DETAILED ACTIONS
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This office action is in response to the amendments/arguments submitted by the Applicant(s) on 02/24/2026.
Status of the Claims
Claims 1-4, 6-11, and 13-14 are pending.
Claims 1 and 8 are amended.
Claims 5 and 12 are canceled.
Response to Arguments
Rejections Under 35 U.S.C. §103
Applicant’s Argument:
Applicant argues in pages 7-8 in the remarks, filed 02/24/2026 with respect to the rejection(s) of Claims under 35 U.S.C. that
“No teaching of the claimed simulated distributions
Turning now to the rejections, there is no teaching in Gu of the claimed simulated
temperature distributions, where each of the plurality of simulated temperature distributions relate to a different situation with respect to simulated operation of a simulated device, and each
different situation involves specific electrical characteristics and connectivity patterns of the simulated device. As mentioned, specific currents may be applied and/or specific electrical connections may be loose for a given situation. Any simulation in Gu simply does not relate to these types of situations.
The other references are silent as to these features.
Since at least one claim feature is not taught or suggested by the art, the claims are
allowable for these reasons.”
Examiner’s Response:
Applicant's arguments, see remarks page 7-8, filed 02/24/2026.
with respect to the rejection(s) of Claims under 35 U.S.C. 103 has been considered, and are not persuasive.
Examiner respectfully disagrees with the argument. Gu teaches determination of Hotspots, and off set values are simulated under different operating conditions. See (Gu, Figure 1, [0026]), The offset temperature values are measured under different “operating conditions” The operating condition could be various criteria”, one of the criteria is considering the rail power, work load see [0017] “The distribution evaluator 106 may determine a type of temperature or workload distribution currently being executed by the unit(s) 110 of an IC(s). For example, a unit(s) 110 or portion thereof may be capable of performing a certain amount of work or processing, and the current amount of work or processing may be compared against a capability of the unit(s) 110 to determine the utilization thereof. As another example, rail power consumption of the unit(s) 110 of the IC(s) may be monitored to determine the workload and/or temperature distributions across the unit(s) of 110 and/or the IC” NOTE: power consumption of the IC, work load processing are different factors/ operation conditions that affect the heat generated and respective temperature distribution. Therefore, the simulated off set value corresponds to each difference operating conditions of the IC).
Therefore, applicant’s argument is not persuasive. The rejections are maintained.
Applicant’s Argument:
Applicant argues in pages 7-8 in the remarks, filed 02/24/2026 with respect to the rejection(s) of Claims under 35 U.S.C. that
“No teaching of comparing measured distributions with simulated distributions
There is also no teaching in Gu of comparing measured distributions with a selected
simulated distribution as is recited in the claims.
First, Gu states that simulations can be used to determine hotspots ("to determine the hotspots 250, thermal simulations of a virtual representation of the IC (e.g., prior to
manufacturer) ... may be executed." Gu, paragraph [0026]).
Gu also teaches (at his paragraph [0026]) that "offset values may also be determined via
thermal simulation and/or observation (using infrared camera to look at hot spots in comparison
to thermal sensor I 04 locations)." The Office Action asserted that this language meant that
"various temperature data for different locations generates a temperature distribution map
periodically and simulated data is compared to the sensor data." The Applicant respectfully
disagrees. In fact, this language from Gu only uses visual data and compares this to a location.
This is not the same as comparing a measured distribution to a simulated distribution as is being
claimed. The Applicant also notes that the "observations" in [0026] of Gu is not a measurement
simulation as being claimed.
The only other mention of simulations in Gu is at his paragraph [0023] noting "to
calculate Dtemp or Dweighted, the distribution evaluator I 06 may be trained by mapping various
criteria ... with infrared imaging ... and/or a simulated thermal map using a thermal simulator.".”
But again, this is not describing comparing measurement distributions to simulated distributions
to find hot spots as is being claimed.
The other references are silent as to these features.
Since at least one additional claim feature is not taught or suggested by the art, the claims
are allowable for these additional reasons.
No teaching of a first accessible location and a hotspot at a second inaccessible location
There is also no teaching in Gu of a first location that is accessible and finding the
hotspot at the second location where the second location is generally inaccessible. The
accessibility of locations is not taught by Gu.
The other references are silent as to these features.
Since at least one additional claim feature is not taught or suggested by the art, the claims
are allowable for these additional reasons.
Examiner’s Response:
Applicant's arguments, see remarks page 8-9, filed 02/24/2026.
with respect to the rejection(s) of Claims under 35 U.S.C. 103 has been considered, and are not persuasive.
