Abstract: An approach to sensor data is based on scenes. One aspect concerns a computer-implemented method for specifying and obtaining a variety of sensor data and processed sensor data related to a scene. The method incorporates a Scene-based API that uses SceneModes and SceneData. An application requesting sensor data communicates a SceneMode to a group of one or more sensor devices and/or sensor modules via the Scene-based API. The SceneMode determines the SceneData to be captured or provided by the sensor group, which typically includes different types of sensor data related to the Scene and also further processed or analyzed data. The application receives the SceneData from the sensor group via the Scene-based API, with the SceneData organized into SceneShots which are samples of the Scene.

Abstract: Apparatus and methods for reverse identification and authentication are provided. The apparatus and methods may include a server receiving a request from a user device to authenticate an entity, forming a communication channel between the entity and the user device, requesting the entity provide authentication credentials, and authenticating the entity. When the entity is authenticated, the server may notify the user through the authentication channel, a mobile device application, or another method. An entity may proactively authenticate itself to a user through the central server, in anticipation of a communication between the entity and user.

Abstract: A method for reducing a storage of duplicated documents is provided. Methods may include hashing each document stored in the centralized data repository by executing a hashing algorithm on the document, outputting a hash-value and adding the hash-value and a hash pointer to a hash table. Methods may further include crawling the hash table to identify duplicate hash-values. For each hash-value recorded on the hash table two or more times, methods may include combining two or more duplicate hash-values into a cluster and for each cluster identifying, on the hash table, a unique hash-value. For the unique hash-value, methods may include maintaining the unique hash-value on the hash table and maintaining the document corresponding to the unique hash-value in the memory address. For each remaining duplicate hash-value stored in the cluster, deleting the corresponding document from the memory address and store the reference pointer at the memory address.

Abstract: A multi-layer technology stack includes a sensor layer including image sensors, a device layer, and a cloud layer, with interfaces between the layers. A method to curate different custom workflows for multiple applications include the following. Requirements for custom sets of data packages for the applications is received. The custom set of data packages include sensor data packages (e.g., SceneData) and contextual metadata packages that contextualize the sensor data packages (e.g., SceneMarks). Based on the received requirements and capabilities of components in the technology stack, the custom workflow for that application is deployed. This includes a selection, configuration and linking of components from the technology stack. The custom workflow is implemented in the components of the technology stack by transmitting workflow control packages directly and/or indirectly via the interfaces to the different layers.

Abstract: A multi-layer technology stack includes a sensor layer including image sensors, a device layer, and a cloud layer, with interfaces between the layers. A method to curate different custom workflows for multiple applications include the following. Requirements for custom sets of data packages for the applications is received. The custom set of data packages include sensor data packages (e.g., SceneData) and contextual metadata packages that contextualize the sensor data packages (e.g., SceneMarks). Based on the received requirements and capabilities of components in the technology stack, the custom workflow for that application is deployed. This includes a selection, configuration and linking of components from the technology stack. The custom workflow is implemented in the components of the technology stack by transmitting workflow control packages directly and/or indirectly via the interfaces to the different layers.

Abstract: Apparatus and methods for reverse identification and authentication are provided. The apparatus and methods may include a server receiving a request from a user device to authenticate an entity, forming a communication channel between the entity and the user device, requesting the entity provide authentication credentials, and authenticating the entity. When the entity is authenticated, the server may notify the user through the authentication channel, a mobile device application, or another method. An entity may proactively authenticate itself to a user through the central server, in anticipation of a communication between the entity and user.

Abstract: A method for reducing a storage of duplicated documents is provided. Methods may include hashing each document stored in the centralized data repository by executing a hashing algorithm on the document, outputting a hash-value and adding the hash-value and a hash pointer to a hash table. Methods may further include crawling the hash table to identify duplicate hash-values. For each hash-value recorded on the hash table two or more times, methods may include combining two or more duplicate hash-values into a cluster and for each cluster identifying, on the hash table, a unique hash-value. For the unique hash-value, methods may include maintaining the unique hash-value on the hash table and maintaining the document corresponding to the unique hash-value in the memory address. For each remaining duplicate hash-value stored in the cluster, deleting the corresponding document from the memory address and store the reference pointer at the memory address.

