Systems and methods are described for tailoring shareable content object reference model (SCORM)-compliant content to one or more users. A learning management system (LMS), configured to be SCORM-compliant, initiates shareable content object (SCO) to provide content to users. The LMS implements an instance of application programming interface (API) comprising a plurality of functions to be called by SCO during runtime to access data model elements accessible via LMS. The LMS is configured to support one or more data model elements undefined by SCORM. Further, LMS receives a call to a function of the plurality of functions of the API from SCO to access information about users. The call references a name of a data model element undefined by SCORM. The data model element identifies information about users. The LMS provides information about the users to SCO and the SCO tailors the content to the users based on the information.

An objective of the present disclosure is to allow a situation in a conversation of a user to be recognized as a context and allow an item appropriate for the situation to be presented. An information recommendation device according to the present disclosure includes a context extraction module 24 that extracts, from the conversation of the user, a keyword representing a topic, a similarity determination module 31 that refers to a knowledge base 13 storing recommended items linked to communication contexts each including the keyword to extract the recommended items and the communication contexts that are linked to the extracted keyword and selects, from among the extracted communication contexts, the communication context similar to the topic, and an information search module 32 that acquires, from the knowledge base 13, the recommended item linked to the selected communication context.

An objective of the present disclosure is to allow a situation in a conversation of a user to be recognized as a context and allow an item appropriate for the situation to be presented. An information recommendation device according to the present disclosure includes a context extraction module 24 that extracts, from the conversation of the user, a keyword representing a topic, a similarity determination module 31 that refers to a knowledge base 13 storing recommended items linked to communication contexts each including the keyword to extract the recommended items and the communication contexts that are linked to the extracted keyword and selects, from among the extracted communication contexts, the communication context similar to the topic, and an information search module 32 that acquires, from the knowledge base 13, the recommended item linked to the selected communication context.

Various embodiments discussed herein are directed to improving existing technologies by causing certain characters to be replaced at a document if such characters are likely to be an error. For example, documents generated using speech-to-text technology or Optical Character Recognition (OCR) technology often contain character errors. A scoring threshold may be utilized to determine one or more characters are not being correctly represented in the document. Alternatively or additionally, various embodiments recommend multiple character sequences as candidates to replace other characters and a user may select which of the candidates will be used for replacement.

Various embodiments discussed herein are directed to improving existing technologies by causing certain characters to be replaced at a document if such characters are likely to be an error. For example, documents generated using speech-to-text technology or Optical Character Recognition (OCR) technology often contain character errors. A scoring threshold may be utilized to determine one or more characters are not being correctly represented in the document. Alternatively or additionally, various embodiments recommend multiple character sequences as candidates to replace other characters and a user may select which of the candidates will be used for replacement.

Embodiments of the present disclosure provide for identification and output of improved predicted operational support data object(s). Embodiments utilize particular data sets and data model implementations to identify and select predicted operational support data object(s) determined as associated with the highest confidence to assist in resolving a particular malfunction affecting a networked device in a dynamic home communications network. Such embodiments enable resolution of the malfunction, utilizing the predicted operational support data object(s), with improved success rates and without requiring performance of additional and/or alternative support processes.

Systems and methods for multi-source reference class identification, base rate calculation, and prediction are disclosed. The systems and method can guide on, then elicit, information about reference class identification on a case-by-case basis, connects to a database in order to calculate historical base rates according to user reference class selections, and collect additional quantitative and qualitative information from users. The systems and methods can then generate predictive estimates based on the combination of the inputs by one or more users.

Systems and methods for data management using machine learning and artificial intelligence techniques related to topic modeling on text comments are described. The text comments may correspond to a particular transaction conducted by a user. Machine learning text analysis is performed on the text comment to determine one or more topics associated with the text comment. The topic with the highest correlation to the text comment is assigned to the transaction claim. Based on the topic assigned to the transaction claim, various actions may be performed, including remedial actions on a user account. These techniques may be applicable to chargeback fraud, in some embodiments.

Systems and methods for data management using machine learning and artificial intelligence techniques related to topic modeling on text comments are described. The text comments may correspond to a particular transaction conducted by a user. Machine learning text analysis is performed on the text comment to determine one or more topics associated with the text comment. The topic with the highest correlation to the text comment is assigned to the transaction claim. Based on the topic assigned to the transaction claim, various actions may be performed, including remedial actions on a user account. These techniques may be applicable to chargeback fraud, in some embodiments.

Described are computer-based methods and apparatuses, including computer program products, comprising the steps of, or structure for, storing a plurality of expert profiles in a database, each of the plurality of expert profiles comprising information associated with a person having knowledge in a particular category, subject or topic; receiving search criteria over a network from a query source; selecting at least one of the plurality of expert profiles comprising information that satisfy the search criteria; and transmitting expert profile data for each of the selected expert profiles to the remote search engine, the expert profile data comprising data that defines a displayable representation of a corresponding expert profile, the expert profile data further comprising data that facilitates a client-initiated, real-time communication session over the network with a person associated with the corresponding expert profile.