DETAILED ACTION
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 Amendments filed on 05/11/2026, wherein Claims 1-13, 15-25, 27-32, and 34-39 are pending. Claims 1, 15, 17, 23, 27, and 34 have been amended. Claims 14, 26, and 33 have been cancelled.
Response to Arguments
Regarding 35 USC 103 rejection: Applicant’s arguments with respect to claims 1-13, 15-25, 27-32, and 34-39, have been considered but are moot because of the new ground of rejection necessitated by the amendments.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 12-13, 15, 23-25, 31-32, 37, and 38 are rejected under 35 U.S.C. 103 as being unpatentable over US20190041417 to Mulage et al. (hereinafter Mulage) in view of US20210278212A1 to Scafidi et al. (hereinafter Scafidi).
Regarding Claim 1: Mulage discloses:
“A system comprising: an inertial sensing device comprising: an inertial sensor arranged on a chip; and a plurality of stress sensors arranged on the chip, the plurality of stress sensors configured to measure stress applied to the inertial sensing device” (Fig. 1; para 0053 – “The non-limiting example of FIG. 1 illustrates various hardware elements of an electronic device 100 (i.e. chip, added by examiner) for gyroscope drift compensation… the electronic device 100 includes a sensor unit 102, a data processing unit 104, a storage unit 106 and a display unit 108”; para 0054 – “the sensor unit 102 includes one or more MEMS (micro electro mechanical systems) sensors. For example, the MEMS sensors include an accelerometer, a gyroscope, or any other inertial sensor… In addition, the electronic device 100 may further include various types of sensors, such as a gesture sensor, a pressure sensor, a magnetic sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor. (i.e. stress sensors and environmental sensors, added by examiner). Each sensor of the sensor unit 102 can be mounted on a separate chip and a plurality of sensors can be mounted on a single chip”);
“at least one computing device configured to: receive sensor data from the inertial sensor” (Abstract – “A method for compensating for gyroscope drift on an electronic device includes receiving by a data processing unit, measurement data from a gyroscope. The method includes computing, by the data processing unit, a compensation parameter by analyzing the measurement data received from the gyroscope”; para 0015 – “a method for compensating gyroscope drift on an electronic device. The method includes receiving by a data processing unit a measurement data from a gyroscope.”); and
“the plurality of stress sensors” (Fig. 2; para 0054 – “the electronic device 100 may further include various types of sensors, such as a gesture sensor, a pressure sensor, a magnetic sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor (i.e. stress sensors and environmental sensors, added by examiner). Each sensor of the sensor unit 102 can be mounted on a separate chip and a plurality of sensors can be mounted on a single chip”; para 0055 – “The data processing unit 104 (i.e. computing device, added by examiner) may include one or more processors (for example, an application processor). The data processing unit 104 can be configured to receive a measurement data from the sensor unit 102.”)
“determine a drift compensation of the inertial sensor based on the sensor data from the inertial sensor” (para 0054 – “the sensor unit 102 includes one or more MEMS (micro electro mechanical systems) sensors. For example, the MEMS sensors include an accelerometer, a gyroscope, or any other inertial sensor. However, the embodiments described herein facilitate the compensation of gyroscope drift”).
Mulage does not specifically disclose:
“determine a drift compensation based on the sensor data from at least one stress sensor of the plurality of stress sensors”.
However, Scafidi discloses:
“determine a drift compensation based on the sensor data from at least one stress sensor of the plurality of stress sensors” (para 0005 – “a microelectromechanical (MEMS) system includes a gyroscope (i.e. inertial sensor, added by examiner) … and an accelerometer (i.e. stress sensor, added by examiner) that outputs a linear acceleration signal. The MEMS system may further include a processing circuitry (i.e. computing device, added by examiner) coupled to the gyroscope and the accelerometer to receive the quadrature signal and the linear acceleration signal (i.e. sensor data, added by examiner).”; para 0021 – “To determine the compensation values, a set of controlled external stresses is applied to the MEMS system over a known range of stress levels known to be typical to the MEMS system environment”; para 0022 – “During regular operation, linear acceleration error compensation (i.e. drift compensation, added by examiner) may be performed on the MEMS chip to negate the effects of the external stresses. The estimated linear acceleration error may be determined during manufacturing, in a controlled environment, or in-field”).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the system of Mulage as taught by Scafidi in order to provide a more accurate and reliable drift determination.
Regarding Claim 2: Mulage/Scafidi combination discloses the system of Claim 1.
Mulage further discloses:
“wherein the inertial sensing device comprises the at least one computing device” (para 0015 – “a method for compensating gyroscope drift on an electronic device. The method includes receiving by a data processing unit (i.e. computing device, added by examiner) a measurement data from a gyroscope”).