Abstract: Apparatus and methods for utilizing non-fungible tokens (“NFTs”) as universal digital identification are provided. A user may be assigned an NFT on a distributed ledger attesting to the user's identity. The user may request to be authenticated. The user may be prompted to transfer the NFT to a smart contract on the distributed ledger. An authentication program may check the NFT and the NFT's history on the ledger to determine that the NFT belongs to the user. When the NFT belongs to the user, the authentication program may authenticate the user. The smart contract may transfer the NFT back to the user, so that the user may use the NFT again to be authenticated.

Abstract: A multi-layer technology stack includes a sensor layer including image sensors, a device layer, and a cloud layer, with interfaces between the layers. A method to curate different custom workflows for multiple applications include the following. Requirements for custom sets of data packages for the applications is received. The custom set of data packages include sensor data packages (e.g., SceneData) and contextual metadata packages that contextualize the sensor data packages (e.g., SceneMarks). Based on the received requirements and capabilities of components in the technology stack, the custom workflow for that application is deployed. This includes a selection, configuration and linking of components from the technology stack. The custom workflow is implemented in the components of the technology stack by transmitting workflow control packages directly and/or indirectly via the interfaces to the different layers.

Abstract: An approach to sensor data is based on scenes. One aspect concerns a computer-implemented method for specifying and obtaining a variety of sensor data and processed sensor data related to a scene. The method incorporates a Scene-based API that uses SceneModes and SceneData. An application requesting sensor data communicates a SceneMode to a group of one or more sensor devices and/or sensor modules via the Scene-based API. The SceneMode determines the SceneData to be captured or provided by the sensor group, which typically includes different types of sensor data related to the Scene and also further processed or analyzed data. The application receives the SceneData from the sensor group via the Scene-based API, with the SceneData organized into SceneShots which are samples of the Scene.

Abstract: Security and access control is provided for sensor devices, the data captured by sensor devices, and the results of processing and analyzing that data. In one aspect, SceneData related to a Scene is requested from a sensor-side technology stack and at least some of the SceneData is secured, for example by encryption. Different SceneData can be secured separately and at different levels of security, thus providing fine-grained security of the SceneData. In yet another aspect, data security is implemented by a separate privacy management system. In yet another aspect, sensor devices themselves are secured against external network threats. The sensor device includes an execution environment and a separate network management layer that secures the execution environment against threats from the external network. In one implementation, the sensor device is partitioned into a trusted region and a non-trusted region, and the network security stack is implemented in the trusted region.

Abstract: An approach to sensor data is based on scenes. One aspect concerns a computer-implemented method for specifying and obtaining a variety of sensor data and processed sensor data related to a scene. The method incorporates a Scene-based API that uses SceneModes and SceneData. An application requesting sensor data communicates a SceneMode to a group of one or more sensor devices and/or sensor modules via the Scene-based API. The SceneMode determines the SceneData to be captured or provided by the sensor group, which typically includes different types of sensor data related to the Scene and also further processed or analyzed data. The application receives the SceneData from the sensor group via the Scene-based API, with the SceneData organized into SceneShots which are samples of the Scene.

Abstract: Automated discovery of the relative positioning of a network of cameras that view a physical environment. The automated discovery is based on comparing TimeLines for the cameras. The TimeLines are time-stamped data relating to the camera's view, for example a sequence of time stamps and corresponding images captured by a camera at those time stamps. In one approach, the relative positioning is represented by a proximity graph of nodes connected by edges. The nodes represent spaces in the physical environment, and each edge between two nodes represents a pathway between the spaces represented by the two nodes.

