Various embodiments provide an approach to controlled access to online content. Such control may be based on a multitude of factors including but not limited to website content, profile for the person consuming the data. In operation, machine-learning techniques are used to classify the websites based on community and social media inputs, crowd-sourced data, as well as access rules implemented by parents or system administrators. Feedback from users/admins of the system, including the instances of allowed or denied access to websites, in conjunction with other relevant parameters, is used for iterative machine-learning techniques. Embodiments may also allow for real, or near real-time, approval or denial of access to websites by registered admins.

A platform for design of a lighting installation generally includes an automated search engine for retrieving and storing a plurality of lighting objects in a lighting object library and a lighting design environment providing a visual representation of a lighting space containing lighting space objects and lighting objects. The visual representation is based on properties of the lighting space objects and lighting objects obtained from the lighting object library. A plurality of aesthetic filters is configured to permit a designer in a design environment to adjust parameters of the plurality of lighting objects handled in the design environment to provide a desired collective lighting effect using the plurality of lighting objects.

A platform for design of a lighting installation generally includes an automated search engine for retrieving and storing a plurality of lighting objects in a lighting object library and a lighting design environment providing a visual representation of a lighting space containing lighting space objects and lighting objects. The visual representation is based on properties of the lighting space objects and lighting objects obtained from the lighting object library. A plurality of aesthetic filters is configured to permit a designer in a design environment to adjust parameters of the plurality of lighting objects handled in the design environment to provide a desired collective lighting effect using the plurality of lighting objects.

Machine data is collected from multiple sources of an operating environment such as an information technology system, factory floor, or the like, into a data intake and query system, in one embodiment. Metrics representative of the environment are included in or derived from the data. Users may interact with an interface to depict a representation of various metrics and interdependencies and that depiction is reflected in a computer storage model. Changes to the computer storage model based on the user interaction may also result in training of a machine learning model according to the user interaction, the machine learning model configured to determine a prediction, classification or clustering of a result of a first search query by utilizing a result of at least a second search query as input to the machine learning model.

Various embodiments of the present application set forth a computer-implemented method that includes processing a first natural language (NL) request, where the first NL request includes a first artifact. The method further includes determining that a first relationship, associated with the first artifact and useable to process the first NL request, is unavailable in a first NL language processing system. The method further includes generating a first data relationship recommendation based on the first NL request. In addition, the method includes causing the first data relationship recommendation to be provided to a user.

A robot configured to perceive a model of an environment, including: a chassis; a set of wheels; a plurality of sensors; a processor; and memory storing instructions that when executed by the processor effectuates operations including: capturing a plurality of data while the robot moves within the environment; perceiving the model of the environment based on at least a portion of the plurality of data, the model being a top view of the environment; storing the model of the environment in a memory accessible to the processor; and transmitting the model of the environment and a status of the robot to an application of a smartphone previously paired with the robot.

A robot configured to perceive a model of an environment, including: a chassis; a set of wheels; a plurality of sensors; a processor; and memory storing instructions that when executed by the processor effectuates operations including: capturing a plurality of data while the robot moves within the environment; perceiving the model of the environment based on at least a portion of the plurality of data, the model being a top view of the environment; storing the model of the environment in a memory accessible to the processor; and transmitting the model of the environment and a status of the robot to an application of a smartphone previously paired with the robot.

Systems and methods for presenting inference models based on interrelationships among inference models are provided. For example, information related to a group of at least three inference models may be obtained. Further, in some examples, a plurality of interrelationship records may be obtained, wherein each interrelationship record may correspond to one subgroup of the group of at least three inference models, and each subgroup may comprise at least two inference models. Further, in some examples, the plurality of interrelationship records may be used to determine information related to a first inference model of the group of at least three inference models. Further, in some examples, the determined information may be used to present the first inference model to a user.

Systems and methods for presenting inference models based on interrelationships among inference models are provided. For example, information related to a group of at least three inference models may be obtained. Further, in some examples, a plurality of interrelationship records may be obtained, wherein each interrelationship record may correspond to one subgroup of the group of at least three inference models, and each subgroup may comprise at least two inference models. Further, in some examples, the plurality of interrelationship records may be used to determine information related to a first inference model of the group of at least three inference models. Further, in some examples, the determined information may be used to present the first inference model to a user.

Computer-implemented systems utilizing sensor networks for sensing temperature and motion environmental parameters, and performing at least operations of electronically establishing, based on pattern recognition criteria, correspondence of a plurality of representative features a plurality of characteristics of an occurrence, where a first instance of the occurrence occurred within a first time period of a plurality of time periods; electronically discovering, based on the correspondence, a second instance of the occurrence in an environment during a second time period of the plurality of time periods; and electronically causing, based on the discovery of the second instance of the occurrence, a change in the environment via an electronically-controlled device.