Regarding Claim 3: Mulage/Scafidi combination discloses the system of Claim 1.
Mulage further discloses:
”wherein the at least one computing device comprises at least one first processor arranged in the sensing device and at least one second processor external to the sensing device” (para 0099 – “overall computing environment 1102 can be composed of multiple homogeneous and/or heterogeneous cores, multiple CPUs of different kinds, special media and other accelerators. The processing unit 1108 is responsible for processing the instructions of the algorithm. Further, the plurality of processing units 1108 may be located on a single chip or over multiple chips”; see also paras 0100, 0101).
Regarding Claim 4: Mulage/Scafidi combination discloses the system of Claim 1.
Mulage further discloses:
“wherein the inertial sensing device further comprises a plurality of environmental sensors in addition to the plurality of stress sensors” (para 0054 – “the sensor unit 102 includes one or more MEMS (micro electro mechanical systems) sensors. For example, the MEMS sensors include an accelerometer, a gyroscope, or any other inertial sensor… In addition, the electronic device 100 may further include various types of sensors, such as a gesture sensor, a pressure sensor, a magnetic sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor. (i.e. stress sensors and environmental sensors, added by examiner). Each sensor of the sensor unit 102 can be mounted on a separate chip and a plurality of sensors can be mounted on a single chip”).
Mulage does not explicitly disclose:
“wherein the drift compensation is determined at least partially based on sensor data received from the plurality of environmental sensors”.
However, Scafidi discloses:
“wherein the drift compensation is determined at least partially based on sensor data received from the plurality of environmental sensors” (para 0005 – “a microelectromechanical (MEMS) system includes a gyroscope (i.e. inertial sensor, added by examiner) … and an accelerometer (i.e. stress sensor, added by examiner) that outputs a linear acceleration signal. The MEMS system may further include a processing circuitry (i.e. computing device, added by examiner) coupled to the gyroscope and the accelerometer to receive the quadrature signal and the linear acceleration signal (i.e. sensor data, added by examiner).”; para 0021 – “To determine the compensation values, a set of controlled external stresses is applied to the MEMS system over a known range of stress levels known to be typical to the MEMS system environment”; para 0022 – “During regular operation, linear acceleration error compensation (i.e. drift compensation, added by examiner) may be performed on the MEMS chip to negate the effects of the external stresses. The estimated linear acceleration error may be determined during manufacturing, in a controlled environment, or in-field”).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the system of Mulage as taught by Scafidi in order to provide a more accurate and reliable drift determination.
Regarding Claim 5: Mulage/Scafidi combination discloses the system of Claim 4.
Mulage further discloses:
“wherein the plurality of environmental sensors comprises at least one of the following: a temperature sensor, a resonator oscillator sensor, a strain sensor, a gas chemical sensor, or any combination thereof” (para 0054 – “the electronic device 100 may further include various types of sensors, such as a gesture sensor, a pressure sensor, a magnetic sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor. (i.e. stress sensors and environmental sensors, added by examiner). Each sensor of the sensor unit 102 can be mounted on a separate chip and a plurality of sensors can be mounted on a single chip”).
Regarding Claim 6: Mulage/Scafidi combination discloses the system of Claim 4.
Mulage does not specifically disclose:
“wherein the sensor data is received while the inertial sensing device is moved”.
However, Scafidi discloses:
“wherein the sensor data is received while the inertial sensing device is moved” (para 0036 – “In the presence of movement, the accelerometer error cannot be readily distinguished from an actual accelerometer output because the movement of the MEMS chip likely adversely affects the multilinear regression calculations on which the estimated acceleration error relies.”).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the system of Mulage as taught by Scafidi in order to provide a more accurate and reliable drift determination.
Regarding Claim 12: Mulage/Scafidi combination discloses the system of Claim 1.
Mulage does not explicitly disclose:
“wherein the inertial sensor comprises an array of accelerometers”.
However, Scafidi discloses:
“wherein the inertial sensor comprises an array of accelerometers” (Claim 14 – “The MEMS system of claim 1, further comprising a second accelerometer”; Claim 15 – “The MEMS system of claim 1, further comprising a third accelerometer ”).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the system of Mulage as taught by Scafidi in order to provide a more accurate and reliable drift determination.
Regarding Claim 13: Mulage/Scafidi combination discloses the system of Claim 1.
Mulage further discloses:
“wherein the inertial sensor comprises at least one gyroscope or an array of gyroscopes” (para 0054 – “the sensor unit 102 includes one or more MEMS (micro electro mechanical systems) sensors”).
