Abstract: A computing system enables individuals and organizations to self-insure a benefit, e.g., a dental plan benefit. The system leverages a patient-centric data construct that evolves in a multi-dimensional manner as a patient continuously interacts with the system, its providers, participating organizations, etc. Using the patient-centric information repositories about the various participants (and their interactions), the system applies machine learning to the data to create data models that, in turn, provide valuable insights, or to otherwise enable more-efficient settlement processing. For example, a model may be used to determine an optimal patient-dentist match (by looking at utilization best provider outcomes), or a dentist over-provision of certain services (indicated by provider codes, thereby possibly indicative of fraud). Using patient-centric data modeling, the system determines if, when and how an organization can reduce expenses (e.g.

Abstract: A computing system enables individuals and organizations to self-insure a benefit, e.g., a dental plan benefit. The system leverages a patient-centric data construct that evolves in a multi-dimensional manner as a patient continuously interacts with the system, its providers, participating organizations, etc. Using the patient-centric information repositories about the various participants (and their interactions), the system applies machine learning to the data to create data models that, in turn, provide valuable insights, or to otherwise enable more-efficient settlement processing. For example, a model may be used to determine an optimal patient-dentist match (by looking at utilization best provider outcomes), or a dentist over-provision of certain services (indicated by provider codes, thereby possibly indicative of fraud). Using patient-centric data modeling, the system determines if, when and how an organization can reduce expenses (e.g.

Abstract: The BIN ENABLED DATA OBJECT ENCRYPTION AND STORAGE APPARATUSES, METHODS AND SYSTEMS (“MBIN”) transforms bin creation requests, bin templates and unencrypted object storage inputs, using MBIN components, into encrypted object storage bins and encrypted bin objects. A method comprises the creation of encrypted object storage bins, the storage of encrypted bin objects, and an application framework that allows privileged applications to leverage the capabilities of and data stored in the encrypted object storage bins.

Abstract: The BIN ENABLED DATA OBJECT ENCRYPTION AND STORAGE APPARATUSES, METHODS AND SYSTEMS (“MBIN”) transforms bin creation requests, bin templates and unencrypted object storage inputs, using MBIN components, into encrypted object storage bins and encrypted bin objects. A method comprises the creation of encrypted object storage bins, the storage of encrypted bin objects, and an application framework that allows privileged applications to leverage the capabilities of and data stored in the encrypted object storage bins.

Abstract: The BIN ENABLED DATA OBJECT ENCRYPTION AND STORAGE APPARATUSES, METHODS AND SYSTEMS (“MBIN”) transforms bin creation requests, bin templates and unencrypted object storage inputs, using MBIN components, into encrypted object storage bins and encrypted bin objects. A method comprises the creation of encrypted object storage bins, the storage of encrypted bin objects, and an application framework that allows privileged applications to leverage the capabilities of and data stored in the encrypted object storage bins.

Abstract: The BIN ENABLED DATA OBJECT ENCRYPTION AND STORAGE APPARATUSES, METHODS AND SYSTEMS (“MBIN”) transforms bin creation requests, bin templates and unencrypted object storage inputs, using MBIN components, into encrypted object storage bins and encrypted bin objects. A method comprises the creation of encrypted object storage bins, the storage of encrypted bin objects, and an application framework that allows privileged applications to leverage the capabilities of and data stored in the encrypted object storage bins.

Abstract: Methods and systems for interpreting and processing data streams from a plurality of sensors on a motion-capture device are described. In various embodiments, an engine module of the system receives a raw input data stream comprising motion and non-motion data. Metadata is associated with data segments within the input data stream to produce a stream of data profiles. In various embodiments, an interpreter converts received data profiles into non-contextual tokens and/or commands recognizable by an application adapted for external control. In various embodiments, a parser converts received non-contextual tokens into contextual tokens and/or commands recognizable by an application adapted for external control. In various embodiments, the system produces commands based upon the non-contextual and/or contextual tokens and provides the commands to the application. The application can be a video game, software operating on a computer, or a remote-controlled apparatus.

Abstract: An apparatus, system and method for turning physical motion into an interpretable language which when formed into sentences reproduces the original motion. This system may be referred to herein as a “Motion Description System.” Physical motion is defined as motion in one, two or three dimensions, with anywhere from 1 to 6 degrees of freedom. Language is defined as meaning applied to an abstraction.

Abstract: An apparatus, system and method for turning physical motion into an interpretable language which when formed into sentences reproduces the original motion. This system may be referred to herein as a “Motion Description System.” Physical motion is defined as motion in one, two or three dimensions, with anywhere from 1 to 6 degrees of freedom. Language is defined as meaning applied to an abstraction.