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 .
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.
Status of Claims
The following is a non-final, first office action in response to the communication filed on 10/10/2024.
Claims 1-20 are currently pending.
Claims 1-20 have been examined.
Information Disclosure Statement
The Information Disclosure Statement received on 10/10/2024 has been reviewed and considered.
Claim Rejections - 35 USC § 103
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 1-3, 8-9, 11-13, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Varley et al. (WO 2021160643 A1; hereinafter Varley) in view of Cheng et al. (Cheng, A., Yang, Z., Yang, L., Sun, Y., & Zhang, Z. (2023, May). Design of anti-pinch control strategy for vehicle sunroof based on Hall motor. In 2023 35th Chinese Control and Decision Conference (CCDC) (pp. 2607-2611). IEEE.; hereinafter Cheng) and further in view of Zhang (CN 112511063 B; hereinafter Zhang).
Regarding claim 1, Varley discloses the subject matter indicated in bold below:
A vehicle, comprising (see Varley at least pg. 1, lines 6-8 “The present invention is related to a pinch protection method for a vehicle, a pinch protection system for a vehicle, [and] a corresponding vehicle . . .”):
a movable gate of the vehicle (see Varley at least pg. 2, lines 13-16 “In particular, the closure component may comprise a tailgate, a boot lid, a hood or a passenger door, in particular a sliding door or a gull-wing door, of the vehicle.”);
a motor configured to control an operation of the movable gate of the vehicle (see Varley at least pg. 3, lines 33-34 “. . . the closure component is designed as an automated or motorized closure component.”);
a sensor associated with the vehicle (see Varley at least pg. 2, lines 30-36 “Here and in the following, a computer vision algorithm can be understood as an algorithm capable of determining properties or characteristics related to a content of an image by analyzing image data, in particular camera data . . . The monitoring of the pinch zone includes, in particular, capturing images repeatedly and generating respective camera data or camera signals by the camera system.”); and
control circuitry coupled to the motor, the control circuitry configured to (see Varley at least pg. 3, lines 7-10 “. . . the computing system may comprise circuitry of an imager chip of the camera system, an image signal processor, ISP, of the camera system or the vehicle and/or an electronic control unit, ECU, of the vehicle.”; pg. 5, lines 9-11 “. . . the control unit may transmit the control signal to a motor or a drive unit of the vehicle and the motor and/or the drive unit of the vehicle is configured to automatically close the closure component depending on the control signal.”): . . .
acquire sensor data associated with the sensor (see Varley at least pg. 3, lines 1-2 “The monitoring of the pinch zone includes, in particular, capturing images repeatedly and generating respective camera data or camera signals by the camera system.”);
apply a machine learning (ML) model on . . . the sensor data (see Varley at least pg. 4, lines 18-19 “The trained algorithm is in particular designed as an algorithm based on machine learning and/or computer vision.”; pg. 7, lines 1-5“. . . an optical flow may be determined based on the camera data to determine the probable intention of the person.”) . . .
determine a type of pinch corresponding to the movable gate of the vehicle, based on the application of the ML model (see Varley at least pg. 6, lines 14-17 “The body pose may, for example, be determined by the trained algorithm . . .”; pg. 6, lines 19-21 “The body pose may indicate how high the probability that the person, in particular a limb or another body part of the person, might be hit or clamped by the closure component is, in case the closing procedure would be carried out.”); and
control the operation of the motor based on the determined type of pinch (see Varley at least pg. 6, lines 23-26 “. . . if, only if or if and only if, it is determined by the computing system that the probability for a clamping or hitting a body part of the person is greater than a predetermined further threshold value based on the determined body pose, the measure for preventing the closing of the closure component is initiated.”).
While Varley discloses a sensor associated with a vehicle, acquiring sensor data associated with the sensor, applying a machine learning model on the sensor data, determining a type of pinch corresponding to a movable gate of the vehicle based on application of the machine learning model, and controlling operation of a motor based on a type of pinch, it does not appear to explicitly disclose a set of sensors associated with the vehicle, acquiring timeseries data associated with operation of a motor, determining statistical features associated with the acquired timeseries data, acquiring sensor data associated with the set of sensors, and applying a machine learning model on the acquired timeseries data and the sensor data, wherein the machine learning model is trained based on the determined statistical features.