Regarding Claim 15: Mulage/Scafidi combination discloses the system of Claim 1.
Mulage further discloses:
“further comprising a data storage device” (Fig. 1, Storage unit 106)
“comprising an association between each stress sensor of the plurality of stress sensors and at least one of the following: an accelerometer of the array of accelerometers, a position on the chip supporting the array of accelerometers, a position on the chip relative to an accelerometer of the array of accelerometers, or any combination thereof” (para 0054 – “the sensor unit 102 includes one or more MEMS (micro electro mechanical systems) sensors. For example, the MEMS sensors include an accelerometer, a gyroscope, or any other inertial sensor… the electronic device 100 may further include various types of sensors, such as a gesture sensor, a pressure sensor, a magnetic sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor. (i.e. stress sensors and environmental sensors, added by examiner). Each sensor of the sensor unit 102 can be mounted on a separate chip and a plurality of sensors can be mounted on a single chip”).
Regarding Claim 23: Mulage discloses:
“An inertial sensing device arranged on a chip comprising: an inertial sensor” (Fig. 1; para 0053 – “The non-limiting example of FIG. 1 illustrates various hardware elements of an electronic device 100 (i.e. chip, added by examiner) for gyroscope drift compensation… the electronic device 100 includes a sensor unit 102, a data processing unit 104, a storage unit 106 and a display unit 108”);
“a plurality of stress sensors arranged on the inertial sensing device and configured to measure stress applied to the inertial sensing device” (para 0054 – “the sensor unit 102 includes one or more MEMS (micro electro mechanical systems) sensors. For example, the MEMS sensors include an accelerometer, a gyroscope, or any other inertial sensor… In addition, the electronic device 100 may further include various types of sensors, such as a gesture sensor, a pressure sensor, a magnetic sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor. (i.e. stress sensors and environmental sensors, added by examiner). Each sensor of the sensor unit 102 can be mounted on a separate chip and a plurality of sensors can be mounted on a single chip”); and
“at least one computing device configured to:” (Abstract – “A method for compensating for gyroscope drift on an electronic device includes receiving by a data processing unit, measurement data from a gyroscope. The method includes computing, by the data processing unit, a compensation parameter by analyzing the measurement data received from the gyroscope”)
“determine a drift compensation of the inertial sensor based on the sensor data from the inertial sensor” (para 0054 – “the sensor unit 102 includes one or more MEMS (micro electro mechanical systems) sensors. For example, the MEMS sensors include an accelerometer, a gyroscope, or any other inertial sensor. However, the embodiments described herein facilitate the compensation of gyroscope drift”).
Mulage does not specifically disclose:
“determine a drift compensation based on the sensor data from at least one stress sensor of the plurality of stress sensors”.
However, Scafidi discloses:
“determine a drift compensation based on the sensor data from at least one stress sensor of the plurality of stress sensors” (para 0005 – “a microelectromechanical (MEMS) system includes a gyroscope (i.e. inertial sensor, added by examiner) … and an accelerometer (i.e. stress sensor, added by examiner) that outputs a linear acceleration signal. The MEMS system may further include a processing circuitry (i.e. computing device, added by examiner) coupled to the gyroscope and the accelerometer to receive the quadrature signal and the linear acceleration signal (i.e. sensor data, added by examiner).”; para 0021 – “To determine the compensation values, a set of controlled external stresses is applied to the MEMS system over a known range of stress levels known to be typical to the MEMS system environment”; para 0022 – “During regular operation, linear acceleration error compensation (i.e. drift compensation, added by examiner) may be performed on the MEMS chip to negate the effects of the external stresses. The estimated linear acceleration error may be determined during manufacturing, in a controlled environment, or in-field”).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the system of Mulage as taught by Scafidi in order to provide a more accurate and reliable drift determination.
Regarding Claim 24: Mulage/Scafidi combination discloses the inertial sensing device of Claim 23.
Mulage does not explicitly disclose:
“further comprising an interface configured to output sensor data from the inertial sensor and the plurality of stress sensors”.
However, Scafidi discloses:
“further comprising an interface configured to output sensor data from the inertial sensor and the plurality of stress sensors” (para 0026 – “the processing circuitry 104 may process data received from the MEMS accelerometer 102 and other sensors 108 and communicate with external components via a communication interface 110 (e.g., a SPI or I2C bus, in automotive applications a controller area network (CAN) or Local Interconnect Network (LIN) bus, or in other applications suitable wired or wireless communications interfaces as is known in the art ”).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the system of Mulage as taught by Scafidi in order to provide a more accurate and reliable drift determination.
Regarding Claim 25: Mulage/Scafidi combination discloses the inertial sensing device of Claim 23.
