# US 10,771,995 B2 · Systems and methods for determining operational status of functional components of a wireless signal transmission environment > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,771,995 B2 - Document ID: US10771995B2 - Status: Granted - Kind: B2 - Application number: 16/377,972 - Filed: 2019-04-08 - Published: 2019-10-03 - Granted: 2020-09-08 - Earliest claimed: 2017-05-19 - Assignees: Ossia Inc. - Applicants: Ossia Inc. - Inventors: Hatem Ibrahim Zeine; Ahmad Moghaddam - Web page: https://patentgrep.com/patent/US10771995B2 ## CPC - H04W 24/08: Testing, {supervising or monitoring} using real traffic - G06T 7/001: {using an image reference approach} - H04W 8/10: between location register and external networks - H04W 24/02: Arrangements for optimising operational condition - H04W 40/04: based on wireless node resources - H04W 40/06: based on characteristics of available antennas - H04W 40/246: {Connectivity information discovery} - H04W 76/25: Maintenance of established connections - G06T 2207/10048: Infrared image - Y02D 30/70: in wireless communication networks - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - Y02D: CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE ## Abstract For determining operational status of components of a wireless signal transmission environment, a signal is received from a client power receiver (CPR), and at least one waveform characteristic value of the signal is calculated. Based on the determined waveform characteristic value(s), a current transceiver system operational status is computed for the CPR at a current location. The determined current transceiver system operational status is compared with a prior-stored last known good transceiver system operational status for the CPR at the current location, and it is determined if, as a result of the comparing, the current transceiver system operational status matches the prior-stored last known good transceiver system operational status for the CPR at the current location. Determining a presence or an absence of the match facilitates identifying one of a presence and an absence of: functional problem(s) in: at least one of the transceiver system and the CPR. ## Claims ### Claim 1 A method for determining an operational status of at least one component in a wireless signal transmission environment, the method comprising: generating, during a learning phase, a predictive framework that models behavior of one or more components of the wireless signal transmission environment, wherein the predictive framework includes one or more machine learning systems or algorithms configured to predict expected behavior of the one or more components including expected waveform characteristics; determining, during a tracking and updating phase, a value of at least one waveform characteristic of a received signal; determining, using the predictive framework, an expected value of the at least one waveform characteristic of the received signal; comparing the value of the at least one waveform characteristic of the received signal with the expected value of the at least one waveform characteristic of the received signal; and determining the operational status of the at least one components of the wireless signal transmission environment based on the comparison. ### Claim 2 (depends on 1) The method of claim 1, wherein the received signal comprises a beacon signal. ### Claim 3 (depends on 1) The method of claim 1, wherein the at least one component comprises a transceiver system or a client power receiver wirelessly communicatively coupled to the transceiver system. ### Claim 4 (depends on 1) The method of claim 1, further comprising: tracking, during the tracking and updating phase, locations of client power receivers in the wireless signal transmission environment. ### Claim 5 (depends on 4) The method of claim 4, further comprising: computing a current location of the client power receiver in the wireless signal transmission environment based at least in part on the determined value of the at least one waveform characteristic. ### Claim 6 (depends on 5) The method of claim 5, further comprising: detecting an anomalous or uncharacteristic event related to the operational status of the operational status of the at least one component of the wireless signal transmission environment. ### Claim 7 (depends on 6) The method of claim 6, wherein detecting the anomalous or uncharacteristic event further comprises: comparing the operational status of the at least one component of the wireless signal transmission environment with a prior-stored last known good operational status of the client power receiver at the current location; and determining, based on the comparison, one of a presence or an absence of a match of the operational status of the at least one component of the wireless signal transmission environment and the prior-stored last known good operational status of the client power receiver at the current location, wherein the presence or the absence of the match is indicative of at least one functional problem in at least one of the transceiver system and the client power receiver. ### Claim 8 (depends on 6) The method of claim 6, further comprising: responsive to detecting the anomalous or uncharacteristic event, implementing one or more corrective actions. ### Claim 9 (depends on 1) The method of claim 1, wherein the predictive framework utilizes supervised learning