Gu, teaches simulating offset. Offset is the difference between two temperatures. Gu teaches in [0004] “identify asymmetric and symmetric temperature distributions in order to adjust thermal offsets of the IC thereby allowing varying temperature offsets corresponding to power adjustments (e.g., throttle back power, turn off power, etc.) of the IC under different operating conditions”. Asymmetric temperature distribution corresponds to different temperature at different location of the IC110 and the varying temperature offset values simulated are the correlations between varying temperature values at each locations. The thermal offset values may be determined in order to estimate a final temperature value for locations on the unit(s) 110 of the IC that may be some physical distance away from the thermal sensors 104”. With time step Adjusting a first thermal offset value corresponding to at least the thermal sensor to a second thermal offset value based at least in part on the temperature difference being greater than the threshold value. Determining the hot spots 250 using these testing techniques, the offset values may also be determined using thermal simulation in comparison to thermal sensor 104 locations.
Gu, teaches junction of the 110 IC are also reads on second locations away from the sensors 104, and in contact with first location. Junctions are inaccessible with a sensor. Measuring sensor temperature and simulating temperature offset junction temperature can be estimated. see abstract “ICs often implement thermal sensors to measure temperatures at junctions or hot spots along the IC. Due to a distance
between the thermal sensors and the various junctions, a thermal offset may be added to the temperature readings from the thermal sensors to more accurately estimate the temperature at the junctions”. In [0025] teaches temperature reading of a thermal sensor 104 to account for a physical, e. g., distance---0ffset between the thermal sensor 104 and a location (or junction) on the unit(s) 110 of the IC where a temperature reading is desired ( e.g., a junction temperature, Ti). As described herein, the thermal sensors 104 may not be positioned directly on or near the desired locations as a result of a floorplan of the IC”. Therefore, Gu teaches inaccessible junctions.
Therefore, applicant’s argument is not persuasive. The rejections are maintained.
Claims 1-4,6-11, and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Gu et al. (US 2022/0026967 A1, hereinafter, Gu, previously cited) and in view of Berkcan et al. US 2015/0130480 A1, hereinafter, Barkcan, previously cited), and further in view of Rajit C. Chandra (US 2013/0298101 A1, hereinafter Chandra, previously cited).
Regarding claim 1, Gu teaches,
A system for monitoring a device, the system comprising (Gu, Figure 1, Monitoring system 102, [0011] Systems and methods are disclosed related to monitoring temperature distributions in integrated circuits (ICs)): at least one temperature sensor (Gu, Figure 1. thermal sensors 104”);
a processing unit (Gu, Figure 1. [0017] evaluator 106); and
an output unit (Gu, Figure 1, display);
wherein the at least one temperature sensor is configured to acquire at least one temperature measurement distribution at a first location of an operational device (Gu, Figure 1. [0014], The system 100 may include any number of thermal sensors 104 that may be configured to generate outputs indicative of a detected temperature of a unit(s) 110 of an IC at a location of the thermal sensor 104” NOTE: sensor 104 location is a first location), and
wherein the first location is in thermal contact with a second location of the operational device, (Gu, Figure 1. Figure 2A, sensor 104 location reads on first location, and location 250 A-250D reads on second locations / a location away from the sensors and in contact with the first location, in addition junction of the 110 IC are also reads on locations away from the sensors 104, and in contact with first location)
wherein the at least one temperature measurement distribution includes point-like temperature measurements captured by distributed sensors (Gu, Figure 1, [0014], “The system 100 may include any number of thermal sensors 104 that may be configured to generate outputs indicative of a detected temperature of a unit(s) 110 of an IC at a location of the thermal sensor 104. [0017] The distribution evaluator 106 may determine a type of temperature or workload distribution currently being executed by the unit(s) 110 of an IC(s). with respect to FIG. 2A, temperature readings from the GPU thermals sensors 104A-104B, temperature readings from the memory thermal sensors 104D, temperature readings from the CPU thermal sensors 104C, or a combination thereof”. NOTE: the temperature distribution is generated for specific temperature measured at specific point)
wherein the first location is accessible to taking temperature measurements (Gu, Figure 2A, the thermal sensor 104” Figure 2C,” a unit(s) 110 of an IC. The location where the thermal sensor is located is accessible);
wherein the at least one temperature sensor is configured to provide the at least one temperature measurement distribution to the processing unit (Gu, Figure 2A, [0017] The distribution evaluator 106 may determine a type of temperature or workload distribution currently being executed by the unit(s) 110 of an IC(s). For example, the distribution evaluator 106 may determine whether the temperature distribution is asymmetric or symmetric based on temperature readings from the thermal sensors 104 (e.g., with respect to FIG. 2A”);