Mulage further discloses:
“further comprising a plurality of temperature sensors” (para 0047 - ”The variation in temperature of the gyroscope is received from a temperature sensor in the gyroscope, or from a thermistor or from any other temperature sensor. ”).
Regarding Claim 31: Mulage/Scafidi combination discloses the inertial sensing device of Claim 23.
Mulage does not explicitly disclose:
“wherein the inertial sensor comprises an array of accelerometers”.
However, Scafidi discloses:
“wherein the inertial sensor comprises an array of accelerometers” (Claim 14 – “The MEMS system of claim 1, further comprising a second accelerometer”; Claim 15 – “The MEMS system of claim 1, further comprising a third accelerometer ”).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the system of Mulage as taught by Scafidi in order to provide a more accurate and reliable drift determination.
Regarding Claim 32: Mulage/Scafidi combination discloses the inertial sensing device of Claim 23.
Mulage further discloses:
“wherein the inertial sensor comprises at least one gyroscope or any array of gyroscopes” (para 0054 – “the sensor unit 102 includes one or more MEMS (micro electro mechanical systems) sensors”).
Regarding Claim 37: Mulage/Scafidi combination discloses the system of Claim 1.
Mulage does not explicitly disclose:
“wherein the inertial sensor comprises an array of accelerometers”.
However, Scafidi discloses:
“wherein the inertial sensor comprises an array of accelerometers” (Claim 14 – “The MEMS system of claim 1, further comprising a second accelerometer”; Claim 15 – “The MEMS system of claim 1, further comprising a third accelerometer ”).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the system of Mulage as taught by Scafidi in order to provide a more accurate and reliable drift determination.
Regarding Claim 38: Mulage/Scafidi combination discloses the system of Claim 1.
Mulage further discloses:
“wherein the inertial sensor comprises a plurality of gyroscopes” (para 0054 – “the sensor unit 102 includes one or more MEMS (micro electro mechanical systems) sensors”).
Mulage does not explicitly disclose:
“wherein the inertial sensor comprises a plurality of accelerometers”.
However, Scafidi discloses:
“wherein the inertial sensor comprises a plurality of accelerometers” (Claim 14 – “The MEMS system of claim 1, further comprising a second accelerometer”; Claim 15 – “The MEMS system of claim 1, further comprising a third accelerometer ”).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the system of Mulage as taught by Scafidi in order to provide a more accurate and reliable drift determination.
Claims 7 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mulage in view of Scafidi and in further view of US20170091870 to Trainor et al. (hereinafter Trainor).
Regarding Claim 7: Mulage/Scafidi combination discloses the inertial sensing device of Claim 6.
Mulage does not specifically disclose:
“wherein the at least one computing device is further configured to record the sensor data to temporally associate measurements from the inertial sensor with stress measurements from the plurality of stress sensors”.
However, Trainor discloses:
“wherein the at least one computing device is further configured to record the sensor data” (para 0143 – “In particular, the sensor-based state prediction system 50 extracts service record data”)
“to temporally associate measurements from the inertial sensor with stress measurements from the plurality of stress sensors” (para 0187 – “Sensor devices can integrate multiple sensors to generate more complex outputs (i.e. associate measurements from the inertial sensor with stress measurements, added by examiner)”).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the system of Mulage/Scafidi combination as taught by Trainor in order to provide a more complete knowledge of drift.
Regarding Claim 17: Mulage discloses:
“A method comprising: capturing a plurality of inertial sensor signals comprising inertial sensor data from at least one inertial sensor arranged in an inertial sensing device arranged on a chip” (para 0015 – “The method includes receiving by a data processing unit a measurement data from a gyroscope”; Fig. 1; para 0053 – “The non-limiting example of FIG. 1 illustrates various hardware elements of an electronic device 100 (i.e. chip, added by examiner) for gyroscope drift compensation… the electronic device 100 includes a sensor unit 102, a data processing unit 104, a storage unit 106 and a display unit 108”);
“capturing a plurality of environmental sensor signals comprising environmental sensor data from a plurality of stress sensors arranged in the inertial sensing device” (para 0054 – “the sensor unit 102 includes one or more MEMS (micro electro mechanical systems) sensors. For example, the MEMS sensors include an accelerometer, a gyroscope, or any other inertial sensor… In addition, the electronic device 100 may further include various types of sensors, such as a gesture sensor, a pressure sensor, a magnetic sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor. (i.e. stress sensors and environmental sensors, added by examiner). Each sensor of the sensor unit 102 can be mounted on a separate chip and a plurality of sensors can be mounted on a single chip”);
Mulage does not specifically disclose:
“wherein the sensor data is received while the inertial sensing device is moved”.
