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 .
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 12-13, 37, and 38 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by WO2002035048A1 to Alft et al. (hereinafter Alft).
Regarding Claim 1: Alft discloses:
“A system comprising: an inertial sensing device comprising:” (page 1, lines 10-11 – “an inertial navigation sensor package” )
“an inertial sensor” (page 14, lines 15-16 – “The location of the boring tool is determined using one or more inertial sensors”); and
“a plurality of stress sensors configured to measure stress applied to the inertial sensing device” (page 15, lines 29-30; page 16, lines 1-2 – “In addition to one or more angular rate sensors, a boring tool may be equipped with an on-board radar unit, such as a ground penetrating radar (GPR) unit. The boring tool may also include one or more geophysical sensors, including a capacitive sensor, acoustic sensor, ultrasonic sensor, seismic sensor, resistive sensor, and electromagnetic sensor, for example”; page 18, lines 28-30; page 19, line 1 – “The universal controller may make gross and subtle adjustments to a boring operation based on various other types of acquired data, including, for example, geophysical data at the drilling site acquired prior to or during the boring operation, drill string/drill head/installation product data such as maximum bend radii and stress/strain data”); and
“at least one computing device” (page 18, lines 8-9 – “The universal controller may be implemented using a single processor or multiple processors”) configured to:
“receive sensor data from the inertial sensor and the plurality of stress sensors” (page 18, lines 16-17 – “The universal controller processes the received boring tool telemetry/GPR or other geophysical sensor data”); and
“determine a drift compensation of the inertial sensor based on the sensor data” (page 17, lines 29-30 – “Using the acquired temperature data, the temperature dependent drift rate 30 may be accounted for and an appropriate offset may be computed”).
Regarding Claim 2: Alft discloses the system of Claim 1.
Alft further discloses:
“wherein the inertial sensing device comprises the at least one computing device” (page 18, lines 8-9 – “The universal controller may be implemented using a single processor or multiple processors”).
Regarding Claim 3: Alft discloses the system of Claim 1.
Alft 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” (page 40, lines 3-8 – “The functions performed by the universal controller 72 may be performed by multiple or distributed processors… all or some of the functions associated with the universal controller may be performed from a remotely located processing facility, such as a remote facility which controls the boring machine operations via a satellite or other high-speed communications link”).
Regarding Claim 4: Alft discloses the system of Claim 1.
Alft further discloses:
“wherein the inertial sensing device further comprises a plurality of environmental sensors in addition to the plurality of stress sensors” (page 17, lines 21-22 – “one or more temperature sensors which sense the ambient temperature”), and
“wherein the drift compensation is determined at least partially based on sensor data received from the plurality of environmental sensors” (page 17, lines 29-30 – “Using the acquired temperature data, the temperature dependent drift rate 30 may be accounted for and an appropriate offset may be computed”).
Regarding Claim 5: Alft discloses the system of Claim 4.
Alft 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” (page 17, lines 21-22 – “one or more temperature sensors which sense the ambient temperature”).
Regarding Claim 6: Alft discloses the system of Claim 4.
Alft further discloses:
“wherein the sensor data is received while the inertial sensing device is moved” (page 29, lines 4-6 – “acquisition and processing of boring tool location, orientation, and physical environment information (e.g., temperature, stress/pressure, operating status), which may include geophysical data, in real-time”).
Regarding Claim 12: Alft discloses the system of Claim 1.
Alft further discloses:
“wherein the inertial sensor comprises an array of accelerometers” (page 15, lines 4-5- “an inertial navigation sensor package which includes one or more angular rate sensors”; page 33, lines 6-7 – “Such sensors typically include a two or three-axis gyroscope, a triad or three-axis accelerometer”).
Regarding Claim 13: Alft discloses the system of Claim 1.
Alft further discloses:
“wherein the inertial sensor comprises at least one gyroscope or an array of gyroscopes” (page 15, lines 4-5- “an inertial navigation sensor package which includes one or more angular rate sensors”; page 33, line 6 – “Such sensors typically include a two or three-axis gyroscope).
Regarding Claim 37: Alft discloses the system of Claim 1.
Alft further discloses:
“wherein the inertial sensor comprises an array of gyroscopes” (page 15, lines 4-5 – “an inertial navigation sensor package which includes one or more angular rate sensors”; page 33, line 6 – “Such sensors typically include a two or three-axis gyroscope”).
Regarding Claim 38: Alft discloses the system of Claim 1.
Alft further discloses:
“wherein the inertial sensor comprises a plurality of accelerometers and
a plurality of gyroscopes” (page 15, lines 4-5 – “an inertial navigation sensor package which includes one or more angular rate sensors”; page 33, line 6-7 – “Such sensors typically include a two or three-axis gyroscope, a triad or three-axis accelerometer”).
