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 .
Foreign Priority
Acknowledgment is made of applicant’s claim for foreign priority to DE 102022209393.3 A1 filed 09/09/2022. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
DETAILED ACTION
The following NON-FINAL Office Action is in response to application 18/447,151 filed on 08/09/2023. This communication is the first action on the merits.
Status of Claims
Claims 1-10 are currently pending and have been rejected as follows.
Drawings
The drawings filed on 08/09/2023 are accepted.
IDS
The information disclosure statement filed on 09/29/2023 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 and is considered.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Specifically, representative Claim 1 recites:
A method for evaluating sensor data, comprising the following steps:
reading in raw sensor data and/or processed sensor data from an acceleration sensor and a rotation rate sensor;
ascertaining measured data from the raw sensor data and/or the processed sensor data;
ascertaining at least one application criterion;
correcting the measured data based on a mathematical model, in the correction, an angle between a direction of a sensor orientation and a motion direction being maximally changed by a predefined value per time unit when the application criterion is met;
outputting the corrected measured data.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements.”
Similar limitations comprise the abstract ideas of claims 9 and 10, which perform the method of claim 1 and comprises:
Regarding claim 9,
A processing unit, comprising:
an input;
an output;
and a processor;
wherein the processing unit is configured to receive raw sensor data and/or processed sensor data via the input, the processing unit being configured, via the processor, to:
ascertain measured data from the raw sensor data and/or the processed sensor data, ascertain at least one application criterion,
correct the measured data based on a mathematical model, in the correction, an angle between a direction of a sensor orientation and a motion direction being maximally changed by a predefined value per time unit when the application criterion is met,
and the processing unit being configured to output the corrected measured data via the output.
Regarding claim 10,
A sensor system, comprising:
a processing unit;
a rotation rate sensor;
and an acceleration sensor,
the rotation rate sensor and the acceleration sensor each being configured to convert a physical measured variable into raw sensor data and/or processed sensor data and output them to an input of the processing unit;
wherein the processing unit includes:
the input, an output;
and a processor;
wherein the processing unit is configured to receive the raw sensor data and/or processed sensor data via the input,
the processing unit being configured, via the processor, to:
ascertain measured data from the raw sensor data and/or the processed sensor data, ascertain at least one application criterion,
correct the measured data based on a mathematical model, in the correction, an angle between a direction of a sensor orientation and a motion direction being maximally changed by a predefined value per time unit when the application criterion is met,
and the processing unit being configured to output the corrected measured data via the output.
Under Step 1 of the analysis, claim 1 belongs to a statutory category, namely it is a process claim. Likewise, claim 9 is a machine claim, and claim 10 is a system claim.
Under Step 2A, prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
In the instant case, claim 1 is found to recite at least one judicial exception (i.e. abstract idea), that being a mathematical concept and/or mental process. This can be seen in the claim limitation of “ascertaining” at least one application criterion given that “one application criterion” may involve “recognized motion at the minimum speed,” which is defined as “0.25 meters per second” or “0.5 meters per second” (See, for example, p.13, lines 20-27 of the Original Specification). This limitation falls in the category of mental process, which includes observations that can be done in the human mind such as “ascertaining” a recognized motion at specified speed.
Additionally, “correcting” measured data based on a “mathematical model” is the judicial exception of a mathematical concept and/or mental process because these limitations merely involve data manipulation, evaluations and/or judgements in order to correct sensor data and are capable of being performed mentally and/or with the aid of pen and paper. Additionally, the aforementioned limitations, when interpreted in light of the specification, set forth determinations, predictions, and estimations as well as an equation used for calculating motion direction. (See, for example, p. 16, lines 4-31 and p.17, lines 1-7 of the Original Specification).
Similar limitations comprise the abstract ideas of claims 9 and 10.
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
In addition, to the abstract ideas recited in Claim 1, the claimed method recites additional elements including “A method for evaluating sensor data, comprising the following steps: reading in raw sensor data and/or processed sensor data from an acceleration sensor and a rotation rate sensor; ascertaining measured data from the raw sensor data and/or the processed sensor data” and “outputting the corrected measured data” however these elements are found to be data gathering, processing and output steps, which are recited at a high level of generality, and thus merely amount to “insignificant extra-solution” activity(ies). See MPEP 2106.05(g) “Insignificant Extra-Solution Activity,”.