techniques and leverages classification algorithms to identify patterns and predict the expected behaviors of the one or more components of the wireless signal transmission environment. ### Claim 10 (depends on 1) The method of claim 1, wherein the signal is received at a plurality of elements of an antenna array from the client power receiver. ### Claim 11 An apparatus comprising: one or more computer readable storage media; and program instructions stored on the one or more computer readable storage media for determining an operational status of at least one component in a wireless signal transmission environment, wherein the program instruction, when executed by a processing system, direct the processing system to: generate, during a learning phase, a predictive framework that models behavior of one or more components of the wireless signal transmission environment, wherein the predictive framework includes one or more machine learning systems or algorithms configured to predict expected behavior of the one or more components including expected waveform characteristics; determine, during a tracking and updating phase, a value of at least one waveform characteristic of a received signal; determine, using the predictive framework, an expected value of the at least one waveform characteristic of the received signal; compare the value of the at least one waveform characteristic of the received signal with the expected value of the at least one waveform characteristic of the received signal; and determine the operational status of the at least one component of the wireless signal transmission environment based on the comparison. ### Claim 12 (depends on 11) The apparatus of claim 11, wherein the received signal comprises a beacon signal. ### Claim 13 (depends on 11) The apparatus of claim 11, wherein the at least one component comprises a transceiver system or a client power receiver wirelessly communicatively coupled to the transceiver system. ### Claim 14 (depends on 11) The apparatus of claim 11, wherein the program instructions, when executed by the processing system, further direct the processing system to: track, during the tracking and updating phase, locations of client power receivers in the wireless signal transmission environment. ### Claim 15 (depends on 14) The apparatus of claim 14, wherein the program instructions, when executed by the processing system, further direct the processing system to: compute a current location of the client power receiver in the wireless signal transmission environment based at least in part on the determined value of the at least one waveform characteristic. ### Claim 16 (depends on 15) The apparatus of claim 15, wherein the program instructions, when executed by the processing system, further direct the processing system to: detect an anomalous or uncharacteristic event related to the operational status of the operational status of the at least one component of the wireless signal transmission environment. ### Claim 17 (depends on 16) The apparatus of claim 16, wherein the program instructions, when executed by the processing system, further direct the processing system to: compare the operational status of the at least one component of the wireless signal transmission environment with a prior-stored last known good operational status of the client power receiver at the current location; and determine, based on the comparison, one of a presence or an absence of a match of the operational status of the at least one component of the wireless signal transmission environment and the prior-stored last known good operational status of the client power receiver at the current location, wherein the presence or the absence of the match is indicative of at least one functional problem in at least one of the transceiver system and the client power receiver. ### Claim 18 (depends on 14) The apparatus of claim 14, wherein the program instructions, when executed by the processing system, further direct the processing system to: responsive to detecting the anomalous or uncharacteristic event, implement one or more corrective actions. ### Claim 19 (depends on 11) The apparatus of claim 11, wherein the predictive framework utilizes supervised learning techniques and leverages classification algorithms to identify patterns and predict the expected behaviors of the one or more components of the wireless signal transmission environment. ### Claim 20 A wireless power transmission system comprising: control circuitry configured to: generate, during a learning phase, a predictive framework that models behavior of one or more components of the wireless signal transmission environment, wherein the predictive framework includes one or more machine learning systems or algorithms configured to predict expected behavior of the one or more components including expected waveform characteristics; determine, during a tracking and updating phase, a value of at least one waveform characteristic of a received signal; determine, using the predictive framework, an expected value of the at least one waveform characteristic of the received signal; compare the value of the at least one waveform characteristic of the received signal with the expected value of the at least one waveform characteristic of the received signal; and determine the operational status of the at least one components of the wireless signal transmission environment based on the comparison.