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.
Claims 14, 15, 23-25, and 31-33 are rejected under 35 U.S.C. 103 as being unpatentable over Alft,
Regarding Claim 14: Alft discloses the system of Claim 1.
Alft further discloses:
“wherein the inertial sensing device comprises a chip” (Fig. 6; page 44, lines 13-14 – “the sensors and electronic devices shown in Fig. 6 are disposed on a printed circuit board (PCB) 101”), and
“wherein the inertial sensor and the plurality of stress sensors are arranged on the chip” (page 44, lines 23-26 – “For example, each of the gyroscope 198, accelerometers 197, and magnetometers 196 may be embodied in integrated circuit (IC) form (i.e., chip form) and disposed in an IC package appropriate for mounting on the PCB 101”).
Yet, Alft does not explicitly disclose a plurality of stress sensors are arranged on the chip.
However, Alft discloses the inertial sensor arranged on the chip.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the limitation of “stress sensors on the chip”, as taught by Alft, in order to allow the stress sensors to sense physical stress to the inertial sensing device directly.
Regarding Claim 15: Alft discloses the system of Claim 14.
Alft further discloses:
“further comprising a data storage device” (page 37, lines 15-17 – “a universal controller 72 which interacts with a number of other controls, sensors, and data storing/processing resources”)
“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” (page 18, lines 28-30; page 19, line 1 – “The universal controller may make gross and subtle adjustments to a boring operation based on various other types of acquired data, including, for example, geophysical data at the drilling site acquired prior to or during the boring operation drill string/drill head/installation product data such as maximum bend radii and stress/strain data”; page 33, lines 4-9 – “sensor data is acquired during the boring operation in real-time from various sensors provided in the navigation sensor package 27 at the boring tool 24. Such sensors typically include a two or three-axis gyroscope, a triad or three-axis accelerometer, and a three-axis magnetometer. The acquired data is communicated to the universal controller 25”).
Regarding Claim 23: Alft discloses:
“An inertial sensing device comprising: an inertial sensor” (page 14, lines 15-16 – “The location of the boring tool is determined using one or more inertial sensors”); and
“a plurality of stress sensors” (page 15, lines 29-30; page 16, lines 1-2 – “In addition to one or more angular rate sensors, a boring tool may be equipped with an on-board radar unit, such as a ground penetrating radar (GPR) unit. The boring tool may also include one or more geophysical sensors, including a capacitive sensor, acoustic sensor, ultrasonic sensor, seismic sensor, resistive sensor, and electromagnetic sensor, for example”; page 18, lines 28-30; page 19, line 1 – “The universal controller may make gross and subtle adjustments to a boring operation based on various other types of acquired data, including, for example, geophysical data at the drilling site acquired prior to or during the boring operation, drill string/drill head/installation product data such as maximum bend radii and stress/strain data”).
Yet, Alft does not explicitly disclose:
“a plurality of stress sensors arranged on the inertial sensing device and configured to measure stress applied to the inertial sensing device”.
However, Alft discloses the inertial sensor arranged on the inertial sensing device.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the limitation of “a plurality of stress sensors arranged on the inertial sensing device”, as taught by Alft, in order to allow the plurality of stress sensors to sense physical stress to the inertial sensing device directly.
Regarding Claim 24: Alft discloses the inertial sensing device of Claim 23.
Alft further discloses:
“further comprising an interface configured to output sensor data from the inertial sensor and the plurality of stress sensors” (page 8, lines 19-20 – “an interface that couples the controller with the navigation sensor unit”, page 18, lines 28-30, and page 19, line 1 – “The universal controller may make gross and subtle adjustments to a boring operation based on various other types of acquired data, including, for example, geophysical data at the drilling site acquired prior to or during the boring operation, drill string/drill head/installation product data such as maximum bend radii and stress/strain data”).
Regarding Claim 25: Alft discloses the inertial sensing device of Claim 23.
Alft further discloses:
“further comprising a plurality of temperature sensors” (page 17, lines 21-22 – “one or more temperature sensors which sense the ambient temperature”).
Regarding Claim 31: Alft discloses the inertial sensing device of Claim 23.
Alft further discloses:
“wherein the inertial sensor comprises an array of accelerometers” (page 15, lines 4-5- “an inertial navigation sensor package which includes one or more angular rate sensors”; page 33, lines 6-7 – “Such sensors typically include a two or three-axis gyroscope, a triad or three-axis accelerometer”).
Regarding Claim 32: Alft discloses the inertial sensing device of Claim 23.