Machine claim 9 recites the same additional elements as parent claim 1 and also recites ”a processing unit, comprising: an input; an output; and a processor; wherein the processing unit is configured to receive raw sensor data and/or processed sensor data via the input, the processing unit being configured, via the processor, to: ascertain measured data from the raw sensor data and/or the processed sensor data, ascertain at least one application criterion, correct the measured data based on a mathematical model, in the correction, an angle between a direction of a sensor orientation and a motion direction being maximally changed by a predefined value per time unit when the application criterion is met, and the processing unit being configured to output the corrected measured data via the output.” However, these elements are found to be data gathering, processing, input and output steps, which are recited at a high level of generality, and thus merely amount to “insignificant extra-solution” activity(ies). See MPEP 2106.05(g) “Insignificant Extra-Solution Activity,”.
System claim 10, recites the same additional elements as parent claim 1 and also recites “a sensor system, comprising: a processing unit; a rotation rate sensor; and an acceleration sensor, the rotation rate sensor and the acceleration sensor each being configured to convert a physical measured variable into raw sensor data and/or processed sensor data and output them to an input of the processing unit; wherein the processing unit includes: the input, an output; and a processor; wherein the processing unit is configured to receive the raw sensor data and/or processed sensor data via the input, the processing unit being configured, via the processor, to: ascertain measured data from the raw sensor data and/or the processed sensor data, ascertain at least one application criterion, correct the measured data based on a mathematical model, in the correction, an angle between a direction of a sensor orientation and a motion direction being maximally changed by a predefined value per time unit when the application criterion is met, and the processing unit being configured to output the corrected measured data via the output.”
The generic data gathering, processing, and output steps, are recited at such a high level of generality (e.g. “ascertaining measured data” and “sensor system” and “processing unit”) that it represents no more than mere instructions to apply the judicial exceptions on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exceptions to the technological environment of a computer. Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. For instance, nothing is done with the result of the correct measured data.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to a general purpose computer system that attempts to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use, and/or merely performs insignificant extra-solution activit(ies) (claims 1 and 9-10). Such insignificant extra-solution activity, e.g. data gathering and output, when re-evaluated under Step 2B is further found to be well-understood, routine, and conventional as evidenced by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, and electronically scanning or extracting data from a physical document).
Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that claims 1 and
9-10 amount to significantly more than the abstract idea.
With regards to the dependent claims, claims 2-8, merely further expand upon the algorithm/abstract idea and do not set forth further additional elements that integrate the recited abstract idea into a practical application or amount to significantly more. Therefore, these claims are found ineligible for the reasons described for parent claims 1 and 9-10. Specifically:
Claim 2 recites the method of claim 1 as well as that the predefined value per time unit from Claim 1 is “…ten degrees per hour.” The specification further recites that the angle may change due to “fatigue during motion, for example walking…” [See Spec. p.5, lines 15-16] and may be intentionally manipulated “only when the application criterion suggests” to do so, and that the “application criterion is checked” and the “mathematical model is appropriately changed” to achieve this desired limitation. [See Spec. p.4, lines 1-14].
Under the broadest reasonable interpretation, consistent with the specification, this step encompasses mathematical concepts requiring specific mathematical calculations and/or mental steps. This is because the manipulation of the angle per unit time involves a mathematical model to determine angle deviation based on certain criteria.
Claim 3 specifically recites the method of claim 1 as well as “…the angle between the direction of the sensor orientation and the motion direction is held constant.” Under the broadest reasonable interpretation, consistent with the specification, this step encompasses mathematical concepts requiring specific mathematical calculations and/or mental steps. This is because the measuring of an angle, and estimating the direction and orientation of a sensor, for example north, south, east and west, can be done mentally or mathematically. Furthermore, the angle between Dmotion and Dsensor along with measured values may signal changes that affect the sensor orientation, which can be calculated using the mathematical equation: D sensor =D motion −D sensor_orientation. [See Spec. p.17, line 5].
Claim 4 specifically recites that the mathematical model from Claim 1 “includes a probabilistic filter including a Kalman filter…” [See, for example, FIG. 8 (313)].