wherein each of the plurality of simulated temperature distributions relates to a different situation with respect to simulated operation of a simulated device, each different situation involving specific electrical characteristics and connectivity patterns of the simulated device, (GU, off set values are simulated(Gu, [0026] “The hot spots 250 may correspond to known locations on the SoC 11 0A (or individually on subcomponents or units of the SoC 110A, such as the GPU 242), where temperatures are known to reach high levels under certain operating conditions”. The offset temperature values are measured under different “operating conditions” The operating condition could be various criteria”, one of the criteria is considering the rail power, work load see [0017] “The distribution evaluator 106 may determine a type of temperature or workload distribution currently being executed by the unit(s) 110 of an IC(s). For example, a unit(s) 110 or portion thereof may be capable of performing a certain amount of work or processing, and the current amount of work or processing may be compared against a capability of the unit(s) 110 to determine the utilization thereof. As another example, rail power consumption of the unit(s) 110 of the IC(s) may be monitored to determine the workload and/or temperature distributions across the unit(s) of 110 and/or the IC” NOTE: power consumption of the IC, work load processing are different factors/ operation conditions that affect the heat generated and respective temperature distribution. Therefore, the simulated off set value corresponds to each difference operating conditions of the IC)
wherein for each of the different situations, a correlation exists between a simulated temperature distribution of the first location and a simulated temperature at a second location at the simulated device, (Gu, [0004] “identify asymmetric and symmetric temperature distributions in order to adjust thermal offsets of the IC thereby allowing varying temperature offsets corresponding to power adjustments (e.g., throttle back power, turn off power, etc.) of the IC under different operating conditions” NOTE: asymmetric temperature distribution corresponds to different temperature at different location of the IC110 and the varying temperature offset values simulated are the correlations between varying temperature values at each locations).
wherein the second location is generally inaccessible for taking temperature measurements; (Gu, junction of the 110 IC are also reads on second locations away from the sensors 104, and in contact with first location. Junctions are inaccessible with a sensor. Measuring sensor temperature and simulating temperature offset junction temperature can be estimated. see abstract “ICs often implement thermal sensors to measure temperatures at junctions or hot spots along the IC. Due to a distance
between the thermal sensors and the various junctions, a thermal offset may be added to the temperature readings from the thermal sensors to more accurately estimate the temperature at the junctions”).
wherein the processing unit (Gu, Figure 1,Evaluator unit 106) is configured to select a simulated temperature distribution of the first location of the simulated device from the plurality of simulated temperature distributions of the first location of the simulated device , wherein the selection comprises a comparison of the at least one temperature measurement distribution with the plurality of simulated temperature distributions of the first location(Gu, Figure 1, Figure 2a,[0026] “In addition to determining the hot spots 250 using these testing techniques, the offset values may also be determined using thermal simulation and/or thermal observation (e.g., using infrared cameras to look at hot spots in comparison to thermal sensor 104 locations. the thermal offset values may be determined in order to estimate a final temperature value for locations on the unit(s) 110 of the IC that may be some physical distance away from the thermal sensors 104”;
wherein the processing unit (Gu, Figure 1. [0017] evaluator 106) is configured to determine that a hot spot exists or is developing at the second location of the operational device (GU, Figure 1, the thermal offset values may be determined in order to estimate a final temperature value for locations on the unit(s) 110 of the IC that may be some physical distance away from the thermal sensors 104”. The location 250A or 250B in figure 2A is a second location away from the thermal sensor 104 location, where hot spots can be measured. [0026] “with respect to FIG. 2A, hot spots 250A-250D may be determined on the SoC 110A.The hot spots 250 may correspond to known locations on the SoC 11 0A (or individually on subcomponents or units of the SoC 110A”, another second location away from the sensors are the component Junctions in accessible with a sensor. See abstract “I Cs often implement thermal sensors to measure temperatures at junctions or hot spots along the IC. Due to a distance between the thermal sensors and the various junctions, a thermal offset may be added to the temperature readings from the thermal sensors to more accurately estimate the temperature at the junctions)”) and
wherein the determination comprises utilization of the correlation between the simulated temperature distribution of the first location and the second location for the selected simulated temperature distribution of the first location of the simulated device (Gu, [0025] The offset manager 108 may leverage the information from the distribution evaluator 106 to adjust the thermal offsets for one or more of the thermal sensors 104 of the unit(s) 110 of the IC. The thermal offset value may correspond to some number of degrees of temperature (e.g., Fahrenheit, Celsius, and/or another type of temperature measurement) that may be added to the actual temperature reading of a thermal sensor 104 to account for a physical, e. g., distance---0ffset between the thermal sensor 104 and a location (or junction) on the unit(s) 110 of the IC where a temperature reading is desired ( e.g., a junction temperature, Ti). As described herein, the thermal sensors 104 may not be positioned directly on or near the desired locations as a result of a floorplan of the IC”)). and
wherein the output unit is configured to output an indication of a fault at the second location of the operational device upon the determination that the hot spot exists or is developing at the second location of the operational device (Gu, [0026] “the thermal offset values may be determined in order to estimate a final temperature value for locations on the unit(s) 110 of the IC that may be some physical distance away from the thermal sensors 104”. NOTE: hotspots at a second location away from the sensors are determined).
