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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
With respect to claim 1,
Step 2A Prong One:
The following bold limitations are considered abstract:
“A method for determining a movement of a vehicle, comprising: reading in a first sensor signal via an interface to a first inertial sensor of the vehicle and a second sensor signal via an interface to a second inertial sensor of the vehicle;
ascertaining a first level signal representing a noise level of the first sensor signal and a second level signal representing a noise level of the second sensor signal using the first sensor signal, the second sensor signal and an activity signal representing an activity of the vehicle;
and determining a movement signal representing the movement of the vehicle using the first sensor signal, the second sensor signal, the first level signal, and the second level signal.”
The above bolded limitations are directed to abstract ideas and would fall within the “Mathematical Concept” and “Mental Process” groupings of abstract ideas. Ascertaining the first level signal and a second level signal is a mathematical concept as seen in Para. [0014] of the specification which shows that the respective level signals are found quantizing the sensor signals. Quantizing a signal is a well-known mathematical concept. According to MPEP 2106.04(C) “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Determining a movement signal involves comparing the level signals to certain thresholds as seen in Para. [0015] of the specification. This comparison is a mental process as it can be done in the human mind using observation, judgement, and opinion.
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements –
“A method for determining a movement of a vehicle, comprising: reading in a first sensor signal via an interface to a first inertial sensor of the vehicle and a second sensor signal via an interface to a second inertial sensor of the vehicle;”
Examiner views these limitations amount to generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
As such Examiner does NOT view that the claims
-Improve the functioning of a computer, or to any other technology or technical field
-Apply the judicial exception with, or by use of, a particular machine - see MPEP
2106.05(b)
-Effect a transformation or reduction of a particular article to a different state or thing -
see MPEP 2106.05(c)
-Apply or use the judicial exception in some other meaningful way beyond generally
linking the use of the judicial exception to a particular technological environment, such that the
claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP
2106.05(e) and Vanda Memo.
Moreover, Examiner views the claims to be merely generally linking the use of the judicial exception to a vehicle. Furthermore, reading sensor signals is viewed as necessary data gathering.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Considering the claim as a whole, one of ordinary skill in the art would not know the practical application of the present invention since the claims do not apply or use the judicial exception in some meaningful way. As currently claimed, Examiner views that the additional elements do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, because the claim fails to recite clearly how the judicial exception is applied in a manner that does not monopolize the exception because the limitations “A method for determining a movement of a vehicle, comprising: reading in a first sensor signal via an interface to a first inertial sensor of the vehicle and a second sensor signal via an interface to a second inertial sensor of the vehicle” just tie the claim to vehicle data. Examiner further notes that such additional elements are viewed to be well known routine and conventional as evidenced by Zhang (US 20230400306 A1) and Laine (US 20160370177 A1).
Dependent claims 2-13 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claims are not directed to an abstract idea, as detailed below:
The dependent claims are directed to a further analysis of the sensor data using mathematical concepts and abstract ideas. Claim 11 includes an additional element of an apparatus. However, it is just a generic apparatus and is viewed as insignificant extra solution activity. Claims 12 and 13 include a computer program and a machine-readable medium which are also additional elements. Nevertheless, they just represent well-known computer and are therefore viewed as just using a computer as a tool.
Therefore, dependent claims 2-13 further limit the abstract idea with an abstract idea and thus the claims are still directed to an abstract idea without significantly more.
Claims 12 and 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because a computer program and a machine-readable storage medium is directed to a computer program per se and/or to a transitory signal per se. The examiner suggests the claims be amended to recite something like “A computer program comprising a non-transitory computer readable medium…” in order to exclude a computer program per se and a transitory signal per se from the scope of the claim.
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.
Claims 1, 2, 4-6 and 11-15 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US 20230400306 A1) in view of Laine (US 20160370177 A1).