Under the broadest reasonable interpretation, consistent with the specification, each of these steps encompasses mathematical concepts requiring specific mathematical calculations and/or mental steps. For example, the Kalman filter involves a “square root” [See Spec. p.18, lines 14-17] and is considered to be a mathematical and/or mental step. Furthermore, the probabilistic filter involves “determining speed” in a “speed determining step” and “determining orientation” and “determining motion direction,” which can be interpreted as a human observing a displayed speed, orientation and motion direction on a generic computer. The same can be said about tracking the orientation of a sensor. A “moving average” can be calculated from “variables” via a mathematical step or mental process. The “checking step” where the sensor is ensured to “align with the y-axis” can reasonably be done via a mathematical step or mental process.
Claim 5 specifically recites the method of claim 1 as well as “…wherein the at least one application criterion includes a recognized motion at a minimum speed.” Under the broadest reasonable interpretation, consistent with the specification, this step encompasses mathematical concepts and/or mental steps requiring specific mathematical calculations. For example, one of ordinary skill in the art could check and recognize a minimum speed value of, for example, 0.25 m/s or 0.5 m/s, on a generic computer. Alternatively, a mathematical model would be capable of checking or recognizing the motion of a sensor at a minimum speed given that certain speed parameters were set in the model.
Claim 6 specifically recites the method of claim 1 as well as “…a sensor position being maintained.” The specification further recites that a “second checking step 134” is used to “show that the sensor position is maintained” [See Spec. FIG. 6]. Under the broadest reasonable interpretation, consistent with the specification, a human would be capable of both maintaining a sensor position and checking to see if a sensor had changed position through observation.
Claim 7 specifically recites the method of claim 1 as well as “…a comparison of an expected motion direction to an actual motion direction.” Under the broadest reasonable interpretation, consistent with the specification, each of these steps encompasses mathematical concepts requiring specific mathematical calculations and/or mental steps. For example, comparing an expected motion with an actual motion to determine if the values match or differ could be done via a mathematical model or mentally through a mental process using pen and paper.
Claim 8 specifically recites the method of claim 1 as well as “…a moving average of multiple measured data that are ascertained in temporal succession…” where the “averages may be computed using a low pass filter, or as a moving average [See Spec. p.6, lines 5-6]. Under the broadest reasonable interpretation, consistent with the specification, each of these steps encompasses mathematical concepts requiring specific mathematical calculations and/or mental steps. For example, calculating a moving average based on orientation and speed can be done mentally with pen and paper or via a mathematical step.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1, 4-5, and 7-8 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Vogel Andreas EPO Patent Publication 1630525 A2 (hereinafter “Andreas” ), an English translation is cited herein and provided with this Action.
Regarding Claim 1,
Andreas teaches: A method for evaluating sensor data, comprising the following steps: [Andreas: Abstract],
reading in raw sensor data and/or processed sensor data from an acceleration sensor and a rotation rate sensor;
(Andreas: [p. 4, para. 9]:[“The output voltage measured at the gyroscope is described by the following formula (1): U = Z + S ω + υ”];
[Andreas: p. 4, para. 10]: [“In this case, U is the measured output voltage at the gyroscope, Z is an offset, S is a scaling factor, ω is the angular speed of the motor vehicle and v is an offset-free noise with a variance of σ(v)=R. The calibration values Z and S drift over time, but can be considered to be constant at sufficiently small time intervals”];
[Andreas: p. 4, para. 11]: [“The inventive method is to find a suitable time interval in which the average rate of rotation is determined from measurements of the direction of movement of the absolute position sensor ”];
[Andreas: p. 5, para. 2]: [“The average angular velocity is obtained by evaluating the velocities calculated by GPS or another position sensor. The method is presented below using the example of GPS. Because the GPS measures the absolute position of the vehicle at regular time intervals, speed vectors can also be determined for each of these points in time. The velocity vectors are followed by the movement directions, which are typically specified as angles between the movement direction and the north direction”]);
ascertaining measured data from the raw sensor data and/or the processed sensor data;
(Andreas: [p.4, para. 5]:[“The measured values, in particular those of the gyroscope, are entered into the history.”]);
[Andreas: p. 4, para. 11]: [“The inventive method is to find a suitable time interval in which the average rate of rotation is determined from measurements of the direction of movement of the absolute position sensor ”];
ascertaining at least one application criterion;
(Andreas: [p.5, para. 2]: [“Reliable calculation of the direction of movement by GPS is, however, only possible if a series of conditions is fulfilled…”]);
correcting the measured data based on a mathematical model, in the correction, an angle between a direction of a sensor orientation and a motion direction being maximally changed by a predefined value per time unit when the application criterion is met;
(Andreas: [p.4, para. 6]: “[After passing through the curve, i.e. at the time t 8 , the measurement of the direction of movement is reliable enough to carry out a filtering step. For this purpose, the history searches for the points in time at which the direction of movement differs particularly greatly from the current direction of movement. This results in the maximum of the rotation at t 4. The time t 4 is now identified in the history, for example, by setting a flag in order to prevent the associated data record from being used a further time. From FIG. 2, the average rotation rate corresponding to this time period can be easily determined. Accordingly, the average measurement value in FIG. 3 can be easily calculated (solid horizontal line). By comparing the corresponding variables, or by applying the Kalman filter, the calibration values can now be determined];
[Andreas: p.5, para. 2]: [“The average angular velocity is obtained by evaluating the velocities calculated by GPS or another position sensor. The method is presented below using the example of GPS. Because the GPS measures the absolute position of the vehicle at regular time intervals, speed vectors can also be determined for each of these points in time. The velocity vectors are followed by the movement directions, which are typically specified as angles between the movement direction and the north direction. Reliable calculation of the direction of movement by GPS is, however, only possible if a series of conditions is fulfilled.