Gu teaches in that a thermal sensor could be any type of sensors. In [0014], (“The thermal sensors 104 may include, without limitation, diodes, bipolar junction transistors (BJTs ), voltage output IC sensors, current output IC sensors, digital output IC sensors, immersion IC sensors, transducer type IC sensors (…) and/or another type of temperature sensor suitable for implementation on the IC or a unit(s) 110 thereof. As such, the thermal sensors 104 may generate an output signal(s) (e.g., corresponding to a voltage, a current, a resistance, etc.) that may be indicative of a corresponding temperature of the unit(s) 110 or the IC at the location of the thermal sensor 104”). transducer type IC sensors could be a wireless sensor.
Gu is silent on a specific that temperature is captured by distributed wireless sensors;
However, Berkcan teaches temperature is captured by distributed wireless sensors (Berkcan, Figures 1- 2, [0023], In a presently contemplated configuration, the remote monitoring system 101 includes a sensor unit 106 and a reader unit 122. The As depicted in FIG. 1, “the sensor unit 106 includes one or more sensor tags 108, 110, 112, 114 that are disposed at a sensing position on the metallic platform 102. [0024], [0024] In addition, the sensor unit 106 includes one or more reference tags 116, 118, 120 that are disposed at a second distance 128 from the reader unit 122.” NOTE: the electrical measurands include temperature see [0022], a remote monitoring system 101 is employed to determine one or more measurands of the electrical device 100.In a non-limiting example, the measurands may represent temperature in the electrical device, thermal joint conditions, hot spot detection”).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Gu’s system in view of Berkcan” system to include a sensor Tag to determine characteristic measurement with the measurement characteristics and the reference characteristics in response to a radio frequency (RF) signal transmitted to the sensor unit as taught by Berkcan with the benefit of the measuring temperature for remotely (Barkcan, [0009]).
Gu is silent on wherein a plurality of simulated temperature distributions are simulated in a process that utilizes finite element analysis,
However, Chandra teaches wherein a plurality of simulated temperature distributions are simulated in a process that utilizes finite element analysis, (Chandra, Figure 11, The mesh-based field solver 908 produces thermal analysis per partition. As shown in block 1104, the scalable thermal model builder 300 assimilates the layout with commercial thermal field solver (such as that shown in block 908) by detailing finite element or finite volume field solvers by placing hypothetical measurement probes at various points to study the temperature fields surrounding the selected configuration”).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Gu’s method of simulation in view of Chandra method to include a finite element analysis to generate temperature distribution to determine characteristic temperature at a location of electrical device as taught by Chandra with the benefit of the measuring hot spots location accurately. (Chandra, [0055], [0066])). It would have been obvious to a person of ordinary skill to include the well-known finite element analysis and simulate thermal data along with the other machine learning network, in order to yield the predicted results of generating accurate hot spots map, yet with higher accuracy (KSR).
Regarding claim 2, combination of Gu, Berkcan, and Chandra teaches the system according to claim 1,
Gu is silent wherein the at least one temperature sensor comprises one or more one or more RFID sensors.
However, Berkcan teaches wherein the at least one temperature sensor comprises one or more one or more RFID sensors. (Berkcan, Figures 1- 2, [0023], In a presently contemplated configuration, the remote monitoring system 101 includes a sensor unit 106 and a reader unit 122. The As depicted in FIG. 1, “the sensor unit 106 includes one or more sensor tags 108, 110, 112, 114 that are disposed at a sensing position on the metallic platform 102. [0024], [0024] In addition, the sensor unit 106 includes one or more reference tags 116, 118, 120 that are disposed at a second distance 128 from the reader unit 122.” NOTE: the electrical measurands include temperature see [0022], a remote monitoring system 101 is employed to determine one or more measurands of the electrical device 100.In a non-limiting example, the measurands may represent temperature in the electrical device, thermal joint conditions, hot spot detection”).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Gu’s system in view of Berkcan” system to include a sensor Tag to determine characteristic measurement with the measurement characteristics and the reference characteristics in response to a radio frequency (RF) signal transmitted to the sensor unit as taught by Berkcan with the benefit of the measuring temperature for remotely (Barkcan, [0009]).