With respect to claim 1,
Zhang teaches,
A method for determining a movement of a vehicle, comprising: reading in a first sensor signal via an interface to a first inertial sensor of the vehicle and a second sensor signal via an interface to a second inertial sensor of the vehicle; (Para. [0039] teaches “The models and measurements can be generated from raw sensor readings, such as from one or more of each of inertial sensors, kinematic sensors, and odometry sensors (e.g., wheel odometry sensors or other rotating member odometry sensors).” Para. [0101] teaches “the computer-implemented method 600 can comprise obtaining, by a system operatively coupled to a processor (e.g., obtaining component 214), from an inertial sensor (e.g., inertial sensor 250), kinematics sensor (e.g., kinematics sensor 251), and odometry sensor (e.g., odometry sensor 252), respectively, an inertial sensor reading (e.g., inertial sensor reading 250-1), a kinematics sensor reading (e.g., kinematics sensor reading 251-1), and odometry sensor reading (e.g., odometry sensor reading 252-1).” (i.e. interface is viewed as the obtaining component 214.))
and determining a movement signal representing the movement of the vehicle using the first sensor signal, the second sensor signal, the first level signal, and the second level signal. (Para. [0149] teaches “The separate measurements, and thus the separate sensor readings from which the measurements are generated, can be combined and/or fused to provide an accurate and efficient global pose estimation of the vehicle, whether stationary or moving.”
Zhang does not explicitly teach,
ascertaining a first level signal representing a noise level of the first sensor signal and a second level signal representing a noise level of the second sensor signal using the first sensor signal, the second sensor signal and an activity signal representing an activity of the vehicle;
Laine teaches,
ascertaining a first level signal representing a noise level of the first sensor signal and a second level signal representing a noise level of the second sensor signal using the first sensor signal, the second sensor signal and an activity signal representing an activity of the vehicle; (Para. [0088] teaches “calibration methods include steps taken to determine the noise or error statistics specific to each individual sensor or class of sensors. This noise or error characterization may be obtained through the measurement of various fixed shapes or the execution of a known movement along a prescribed path.” (i.e. noise statistics are viewed as level signals. Known movement is viewed as signal representing activity.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang with ascertaining a first level signal representing a noise level of the first sensor signal and a second level signal representing a noise level of the second sensor signal using the first sensor signal, the second sensor signal and an activity signal representing an activity of the vehicle such as that of Laine.
One of ordinary skill would have been motivated to modify Zhang, because as seen in Para. [0088] of Laine the noise or error characterization used in Laine can be used to refine the Kalman filtering to produce a better estimate of the state of the sensor system and remove error in estimation models. Kalman filtering is used in Zhang to combine the sensor data as seen in Para. [0089] of Zhang. Therefore, one would be motivated to combine the references in order to improve the filtering method and reduce error in the estimates.
With respect to claim 2,
Zhang teaches,
providing the activity signal using at least one of the sensor signals. (Para. [0092] teaches “For the prediction step 240, the inertial model 250-3 (e.g., the inertial measurements 250-2 and/or inertial sensor readings 250-1) that have been adjusted via the covariance adjustment 216-1 performed by the covariance component 216, the vehicle internal sensor readings (e.g., auxiliary measurement data 254-1), and the fused pose estimation from the previous cycle (e.g., historical pose estimation 246-2)” (i.e. activity signal is viewed as the vehicle internal sensor readings (e.g., auxiliary measurement data 254-1 ))
With respect to claim 4,
Zhang further teaches,
The method according to claim 1, wherein: in the providing step, the activity is recognized as a stopped vehicle, an off-road driving of the vehicle, or a highway driving of the vehicle. (Para. [0016] teaches “The separate measurements, and thus the separate sensor readings from which the measurements are generated, can be combined and/or fused to provide an accurate and efficient global pose estimation of the vehicle, whether stationary or moving.”)
With respect to claim 5,
Zhang further teaches,
The method according to claim 1, wherein: in the ascertaining step, a first filter is set for filtering the first sensor signal and a second filter is set for filtering the second sensor signal using the activity signal, the first sensor signal is filtered using the first filter to obtain a first filtered sensor signal, the second sensor signal is filtered using the second filter to obtain a second filtered sensor signal, and the first level signal is ascertained using the first filtered sensor signal and the second level signal is ascertained using the second filtered sensor signal. (Para. [0089] teaches “A sequential Kalman Filter can be configured, such as designed, to fuse measurements from different sensor types for each fusion cycle (e.g., iteration of use of the pose estimation system 202 and/or iteration of provision of a pose estimation 246-1).” (i.e. a sequential Kalaman filter processes one sensor at a time. Therefore, each sensor signal will be filtered independently as claimed.)