These include a minimum speed of the vehicle, not too high acceleration or rotation rates and good visibility of the GPS satellites, i.e. a sufficient value of the DOP (dilution of precision). The error in the measurement of the direction of motion can be expressed simply trigonometrically, but for speed measurement errors that are not too large it is given simply by the quotient of GPS speed measurement error and the absolute GPS speed. When a suitable time interval in which the average rotation rate is to be determined has been set, practically a time interval in which the rotation of the vehicle was at a maximum, the average rotation rate is given by Equation 3”]);
outputting the corrected measured data;
(Andreas: [p.4, para. 5]: [“Nevertheless, the measured values, in particular those of the gyroscope, are entered into the history”];
[Andreas: p.4, para. 6]: [“By comparing the corresponding variables, or by applying the Kalman filter, the calibration values can now be determined”]).
Regarding Claim 4,
Andreas teaches all the limitations of the parent claim 1 as shown above. Andreas additionally teaches using a Kalman filter in conjunction with Equations 1-6 to calibrate or correct sensor data (Andreas: [p.4, para. 6]; [Equations 1-6]).
Regarding Claim 5,
Andreas teaches all the limitations of the parent claim 1 as shown above. Andreas additionally teaches that reliable calculation of the direction of movement by GPS is only possible if certain conditions are fulfilled, one of which being [“a minimum speed of the vehicle”]; (Andreas: p.5, para. 2).
Regarding Claim 7,
Andreas teaches all the limitations of the parent claim 1 as shown above. Andreas additionally teaches the at least one application criterion includes a comparison of an expected motion direction to an actual motion direction (Andreas: [p.4, para. 6]: [“…a data history searching for points in time at which “the direction of movement differs particularly greatly from the current direction of movement”];
[Andreas: p.4, para. 7]: [“The next update of the filter occurs at the time t 9. Again, the data record is sought in the history, which maximizes the difference of the measured movement directions”]).
Regarding Claim 8,
Andreas teaches all the limitations of the parent claim 1 as shown above. Andreas additionally teaches a moving average of multiple measured data that are ascertained in temporal succession is used to assess whether the at least one application criterion is met (Andreas: [FIG. 2; p.4, para. 8]: [“Shortly after the curve, the described procedure automatically prefers those values of the direction of movement which were measured on the straight line before the curve, which are the lowest in FIG. 2. This leads to an undesired, slight overestimate of the angle of rotation and thus also of the rate of rotation of the vehicle. This can be avoided by averaging over a plurality of successive data sets on a straight line”]).
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.
Claim(s) 6, 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Andreas in view of Spasovski U.S. Patent Publication 2021/0364652 A1.
Regarding Claim 6,
Andreas teaches all the limitations of the parent claim 1 as discussed above. However, Andreas does not explicitly teach a sensor position being maintained as at least one of the application criteria.