However, Scafidi discloses:
“wherein the sensor data is received while the inertial sensing device is moved” (para 0036 – “In the presence of movement, the accelerometer error cannot be readily distinguished from an actual accelerometer output because the movement of the MEMS chip likely adversely affects the multilinear regression calculations on which the estimated acceleration error relies.”).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the system of Mulage as taught by Scafidi in order to provide a more accurate and reliable drift determination.
Mulage/Scafidi combination does not explicitly disclose:
“temporally associating the inertial sensor data with the environmental sensor data; and determining, with at least one processor, a drift compensation for the at least one inertial sensor based on the inertial sensor data and the environmental sensor data”.
However, Trainor discloses:
“temporally associating the inertial sensor data with the environmental sensor data” (para 0187 – “Sensor devices can integrate multiple sensors to generate more complex outputs (i.e. associate measurements from the inertial sensor with stress measurements, added by examiner)”); and
“determining, with at least one processor” (Fig. 16; para 0158 – “the three sensors share common infrastructure, i.e., a common processor device 21”),
“a drift compensation for the at least one inertial sensor” (para 0005 – “determine a prediction based on the detected drift sensor state, and send the prediction to an external device”)
“based on the inertial sensor data and the environmental sensor data” (para 0149 – “The sensor-based state prediction system 50 monitors the sensor data from plural sensors for drift states in various physical systems for the insured premises, and when sensor-based state prediction system 50 determines existence of drift states, the sensor-based state prediction system 50 determines a suitable action alert (based on that drift state)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, disclosed by Mulage/Scafidi combination, as taught by Trainor, to include multiple sensor input drift detection in order to accurately predict sensor drift.
Regarding Claim 18: Mulage/Scafidi/Trainor combination discloses the method of Claim 17.
Mulage further discloses:
“further comprising: adjusting a configuration of the at least one inertial sensor based on the drift compensation” (para 0057 – “data processing unit 104 is configured to correct the measurement data received from the gyroscope. The data processing unit 104 is configured to compute the compensation parameters such as static drift, dynamic drift, and temperature drift. Further, the data processing unit 104 is configured to compensate the measurement data by correcting the measurement data with the computed compensation parameters”).
Regarding Claim 19: Mulage/Scafidi/Trainor combination discloses the method of Claim 18.
Mulage does not specifically disclose:
“wherein adjusting the configuration of the at least one inertial sensor comprises at least one of the following: modifying an attribute of the at least one inertial sensor, modifying an algorithm that processes the inertial sensor data, or any combination thereof”.
However, Trainor discloses:
“wherein adjusting the configuration of the at least one inertial sensor comprises at least one of the following: modifying an attribute of the at least one inertial sensor, modifying an algorithm that processes the inertial sensor data, or any combination thereof” (para 0100 – “This data and states can be stored in the database 51 and serves as training data for a machine learning model (i.e. algorithm, added by examiner)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, disclosed by Mulage/Scafidi/Trainor combination, as taught by Trainor, to include algorithm modification in order to produce a more accurate algorithm.
Regarding Claim 20: Mulage/Scafidi/Trainor discloses the method of Claim 17.
Mulage does not specifically disclose:
“wherein determining the drift compensation comprises: inputting the environmental sensor data into a machine-learning model configured to output a predicted drift value, wherein the drift compensation is based on the predicted drift value”.
However, Trainor discloses:
“wherein determining the drift compensation comprises: inputting the environmental sensor data” (para 0005 – “cause a processor to collect sensor information from plural sensors deployed in a premises with sensor information including a sensor data value”)
“into a machine-learning model” (para 0066 – “The sensor based state prediction system 50 applies unsupervised algorithm learning models to analyze historical and current sensor data records”)
“configured to output a predicted drift value” (para 0005 – “determine a prediction based on the detected drift sensor state”),
“wherein the drift compensation is based on the predicted drift value” (para 0005 – “determine a prediction based on the detected drift sensor state, and send the prediction to an external device”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, disclosed by Mulage/Scafidi/Trainor combination, as taught by Trainor, to include sensor data based machine learning drift prediction in order to predict drift based on historical data trends.
Claims 8-10 and 27-29 are rejected under 35 U.S.C. 103 as being unpatentable based on Mulage/Scafidi combination in view of US20130041859 to Esterlilne (hereinafter Esterlilne).
Regarding Claim 8: Mulage/Scafidi combination discloses the system of Claim 1.
Mulage does not specifically disclose:
“wherein determining the drift compensation is based on a machine-learning algorithm”.
