Abstract: In one aspect, a system for auto-tracking with machine-learning trained local-area identifier tracking for radio state control of 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; 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: training an asset tracker location model using at least one machine learning (ML) method to identify a region by building a multi-modality fingerprint of a region, and with the asset tracker location model, automatically identifying a relationship of the region with respect to a location of each asset tracker of the plurality of asset trackers; with each asset tracker, use the relationship of the region with respect to the location of each asset tracker to determine a behavior of each asset tracker without a need turn on a high-power radio in each asset track

Abstract: In one aspect, a computer system for implementing asset tracker safety enhancements in an internet of things network 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: enhance the plurality of asset trackers with a plurality of sensors and a plurality of algorithms to detect and transmit an alert when a hazardous condition is detected, wherein the plurality of asset trackers monitor one or more IoT assets sensor data; and initiate a risk prevention or risk detection warning protocol when a specified probability of a hazardous material is detected by at least one asset tracker of the plurality of asset trackers.

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 t

Abstract: In one aspect, a computerized method for augmented reality tracking of asset trackers comprising: integrating an asset tracker with a specific asset; configuring the asset tracker to communicate a location of the asset tracker to an Augmented Reality (AR) tracking system; and overlaying an AR visualization system overlay digital location data with a real-time camera view of a search area, wherein the search area comprises the specific asset; wherein the AR tracking system comprises an RTL (Real-Time Location) system, with the AR tracking system software: detecting an RTL link of the RTL system is within a range of the specific asset, and automatically launching the AR viewer with an AR view of the RTL link over laying a view of the specific asset.

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

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

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 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 experie

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: 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: 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.

Abstract: Systems and methods are described for managing communications amongst mobile computing units. The invention includes location detection, three-dimensional proximity detection and alert systems for and amongst mobile units, through the utilization of low-power local communications protocols. The systems and methods minimize power consumption by the mobile units, and enable for robust, reconfigurable real-time location and asset tracking.