In an analogous field, Spasovski is directed to providing a method and device for position determination by inertial navigation (Spasovski: Abstract). Therein Spasovski teaches a sensor position being maintained as at least one of the application criteria (Spasovski, FIG. 4; para. 0052); [“FIG. 4 shows the calculated current positions 41 of the individual sensors of an arrangement of 36 IMU sensors (crosses) after a calibration time duration of 10 minutes (the Z-coordinate has been omitted); the system was in the rest state during the calibration.”]).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to correct sensor data, as taught by Andreas, by maintaining a sensor position, as taught by Spasovski, in order to mitigate errors or deviations in sensor data. This method of improving Andreas was within the ability of one ordinary skilled in the art based on the teachings of Spasovski. Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Andreas and Spasovski to obtain the invention as specified in claim 6.
Regarding Claim 9,
Andreas teaches:
received raw sensor data and/or processed sensor data;
(Andreas: [p.4, para. 9]: [“The output voltage measured at the gyroscope is described by the following formula (1): U = Z + S ω + υ”];
[Andreas: p.4, para. 10]: [“In this case, U is the measured output voltage at the gyroscope, Z is an offset, S is a scaling factor, ω is the angular speed of the motor vehicle and v is an offset-free noise with a variance of σ(v)=R. The calibration values Z and S drift over time, but can be considered to be constant at sufficiently small time intervals”];
[Andreas: p.4, para. 11]: [“The method according to the invention consists in finding a suitable time interval in which the average rotation rate is determined from measurements of the direction of movement of the absolute position sensor”];
[Andreas: p.5, para. 2]: [“The average angular velocity is obtained by evaluating the velocities calculated by GPS or another position sensor. The method is presented below using the example of GPS. Because the GPS measures the absolute position of the vehicle at regular time intervals, speed vectors can also be determined for each of these points in time. The velocity vectors are followed by the movement directions, which are typically specified as angles between the movement direction and the north direction”]);
ascertained measured data from the raw sensor data and/or the processed sensor data;
(Andreas: [p.4, para. 5]: [“The measured values, in particular those of the gyroscope, are entered into the history.”]);
ascertain at least one application criterion;
(Andreas: [p.5, para. 2]: [“Reliable calculation of the direction of movement by GPS is, however, only possible if a series of conditions is fulfilled…”]);
correct the measured data based on a mathematical model, in the correction, an angle between a direction of a sensor orientation and a motion direction being maximally changed by a predefined value per time unit when the application criterion is met;
(Andreas: [p.4, para. 6; Including Eqns: 3-5]: [“After passing through the curve, i.e. at the time t 8 , the measurement of the direction of movement is reliable enough to carry out a filtering step. For this purpose, the history searches for the points in time at which the direction of movement differs particularly greatly from the current direction of movement. This results in the maximum of the rotation at t 4. The time t 4 is now identified in the history, for example, by setting a flag in order to prevent the associated data record from being used a further time. From FIG. 2, the average rotation rate corresponding to this time period can be easily determined. Accordingly, the average measurement value in FIG. 3 can be easily calculated (solid horizontal line). By comparing the corresponding variables, or by applying the Kalman filter, the calibration values can now be determined];
[Andreas: p.5, para. 2]: [“The average angular velocity is obtained by evaluating the velocities calculated by GPS or another position sensor. The method is presented below using the example of GPS. Because the GPS measures the absolute position of the vehicle at regular time intervals, speed vectors can also be determined for each of these points in time. The velocity vectors are followed by the movement directions, which are typically specified as angles between the movement direction and the north direction. Reliable calculation of the direction of movement by GPS is, however, only possible if a series of conditions is fulfilled. These include a minimum speed of the vehicle, not too high acceleration or rotation rates and good visibility of the GPS satellites, i.e. a sufficient value of the DOP (dilution of precision). The error in the measurement of the direction of motion can be expressed simply trigonometrically, but for speed measurement errors that are not too large it is given simply by the quotient of GPS speed measurement error and the absolute GPS speed. When a suitable time interval in which the average rotation rate is to be determined has been set, practically a time interval in which the rotation of the vehicle was at a maximum, the average rotation rate is given by Equation 3”]);
output the corrected measured data via the output;
(Andreas: [p.4, para. 5]: [Nevertheless, the measured values, in particular those of the gyroscope, are entered into the history];
[Andreas: p.4, para. 6]: [“By comparing the corresponding variables, or by applying the Kalman filter, the calibration values can now be determined”]).