However, Esterlilne discloses:
“wherein determining the drift compensation is based on a machine-learning algorithm” (Fig. 27; para 0051 – “FIG. 27 is a block diagram of an embodiment of a neural network frequency-drift compensation system”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system, disclosed by Mulage/Scafidi combination, as taught by Esterlilne, to include a machine learning generated drift compensation in order to accurately correct drift.
Regarding Claim 9: Mulage/Scafidi/Esterlilne combination discloses the system of Claim 8.
Mulage does not specifically disclose:
“where the machine-learning algorithm comprises a deep neural network”.
However, Esterlilne discloses:
“where the machine-learning algorithm comprises a deep neural network” (para 0062 – “In some embodiments, an ANN may include one or more “hidden” layers”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system, disclosed by Alft, as taught by Esterlilne, to include a deep neural network in order to provide the ability to compute complex, non-linear drift compensation.
Regarding Claim 10: Mulage/Scafidi/Esterlilne combination discloses the system of Claim 9.
Mulage does not specifically disclose:
“wherein the deep neural network comprises a first fully-connected hidden layer, a second fully-connected hidden layer, and a third fully-connected hidden layer”.
However, Esterlilne discloses:
“wherein the deep neural network comprises a first fully-connected hidden layer” (Fig. 1; para 0062 – “As shown in FIG. 1, the second layer may be considered a hidden layer in some embodiments”),
“a second fully-connected hidden layer and a third fully-connected hidden layer” (Fig. 1; para 0061 – “the model illustrated in FIG. 1 consists of nine neurons organized into three layers”; para 0062 – “In some embodiments, an ANN may include one or more “hidden” layers”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system, disclosed by Mulage/Scafidi combination, as taught by Esterlilne, in order to allow specific transformations of sensor data.
Regarding Claim 27: Mulage/Scafidi combination discloses the method of Claim 23.
Mulage does not specifically disclose:
“wherein the drift compensation is determined based on a machine-learning model”.
However, Esterlilne discloses:
“wherein the drift compensation is determined based on a machine-learning model” (Fig. 27; para 0051 – “FIG. 27 is a block diagram of an embodiment of a neural network frequency-drift compensation system”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system, disclosed by Mulage/Scafidi combination, to include a machine learning generated drift compensation, as taught by Esterlilne, in order to accurately correct drift.
Regarding Claim 28: Mulage/Scafidi/Esterlilne combination discloses the method of Claim 27.
Mulage does not specifically disclose:
“wherein the machine- learning model comprises a deep neural network”.
However, Esterlilne discloses:
“wherein the machine- learning model comprises a deep neural network” (para 0062 – “In some embodiments, an ANN may include one or more “hidden” layers”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system, disclosed by Mulage/Scafidi/Esterlilne combination, to include a deep neural network as taught by Esterlilne, in order to provide the ability to compute complex, non-linear drift compensation.
Regarding Claim 29: Mulage/Scafidi/Esterlilne combination discloses the method of Claim 28.
Mulage does not specifically disclose:
“wherein the deep neural network comprises a first fully-connected hidden layer, a second fully-connected hidden layer, and a third fully-connected hidden layer”.
However, Esterlilne discloses:
“wherein the deep neural network comprises a first fully-connected hidden layer” (Fig. 1; para 0062 – “As shown in FIG. 1, the second layer may be considered a hidden layer ”)”,
“a second fully-connected hidden layer, and a third fully-connected hidden layer” (Fig. 1; para 0061 – “ the model illustrated in FIG. 1 consists of nine neurons organized into three layers”; para 0062 – “an ANN may include one or more “hidden” layers”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system, disclosed by Mulage/Scafidi/Esterlilne combination, to include multiple hidden neural network layers, as taught by Esterlilne, in order to allow specific transformations of sensor data.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Mulage in view of Scafidi and in further view of US20160210775 to Alaniz et al. (hereinafter Alaniz).
Regarding Claim 16: Mulage/Scafidi combination discloses the system of Claim 1.
Mulage does not specifically disclose:
“further comprising: a testbed computing device in communication with the inertial sensing device, the testbed computing device configured to generate signals configured to produce the sensor data from the inertial sensor and the plurality of stress sensors”.
However, Alaniz discloses:
“further comprising: a testbed computing device” (para 0008 – “The disclosed virtual environment may include a virtual test bed”)
“in communication with the inertial sensing device” (para 0008 – “The tool may be used during the development of sensor fusion processes”),
“the testbed computing device configured to generate signals configured to produce the sensor data from the inertial sensor and the plurality of stress sensors” (para 0014 – “The output of the computing device 110 may include virtual sensor data that may be used for testing purposes, training purposes, or both, and may represent the sensor data collected by virtual sensors”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system, disclosed by Mulage/Scafidi combination, as taught by Alaniz, to include a testbed computing device in order to train the sensing system.