With respect to claim 6,
Zhang further teaches,
The method according to claim 5, wherein: in the ascertaining step, the first filtered sensor signal is quantized to obtain a first quantization value and the second filtered sensor signal is quantized to obtain a second quantization value, the first level signal representing the first quantization value and the second level signal representing the second quantization value. (Para. [0150] teaches “Moreover, a dynamic noise covariance estimator can be employed for the hybrid model localizer that can dynamically adjust the noise covariance matrices for each sensor model at runtime” (i.e. the values in the matrices are viewed as the quantization values.) Para. [0092] teaches “For the prediction step 240, the inertial model 250-3 (e.g., the inertial measurements 250-2 and/or inertial sensor readings 250-1) that have been adjusted via the covariance adjustment 216-1 performed by the covariance component 216, the vehicle internal sensor readings (e.g., auxiliary measurement data 254-1),”)
With respect to claim 11,
Zhang further teaches,
An apparatus configured to execute and/or control the steps of the method according to claim 1 in corresponding units. (Fig. 2 shows pose estimation system.)
With respect to claim 12,
Zhang further teaches,
A computer program configured to execute and/or control the steps of the method according to claim 1. (Para. [0010] teaches “a computer program product”)
With respect to claim 13,
Zhang further teaches,
A machine-readable storage medium on which the computer program according to claim 12 is stored. ( Para. [0005] teaches “According to an embodiment, a system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory,”)
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US 20230400306 A1) and Laine (US 20160370177 A1) as applied to claim 2 above, and further in view of Kappi (US 20240110792 A1).
With respect to claim 3,
Zhang further teaches,
The method according to claim 2, wherein: in the providing step, the at least one sensor signal is filtered. (Para. [0080] “That is, the hybrid motion model can leverage a Kalman Filter with a dynamic noise covariance generator to fuse the pose.)
Zhang does not explicitly teach,
a comparison is made with at least one threshold value, wherein the activity of the vehicle is recognized depending on a result of the comparison.
Kappi teaches,
a comparison is made with at least one threshold value, wherein the activity of the vehicle is recognized depending on a result of the comparison. (Para. [0041] teaches “The INS 20 also includes a plurality of filters 29, such as one or more Kalman filters. The filters receive the navigation solution, such as the position and velocity, that has been generated by the motion equations 28 and provides feedback to the motion equations in order to reduce the error in the navigation solution.” Para. [0047] teaches “For example, the apparatus, such as the processing circuitry, may be configured to receive information from the vehicle identifying the steering angle. In this embodiment, the apparatus, such as the processing circuitry, may be configured to determine that the vehicle is making a turn in an instance in which the steering angle has an absolute value that exceeds a predefined threshold, such as an absolute value of greater than seven degrees, for at least a predetermined length of time.”)
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 combination of Zhang and Laine where a comparison is made with at least one threshold value, wherein the activity of the vehicle is recognized depending on a result of the comparison such as that of Kappi.
One of ordinary skill would have been motivated to modify the combination of Zhang and Laine, because setting a threshold would reduce errors in the system by reducing false positives such as determining that the vehicle Is turning when it is not. Therefore, increasing the accuracy of the determination and the activity signal.
Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US 20230400306 A1) and Laine (US 20160370177 A1) as applied to claim 1 above, and further in view of Adams (US 11897486 B1).
With respect to claim 7,
The combination of Zhang and Laine does not explicitly teach,
The method according to claim 1, wherein: in the determining step, the first level signal is compared with a first threshold value defined using the activity signal to create a first quality value representing a quality of the first sensor signal and the second level signal is compared with a second threshold value defined using the activity signal to create a second quality value representing a quality of the second sensor signal, wherein the movement signal is determined using the first sensor signal, the second sensor signal, the first quality value and the second quality value.