Regarding claim 3, combination of Gu, Berkcan, and Chandra teaches the system according to claim 1,
Gu further teaches further teaches wherein the at least one temperature measurement comprises a plurality of temperature measurements, and wherein the plurality of temperature measurements were acquired at the same time (Gu, Figure 1, 2A, [0017] The distribution evaluator 106 may determine a type of temperature or workload distribution currently being executed by the unit(s) 110 of an IC(s). For example, the distribution evaluator 106 may determine whether the temperature distribution is asymmetric or symmetric based on temperature readings from the thermal sensors 104 (e.g.,with respect to FIG. 2A, temperature readings from the GPU thermals sensors 104A-104B, temperature readings from the memory thermal sensors 104D, temperature readings from the CPU thermal sensors 104C, or a combination thereof”. NOTE: thermal sensors collect plurality of temperature data at the same time.);
Regarding claim 4, combination of Gu, Berkcan, and Chandra teaches the system according to claim 2,
Gu further teaches wherein the at least one temperature sensor is an infrared camera and wherein the at least one temperature measurement comprises an infrared image of the first location. (Gu, Figure 1, and figure 2A, [0026], “to determining the hot spots 250 using these testing techniques, the offset values may also be determined using thermal simulation and/or thermal observation (e.g., using infrared cameras to look at hot spots in comparison to thermal sensor 104 locations” NOTE: 250A is a first location, fig. 2A).
Regarding claim 6, combination of Gu,and Barkcan teaches the system according to claim 1,
Gu teaches wherein the correlation between the simulated temperature distribution of the first location and the simulated temperature at the second location of the simulated device
Gu is silent on finite element analysis to determine different situations.
However, Chandra teaches (Chandra, Figure 11, The mesh-based field solver 908 produces thermal analysis per partition. As shown in block 1104, the scalable thermal model builder 300 assimilates the layout with commercial thermal field solver (such as that shown in block 908) by detailing finite element or finite volume field solvers by placing hypothetical measurement probes at various points to study the temperature fields surrounding the selected configuration”).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Gu’s method of simulation in view of Chandra method to include a finite element analysis to generate temperature distribution to determine characteristic temperature at a location of electrical device as taught by Chandra with the benefit of the measuring hot spots location accurately. (Chandra, [0055], [0066])). It would have been obvious to a person of ordinary skill to include the well-known finite element analysis and simulate thermal data along with the other machine learning network, in order to yield the predicted results of generating accurate hot spots map, yet with higher accuracy (KSR).
Regarding claim 7 combination of Gu, Berkcan, and Chandra teaches the system according to claim 1,
Gu further teaches wherein the comparison of the at least one temperature measurement with the plurality of simulated temperature distributions comprises utilization of a matrix norm or a machine learning algorithm implemented by the processing unit (Gu, [0012] In addition, the thermal management for ICs
described herein may be implemented within any type of system, such as. without limitation, machine learning and/or artificial intelligence systems”).
Regarding claim 8, Gu teaches,
A method for monitoring a device (Gu, Figure 1, Monitoring system 102), the method comprising:
a) acquiring by at least one temperature sensor (Gu, Figure 1. thermal sensors 104”); at least one temperature measurement distribution at a first location of an operational device (Gu, Figure 1. [0014], The system 100 may include any number of thermal sensors 104 that may be configured to generate outputs indicative of a detected temperature of a unit(s) 110 of an IC at a location of the thermal sensor 104” NOTE: sensor 104 location is a first location), and wherein the first location is in thermal contact with a second location of the operational device, (Gu, Figure 1. Figure 2A, sensor 104 location reads on first location, and location 250 A-250D reads on second locations / a location away from the sensors and in contact with the first location, in addition junction of the 110 IC are also reads on locations away from the sensors 104, and in contact with first location)
wherein the at least one temperature measurement distribution includes point-like temperature measurements captured by distributed sensors (Gu, Figure 1, [0014], “The system 100 may include any number of thermal sensors 104 that may be configured to generate outputs indicative of a detected temperature of a unit(s) 110 of an IC at a location of the thermal sensor 104. [0017] The distribution evaluator 106 may determine a type of temperature or workload distribution currently being executed by the unit(s) 110 of an IC(s). with respect to FIG. 2A, temperature readings from the GPU thermals sensors 104A-104B, temperature readings from the memory thermal sensors 104D, temperature readings from the CPU thermal sensors 104C, or a combination thereof”. NOTE: the temperature distribution is generated for specific temperature measured at specific point)
wherein the first location is accessible to taking temperature measurements (Gu, Figure 2A, the thermal sensor 104” Figure 2C,” a unit(s) 110 of an IC. The location where the thermal sensor is located is accessible);
b) providing the at least one temperature measurement distribution to a processing unit (Gu, Figure 2A, [0017] The distribution evaluator 106 may determine a type of temperature or workload distribution currently being executed by the unit(s) 110 of an IC(s). For example, the distribution evaluator 106 may determine whether the temperature distribution is asymmetric or symmetric based on temperature readings from the thermal sensors 104 (e.g., with respect to FIG. 2A”);