Cheng teaches the subject matter underlined below:
. . . acquire time-series data associated with an operation of the motor (see Cheng at least pg. 2609, col. 2, paragraph 2“When the sunroof moves in the anti-pinch area, each time it enters a partition, the average current value of each partition is obtained [(i.e., multiple values over time)], and the difference is obtained by subtracting the average current value of the current partition from the average current value of the previous partition. Store this difference value as a reference value for adaptive storage of the current area [(i.e., timeseries)].”);
determine statistical features associated with the acquired time-series data (see Cheng at least pg. 2609, col. 2, paragraph 2 “When the sunroof moves in the anti-pinch area, each time it enters a partition, the average current value [(i.e., mean)] of each partition is obtained, and the difference is obtained by subtracting the average current value of the current partition from the average current value of the previous partition. Store this difference value as a reference value for adaptive storage of the current area.”); . . .
apply a machine learning (ML) model on the acquired time-series data . . . wherein the ML model is trained based on the determined statistical features (see Cheng at least pg. 2609, col. 2, paragraph 2 “When the sunroof moves in the anti-pinch area, each time it enters a partition, the average current value [(i.e., mean)] of each partition is obtained, and the difference is obtained by subtracting the average current value of the current partition from the average current value of the previous partition. Store this difference value as a reference value for adaptive storage of the current area.”; pg. 2609, col. 2, paragraph 3 “. . . RF is the current difference value to be stored, which is also the value to be updated during adaptive learning . . .”; Fig. 7- process is self-adaptive (i.e., machine learning used));
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the data acquisition and machine learning model application of Varley with the timeseries data acquisition, determination of statistical features of the timeseries data, and application of a machine learning model on the acquired timeseries data, wherein the machine learning model is trained based on the determined statistical features as taught by Cheng to acquire time-series data associated with an operation of the motor, determine statistical features associated with the acquired time-series data, and apply a machine learning model on the acquired time-series data and the sensor data, wherein the ML model is trained based on the determined statistical features. Doing so would improve the machine learning control functions for anti-pinch mechanisms as described in Varley by enabling additional motor-related data to be exploited, as recognized by Cheng (see Cheng at least pg. 2608, col. 1, paragraph 2 “A magnetic ring is installed in the motor bearing and the Hall pulse is recorded by the control module of the Hall sensor. The Hall pulse period corresponds to the motor load, and from the pulse width, we can determine whether the sunroof encounters an obstacle, and from the number of pulses, we can calculate the position of the sunroof opening . . .”). In machine learning applications, this type of data selection process is often referred to as “feature engineering,” which one of ordinary skill in the art would recognize results in a number of performance-related improvements including increased accuracy and mitigation of model overfitting.
While Varley and Cheng disclose a sensor associated with a vehicle, acquiring timeseries data associated with operation of a motor, determining statistical features associated with the acquired timeseries data, acquiring sensor data associated with the sensor, applying a machine learning model on the acquired timeseries data and the sensor data, wherein the machine learning model is trained based on the determined statistical features, determining a type of pinch corresponding to a movable gate of the vehicle based on application of the machine learning model, and controlling operation of a motor based on a type of pinch, they do not appear to explicitly disclose a set of sensors associated with the vehicle and acquiring sensor data associated with the set of sensors.