Claims 11 and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Mulage in view of Scafidi in view of Esterlilne and in further view of Trainor.
Regarding Claim 11: Mulage/Scafidi/Esterlilne combination discloses the system of Claim 8.
Mulage does not specifically disclose:
“wherein the machine-learning algorithm outputs a predicted drift value, and wherein the drift compensation is based on the predicted drift value”.
However, Trainor discloses:
“wherein the machine-learning algorithm” (para 0066 – “The prediction system 50 uses various types of unsupervised machine learning models ”)
“outputs a predicted drift value, and wherein the drift compensation is based on the predicted drift value ” (para 0005 - “determine a prediction based on the detected drift sensor state, and send the prediction to an external device”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system, disclosed by Mulage/Scafidi/Esterlilne combination, as taught by Trainor, to include machine learning drift prediction in order to preemptively correct for the drift.
Regarding Claim 30: Mulage/Scafidi/Esterlilne combination discloses the method of Claim 27.
Mulage does not specifically disclose:
“wherein the machine- learning model outputs a predicted drift value, and wherein the drift compensation is based on the predicted drift value”.
However, Trainor discloses:
“wherein the machine- learning model outputs a predicted drift value” (para 0066 – “The prediction system 50 uses various types of unsupervised machine learning models”; para 0005 – “determine a prediction based on the detected drift sensor state”), and
“wherein the drift compensation is based on the predicted drift value” (para 0005 – “determine a prediction based on the detected drift sensor state, and send the prediction to an external device”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system, disclosed by Mulage/Scafidi/Esterlilne combination, to include machine learning drift prediction, as taught by Trainor, in order to preemptively correct for drift.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Mulage/Scafidi combination in view of Trainor and in further view of Esterlilne.
Regarding Claim 21: Mulage/Scafidi/Trainor combination discloses the method of Claim 20.
Mulage does not specifically disclose:
“wherein the machine-learning model comprises a deep neural network comprising a first fully-connected hidden layer, a second fully-connected hidden layer, and a third fully-connected hidden layer”.
However, Esterlilne discloses:
“wherein the machine-learning model comprises a deep neural network comprising a first fully-connected hidden layer” (Fig. 1; para 0062 – “As shown in FIG. 1, the second layer may be considered a hidden layer in some embodiments”),
“a second fully-connected hidden layer and a third fully-connected hidden layer” (Fig. 1; para 0061 – “the model illustrated in FIG. 1 consists of nine neurons organized into three layers”; para 0062 – “In some embodiments, an ANN may include one or more “hidden” layers”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system, disclosed by Mulage/Scafidi/Trainor combination, as taught by Esterlilne, in order to allow specific transformations of sensor data.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable based on Mulage/Scafidi combination in view of Trainor and in further view of Alaniz.
Regarding Claim 22: Mulage/Scafidi/Trainor combination discloses the method of Claim 20.
Mulage does not specifically disclose:
“further comprising: training the machine-learning model based on training input data generated with a testbed computing device”.
However, Alaniz discloses:
“further comprising: training the machine-learning model” (para 0010 – “A machine learning process may take in these images as input … and train classifiers to recognize each sign type (i.e. each type of data, added by examiner)”).
“based on training input data generated with a testbed computing device” (para 0014 – “The output of the computing device 110 may include virtual sensor data that may be used for testing purposes, training purposes, or both, and may represent the sensor data collected by virtual sensors”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system, disclosed by Mulage/Scafidi/Trainor combination, as taught by Alaniz, to include a testbed computing device in order to train the sensing system.
Claims 34-36 and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Mulage/Scafidi combination in view of US20220205784A1 to Gattere et al. (hereinafter Gattere).
Regarding Claim 34: Mulage/Scafidi combination discloses:
“A system comprising: a sensing device comprising: a plurality of environmental sensors configured to measure at least one environmental parameter of the sensing device” (Fig. 1; para 0053 – “The non-limiting example of FIG. 1 illustrates various hardware elements of an electronic device 100 (i.e. chip, added by examiner) for gyroscope drift compensation… the electronic device 100 includes a sensor unit 102, a data processing unit 104, a storage unit 106 and a display unit 108”; para 0054 – “the sensor unit 102 includes one or more MEMS (micro electro mechanical systems) sensors. For example, the MEMS sensors include an accelerometer, a gyroscope, or any other inertial sensor… In addition, the electronic device 100 may further include various types of sensors, such as a gesture sensor, a pressure sensor, a magnetic sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor. (i.e. stress sensors and environmental sensors, added by examiner). Each sensor of the sensor unit 102 can be mounted on a separate chip and a plurality of sensors can be mounted on a single chip”);
Mulage/Scafidi combination does not specifically disclose:
“at least one micromechanical sensor; and at least one computing device configured to: receive sensor data from the at least one micromechanical sensor and the plurality of environmental sensors; and determine a drift compensation of the micromechanical sensor based on the sensor data”.