wherein each of the plurality of simulated temperature distributions relates to a different situation with respect to simulated operation of a simulated device, each different situation involving specific electrical characteristics and connectivity patterns of the simulated device, (GU, off set values are simulated(Gu, [0026] “The hot spots 250 may correspond to known locations on the SoC 11 0A (or individually on subcomponents or units of the SoC 110A, such as the GPU 242), where temperatures are known to reach high levels under certain operating conditions”. The offset temperature values are measured under different “operating conditions” The operating condition could be various criteria”, one of the criteria is considering the rail power, work load see [0017] “The distribution evaluator 106 may determine a type of temperature or workload distribution currently being executed by the unit(s) 110 of an IC(s). For example, a unit(s) 110 or portion thereof may be capable of performing a certain amount of work or processing, and the current amount of work or processing may be compared against a capability of the unit(s) 110 to determine the utilization thereof. As another example, rail power consumption of the unit(s) 110 of the IC(s) may be monitored to determine the workload and/or temperature distributions across the unit(s) of 110 and/or the IC” NOTE: power consumption of the IC, work load processing are different factors/ operation conditions that affect the heat generated and respective temperature distribution. Therefore, the simulated off set value corresponds to each difference operating conditions of the IC)
wherein for each of the different situations, a correlation exists between a simulated temperature distribution of the first location and a simulated temperature at a second location at the simulated device, (Gu, [0004] “identify asymmetric and symmetric temperature distributions in order to adjust thermal offsets of the IC thereby allowing varying temperature offsets corresponding to power adjustments (e.g., throttle back power, turn off power, etc.) of the IC under different operating conditions” NOTE: asymmetric temperature distribution corresponds to different temperature at different location of the IC110 and the varying temperature offset values simulated are the correlations between varying temperature values at each locations).
wherein the second location is generally inaccessible for taking temperature measurements; (Gu, junction of the 110 IC are also reads on second locations away from the sensors 104, and in contact with first location. Junctions are inaccessible with a sensor. Measuring sensor temperature and simulating temperature offset junction temperature can be estimated. see abstract “ICs often implement thermal sensors to measure temperatures at junctions or hot spots along the IC. Due to a distance
between the thermal sensors and the various junctions, a thermal offset may be added to the temperature readings from the thermal sensors to more accurately estimate the temperature at the junctions”).
wherein the processing unit (Gu, Figure 1,Evaluator unit 106) is configured to select a simulated temperature distribution of the first location of the simulated device from the plurality of simulated temperature distributions of the first location of the simulated device , wherein the selection comprises a comparison of the at least one temperature measurement distribution with the plurality of simulated temperature distributions of the first location(Gu, Figure 1, Figure 2a,[0026] “In addition to determining the hot spots 250 using these testing techniques, the offset values may also be determined using thermal simulation and/or thermal observation (e.g., using infrared cameras to look at hot spots in comparison to thermal sensor 104 locations. the thermal offset values may be determined in order to estimate a final temperature value for locations on the unit(s) 110 of the IC that may be some physical distance away from the thermal sensors 104”;
wherein the processing unit (Gu, Figure 1. [0017] evaluator 106) is configured to determine that a hot spot exists or is developing at the second location of the operational device (GU, Figure 1, the thermal offset values may be determined in order to estimate a final temperature value for locations on the unit(s) 110 of the IC that may be some physical distance away from the thermal sensors 104”. The location 250A or 250B in figure 2A is a second location away from the thermal sensor 104 location, where hot spots can be measured. [0026] “with respect to FIG. 2A, hot spots 250A-250D may be determined on the SoC 110A.The hot spots 250 may correspond to known locations on the SoC 11 0A (or individually on subcomponents or units of the SoC 110A”, another second location away from the sensors are the component junctions in accessible with a sensor. See abstract “I Cs often implement thermal sensors to measure temperatures at junctions or hot spots along the IC. Due to a distance between the thermal sensors and the various junctions, a thermal offset may be added to the temperature readings from the thermal sensors to more accurately estimate the temperature at the junctions)”) and
wherein the determination comprises utilization of the correlation between the simulated temperature distribution of the first location and the second location for the selected simulated temperature distribution of the first location of the simulated device (Gu, [0025] The offset manager 108 may leverage the information from the distribution evaluator 106 to adjust the thermal offsets for one or more of the thermal sensors 104 of the unit(s) 110 of the IC. The thermal offset value may correspond to some number of degrees of temperature (e.g., Fahrenheit, Celsius, and/or another type of temperature measurement) that may be added to the actual temperature reading of a thermal sensor 104 to account for a physical, e. g., distance---0ffset between the thermal sensor 104 and a location (or junction) on the unit(s) 110 of the IC where a temperature reading is desired ( e.g., a junction temperature, Ti). As described herein, the thermal sensors 104 may not be positioned directly on or near the desired locations as a result of a floorplan of the IC”)). and
wherein the output unit is configured to output an indication of a fault at the second location of the operational device upon the determination that the hot spot exists or is developing at the second location of the operational device (Gu, [0026] “the thermal offset values may be determined in order to estimate a final temperature value for locations on the unit(s) 110 of the IC that may be some physical distance away from the thermal sensors 104”. NOTE: hotspots at a second location away from the sensors are determined).