Abstract: Security and access control is provided for sensor devices, the data captured by sensor devices, and the results of processing and analyzing that data. In one aspect, SceneData related to a Scene is requested from a sensor-side technology stack and at least some of the SceneData is secured, for example by encryption. Different SceneData can be secured separately and at different levels of security, thus providing fine-grained security of the SceneData. In yet another aspect, data security is implemented by a separate privacy management system. In yet another aspect, sensor devices themselves are secured against external network threats. The sensor device includes an execution environment and a separate network management layer that secures the execution environment against threats from the external network. In one implementation, the sensor device is partitioned into a trusted region and a non-trusted region, and the network security stack is implemented in the trusted region.

Abstract: The present disclosure overcomes the limitations of the prior art by providing approaches to marking points of interest in scenes. In one aspect, a Scene of interest is identified based on SceneData provided by a sensor-side technology stack that includes a group of one or more sensor devices. The SceneData is based on a plurality of different types of sensor data captured by the sensor group, and typically requires additional processing and/or analysis of the captured sensor data. A SceneMark marks the Scene of interest or possibly a point of interest within the Scene.

Abstract: A multi-layer technology stack includes a sensor layer including image sensors, a device layer, and a cloud layer, with interfaces between the layers. Each layer contains multiple nodes. A method to develop contextual understanding of related events detected by the nodes includes the following. Sensor nodes capture sensor data, including multiple image sensors capturing images. Various nodes receive sensor data and/or metadata packages from other nodes, analyze the received data for events, and generate and/or augment metadata packages describing the detected events. The analysis includes image understanding. Events that are related are identified, based on the metadata packages describing the events. A contextual understanding of the related events is developed, based on analysis of the metadata packages describing the events.

Abstract: Automated discovery of the relative positioning of a network of cameras that view a physical environment. The automated discovery is based on comparing TimeLines for the cameras. The TimeLines are time-stamped data relating to the camera's view, for example a sequence of time stamps and corresponding images captured by a camera at those time stamps. In one approach, the relative positioning is represented by a proximity graph of nodes connected by edges. The nodes represent spaces in the physical environment, and each edge between two nodes represents a pathway between the spaces represented by the two nodes.

Abstract: A multi-layer technology stack includes a sensor layer including image sensors, a device layer, and a cloud layer, with interfaces between the layers. Functions within the stack are represented as nodes. Various nodes receive sensor data and/or metadata packages from other nodes, analyze the received data for events, and generate and/or augment metadata packages describing the detected events. The analysis includes AI functions, such as machine learning and image understanding. The overall workflow to develop a contextual understanding of the captured images is represented as a multi-layer graph of interconnected nodes, where some of the nodes perform the AI tasks. The graph is automatically reconfigured in response to outcomes of the AI analysis.

Abstract: A multi-layer technology stack includes a sensor layer including image sensors, a device layer, and a cloud layer, with interfaces between the layers. A method to curate different custom workflows for multiple applications include the following. Requirements for custom sets of data packages for the applications is received. The custom set of data packages include sensor data packages (e.g., SceneData) and contextual metadata packages that contextualize the sensor data packages (e.g., SceneMarks). Based on the received requirements and capabilities of components in the technology stack, the custom workflow for that application is deployed. This includes a selection, configuration and linking of components from the technology stack. The custom workflow is implemented in the components of the technology stack by transmitting workflow control packages directly and/or indirectly via the interfaces to the different layers.

Abstract: An approach to sensor data is based on scenes. One aspect concerns a computer-implemented method for specifying and obtaining a variety of sensor data and processed sensor data related to a scene. The method incorporates a Scene-based API that uses SceneModes and SceneData. An application requesting sensor data communicates a SceneMode to a group of one or more sensor devices and/or sensor modules via the Scene-based API. The SceneMode determines the SceneData to be captured or provided by the sensor group, which typically includes different types of sensor data related to the Scene and also further processed or analyzed data. The application receives the SceneData from the sensor group via the Scene-based API, with the SceneData organized into SceneShots which are samples of the Scene.