Adams teaches,
wherein: in the determining step, the first level signal is compared with a first threshold value defined using the activity signal to create a first quality value representing a quality of the first sensor signal and the second level signal is compared with a second threshold value defined using the activity signal to create a second quality value representing a quality of the second sensor signal, (Col. 7 Ln(s). [8- 41] teach “In some examples, residuals are used as discrepancy metrics to determine IMU data consensus, and a residual may include a comparison of converted filtered IMU data (e.g., of a given IMU) to the mean converted filtered IMU data (e.g., from all IMUs being compared). For instance, in association with a given time (e.g., a selected timestamp), a mean of converted filtered gyro values in the z-axis for all of IMUs 122, 124, and 126 may be determined. Then, to determine the residual for a given IMU, the mean may be subtracted from the converted filtered gyro value in the z-axis for the given IMU. Similar residual determinations may be performed for the gyro data in any axis and the accelerometer data in any axis. For example, a residual for any given IMU may be compared to a residual threshold, and if the residual is larger than the threshold, then the given IMU may be deemed invalid (e.g., until some corrective measure is taken).” ))
wherein the movement signal is determined using the first sensor signal, the second sensor signal, the first quality value and the second quality value. (Col. 10 Ln(s) [1-6] teach “When an IMU is deemed invalid or in a fault state, various steps may be taken. For example, corrective action may be taken with respect to the identified IMU (e.g., calibration or recalibration). In addition, one or more of the other IMUs (e.g., not associated with the maximum residual) may be used, instead of the invalid IMU, to control operations of the vehicle. For example, the one or more other IMUs may be used to determine a relative position of the vehicle.” )
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 combination of Zhang and Laine wherein: in the determining step, the first level signal is compared with a first threshold value defined using the activity signal to create a first quality value representing a quality of the first sensor signal and the second level signal is compared with a second threshold value defined using the activity signal to create a second quality value representing a quality of the second sensor signal, wherein the movement signal is determined using the first sensor signal, the second sensor signal, the first quality value and the second quality value such as that of Adams.
One of ordinary skill would have been motivated to modify the combination of Zhang and Laine, because the quality of the sensed data from the different inertial sensors would affect the determined motion signal. Therefore, the quality of each sensor should be accounted for to increase the reliability of the system as shown in Col. 11 of Adams.
With respect to claim 8,
The combination of Zhang and Laine does not explicitly teach,
The method according to claim 1, wherein: in the determining step, a reference value is determined using the first level signal and the second level signal, a first noise indicator value is determined as the difference between the first level signal and the reference value, and a second noise indicator value is determined as the difference between the second level signal and the reference value, wherein the movement signal is determined using the first sensor signal, the second sensor signal, the first noise indicator value and the second noise indicator value.
Adams teaches,
wherein: in the determining step, a reference value is determined using the first level signal and the second level signal, a first noise indicator value is determined as the difference between the first level signal and the reference value, and a second noise indicator value is determined as the difference between the second level signal and the reference value, wherein the movement signal is determined using the first sensor signal, the second sensor signal, the first noise indicator value and the second noise indicator value. (Col. 2 Ln(s). 29-53 teaches “Error detection may include various techniques, and in some examples, sensor data from multiple sensors is compared to assess variability. Larger variability (e.g., less consistency or consensus) among the sensor data often suggests some error or fault among the sensors, whereas smaller variability (e.g., more consistency or consensus) may suggest absence of an error or fault. Typically, sensor data is intended to represent a detected signal, and often the sensor data also reflects sensor noise and sensor bias (e.g., in addition to the actual signal). As such, absent examples of the present disclosure, it may be challenging to determine when discrepancies among sensor data is attributable to sensor noise and/or bias or to differences in the actual signal. Examples of the present disclosure include filtering sensor data (e.g., from multiple sensors) over time to reduce the noise and bias and comparing the filtered sensor data to assess variability. For example, a low-pass filter, high-pass filter, bandpass filter, etc. may be used to remove high-frequency noise and low-frequency bias. Once filtered, the sensor data may more accurately represent the actual detected signal, and as such, any discrepancy determined among the sensor data may be more likely attributable to differences in detected signal. When the discrepancy is high enough (e.g., exceeds a threshold), the discrepancy may indicate some type of error among the sensors and/or sensor data.”
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 combination of Zhang and Laine wherein: in the determining step, a reference value is determined using the first level signal and the second level signal, a first noise indicator value is determined as the difference between the first level signal and the reference value, and a second noise indicator value is determined as the difference between the second level signal and the reference value, wherein the movement signal is determined using the first sensor signal, the second sensor signal, the first noise indicator value and the second noise indicator value such as that of Adams.
