Abstract:
In one aspect, a system for auto-tracking with just-in-time training and goal seeking AI agents comprising: a plurality of asset trackers, wherein each asset tracker tracks one or more IoT assets and obtains a set of IoT data; one or more communications hubs; a base station; one or more communication networks; wherein each asset tracker is configured to be in communication with a base station and one or more of the communications hubs; wherein in the one or more communications hubs are configured to be in communication with one or more of the mobile units, and the one or more network; wherein the base station is configured to be in communication with the plurality of asset trackers; a server computing device configured to be in communication with the one or more networks, wherein the server computing device is further configured to implement the following logic: wherein one or more asset trackers transmit raw data directly to the server computing device, instead of device ML model-derived inference or device computed data, storing the raw data of the one or more asset trackers in the server computing device without analytical computation; receiving a user query; initiating a just-in-time process to find an answer to the user query; communicating the question to an AI agent in the server computing device; with the AI agent: seeking a goal of answering the question, by breaking the question down into multiple steps, and executing the multiple steps until the goal is reached.
Abstract:
In one aspect, a system of an adaptive asset tracking insurance rates for IoT asset tracking comprising: a plurality of asset trackers, wherein each asset tracker tracks one or more IoT assets and obtains a set of IoT data; one or more communications hubs; a base station; one or more communication networks; wherein each asset tracker is configured to be in communication with a base station and one or more of the communications hubs; wherein in the one or more communications hubs are configured to be in communication with one or more of the mobile units, and the one or more network; wherein the base station is configured to be in communication with the plurality of asset trackers; a server computing device configured to be in communication with the one or more networks, wherein the server computing device is further configured to implement the following logic: obtain data from the plurality of asset trackers, and dynamically issue an insurance coverage cost of each IoT asset tracked by the plurality of asset trackers.
Abstract:
A computerized method of an integrated microphone to monitor environmental conditions of battery-operated asset tracker comprising: integrating one or more microphones into each asset tracker of a cluster of asset trackers; using the one or more microphones to monitor an environmental condition of a battery-operated asset tracker; correlate a set of sound data received form the one or more microphones with another set of data from other sensors of each asset tracker of the cluster of asset trackers; using an IMU (Inertial Management Unit) to determine an orientation in space of each asset tracker; with the data of the one or more microphones and the orientation in space of each asset tracker, determining a direction of a sound in a proximity of the cluster of asset trackers; and providing a pre-learned machine learning sound identification model to identify a sound source of the sound.
Abstract:
Methods and apparatus for enhancing efficiency (e.g., reducing power consumption and bus activity) in a data bus. In an exemplary embodiment, a client-driven host device state machine switches among various states, each comprising a different polling frequency. A client device on the data bus (e.g., serial bus) checks for non-productive periods of polling activity, and upon discovering such a period, informs the host. The state machine then alters its polling scheme; e.g., switches to a lower state comprising a reduced polling frequency, and polling continues at this reduced frequency. In one variant, the client device continuously monitors itself to determine whether it has any data to transmit to a host device and if so, the host is informed, and the state machine restarts (e.g., to its highest polling state). By eliminating extraneous polling, power consumption and serial bus activity is optimized, potentially on both the host and the client.
Abstract:
One embodiment of the present invention provides a universal remote control, which includes a display screen and a user input mechanism. The universal remote control also includes a processing unit that is configured to display information on the display screen and to accept selection data from the user input mechanism. The universal remote control additionally includes a wireless communication mechanism that is configured to provide communications between the processing unit and an appliance or computer program running on a computer system. The appliance provides information to be displayed on the display screen, and information entered through the user input mechanism is communicated to the appliance. Since the appliance provides the information to be displayed on the display screen and also interprets the entries on the input mechanism, the universal remote control needs no special knowledge about the appliance.
Abstract:
In one aspect, a system for application-free tracker interfaces and auto-tracking for asset trackers comprising: a plurality of asset trackers, wherein each asset tracker tracks one or more IoT assets and obtains a set of IoT data; one or more communications hubs; a server computing device configured to be in communication with the one or more networks, wherein the server computing device is further configured to implement the following logic: with a underlying tracking system operative in the server computing device, wherein the underlying tracking system manages a goal-seeking large language model (LLM) that is trained to translate a natural language into specific tracker device configurations in order to fulfill set goals, and wherein the LLM receives a user's spoken or written request and: constructs a data display based on a user's spoken or written request, answers a user inquiry in the user's spoken or written request with the underlying tracking system facilitates answering to enhance the user experience, and automatically adjusts an asset tracker setting of the plurality of asset trackers based on the user's spoken or written request.
Abstract:
In one aspect, a system for auto-tracking with just-in-time training and goal seeking AI agents comprising: a plurality of asset trackers, wherein each asset tracker tracks one or more IoT assets and obtains a set of IoT data; one or more communications hubs; a base station; one or more communication networks; wherein each asset tracker is configured to be in communication with a base station and one or more of the communications hubs; wherein in the one or more communications hubs are configured to be in communication with one or more of the mobile units, and the one or more network; wherein the base station is configured to be in communication with the plurality of asset trackers; a server computing device configured to be in communication with the one or more networks, wherein the server computing device is further configured to implement the following logic: wherein one or more asset trackers transmit raw data directly to the server computing device, instead of device ML model-derived inference or device computed data, storing the raw data of the one or more asset trackers in the server computing device without analytical computation; receiving a user query; initiating a just-in-time process to find an answer to the user query; communicating the question to an AI agent in the server computing device; with the AI agent: seeking a goal of answering the question, by breaking the question down into multiple steps, and executing the multiple steps until the goal is reached.
Abstract:
In one aspect, a computer system for implementing an asset tracker-based virtual tether comprising: a plurality of asset trackers, wherein each asset tracker tracks one or more IoT assets and obtains a set of IoT data; a server computing device configured to be in communication with the one or more networks, wherein the server computing device is further configured to implement the following logic: attach an asset tracker of the plurality of asset tracker to an animal to be tracked; and use precision location technology in a training regime of animals and the recording of animal interactions; wherein an asset tracker tether device in the asset tracker is equipped with a precision location and a feedback mechanism that reinforces for the animal a need to remain with a pre-specified boundary.
Abstract:
In one aspect, a system of a live-stock carbon footprint assessment in an internet of things network tracking comprising: a plurality of asset trackers, wherein each asset tracker tracks one or more IoT assets and obtains a set of IoT data; one or more communications hubs; a base station; one or more communication networks; wherein each asset tracker is configured to be in communication with a base station and one or more of the communications hubs; a server computing device configured to be in communication with the one or more networks, wherein the server computing device is further configured to implement the following logic: periodically received sensor data from an attached live-stock carbon footprint tracker that is attached to a live-stock entity; and use the sensor data to calculate a livestock carbon footprint.
Abstract:
In one aspect, a method for detecting lithium polymer battery swell due to exposure to heat or battery aging comprising: integrating an integrated force sensor with a lithium polymer battery; monitoring a lithium polymer battery swell of the lithium polymer battery with the integrated force sensor; with the integrated force sensor, detecting the lithium polymer battery swell beyond a specific swelling threshold; and determining that the lithium polymer battery swell due to exposure to heat or battery aging.