Spasovski teaches:
A processing unit, comprising: an input; an output; and a processor;
(Spasovski, para. 0095; [“Furthermore, provision is made of an evaluation unit 13 in the form of a plurality of processors or microcontrollers (MCU) 13 a , 13 b , which are arranged on the rear side B of the circuit board 11 . The main task of the processors or MCUs 13 a , 13 b is reading out the IMU data and calculating the position. In this case, the MCU 13 b serves for weighting the individual results and for carrying out further calculation steps, and for presenting the result on a display 16 and/or for transmission to a receiving station by way of a transmitter and receiver 17 with the aid of an MCU 13 c (see also FIG. 6B)”];
[Spasovski, para. 0023]: [“Advantageously, the evaluation unit is configured for carrying out the method according to the disclosure herein”]).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to correct sensor data, as taught by Andreas, using a processing unit comprising an input, output and processor, as taught by Spasovski, in order to mitigate errors or deviations in sensor data by receiving raw and/or processed sensor data via the input, processing the data via a processor and outputting the corrected measured data to a display or storage medium, the references being in the same field, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements, the results of the combination were predictable.
Regarding Claim 10,
Andreas teaches:
a rotation rate sensor;
(Andreas: [p. 4, para. 9]: [“The output voltage measured at the gyroscope is described by the following formula (1): U = Z + S ω + υ”];
[Andreas: p.4, para. 10]: [“In this case, U is the measured output voltage at the gyroscope, Z is an offset, S is a scaling factor, ω is the angular speed of the motor vehicle and v is an offset-free noise with a variance of σ(v)=R. The calibration values Z and S drift over time, but can be considered to be constant at sufficiently small time intervals”];
[Andreas: p.4, para. 11]: [“The method according to the invention consists in finding a suitable time interval in which the average rotation rate is determined from measurements of the direction of movement of the absolute position sensor”];
[Andreas: p.5, para. 2]: [“The average angular velocity is obtained by evaluating the velocities calculated by GPS or another position sensor. The method is presented below using the example of GPS. Because the GPS measures the absolute position of the vehicle at regular time intervals, speed vectors can also be determined for each of these points in time. The velocity vectors are followed by the movement directions, which are typically specified as angles between the movement direction and the north direction”]);
Andreas teaches:
and an acceleration sensor, the rotation rate sensor and the acceleration sensor each being configured to convert a physical measured variable into raw sensor data and/or processed sensor data…
(Andreas: [p.4, para. 9]: [“The output voltage measured at the gyroscope is described by the following formula (1): U = Z + S ω + υ”];
[Andreas: p.4, para. 10]: [“In this case, U is the measured output voltage at the gyroscope, Z is an offset, S is a scaling factor, ω is the angular speed of the motor vehicle and v is an offset-free noise with a variance of σ(v)=R. The calibration values Z and S drift over time, but can be considered to be constant at sufficiently small time intervals”]; [“The method according to the invention consists in finding a suitable time interval in which the average rotation rate is determined from measurements of the direction of movement of the absolute position sensor”];
[Andreas: p.5, para. 2]: [“The average angular velocity is obtained by evaluating the velocities calculated by GPS or another position sensor. The method is presented below using the example of GPS. Because the GPS measures the absolute position of the vehicle at regular time intervals, speed vectors can also be determined for each of these points in time. The velocity vectors are followed by the movement directions, which are typically specified as angles between the movement direction and the north direction”]);
[Andreas: p.4, para. 5]: [“The measured values, in particular those of the gyroscope, are entered into the history.”]);
Andreas teaches:
…to: ascertain measured data from the raw sensor data and/or the processed sensor data, ascertain at least one application criterion, correct the measured data based on a mathematical model, in the correction, an angle between a direction of a sensor orientation and a motion direction being maximally changed by a predefined value per time unit when the application criterion is met…
(Andreas: [p. 4, para. 9]: [“The output voltage measured at the gyroscope is described by the following formula (1): U = Z + S ω + υ”];