One of ordinary skill would have been motivated to modify the combination of Zhang and Laine, because determining what is noise and what is an actual discrepancy in the sensor data may reduce the likelihood that a false positive sensitive error is detected as seen in Col. 5 of Adams.
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US 20230400306 A1) and Laine (US 20160370177 A1) as applied to claim 1 above, and further in view of Kalkkuhl (US 20240003689 A1).
With respect to claim 9,
The combination of Zhang and Laine does not explicitly teach,
The method according to claim 1, wherein: in the determining step, a first error signal indicative of an error state of the first inertial sensor is determined using the first level signal, and a second error signal indicative of an error state of the second inertial sensor is determined using the second level signal.
Kalkkuhl teaches,
wherein: in the determining step, a first error signal indicative of an error state of the first inertial sensor is determined using the first level signal, and a second error signal indicative of an error state of the second inertial sensor is determined using the second level signal. (Para. [0038] teaches “In one embodiment of the invention, co-variances of estimation errors evaluated in the Kalman filters can be used in assessing sensor signal deviations when detecting the significance of signal deviations.” Para. [0056] teaches “According to the invention, a first inertial measurement unit 4 is used as the master inertial measurement unit, and a second inertial measurement unit 5 and a third inertial measurement unit 6, whose performance reliability can be lower than that of the first inertial measurement unit 4, are used as slave inertial measurement units, wherein messages from the master inertial measurement unit 4 are used in a detection unit 7 to estimate systematic error parameters in slave inertial measurement units 5, 6 relative to the master inertial measurement unit 4 in models, and to detect malfunctions in individual sensors through 2-out-of-3 voting on the compensated signals. In 2-out-of-3 voting, the two signals with the smallest difference are considered error-free and the third signal is tested against them. If the difference between the third signal and the signals considered error-free is too great, then a potential malfunction of the evaluated third signal is assumed.”)
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 combination of Zhang and Laine wherein: in the determining step, a first error signal indicative of an error state of the first inertial sensor is determined using the first level signal, and a second error signal indicative of an error state of the second inertial sensor is determined using the second level signal such as that of Kalkkuhl.
One of ordinary skill would have been motivated to modify the combination of Zhang and Laine, because errors could indicate sensor malfunction which can lead to significant errors or grossly false values in the operating signals and thus represent a safety risk in safety-related applications as seen in Para. [0006] of Kalkkuhl.
With respect to claim 10,
The combination of Zhang and Laine does not explicitly teach,
The method according to claim 1, wherein: in the reading-in step, a further sensor signal is read in via an interface to a further inertial sensor of the vehicle, and in the ascertaining step, a further level signal representing a noise level of the further sensor signal is ascertained using the further sensor signal and the activity signal.
Kalkkuhl teaches,
wherein: in the reading-in step, a further sensor signal is read in via an interface to a further inertial sensor of the vehicle, and in the ascertaining step, a further level signal representing a noise level of the further sensor signal is ascertained using the further sensor signal and the activity signal. (Para. [0057] teaches “In the diagrams, the three inertial measurement units 4, 5, 6 are also designated as IMU A, IMU B, IMU C. Each of them has a vector {right arrow over (w)}.sub.ib.sup.b with three angular velocity signals and a vector {right arrow over (f)}.sub.ib.sup.b with three specific force signals. For easier representation of these signals, the highest and deepest positions are not shown. We can then refer to the sensor signals as {right arrow over (ω)}.sub.A, {right arrow over (ω)}.sub.B, {right arrow over (ω)}.sub.C or {right arrow over (f)}.sub.A, {right arrow over (f)}.sub.B, {right arrow over (f)}.sub.C.”)
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 combination of Zhang and Laine wherein: in the reading-in step, a further sensor signal is read in via an interface to a further inertial sensor of the vehicle, and in the ascertaining step, a further level signal representing a noise level of the further sensor signal is ascertained using the further sensor signal and the activity signal such as that of Kalkkuhl.
One of ordinary skill would have been motivated to modify the combination of Zhang and Laine, because a third sensor would increase redundancy should one of the other sensors fail.
Conclusion
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should be directed to JOSHUA L FORRISTALL whose telephone number is 703-756-4554. The examiner
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/JOSHUA L FORRISTALL/Examiner, Art Unit 2857
/ALEXANDER SATANOVSKY/Primary Examiner, Art Unit 2857
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