Zhang discloses the use of multiple sensors in determining anti-pinch determinations (see Zhang at least pg. 5, paragraph 6 “In the process that the window glass rises to the top from the bottom, an interval time (set to 10 ms) sends a timing interruption request signal to a current collector and a Hall counter, the current collector responds to the interruption request and periodically collects a motor sampling voltage value through a current, the sampling voltage value is sent to a window anti-clamping controller through the current sensor, the window anti-clamping controller divides the voltage values at two ends of a sampling resistor by the resistance value of the sampling resistor to be converted into motor current, and meanwhile, the motor currents at different moments calculated in a reasonable learning process are recorded.”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the sensor data acquisition and machine learning model application of Varley and Cheng with the multiple sensors as taught by Zhang to have a set of sensors associated with the vehicle and acquire sensor data associated with the set of sensors. Doing so would improve the machine learning control functions for anti-pinch mechanisms as described in Varley and Cheng by enabling additional data to be exploited. In machine learning applications, this type of data selection process is often referred to as “feature engineering,” which one of ordinary skill in the art would recognize results in a number of performance-related improvements including increased accuracy and mitigation of model overfitting.
Regarding claim 2, Varley, Cheng, and Zhang disclose the subject matter of claim 1 as recited in the claim and applied above. Additionally, Varley discloses the subject matter indicated in bold below:
. . . wherein the movable gate corresponds to at least one of: a door of the vehicle, a tailgate of the vehicle, a liftgate of the vehicle, a window of the vehicle, a bonnet of the vehicle, a sunroof of the vehicle, or a trunk of the vehicle (see Varley at least pg. 2, lines 13-16 “In particular, the closure component may comprise a tailgate, a boot lid, a hood or a passenger door, in particular a sliding door or a gull-wing door, of the vehicle.”).
Regarding claim 3, Varley, Cheng, and Zhang disclose the subject matter of claim 1 as recited in the claim and applied above. Additionally, Varley discloses the subject matter indicated in bold below:
. . . wherein the control circuitry is further configured to:
compare the acquired sensor data with a first threshold range (see Varley at least pg. 4, lines 9-13 “In other words, the computing system determines that there is an object in the pinch zone and what is the probability for the object corresponding to a person. This may, for example, be accomplished by a computer vision algorithm. The computing system may then compare the determined probability with the predefined threshold to identify the object as a person. In this way, the probability for false positives is further reduced. According to several implementations, a trained algorithm is used by the computing system to detect the person in the pinch zone.”); and
determine the type of pinch based on the comparison of the acquired sensor data with the first threshold range (see Varley at least pg. 4, lines 9-13 “In other words, the computing system determines that there is an object in the pinch zone and what is the probability for the object corresponding to a person. This may, for example, be accomplished by a computer vision algorithm. The computing system may then compare the determined probability with the predefined threshold to identify the object as a person [(i.e., pinch type determination made- person pinch or not)]. In this way, the probability for false positives is further reduced. According to several implementations, a trained algorithm is used by the computing system to detect the person in the pinch zone.”).
While Varley discloses comparing acquired sensor data with a first threshold range and determining a type of pinch based on the comparison of the acquired sensor data with the first threshold range, it does not appear to explicitly disclose comparing acquired timeseries data with a first threshold range and determining a type of pinch based on the comparison of the acquired timeseries data with the first threshold range
Zhang teaches the subject matter underlined below:
. . . wherein the control circuitry is further configured to:
compare the acquired time-series data with a first threshold range (see Zhang at least pg. 4, paragraph 8 “. . . the real-time current corresponding to the target distance segment is sequentially collected, whether the real-time current, the learning current corresponding to the target distance segment and the preset anti-pinch threshold meet a first preset corresponding relation or not is judged . . .”; pg. 5, paragraph 4 “Based on the set anti-pinch current threshold, if the detected actual current of the motor is greater than the anti-pinch current threshold in the running process of the vehicle window, the clamping is considered to occur, and corresponding anti-pinch rebound operation is executed so as to reduce the damage.”); and
determine the type of pinch based on the comparison of the acquired time-series data with the first threshold range (see Zhang at least pg. 5, paragraph 4 “Based on the set anti-pinch current threshold, if the detected actual current of the motor is greater than the anti-pinch current threshold in the running process of the vehicle window, the clamping is considered to occur, and corresponding anti-pinch rebound operation is executed so as to reduce the damage [(i.e., pinch type (no pinch vs. pinch) determined)].”)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the comparison of the acquired sensor data with a first threshold range and determination of the type of pinch based on the comparison of the acquired sensor data with the first threshold range of Varley with the timeseries data threshold comparison as taught by Zhang to compare the acquired time-series data with a first threshold range and determine the type of pinch based on the comparison of the acquired time-series data with the first threshold range. Doing so would extend the method of Varley to cover all of the data types of the combined invention of Varley and Zhang. The examiner supplies the same rationale for the combination of these references as applied above with regard to claim 1.
