# US 2025/0085751 A1 · Thermal Power Budget Optimization Method, Heating device and Thermal Power Budget Optimization System > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0085751 A1 - Document ID: US20250085751A1 - Status: Published application - Kind: A1 - Application number: 18/788,086 - Filed: 2024-07-29 - Published: 2025-03-13 - Earliest claimed: 2023-09-07 - Assignees: Mediatek Inc. - Applicants: Mediatek Inc. - Inventors: Yu-Chia Chang; Chien-Chih Huang; Ta-Chang Liao; Chia-Feng Yeh; Ching-Lin Hsiao; Wei-Te Wu - Web page: https://patentgrep.com/patent/US20250085751A1 ## CPC - G06F 1/203: {for portable computers, e.g. for laptops} - G06F 1/3234: Power saving characterised by the action undertaken - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A thermal power budget optimization method includes acquiring sensor log information from a plurality of sensors of a heating device, generating a virtual surface temperature of the heating device according to the sensor log information, setting a target surface temperature of the heating device, and dynamically adjusting a thermal power budget of the heating device according to the virtual surface temperature and the target surface temperature over time. ## Claims ### Claim 1 A thermal power budget optimization method comprising: acquiring sensor log information from a plurality of sensors of a heating device; generating a virtual surface temperature of the heating device according to the sensor log information; setting a target surface temperature of the heating device; and dynamically adjusting a thermal power budget of the heating device according to the virtual surface temperature and the target surface temperature over time. ### Claim 2 (depends on 1) The method of claim 1, further comprising: acquiring a real surface temperature of the heating device; and executing model training according to the sensor log information and the real surface temperature for obtaining a trained surface temperature prediction model of the heating device. ### Claim 3 (depends on 2) The method of claim 2, wherein generating the virtual surface temperature of the heating device according to the sensor log information comprises: inputting the sensor log information to the trained surface temperature prediction model to generate the virtual surface temperature. ### Claim 4 (depends on 3) The method of claim 3, wherein the trained surface temperature prediction model is configured to ensure the difference between the virtual surface temperature and the real surface temperature less than a threshold, or the trained surface temperature prediction model is configured to ensure the virtual surface temperature closest to the real surface temperature compared to other training models. ### Claim 5 (depends on 1) The method of claim 1, wherein dynamically adjusting a thermal power budget comprises generating a new thermal power budget according to a sustainable power budget and a function-estimated power budget, the sustainable power budget is a pre-determined power budget, and the function-estimated power budget is a proportional integral derivative (PID) function output generated according to the virtual surface temperature and the target surface temperature. ### Claim 6 (depends on 5) The method of claim 5, wherein the sustainable power budget is obtained according to a real surface temperature. ### Claim 7 (depends on 5) The method of claim 5, wherein when the virtual surface temperature is greater than the target surface temperature, the function-estimated power budget is decreased, and when the virtual surface temperature is smaller than the target surface temperature, the function-estimated power budget is increased. ### Claim 8 (depends on 1) The method of claim 1, wherein the thermal power budget is dynamically adjusted during a turbo stage, a transition stage, or a sustained stage, the transition stage follows the turbo stage, and the sustained stage follows the transition stage. ### Claim 9 (depends on 8) The method of claim 8, wherein a first thermal power budget in the turbo stage is greater than a second thermal power budget in the transition stage, and the second thermal power budget in the transition stage is greater than a third thermal power budget in the sustained stage. ### Claim 10 A heating device, comprises: a plurality of sensors; at least one heating source; a storage device; a cache memory; a processor coupled to the plurality of sensors, the at least one heating source, the storage device, and the cache memory; and a case configured to cover the plurality of sensors, the at least one heating source, the storage device, the cache memory, and the processor; wherein the processor is configured to acquire sensor log information from the plurality of sensors of the heating device, the processor is further configured to generate a virtual surface temperature of the heating device according to the sensor log information, the processor is further configured to set a target surface temperature of the heating device, the processor is further configured to dynamically adjust a thermal power budget of the heating device according to the virtual surface temperature and the target surface temperature over time, and the sensor log information, the virtual surface temperature, the target surface temperature, and the thermal power budget are buffered in the cache memory. ### Claim 11 (depends on 10) The heating device of claim 10, wherein the storage device is configured to receive a trained surface temperature prediction model from a memory outside of the heating device, the processor is further configured to control the cache memory to buffer the trained surface temperature prediction model from the storage device, the processor is further configured to input the sensor log information to the trained surface temperature prediction model to generate the virtual surface temperature. ### Claim 12 (depends on 11) The heating device of claim 11, wherein the trained surface temperature prediction model is configured to ensure a difference between the virtual surface temperature and the real surface temperature less than a threshold, or the trained surface temperature prediction model is configured to ensure the virtual surface temperature closest to the real surface temperature compared to other training models. ### Claim 13 (depends on 10) The heating device of claim 10, wherein the processor is further configured to generate a new thermal power budget according to a sustainable power budget and a function-estimated power budget, the sustainable power budget is a pre-determined power budget, and the function-estimated power budget is a proportional integral derivative (PID) function output generated according to the virtual surface temperature and the target surface temperature. ### Claim 14 (depends on 13) The heating device of claim 13, wherein the sustainable power budget is obtained according to a real surface temperature. ### Claim 15 (depends on 13) The heating device of claim 13, wherein when the virtual surface temperature is greater than the target surface temperature, the function-estimated power budget is decreased, and when the virtual surface temperature is smaller than the target surface temperature, the function-estimated power budget is increased. ### Claim 16 (depends on 10) The heating device of claim 10, wherein the thermal power budget is dynamically adjusted by the processor during a turbo stage, a transition stage, or a sustained stage, the transition stage follows the turbo stage, and the sustained stage follows the transition stage. ### Claim 17 (depends on 16) The heating device of claim 16, wherein a first thermal power budget in the turbo stage is greater than a second thermal power budget in the transition stage, and the second thermal power budget in the transition stage is greater than a third thermal power budget in the sustained stage. ### Claim 18 (depends on 10) The heating device of claim 10, wherein the heating device is a foldable phone, or a notebook. ### Claim 19 A thermal power budget optimization system comprising a heating device, a memory, and a first processor coupled to the heating device and the memory, wherein the heating device comprises: a plurality of sensors; at least one heating source; a storage device; a cache memory; a second processor coupled to the plurality of sensors, the at least one heating source, the storage device, and the cache memory; and a case configured to cover the plurality of sensors, the at least one heating source, the storage device, the cache memory, and the second processor; wherein the second processor is configured to acquire sensor log information from the plurality of sensors of the heating device, the second processor is further configured to generate a virtual surface temperature of the heating device according to the sensor log information, the second processor is further configured to set a target surface temperature of the heating device, the second processor is further configured to dynamically adjust a thermal power budget of the heating device according to the virtual surface temperature and the target surface temperature over time, and the sensor log information, the virtual surface temperature, the target surface temperature, and the thermal power budget are buffered in the cache memory. ### Claim 20 (depends on 19) The system of claim 19, wherein the first processor is configured to acquire a real surface temperature of the case of the heating device, the first processor is further configured to execute model training according to the sensor log information and the real surface temperature for obtaining a trained surface temperature prediction model of the heating device, and the trained surface temperature prediction model is saved in the memory; wherein the storage device is configured to receive the trained surface temperature prediction model from the memory, the second processor is further configured to control the cache memory to buffer the trained surface temperature prediction model from the storage device, the second processor is further configured to input the sensor log information to the trained surface temperature prediction model to generate the virtual surface temperature.