[Andreas: p.4, para. 10]: [“In this case, U is the measured output voltage at the gyroscope, Z is an offset, S is a scaling factor, ω is the angular speed of the motor vehicle and v is an offset-free noise with a variance of σ(v)=R. The calibration values Z and S drift over time, but can be considered to be constant at sufficiently small time intervals”];
[Andreas: p.4, para. 11]: [“The method according to the invention consists in finding a suitable time interval in which the average rotation rate is determined from measurements of the direction of movement of the absolute position sensor”];
[Andreas: p.5, para. 2]: [“The average angular velocity is obtained by evaluating the velocities calculated by GPS or another position sensor. The method is presented below using the example of GPS. Because the GPS measures the absolute position of the vehicle at regular time intervals, speed vectors can also be determined for each of these points in time. The velocity vectors are followed by the movement directions, which are typically specified as angles between the movement direction and the north direction”]);
(Andreas: p.4, para.5]: [“The measured values, in particular those of the gyroscope, are entered into the history.”];
[Andreas: p.5, para. 2]: [“Reliable calculation of the direction of movement by GPS is, however, only possible if a series of conditions is fulfilled”];
(Andreas, para. 0024; [“After passing through the curve, i.e. at the time t 8 , the measurement of the direction of movement is reliable enough to carry out a filtering step. For this purpose, the history searches for the points in time at which the direction of movement differs particularly greatly from the current direction of movement. This results in the maximum of the rotation at t 4. The time t 4 is now identified in the history, for example, by setting a flag in order to prevent the associated data record from being used a further time. From FIG. 2, the average rotation rate corresponding to this time period can be easily determined. Accordingly, the average measurement value in FIG. 3 can be easily calculated (solid horizontal line). By comparing the corresponding variables, or by applying the Kalman filter, the calibration values can now be determined”];
[Andreas: p.5, para. 2]: [“The average angular velocity is obtained by evaluating the velocities calculated by GPS or another position sensor. The method is presented below using the example of GPS. Because the GPS measures the absolute position of the vehicle at regular time intervals, speed vectors can also be determined for each of these points in time. The velocity vectors are followed by the movement directions, which are typically specified as angles between the movement direction and the north direction. Reliable calculation of the direction of movement by GPS is, however, only possible if a series of conditions is fulfilled.
These include a minimum speed of the vehicle, not too high acceleration or rotation rates and good visibility of the GPS satellites, i.e. a sufficient value of the DOP (dilution of precision). The error in the measurement of the direction of motion can be expressed simply trigonometrically, but for speed measurement errors that are not too large it is given simply by the quotient of GPS speed measurement error and the absolute GPS speed. When a suitable time interval in which the average rotation rate is to be determined has been set, practically a time interval in which the rotation of the vehicle was at a maximum, the average rotation rate is given by Equation 3”];
[Andreas: p.4, para. 6]: [“By comparing the corresponding variables, or by applying the Kalman filter, the calibration values can now be determined”]);
Andreas teaches the limitations of claim 10 as described above. However, Andreas does not explicitly teach a sensor system comprising a processing unit including an input, output and processor.
In an analogous field, Spasovski is directed to providing a sensor system comprising a processing unit including an input, output and processor, specifically Spasovski teaches:
A sensor system, comprising: a processing unit;
[Spasovski, para. 0010]: [“Preferably, for the purpose of ascertaining the current position, a nonlinear combination of the vector components of the positions of a multiplicity of sensors ( 10 ) is formed”]);
[Spasovski, para. 0094]: [A multiplicity of sensors 10 embodied as inertial measurement units (IMUs) form a sensor arrangement 20 , which is arranged on a carrier unit 11 embodied as a circuit board. The sensors 10 serve for measuring accelerations and rotation rates along and about the sensor body axes and are arranged on the front side A of the circuit board 11 . In this example, the sensor or IMU arrangement consists of 25 sensors”]).