Regarding claim 8, Varley, Cheng, and Zhang disclose the subject matter of claim 1 as applied above and recited in the claim. Additionally, Varley discloses the subject matter indicated in bold below:
. . . wherein the ML model corresponds to a Convolutional Neural Network (CNN) model (see Varley at least pg. 4, lines 29-30 “. . . the trained algorithm is based on a trained artificial neural network, in particular a convolutional neural network, CNN.”).
Regarding claim 9, Varley, Cheng, and Zhang disclose the subject matter of claim 1 as applied above and recited in the claim.
While Varley discloses the use of sensor data (see Varley at least pg. 3, lines 1-2 “The monitoring of the pinch zone includes, in particular, capturing images repeatedly and generating respective camera data or camera signals by the camera system.”), it does not appear to explicitly disclose acquiring timeseries data and determining statistical features associated with the acquired timeseries data.
Cheng teaches the subject matter underlined below:
. . . wherein the determined statistical features include at least one of: a mean, a standard deviation, a skewness, a kurtosis, a variance, Fast Fourier Transform (FFT) coefficients, or a data length (see Cheng at least pg. 2609, col. 2, paragraph 2 “When the sunroof moves in the anti-pinch area, each time it enters a partition, the average current value of each partition is obtained, and the difference is obtained by subtracting the average current value [(i.e., mean)] of the current partition from the average current value of the previous partition. Store this difference value as a reference value for adaptive storage of the current area.”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the use of sensor data of Varley with the acquiring timeseries data and determining statistical features associated with the acquired timeseries data, wherein the determined statistical features include a mean as taught by Cheng to acquire timeseries data and determine statistical features associated with the acquired timeseries data, wherein the determined statistical features include a mean. The examiner supplies the same rationale for the combination of these references as applied above with regard to claim 1.
Regarding claim 11, the limitations of the claim are analogous to the limitations of claim 1 and the analysis of claim 1 is applied to claim 11. Additionally, claim 11 recites an electronic device comprising the control circuitry described in claim 1.
Varley discloses the subject matter indicated in bold below:
An electronic device, comprising (see Varley at least pg. 3, lines 7-10 “The computing system may comprise one or more computing units . . . the computing system may comprise circuitry of . . . an electronic control unit, ECU . . .”) . . .
Regarding claim 12, Varley, Cheng, and Zhang disclose the subject matter of claim 11 as recited in the claim and applied above. Additionally, the limitations of claim 12 are analogous to claim 2 and the analysis of claim 2 is applied to claim 12.
Regarding claim 13, Varley, Cheng, and Zhang disclose the subject matter of claim 11 as recited in the claim and applied above. Additionally, the limitations of claim 13 are analogous to claim 3 and the analysis of claim 3 is applied to claim 13.
Regarding claim 18, Varley, Cheng, and Zhang disclose the subject matter of claim 11 as recited in the claim and applied above. Additionally, the limitations of claim 18 are analogous to claim 9 and the analysis of claim 9 is applied to claim 18.
Regarding claim 20, the limitations of the claim are analogous to the limitations of claim 1 and the analysis of claim 1 is applied to claim 20. Additionally, claim 20 recites a method for executing the steps described in claim 1.
Varley discloses the subject matter indicated in bold below:
A method, comprising (see Varley at least pg. 1, lines 6-8 “The present invention is related to a pinch protection method for a vehicle . . .”) . . .
Claims 4-6 and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Varley in view of Cheng and further in view of Zhang and Gifford et al. (CA 2464747 C; hereinafter Gifford).