Alft further discloses:
“wherein the inertial sensor comprises at least one gyroscope or any array of gyroscopes” (page 15, lines 4-5- “an inertial navigation sensor package which includes one or more angular rate sensors”; page 33, lines 6-7 – “Such sensors typically include a two or three-axis gyroscope, a triad or three-axis accelerometer”).
Regarding Claim 33: Alft discloses the inertial sensing device of Claim 23.
Alft further discloses:
“further comprising a chip, the chip including the inertial sensor” (Fig. 6; page 44, lines 13-14 – “the sensors and electronic devices shown in Fig. 6 are disposed on a printed circuit board (PCB) 101”; page 44, lines 23-26 – “For example, each of the gyroscope 198, accelerometers 197, and magnetometers 196 may be embodied in integrated circuit (IC) form (i.e., chip form) and disposed in an IC package appropriate for mounting on the PCB 101”).
Yet, Alft does not explicitly disclose:
“a plurality of stress sensors arranged on the chip”.
However, Alft discloses the inertial sensor arranged on chip.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the limitation of “a plurality of stress sensors arranged on the chip”, as taught by Alft, in order to allow the plurality of stress sensors to sense physical stress to the inertial sensing device directly.
Claims 7, 17-20, and 26 are rejected under 35 U.S.C. 103 as being unpatentable based on Alft in view of US20170091870 to Trainor et al. (hereinafter Trainor).
Regarding Claim 7: Alft discloses the inertial sensing device of Claim 6.
Alft further discloses:
“measurements from the inertial sensor” (page 14, lines 15-16 – “The location of the boring tool is determined using one or more inertial sensors”)
“stress measurements from the plurality of stress sensors” (page 18, lines 28-30; page 19, line 1 – “The universal controller may make gross and subtle adjustments to a boring operation based on various other types of acquired data, including, for example, geophysical data at the drilling site acquired prior to or during the boring operation, drill string/drill head/installation product data such as maximum bend radii and stress/strain data”).
Alft 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 Alft as taught by Trainor in order to provide a more complete knowledge of drift.
Regarding Claim 17: Alft 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” (page 84, lines 14-16 – “the universal controller 72 receives input signals from the various sensors of the boring tool navigation sensor package 189, which may include a gyroscope 198, accelerometers 197”)
“while the inertial sensing device is moved” (page 84, lines 17-18 – “The sensor input signals are preferable acquired by the universal controller 72 in real-time” (i.e. while being moved, added by examiner));
“capturing a plurality of environmental sensor signals comprising environmental sensor data from a plurality of stress sensors arranged in the inertial sensing device” (page 15, lines 29-30; page 16, lines 1-2 – “In addition to one or more angular rate sensors, a boring tool may be equipped with an on-board radar unit, such as a ground penetrating radar (GPR) unit. The boring tool may also include one or more geophysical sensors, including a capacitive sensor, acoustic sensor, ultrasonic sensor, seismic sensor, resistive sensor, and electromagnetic sensor, for example”; page 18, lines 28-30; page 19, line 1 – “The universal controller may make gross and subtle adjustments to a boring operation based on various other types of acquired data, including, for example, geophysical data at the drilling site acquired prior to or during the boring operation, drill string/drill head/installation product data such as maximum bend radii and stress/strain data”)
“while the inertial sensing device is moved” (page 84, lines 17-18 – “The sensor input signals are preferable acquired by the universal controller 72 in real-time” (i.e. while being moved, added by examiner)).
Alft does not specifically 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”)
“for the at least one inertial sensor 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 Alft, as taught by Trainor, to include multiple sensor input drift detection in order to accurately predict sensor drift.
Regarding Claim 18: Alft in view of Trainor discloses the method of Claim 17.
Alft further discloses:
“further comprising: adjusting a configuration of the at least one inertial sensor based on the drift compensation” (page 17, lines 27-30 – “For example, a given solid-state gyroscope may have a known drift rate that varies as a function of gyroscope temperature. Using the acquired temperature data, the temperature dependent drift rate 30 may be accounted for and an appropriate offset may be computed”).
Regarding Claim 19: Alft in view of Trainor discloses the method of Claim 18.
Alft 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 Alft, as taught by Trainor, to include algorithm modification in order to produce a more accurate algorithm.
Regarding Claim 20: Alft in view of Trainor discloses the method of Claim 17.
Alft 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 Alft, as taught by Trainor, to include sensor data based machine learning drift prediction in order to predict drift based on historical data trends.
Regarding Claim 26: Alft discloses the inertial sensing device of Claim 23.
Alft further discloses:
“further comprising at least one computing device” (page 18, lines 8-9 – “The universal controller may be implemented using a single processor or multiple processors”).
Alft does not explicitly disclose:
“at least one computing device configured to determine a drift compensation of the inertial sensor based on sensor data from the inertial sensor and the plurality of stress sensors”.