However, Gattere discloses:
“at least one micromechanical sensor” (para 0026 – “The present disclosure provides a micromechanical structure of a multi-axis MEMS gyroscope having reduced drift of its electrical parameters, for example in terms of output signal in response to a zero input (ZRL) and of in terms of sensitivity, in the presence of deformations of the corresponding substrate”)
“at least one computing device configured to: receive sensor data from the at least one micromechanical sensor and the plurality of environmental sensors; and” (para 0093 – “The electronic device 40 is generally able to process, store, and/or transmit and receive signals and information, and comprises: a microprocessor 44, which receives the signals (i.e. sensor data, added by examiner) detected by the MEMS gyroscope 42; and an input/output interface 45, for example, provided with a keypad and a display, coupled to the microprocessor 44.”)
“determine a drift compensation of the micromechanical sensor based on the sensor data” (para 0083 – “the coupling assembly 24 of the micromechanical structure 10 enables effective compensation of possible deformations of the substrate 12 and reduction and minimization of drifts of the electrical parameters for detection of the angular velocity”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system, disclosed by Mulage/Scafidi combination, as taught by Gattere, to include the micromechanical sensor into the sensing device in order to more efficiently reduce sensor drift and to capture stress states across the device.
Regarding Claim 35: Mulage/Scafidi/Gattere combination discloses the system of Claim 34.
Mulage further discloses:
“wherein the plurality of environmental sensors comprises at least one of the following types of sensors: a stress sensor, a temperature sensor, a resonance oscillator sensor, a strain sensor, a gas chemical sensor, or any combination thereof” (para 0054 – “the sensor unit 102 includes one or more MEMS (micro electro mechanical systems) sensors. For example, the MEMS sensors include an accelerometer, a gyroscope, or any other inertial sensor… In addition, the electronic device 100 may further include various types of sensors, such as a gesture sensor, a pressure sensor, a magnetic sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor. (i.e. stress sensors and environmental sensors, added by examiner). Each sensor of the sensor unit 102 can be mounted on a separate chip and a plurality of sensors can be mounted on a single chip”).
Regarding Claim 36: Mulage/Scafidi/Gattere combination discloses the system of Claim 34.
Mulage does not specifically disclose:
“wherein the at least one micromechanical sensor comprises at least one of the following: an inertial sensor, a resonant gravimetric sensor, a resonant timing device, or any combination thereof”.
However, Gattere discloses:
“wherein the at least one micromechanical sensor comprises at least one of the following: an inertial sensor, a resonant gravimetric sensor, a resonant timing device, or any combination thereof” (para 0026 – “The present disclosure provides a micromechanical structure of a multi-axis MEMS gyroscope (i.e. inertial sensor, added by examiner) having reduced drift of its electrical parameters”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system, disclosed by Mulage/Scafidi combination, as taught by Gattere, to include the inertial sensor into the micromechanical sensor in order to more efficiently reduce sensor drift since the inertial sensors feature very low bias instability.
Regarding Claim 39: Mulage/Scafidi/Gattere combination discloses the system of Claim 34.
Mulage further discloses:
“wherein the plurality of environmental sensors comprises a plurality of magnetometers” (para 0054 – “the electronic device 100 may further include various types of sensors, such as a gesture sensor, a pressure sensor, a magnetic sensor”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US20020158796 to Humphrey et al. (hereinafter Humphrey) discloses integrated GPS and IGS system and method.
US20220306089 to Seth (hereinafter Seth) discloses relative Position Tracking Using Motion Sensor With Drift Correction.
US20180120112 to Yost (hereinafter Yost) discloses performance of inertial sensing systems using dynamic stability compensation.
US8762091 to Foxlin et al. (hereinafter Foxlin) discloses inertial measurement system.
US20160245667 to Najafi et al. (hereinafter Najafi) discloses actuation And Sensing Platform For Sensor Calibration And Vibration Isolation. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Lyudmila Zaykova-Feldman whose telephone number is (469)295-9269. The examiner can normally be reached 8:30am - 5:30pm, Monday through Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen M. Vazquez can be reached on 571-272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LYUDMILA ZAYKOVA-FELDMAN/
Examiner, Art Unit 2857 /LINA CORDERO/Primary Examiner, Art Unit 2857
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