Regarding claim 4, Varley, Cheng, and Zhang disclose the subject matter of claim 3 as applied above and recited in the claim. Additionally, Varley discloses the subject matter indicated in bold below:
. . . wherein the type of pinch corresponds to a no-type of pinch based on the acquired data being below the first threshold range, the type of pinch corresponds to an affirmative type of pinch based on the acquired data being within the first threshold range (see Varley at least pg. 4, lines 4-7 “. . . an object is detected in the pinch zone by the computing system based on the monitoring for detecting the person in the pinch zone and a probability for the object being a person is determined by the computing system to be greater than a predefined threshold value based on the monitoring.”). . .
While Varley discloses the type of pinch corresponding to a no-type of pinch based on the acquired data being below the first threshold range and the type of pinch corresponding to an affirmative type of pinch based on the acquired data being within the first threshold range, it does not appear to explicitly disclose the acquired data being time-series data nor the type of pinch corresponding to a small-type of pinch based on the acquired time-series data being within the first threshold range and the type of pinch corresponding to a large-type of pinch based on the acquired time-series data being above the first threshold range.
Cheng teaches the acquiring time-series data (see Cheng at least pg. 2609, col. 2, paragraph 2“When the sunroof moves in the anti-pinch area, each time it enters a partition, the average current value of each partition is obtained [(i.e., multiple values over time)], and the difference is obtained by subtracting the average current value of the current partition from the average current value of the previous partition. Store this difference value as a reference value for adaptive storage of the current area [(i.e., timeseries)].”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the data acquisition of Varley with the time-series data acquisition as taught by Cheng to acquire time-series data. The examiner supplies the same rationale for the combination of these references as provided above with regard to claim 1.
While Varley and Cheng disclose the type of pinch corresponding to a no-type of pinch based on the acquired time-series data being below the first threshold range and the type of pinch corresponding to an affirmative type of pinch based on the acquired time-series data being within the first threshold range, they do not appear to explicitly disclose the type of pinch corresponding to a small-type of pinch based on the acquired time-series data being within the first threshold range and the type of pinch corresponding to a large-type of pinch based on the acquired time-series data being above the first threshold range.
Gifford teaches the subject matter underlined below:
. . . the type of pinch corresponds to a small-type of pinch based on the acquired time-series data being within the first threshold range, or the type of pinch corresponds to a large-type of pinch based on the acquired time-series data being above the first threshold range (see Gifford at least pg. 8, lines 11-13 “In the lower or primary zone 58, the controller 18 increases the sensitivity of the capacitive sensor 32 to allow it to detect the presence of an object even when the object is low enough to avoid physically moving the capacitive sensor 32 [(i.e., small pinch type- avoidable)].”; pg. 8, lines 14-18 “In the secondary zone 60, usually about 4 mm separating the upper edge 13 of the windowpane 12 from the sensor 32, the controller 18 decreases the sensitivity of the capacitive sensor 32. The position sensor 30 generates the position signal and the controller 18 responsively determines when the windowpane 12 enters the secondary zone 60 [(i.e., large pinch type- imminent)].”; pg. 8, lines 19-21“. . . the controller 18 applies a second predetermined threshold that has a magnitude and/or duration greater than the first predetermined threshold [(i.e., first threshold range and second threshold range)].”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the threshold value comparison for determining whether a pinch as occurred or not of Varley and Cheng with the second threshold for categorizing pinch conditions as small or large pinch types as taught by Gifford to have the type of pinch correspond to a small-type of pinch based on the acquired time-series data being within the first threshold range, or the type of pinch correspond to a large-type of pinch based on the acquired time-series data being above the first threshold range. Doing so would enable multi-zone control of movable gates and further allow the gates to approach a closed position without a controller misidentifying the gate as an object, as recognized by Gifford (see Gifford at least pg. 8, lines 22-28 “The reduction in sensitivity allows the windowpane 12 to approach the capacitive sensor 32 without the controller 18 misidentifying the windowpane 12 as an object that might be pinched between the windowpane 12 and the window frame 40.”).