However, Trainor discloses:
“at least one computing device configured to determine a drift compensation of the inertial sensor based on sensor data from the inertial sensor and the plurality of stress sensors” (para 0005 – “cause a processor to collect sensor information from plural sensors deployed in a premises with sensor information including a sensor data value… determine a prediction based on the detected drift sensor 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 Alft, as taught by Trainor, to use the data for drift compensation, received from multiple sensors, in order to accurately predict sensor drift.
Claims 8-10 are rejected under 35 U.S.C. 103 as being unpatentable based on Alft in view of US20130041859 to Esterlilne (hereinafter Esterlilne).
Regarding Claim 8: Alft discloses the system of Claim 1.
Alft 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 Alft, as taught by Esterlilne, to include a machine learning generated drift compensation in order to accurately correct drift.
Regarding Claim 9: Alft in view of Esterlilne discloses the system of Claim 8.
Alft 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: Alft in view of Esterlilne discloses the system of Claim 9.
Alft 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 Alft, 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 based on Alft in view of US20160210775 to Alaniz et al. (hereinafter Alaniz).
Regarding Claim 16: Alft discloses the system of Claim 1.
Alft 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 Alft, as taught by Alaniz, to include a testbed computing device in order to train the sensing system.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable based on Alft in view of Esterlilne and in further view of Trainor.
Regarding Claim 11: Alft in view of Esterlilne discloses the system of Claim 8.
Alft 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 Alft/Esterlilne combination, as taught by Trainor, to include machine learning drift prediction in order to preemptively correct for the drift.
Claims 21 and 27-30 are rejected under 35 U.S.C. 103 as being unpatentable over Alft in view of Trainor and in further view of Esterlilne.
Regarding Claim 21: Alft in view of Esterlilne discloses the method of Claim 20.
Alft 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 Alft, as taught by Esterlilne, in order to allow specific transformations of sensor data.
Regarding Claim 27: Alft in view of Trainor discloses the method of Claim 26.
Alft 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 Alft/Trainor combination, to include a machine learning generated drift compensation, as taught by Esterlilne, in order to accurately correct drift.
Regarding Claim 28: Alft/Trainor/Esterlilne combination discloses the method of Claim 26.
Alft 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 Alft/Trainor/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: Alft/Trainor/Esterlilne combination discloses the method of Claim 28.
Alft 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 Alft/Trainor/Esterlilne combination, to include multiple hidden neural network layers, as taught by Esterlilne, in order to allow specific transformations of sensor data.
Regarding Claim 30: Alft/Trainor/Esterlilne combination discloses the method of Claim 27.
Alft 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 Alft/Trainor/Esterlilne combination, to include machine learning drift prediction, as taught by Trainor, in order to preemptively correct for drift.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable based on Alft in view of Trainor and in further view of Alaniz.
Regarding Claim 22: Alft in view of Trainor discloses the method of Claim 26.
Alft 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 Alft/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 based on Alft in view of US20220205784A1 to Gattere et al. (hereinafter Gattere).
Regarding Claim 34: Alft discloses:
“A system comprising: a sensing device comprising:” (page 5, lines 4-5 – “A control system of an underground boring machine 5 receives data from sensors provided at the boring machine”)
“a plurality of environmental sensors configured to measure at least one environmental parameter of the sensing device” (page 17, lines 21-22 – “one or more temperature sensors which sense the ambient temperature”).
Alft 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 Alft, 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: Alft/Gattere combination discloses the system of Claim 34.
Alft 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” (page 17, lines 21-22 – “one or more temperature sensors which sense the ambient temperature”).
Regarding Claim 36: Alft/Gattere combination discloses the system of Claim 34.
Alft 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 Alft, 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: Alft/Gattere combination discloses the system of Claim 34.
Alft further disclose:
“wherein the plurality of environmental sensors comprises a plurality of magnetometers” (page 44, lines 23-26 – “For example, each of the gyroscope 198, accelerometers 197, and magnetometers 196 may be embodied in integrated circuit (IC) form (i.e., chip form) and disposed in an IC package appropriate for mounting on the PCB 101”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US-20050285692-A1 to Mattila et al. (hereinafter Mattila) discloses frequency synthesizer.
US-20160327446-A1 to Classen et al. (hereinafter Classen) discloses micromechanical pressure sensor device and corresponding manufacturing method.
US-20180118561-A1 to Lasalandra et al. (hereinafter Lasalandra) discloses temperature-compensated micro-electromechanical device, and method of temperature compensation in a micro-electromechanical device.
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/LYUDMILA ZAYKOVA-FELDMAN/Examiner, Art Unit 2857
/LINA CORDERO/Primary Examiner, Art Unit 2857