region. The illuminating light is dynamically adjustable to generate a dynamic light beam on the eye region, and a sensor is configured to capture reflected light on the eye region and generate reflection eye data. The apparatus can maintain user profile information, adjust spectral power distribution of the light source based on the user profile information, receive the reflection eye data, and generate the gaze direction information based on the reflection eye data.

The present disclosure provides a method for identifying and representing potential next positions based on current position of user of an opportunity network system, the method including: (a) collecting and pre-analysing a comprehensive database of resumes or profiles of users, (b) extracting normalized entity information from resumes or profiles about backgrounds of users, (c) normalizing of the entity information of the users using at least one of machine learning techniques or statistical techniques to obtain normalized entity information, (d) identifying a comprehensive set of possible subsequent positions for the user based on the current position, (e) generating a position-detail profile for one or more of possible subsequent positions based on the profiles of people who are currently in that position or who may have previously worked at the position, and (f) determining a degree of match between resume or profile information of the user and at least one the position-detail profile of the target position.

A method is disclosed. The method includes receiving a text phrase from a user and parsing the text phrase using a natural language parser to generate distinct words. A vector of values relating to the distinct words is generated and compared to vectors in a graph database which relate to learned communities of language. Based on the comparing, a most similar community in the learned communities of language is determined and an action set action set associated with the most similar community is queried. A response to the text phrase from the user is then generated based on the action set and provided to the user.

Natural language processing is enhanced by linguistically extracting intelligence about a user. A history of user queries is analyzed by a natural language classifier to determine various user intents, and these intents are combined to form a user intent profile. The profile includes elements of sentiment, emotion and tone. The profile can be used in various ways including restricting access to documents in a collection, or refining a cognitive analysis of a query. For access restriction, a determination is made that the user intent is inconsistent with a document, and the user is denied access to the document. This determination involves a user intent score which is compared to a score of the document. For cognitive analysis, searching of reference documents is filtered by excluding documents based on the user intent. The searching includes a comparison of meta-data tags of the documents to the user intent.

A determination device 1 includes: a history storage unit 101 configured to accumulate and store use history information in which application identification information, position information indicating a position of a user, and user identification information are correlated with each other; a use region UU number totaling unit 105 configured to total a UU number for each of a plurality of regions on the basis of the use history information for a specific application; a use region number totaling unit 106 configured to total the number of regions on the basis of the use history information for the specific application; a score calculating unit 107 configured to calculate a regionality score for each of the plurality of regions on the basis of the UU number and the number of regions for the specific application; and a determination unit 108 configured to determine whether there is regionality for the specific application on the basis of the regionality score.

A system and method for mining social media signals and cues i) created by a user (for example, a customer) and/or ii) to which the user is exposed (the “data”), and for processing that data as it relates to a service (including a fee or subscription-based service), in order to predict the user's predisposition or likelihood to either leave the subscription or the service or reduce his/her engagement with the subscription or the service.

Some aspects of this disclosure involve generation of crowd-based results based on measurements of affective response of users. In some embodiments described herein, sensors are used to take measurements of affective response of at least ten users who were at a certain location. The measurements may include various values indicative of physiological signals and/or behavioral cues of the at least ten users. Some examples of locations in this disclosure include vacation destinations, businesses, establishments that provide entertainment, certain geographical regions, and virtual environments. User interfaces are configured to receive data describing a location score computed based on the measurements of the at least ten users, which represents the affective response of the at least ten users to being at the certain location. The user interfaces may be used to report the location score (e.g., to a user who may be interested in visiting the certain location).

As a client device accesses and interacts with a web server of an online retailer, an engagement evaluation server gathers data from both the client device and the web server. Over time, as the client device is used to access the web server and other web servers within the evaluation server network, a profile is built and maintained that describes some aspects of the client device interaction with the web server, including recency of visits, frequency of visits, frequency of views of products, frequency of shopping cart creation and modification, and other factors indicative of the user being engaged with the online retailer. The evaluation server performs statistical analysis and data modeling on profiles in order to generate an engagement score, and then provides the contact information for profiles meeting certain criteria.

Method and system for managing user profile information across plurality of locations in real time Embodiments herein disclose a system and method for managing user profile information across a plurality of locations in real time. The method includes receiving, by a central server, usage information of a tag associated with a user and determining, by the central server, a zone associated with the user based on the usage information of the tag, where the zone indicates a plurality of locations associated with user. Further, the method includes automatically replicating, by the central server, a user profile information of the user and a tag data of the user into a local database of each of the locations indicated in the determined zone associated with the user.