Regarding claim 5, Varley, Cheng, and Zhang disclose the subject matter of claim 1 as applied above and recited in the claim.
While Varley discloses comparing data to threshold ranges to determine a pinch type (see Varley at least pg. 6, lines 23-26 “. . . if, only if or if and only if, it is determined by the computing system that the probability for a clamping or hitting a body part of the person is greater than a predetermined further threshold value based on the determined body pose, the measure for preventing the closing of the closure component is initiated.”), it does not appear to explicitly disclose comparing the determined statistical features associated with the acquired time-series data with a second threshold range and determining the type of pinch based on the comparison of the determined statistical features with the second threshold range.
Cheng teaches the acquiring time-series data (see Cheng at least pg. 2609, col. 2, paragraph 2“When the sunroof moves in the anti-pinch area, each time it enters a partition, the average current value of each partition is obtained [(i.e., multiple values over time)], and the difference is obtained by subtracting the average current value of the current partition from the average current value of the previous partition. Store this difference value as a reference value for adaptive storage of the current area [(i.e., timeseries)].”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the data acquisition of Varley with the time-series data acquisition as taught by Cheng to acquire time-series data and determine statistical features associated with the acquired time-series data. The examiner supplies the same rationale for the combination of these references as provided above with regard to claim 1.
While Varley and Cheng disclose comparing statistical features associated with acquired time-series data to threshold ranges to determine a pinch type, they do not appear to explicitly disclose comparing the determined statistical features associated with the acquired time-series data with a second threshold range and determining the type of pinch based on the comparison of the determined statistical features with the second threshold range.
Gifford teaches the subject matter underlined below:
. . . compare . . . the acquired time-series data with a second threshold range (see Gifford at least pg. 8, lines 19-21“. . . the controller 18 applies a second predetermined threshold that has a magnitude and/or duration greater than the first predetermined threshold.”); and
determine the type of pinch based on the comparison of the acquired time-series data with the second threshold range (see Gifford at least pg. 8, lines 22-28 “. . . should an object remain in the path of the windowpane 12 as the upper edge 13 approaches the sealing system 37, the controller 18 will still be able to detect it [(i.e., pinch type is imminent in second threshold range)] . . .”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the threshold value comparison for determining whether a pinch as occurred or not of Varley and Cheng with the second threshold for categorizing pinch conditions as small or large pinch types as taught by Gifford to compare the determined statistical features associated with the acquired time-series data with a second threshold range and determine the type of pinch based on the comparison of the determined statistical features with the second threshold range. The examiner supplies the same rationale for the combination of these references as provided above with regard to claim 4.
Regarding claim 6, Varley, Cheng, Zhang, and Gifford disclose the subject matter of claim 4 as recited in the claim and applied above. Additionally, Varley discloses the subject matter indicated in bold below:
. . . wherein the type of pinch corresponds to a no-type of pinch based on the acquired data being below the first threshold range, the type of pinch corresponds to an affirmative type of pinch based on the acquired data being within the first threshold range (see Varley at least pg. 4, lines 4-7 “. . . an object is detected in the pinch zone by the computing system based on the monitoring for detecting the person in the pinch zone and a probability for the object being a person is determined by the computing system to be greater than a predefined threshold value based on the monitoring.”). . .
While Varley discloses the type of pinch corresponding to a no-type of pinch based on the acquired data being below the first threshold range and the type of pinch corresponding to an affirmative type of pinch based on the acquired data being within the first threshold range, it does not appear to explicitly disclose the acquired data being time-series data nor the type of pinch corresponding to a small-type of pinch based on the acquired time-series data being within the first threshold range and the type of pinch corresponding to a large-type of pinch based on the acquired time-series data being above the first threshold range.