(Spasovski, para. 0095; [“Furthermore, provision is made of an evaluation unit 13 in the form of a plurality of processors or microcontrollers (MCU) 13 a , 13 b , which are arranged on the rear side B of the circuit board 11 . The main task of the processors or MCUs 13 a , 13 b is reading out the IMU data and calculating the position. In this case, the MCU 13 b serves for weighting the individual results and for carrying out further calculation steps, and for presenting the result on a display 16 and/or for transmission to a receiving station by way of a transmitter and receiver 17 with the aid of an MCU 13 c (see also FIG. 6B)”];
[Spasovski, para. 0023]: [“Advantageously, the evaluation unit is configured for carrying out the method according to the disclosure herein”]);
and output them to an input of the processing unit;
(Spasovski, para. 0022; [“Another aspect of the disclosure herein provides a device for position determination by inertial navigation, comprising a multiplicity of sensors for detecting accelerations and rotation rates along or about the respective sensor axes thereof, and an evaluation unit for calculating a current position from the detected accelerations and rotation rates, wherein the evaluation unit is designed in such a way that it ascertains a position in each case from the data of the individual sensors and subsequently adds the vector components of the ascertained positions in a weighted manner, wherein the weights are ascertained by a calibration of the sensors”]);
(Spasovski, para. 0095; [“Furthermore, provision is made of an evaluation unit 13 in the form of a plurality of processors or microcontrollers (MCU) 13 a , 13 b , which are arranged on the rear side B of the circuit board 11 . The main task of the processors or MCUs 13 a , 13 b is reading out the IMU data and calculating the position. In this case, the MCU 13 b serves for weighting the individual results and for carrying out further calculation steps, and for presenting the result on a display 16 and/or for transmission to a receiving station by way of a transmitter and receiver 17 with the aid of an MCU 13 c (see also FIG. 6B)”];
wherein the processing unit includes: the input, an output; and a processor;
(Spasovski, para. 0095; [“Furthermore, provision is made of an evaluation unit 13 in the form of a plurality of processors or microcontrollers (MCU) 13 a , 13 b , which are arranged on the rear side B of the circuit board 11 . The main task of the processors or MCUs 13 a , 13 b is reading out the IMU data and calculating the position. In this case, the MCU 13 b serves for weighting the individual results and for carrying out further calculation steps, and for presenting the result on a display 16 and/or for transmission to a receiving station by way of a transmitter and receiver 17 with the aid of an MCU 13 c (see also FIG. 6B)”];
wherein the processing unit is configured to receive the raw sensor data and/or processed sensor data via the input, the processing unit being configured, via the processor,
(Spasovski, para. 0022; [“Another aspect of the disclosure herein provides a device for position determination by inertial navigation, comprising a multiplicity of sensors for detecting accelerations and rotation rates along or about the respective sensor axes thereof, and an evaluation unit for calculating a current position from the detected accelerations and rotation rates, wherein the evaluation unit is designed in such a way that it ascertains a position in each case from the data of the individual sensors and subsequently adds the vector components of the ascertained positions in a weighted manner, wherein the weights are ascertained by a calibration of the sensors”]);
(Spasovski, para. 0095; [“Furthermore, provision is made of an evaluation unit 13 in the form of a plurality of processors or microcontrollers (MCU) 13 a , 13 b , which are arranged on the rear side B of the circuit board 11 . The main task of the processors or MCUs 13 a , 13 b is reading out the IMU data and calculating the position. In this case, the MCU 13 b serves for weighting the individual results and for carrying out further calculation steps, and for presenting the result on a display 16 and/or for transmission to a receiving station by way of a transmitter and receiver 17 with the aid of an MCU 13 c (see also FIG. 6B)”];
and the processing unit being configured to output the corrected measured data via the output.
(Spasovski, para. 0095; [“Furthermore, provision is made of an evaluation unit 13 in the form of a plurality of processors or microcontrollers (MCU) 13 a , 13 b , which are arranged on the rear side B of the circuit board 11 . The main task of the processors or MCUs 13 a , 13 b is reading out the IMU data and calculating the position. In this case, the MCU 13 b serves for weighting the individual results and for carrying out further calculation steps, and for presenting the result on a display 16 and/or for transmission to a receiving station by way of a transmitter and receiver 17 with the aid of an MCU 13 c (see also FIG. 6B)”];
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to correct sensor data, as taught by Andreas, using a sensor system comprising a processing unit including an input, output and processor, as taught by Spasovski, in order to mitigate errors or deviations in sensor data by converting a physically measured variable into raw and/or processed sensor data and output the data to an input, such as a receiver or receiving station, of the processing unit, the references being in the same field, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements, the results of the combination were predictable.
Reasons for Overcoming the Prior Art
Regarding the prior art, none of the prior art of record, taken individually or in combination, teach or reasonably suggest the combination of elements in Claims 2 and 3. Specifically, although the prior art describes an angle between a direction of a sensor orientation, specifically acceleration sensors and rotation rate sensors, and a motion direction being maximally changed by a predefined value per time unit, the prior art fails to specifically describe a predefined unit time being “ten degrees per hour” as well as this angle explicitly being held constant in combination with the other elements in the claim.
Conclusion
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/LOGAN D COONS/Examiner, Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857