Gu teaches in that a thermal sensor could be any type of sensors. In [0014], (“The thermal sensors 104 may include, without limitation, diodes, bipolar junction transistors (BJTs), voltage output IC sensors, current output IC sensors, digital output IC sensors, immersion IC sensors, transducer type IC sensors (…) and/or another type of temperature sensor suitable for implementation on the IC or a unit(s) 110 thereof. As such, the thermal sensors 104 may generate an output signal(s) (e.g., corresponding to a voltage, a current, a resistance, etc.) that may be indicative of a corresponding temperature of the unit(s) 110 or the IC at the location of the thermal sensor 104”). transducer type IC sensors could be a wireless sensor.
Gu is silent on a specific that temperature is captured by distributed wireless sensors;
However, Berkcan teaches temperature is captured by distributed wireless sensors (Berkcan, Figures 1- 2, [0023], In a presently contemplated configuration, the remote monitoring system 101 includes a sensor unit 106 and a reader unit 122. The As depicted in FIG. 1, “the sensor unit 106 includes one or more sensor tags 108, 110, 112, 114 that are disposed at a sensing position on the metallic platform 102. [0024], [0024] In addition, the sensor unit 106 includes one or more reference tags 116, 118, 120 that are disposed at a second distance 128 from the reader unit 122.” NOTE: the electrical measurands include temperature see [0022], a remote monitoring system 101 is employed to determine one or more measurands of the electrical device 100.In a non-limiting example, the measurands may represent temperature in the electrical device, thermal joint conditions, hot spot detection”).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Gu’s system in view of Berkcan” system to include a sensor Tag to determine characteristic measurement with the measurement characteristics and the reference characteristics in response to a radio frequency (RF) signal transmitted to the sensor unit as taught by Berkcan with the benefit of the measuring temperature for remotely (Barkcan, [0009]).
Gu is silent on wherein a plurality of simulated temperature distributions are simulated in a process that utilizes finite element analysis,
However, Chandra teaches wherein a plurality of simulated temperature distributions are simulated in a process that utilizes finite element analysis, (Chandra, Figure 11, The mesh-based field solver 908 produces thermal analysis per partition. As shown in block 1104, the scalable thermal model builder 300 assimilates the layout with commercial thermal field solver (such as that shown in block 908) by detailing finite element or finite volume field solvers by placing hypothetical measurement probes at various points to study the temperature fields surrounding the selected configuration”).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Gu’s method of simulation in view of Chandra method to include a finite element analysis to generate temperature distribution to determine characteristic temperature at a location of electrical device as taught by Chandra with the benefit of the measuring hot spots location accurately. (Chandra, [0055], [0066])). It would have been obvious to a person of ordinary skill to include the well-known finite element analysis and simulate thermal data along with the other machine learning network, in order to yield the predicted results of generating accurate hot spots map, yet with higher accuracy (KSR).
Regarding claim 9, combination of Gu, Berkcan, and Chandra teaches the method according to claim 8,
Gu is silent wherein the at least one temperature sensor comprises one or more one or more RFID sensors.
However, Berkcan teaches wherein the at least one temperature sensor comprises one or more one or more RFID sensors. (Berkcan, Figures 1- 2, [0023], In a presently contemplated configuration, the remote monitoring system 101 includes a sensor unit 106 and a reader unit 122. The As depicted in FIG. 1, “the sensor unit 106 includes one or more sensor tags 108, 110, 112, 114 that are disposed at a sensing position on the metallic platform 102. [0024], [0024] In addition, the sensor unit 106 includes one or more reference tags 116, 118, 120 that are disposed at a second distance 128 from the reader unit 122.” NOTE: the electrical measurands include temperature see [0022], a remote monitoring system 101 is employed to determine one or more measurands of the electrical device 100.In a non-limiting example, the measurands may represent temperature in the electrical device, thermal joint conditions, hot spot detection”).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Gu’s system in view of Berkcan” system to include a sensor Tag to determine characteristic measurement with the measurement characteristics and the reference characteristics in response to a radio frequency (RF) signal transmitted to the sensor unit as taught by Berkcan with the benefit of the measuring temperature for remotely (Berkcan, [0009]).