Cheng teaches the acquiring time-series data (see Cheng at least pg. 2609, col. 2, paragraph 2“When the sunroof moves in the anti-pinch area, each time it enters a partition, the average current value of each partition is obtained [(i.e., multiple values over time)], and the difference is obtained by subtracting the average current value of the current partition from the average current value of the previous partition. Store this difference value as a reference value for adaptive storage of the current area [(i.e., timeseries)].”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the data acquisition of Varley with the time-series data acquisition as taught by Cheng to acquire time-series data. The examiner supplies the same rationale for the combination of these references as provided above with regard to claim 1.
While Varley and Cheng disclose the type of pinch corresponding to a no-type of pinch based on the acquired time-series data being below the first threshold range and the type of pinch corresponding to an affirmative type of pinch based on the acquired time-series data being within the first threshold range, they do not appear to explicitly disclose the type of pinch corresponding to a small-type of pinch based on the acquired time-series data being within the first threshold range and the type of pinch corresponding to a large-type of pinch based on the acquired time-series data being above the first threshold range.
Gifford teaches the subject matter underlined below:
. . . the type of pinch corresponds to a small-type of pinch based on the acquired time-series data being within the first threshold range, or the type of pinch corresponds to a large-type of pinch based on the acquired time-series data being above the first threshold range (see Gifford at least pg. 8, lines 11-13 “In the lower or primary zone 58, the controller 18 increases the sensitivity of the capacitive sensor 32 to allow it to detect the presence of an object even when the object is low enough to avoid physically moving the capacitive sensor 32 [(i.e., small pinch type- avoidable)].”; pg. 8, lines 14-18 “In the secondary zone 60, usually about 4 mm separating the upper edge 13 of the windowpane 12 from the sensor 32, the controller 18 decreases the sensitivity of the capacitive sensor 32. The position sensor 30 generates the position signal and the controller 18 responsively determines when the windowpane 12 enters the secondary zone 60 [(i.e., large pinch type- imminent)].”; pg. 8, lines 19-21“. . . the controller 18 applies a second predetermined threshold that has a magnitude and/or duration greater than the first predetermined threshold [(i.e., first threshold range and second threshold range)].”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the threshold value comparison for determining whether a pinch as occurred or not of Varley and Cheng with the second threshold for categorizing pinch conditions as small or large pinch types as taught by Gifford to have the type of pinch correspond to a small-type of pinch based on the acquired time-series data being within the first threshold range, or the type of pinch correspond to a large-type of pinch based on the acquired time-series data being above the first threshold range. The examiner supplies the same rationale for the combination of these references as provided above with regard to claim 4.
Regarding claim 14, Varley, Cheng, Zhang, and Gifford disclose the subject matter of claim 13 as recited in the claim and applied above. Additionally, the limitations of claim 14 are analogous to claim 4 and the analysis of claim 4 is applied to claim 14.
Regarding claim 15, Varley, Cheng, and Zhang disclose the subject matter of claim 11 as recited in the claim and applied above. Additionally, the limitations of claim 15 are analogous to claim 5 and the analysis of claim 5 is applied to claim 15.
Regarding claim 16, Varley, Cheng, and Zhang disclose the subject matter of claim 15 as recited in the claim and applied above. Additionally, the limitations of claim 16 are analogous to claim 6 and the analysis of claim 6 is applied to claim 16.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Varley in view of Cheng and further in view of Zhang and Unrau (Unrau, J. (2015, November 20). How does the power windows anti-pinch safety feature work?. YourMechanic.; hereinafter Unrau).
Regarding claim 7, Varley, Cheng, and Zhang disclose the subject matter of claim 1 as recited in the claim and applied above.
While Varley discloses acquiring sensor data (see Varley at least pg. 3, lines 1-2 “The monitoring of the pinch zone includes, in particular, capturing images repeatedly and generating respective camera data or camera signals by the camera system.”), it does not appear to explicitly disclose the acquired sensor data including at least one of an orientation of the vehicle, a temperature of the vehicle, or a battery voltage of the vehicle, and acquiring time-series data including at least one of a current associated with the motor or a rotation speed associated with the motor.
