Provided herein are system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof for displaying a database record in a chat platform. In a given embodiment, a server may detect a request to launch a search window, including an input field. The server may cause the display of the search window, including the input field, in response to detecting the request to launch the search window. The server may receive a string via the input field. The server may query a database for a database record matching the string. The server may receive a selection of the database record from the chat participant. The server may cause display of a subset of fields of the database record in the chat session.

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.

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.

Systems are configured for processing user queries to generate search results that are contextually relevant for the user based on customer values associated with a corresponding customer schema associated with the user and that are indexed in a customer value index. When search queries are received, they are processed to identify customer values associated with the user context and to perform an initial search query based on the initial search query terms. The systems also generate additional altered search queries to perform contemporaneously, based on the initial search terms and restructured/reformatted based on the customer values. Resulting supplement search results are obtained for the additional altered search queries, which are merged with the initial search results. The merged results are then ranked and provided to the user. These systems facilitate obtaining search results that are more relevant than results obtained by conventional systems.

A method, system and/or computer usable program product for automatically managing the conveying of messages among multiple communication channels including (i) receiving, from a first computing system, an on-line message addressed to a user, (ii) automatically categorizing the message among a predetermined set of message categories stored in memory, (iii) identifying a set of on-line message channels preselected by the addressee user for receiving messages for each of the predetermined set of message categories, (iv) identifying a set of performance metrics stored in memory for optimizing message channel selection, (v) utilizing the performance metrics to automatically select an optimum message channel from the preselected message channels for sending the categorized message to a second computing system of the addressee user, (vi) automatically formatting the categorized message for the optimum message channel, and (vii) sending the formatted message on-line to the second computing system of the addressee user across the optimum message channel.

The described technology is generally directed towards quasi-listing table entries that facilitate the efficient return of an object list in response to a query for relevant objects in a data storage system. A quasi-listing table entry is created when an object is created, and subsequently converted to a normal listing table entry. A normal listing table entry is converted to a quasi-listing table entry when an object is deleted, with the quasi-listing table entry subsequently deleted. When an object list request is received, a normal listing table entry can be used directly to add to the object list; only if a quasi-listing table entry is encountered (before it can be converted or deleted) does the object table need to be accessed to determine if the object actually exists. This eliminates most object table accesses, including those in remote geographic zones, thereby making object listing significantly more efficient.

The described technology is generally directed towards quasi-listing table entries that facilitate the efficient return of an object list in response to a query for relevant objects in a data storage system. A quasi-listing table entry is created when an object is created, and subsequently converted to a normal listing table entry. A normal listing table entry is converted to a quasi-listing table entry when an object is deleted, with the quasi-listing table entry subsequently deleted. When an object list request is received, a normal listing table entry can be used directly to add to the object list; only if a quasi-listing table entry is encountered (before it can be converted or deleted) does the object table need to be accessed to determine if the object actually exists. This eliminates most object table accesses, including those in remote geographic zones, thereby making object listing significantly more efficient.

Methods, systems, and devices for data capture trigger configuration for asset tracking are provided. Another example method capturing raw data involves obtaining a rich data capture trigger that defines when a controller of an asset tracking device onboard an asset is to identify and log an unsimplified block of raw data in raw data on the asset tracking device for rich data analysis, transmitting data capture instructions to the asset tracking device that contains the rich data capture trigger, and receiving the simplified set of raw data and the unsimplified block of raw data from the asset tracking device.

Methods, systems, and devices for data capture trigger configuration for asset tracking are provided. Another example method capturing raw data involves obtaining a rich data capture trigger that defines when a controller of an asset tracking device onboard an asset is to identify and log an unsimplified block of raw data in raw data on the asset tracking device for rich data analysis, transmitting data capture instructions to the asset tracking device that contains the rich data capture trigger, and receiving the simplified set of raw data and the unsimplified block of raw data from the asset tracking device.

According to one embodiment, a method, computer system, and computer program product for three-dimensional printing is provided. The embodiment may include analyzing data of a user. The data is collected while the user is performing an activity. The embodiment may include deriving a user behavior model (UBM) of the user based on the analysis of the data. The embodiment may include calculating a relative comfort coefficient (RCC) of the user for the activity based on attributes of the UBM. The embodiment may include predicting adjustments to the attributes of the UBM which result in the RCC exceeding a threshold value. The predicted adjustments are derived using a convolutional neural network classifier. The embodiment may include defining one or more parameters of a tangible component of an object utilized by the user when performing the activity based on the predicted adjustments.