Regarding claim 10, combination of Gu, Berkcan, and Chandra teaches the method according to claim 8,
Gu further teaches wherein the at least one temperature measurement comprises a plurality of temperature measurements, and wherein the plurality of temperature measurements were acquired at the same time. ((Gu, Figure 1, 2A, [0017] The distribution evaluator 106 may determine a type of temperature or workload distribution currently being executed by the unit(s) 110 of an IC(s). For example, the distribution evaluator 106 may determine whether the temperature distribution is asymmetric or symmetric based on temperature readings from the thermal sensors 104 (e.g.,with respect to FIG. 2A, temperature readings from the GPU thermals sensors 104A-104B, temperature readings from the memory thermal sensors 104D, temperature readings from the CPU thermal sensors 104C, or a combination thereof”. NOTE: thermal sensors collect plurality of temperature data at the same time.);
Regarding claim 11, combination of Gu, Berkcan, and Chandra teaches the method according to claim 8,
Gu further teaches wherein the at least one temperature sensor is an infrared camera and wherein the at least one temperature measurement comprises an infrared image of the first location. (Gu, Figure 1, and figure 2A, [0026], “to determining the hot spots 250 using these testing techniques, the offset values may also be determined using thermal simulation and/or thermal observation (e.g., using infrared cameras to look at hot spots in comparison to thermal sensor 104 locations” NOTE: 250A is a first location, fig. 2A).
Regarding claim 13, combination of Gu, Berkcan, and Chandra teaches the method according to claim 8,
Gu teaches wherein the correlation between the simulated temperature distribution of the first location and the simulated temperature at the second location of the simulated device (Gu, [0025] The offset manager 108 may leverage the information from the distribution evaluator 106 to adjust the thermal offsets for one or more of the thermal sensors 104 of the unit(s) 110 of the IC. The thermal offset value may correspond to some number of degrees of temperature (e.g., Fahrenheit, Celsius, and/or another type of temperature measurement) that may be added to the actual temperature reading of a thermal sensor 104 to account for a physical, e. g., distance---0ffset between the thermal sensor 104 and a location (or junction) on the unit(s) 110 of the IC where a temperature reading is desired ( e.g., a junction temperature, Ti). As described herein, the thermal sensors 104 may not be positioned directly on or near the desired locations as a result of a floorplan of the IC”)).
Gu is silent on finite element analysis to determine different situations.
However, Chandra teaches (Chandra, Figure 11, The mesh-based field solver 908 produces thermal analysis per partition. As shown in block 1104, the scalable thermal model builder 300 assimilates the layout with commercial thermal field solver (such as that shown in block 908) by detailing finite element or finite volume field solvers by placing hypothetical measurement probes at various points to study the temperature fields surrounding the selected configuration”).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Gu’s method of simulation in view of Chandra method to include a finite element analysis to generate temperature distribution to determine characteristic temperature at a location of electrical device as taught by Chandra with the benefit of the measuring hot spots location accurately. (Chandra, [0055], [0066])). It would have been obvious to a person of ordinary skill to include the well-known finite element analysis and simulate thermal data along with the other machine learning network, in order to yield the predicted results of generating accurate hot spots map, yet with higher accuracy (KSR).
Regarding claim 14, combination of Gu, Berkcan, and Chandra teaches the method according to claim 8,
Gu further teaches wherein the comparison of the at least one temperature measurement with the plurality of simulated temperature distributions comprises utilization of a matrix norm or a machine learning algorithm implemented by the processing unit (Gu, [0012] In addition, the thermal management for ICs
described herein may be implemented within any type of system, such as. without limitation, machine learning and/or artificial intelligence systems”).
Conclusions
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Keskin et al. (US 2018/0073933 A1) recites “In certain aspects, a method for temperature monitoring comprises receiving temperature readings from a plurality of
temperature sensors on a chip, and determining an average or a sum of the temperature readings from the temperature sensors. The sum may be a weighted sum of the temperature readings. The method also comprises computing a temperature
at a location on the chip based on the average or sum of the temperature readings. The location may be located at approximately a centroid of the locations of the temperature sensors, an estimated hotspot location on the chip, or another
location on the chip” (Abstract).
Coutts et al. (US 10401235 B2) discloses “In one embodiment, a temperature management system comprises a plurality of thermal sensors at different locations on a chip, and a temperature manager. The temperature manager is configured to receive a plurality of temperature readings from the thermal sensors, to fit a quadratic temperature model to the received temperature readings, and to estimate a hotspot temperature on the chip using the fitted quadratic temperature model. (Abstract).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/DILARA SULTANA/Examiner, Art Unit 2858
06/01/2026
/EMAN A ALKAFAWI/Supervisory Patent Examiner, Art Unit 2858 6/5/2026
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