Cheng teaches acquiring time-series data associated with the motor (see Cheng at least pg. 2609, col. 2, paragraph 2“When the sunroof moves in the anti-pinch area, each time it enters a partition, the average current value of each partition is obtained [(i.e., multiple values over time)], and the difference is obtained by subtracting the average current value of the current partition from the average current value of the previous partition. Store this difference value as a reference value for adaptive storage of the current area [(i.e., timeseries)].”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the data acquisition of Varley with the acquisition of time-series data associated with the motor as taught by Cheng to acquire time-series data associated with the motor. The examiner supplies the same rationale for the combination of these references as provided above with regard to claim 1.
While Varley and Cheng disclose acquiring sensor data and time-series data associated with the motor, they do not appear to explicitly disclose the acquired sensor data including at least one of an orientation of the vehicle, a temperature of the vehicle, or a battery voltage of the vehicle.
Unrau teaches anti-pinch mechanisms for movable liftgates in vehicles being dependent on battery voltage (see Unrau at least paragraph 4 “If the vehicle loses battery power or the power windows require a repair, the power windows will not know their upper and lower limits. The window motor will need to be re-trained so it can learn the window travel limits.”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the sensor data acquisition for use in vehicle movable gate anti-pinch mechanisms of Varley and Cheng with the anti-pinch mechanisms for movable liftgates in vehicles being dependent on battery voltage as taught by Unrau to have the sensor data be a battery voltage of the vehicle. Doing so would be a mere substitution of one type of data known in the art to be useful for anti-pinch mechanism functioning for another type of data known in the art to be useful for anti-pinch mechanism functioning.
Regarding claim 17, Varley, Cheng, and Zhang disclose the subject matter of claim 11 as recited in the claim and applied above. Additionally, the limitations of claim 17 are analogous to claim 7 and the analysis of claim 7 is applied to claim 17.
Claims 10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Varley in view of Cheng and further in view of Zhang and Guo et al. (Guo, Y., Wang, G., & Kang, W. (2024, April). Analysis and Research on the Parameterization of Automotive Power Tailgate. In 2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology (ICCECT) (pp. 1451-1455). IEEE.; hereinafter Guo).
Regarding claim 10, Varely, Cheng, and Zhang disclose the subject matter of claim 1 as recited in the claim and applied above.
While Varley discloses determining a type of pinch (see Varley at least pg. 4, lines 4-7 “. . . an object is detected in the pinch zone by the computing system based on the monitoring for detecting the person in the pinch zone and a probability for the object being a person is determined by the computing system to be greater than a predefined threshold value based on the monitoring.”), it does not appear to explicitly disclose the type of pinch corresponding to an angle between a position of the movable gate and a vehicle body portion associated with the movable gate.
Guo discloses the subject matter underlined below:
. . . wherein the type of pinch corresponds to an angle between a position of the movable gate and a vehicle body portion associated with the movable gate (see Guo at least pg. 1452, col. 2, paragraph 7 “. . . it can be seen that the anti-pinch force is related to . . . the opening angle parameters of the tailgate.”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the pinch type determination of Varley with the type of pinch corresponding to an angle between a position of the movable gate and a vehicle body portion associated with the movable gate as taught by Guo to have the type of pinch correspond to an angle between a position of the movable gate and a vehicle body portion associated with the movable gate. Doing so would extend the system of Varley, which is directed toward a sunroof type of movable gate to a tailgate type of movable gate.
Regarding claim 19, Varley, Cheng, and Zhang disclose the subject matter of claim 11 as recited in the claim and applied above. Additionally, the limitations of claim 19 are analogous to claim 10 and the analysis of claim 10 is applied to claim 19.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
AVR Microcontrollers (AVR Microcontrollers. (2006, December). AVR480: Anti-Pinch System for Electrical Window. Atmel Corporation.) discloses an anti-pinch system for vehicle windows.
Ullatil (Ullatil, S. (2020). Anti-Pinch Protection Approaches on Smart Tailgate. Global Journal of Researches in Engineering: J General Engineering, 20(3).) discloses anti-pinch systems for vehicle tailgates.
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/TABITHA KRESS/Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
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