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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under step 1, claim 1 and 11 belongs to a statutory category.
Under Step 2A prong 1, the claims as a whole are identified as being directed to a judicial exception as claim 1 and similarly 11 recite(s) “A method for inertial navigation applying an Extended Kalman Filter (EKF)”, “in response to the detecting of the degraded quality, temporarily increasing a noise estimate used by the EKF for the inertial sensor data from a first noise estimate value to a second noise estimate value, the noise estimate relating to the at least one inertial axis or the inertial sensor associated with the inertial sensor data, and the second noise estimate value being greater than the first noise estimate value” and “in response to the detecting of the normalization of quality, resetting the temporarily increased noise estimate value used by the EKF back to the first noise estimate value after a predetermined delay period after detecting the normalization of quality” which are directed to mathematical concepts and/or mental processes in view of applicants specification, for example see Par. 5-8, 53-56.
Under Step 2A prong 2, evaluating whether the claim as a whole integrates the exception into a practical application of that exception, the judicial exception is not integrated into a practical application because “detecting a degraded quality of inertial sensor data provided by an inertial sensor device, the inertial sensor data relating to at least one inertial axis, and the degraded quality relating to at least one of a detecting of an error flag indicating an error of at least one type of inertial sensor data and a detecting of an absence of at least one type of incoming inertial sensor data;”, “detecting a normalization of quality of the at least one type of inertial sensor data” and additionally in claim 11, “at least one inertial sensor device” are considered to be data gathering steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity. Claim 11 additionally recites “a computing device configured to perform an EKF calculation based on inertial sensor data received from the at least one inertial sensor device, the computing device that, when executing program code stored on memory” are considered to be generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer.
Under Step 2B, evaluating additional elements to determine whether they amount to an inventive concept both individually and in combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because “detecting a degraded quality of inertial sensor data provided by an inertial sensor device, the inertial sensor data relating to at least one inertial axis, and the degraded quality relating to at least one of a detecting of an error flag indicating an error of at least one type of inertial sensor data and a detecting of an absence of at least one type of incoming inertial sensor data;”, “detecting a normalization of quality of the at least one type of inertial sensor data” and additionally in claim 11, “at least one inertial sensor device” are considered to be adding insignificant extra-solution activity to the judicial exception per MPEP 2106.05(g)(ii) and are well-understood, routine, conventional activities/elements previously known to the industry per MPEP 2106.05(d)(i)(also see prior art of record). Claim 11 additionally recites “a computing device configured to perform an EKF calculation based on inertial sensor data received from the at least one inertial sensor device, the computing device that, when executing program code stored on memory” are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d)
Claims 2-6, 8-10, 12-16, and 18-20 further describe the abstract ideas cited above.
In claims 7 and 17 the judicial exception is not integrated into a practical application or not include additional elements that are sufficient to amount to significantly more than the judicial exception because “upon detecting the error, resetting the inertial sensor providing the degraded quality inertial sensor data;” and “resuming use of the inertial sensor data from the inertial sensor by the EKF before the resetting of the temporarily increased noise estimate” are considered to be generally linking the use of a judicial exception to a particular technological environment or field of use and are considered to be merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself per MPEP 2106.05(h) and are well-understood, routine, and conventional activities/elements previously known to the industry per MPEP 2106.05(d) (see prior art of record). The elements “detecting the inertial sensor resuming a normal operation after the resetting” are considered to be data gathering steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity and are considered to be adding insignificant extra-solution activity to the judicial exception per MPEP 2106.05(g) and are well-understood, routine, conventional activities/elements previously known to the industry per MPEP 2106.05(d).
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) 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yang (US 20050240347 A1).
In claim 1, Yang discloses a method for inertial navigation applying an Extended Kalman Filter (EKF) (Par. 14 “extended Kalman filter”), the method including: detecting a degraded quality of inertial sensor data (Par. 15 “an estimated error of an inertial device”) provided by an inertial sensor device (Fig. 1, 110, 120, 130), the inertial sensor data relating to at least one inertial axis (Par. 102 “3-axis”), and the degraded quality relating to at least one of a detecting of an error flag (Par. 108 “error”) indicating an error of at least one type of inertial sensor data (Par. 108 “small angle errors”) and a detecting of an absence of at least one type of incoming inertial sensor data; in response to the detecting of the degraded quality, temporarily increasing a noise estimate used by the EKF for the inertial sensor data from a first noise estimate value to a second noise estimate value (Par. 85 “measurement noise of the accelerometer and magnetic compass”), the noise estimate relating to the at least one inertial axis or the inertial sensor associated with the inertial sensor data (Par. 85 “measurement noise of the accelerometer and magnetic compass” “analysis of the process noise of the gyroscope”), and the second noise estimate value being greater than the first noise estimate value (Par. 85-90 examiner notes that the second value being greater would be required to show an increase in noise); detecting a normalization of quality of the at least one type of inertial sensor data (Fig. 4 435 450 455, Par. 117); and in response to the detecting of the normalization of quality, resetting the temporarily increased noise estimate value used by the EKF back to the first noise estimate value (Par. 107 “attitude may be periodically re-initialized”) after a predetermined delay period after detecting the normalization of quality (Par. 107 “at certain appropriate times”).
In claim 2, Yang further discloses wherein the degraded quality indicates an error causing unreliable or missing inertial sensor data (Par. 85 “process noise of the gyroscope and its associated drift”).
In claim 3, Yang further discloses temporarily replacing the unreliable or missing inertial sensor data used by the EKF with replacement data having a constant value (Par. 128 “constant speed”); and resuming using the inertial sensor data by the EKF after detecting that quality of the at least one type of inertial data is normalized (Par. 128).
In claim 4, Yang further discloses wherein the replacement data is at least one of: i) a zero value of the unreliable or missing inertial sensor data, ii) an estimate of a system state parameter determined by the EKF immediately before the detecting of the unreliable or missing inertial sensor data, the system state parameter corresponding to the unreliable or missing inertial sensor data (Par. 118), and iii) inertial sensor data obtained immediately before the detecting of the respective inertial sensor data to be unreliable or missing.
In claim 5, Yang further discloses wherein the at least one type of inertial sensor data comprises at least one of acceleration data relating to acceleration along at least one of three different axes (Par. 111 “acceleration”), and angular velocity data relating to a rotation about any one of the three different axes (Par. 53 “real time attitude rotation matrix, latitude, height, and velocity”).
In claim 6, Yang further discloses wherein the noise estimate is an observation noise covariance (Par. 53 “process noise covariance matrix”).
In claim 7, Yang further discloses upon detecting the error, resetting the inertial sensor providing the degraded quality inertial sensor data (Par. 107 “attitude may be periodically re-initialized”); detecting the inertial sensor resuming a normal operation after the resetting (Fig. 4 435 450 455, Par. 117); and resuming use of the inertial sensor data from the inertial sensor by the EKF before the resetting of the temporarily increased noise estimate (Par. 128).
In claim 8, Yang further discloses comprising implementing, by the EKF, a position estimation (Par. 19 “updated attitude”).
In claim 9, Yang further discloses wherein the position estimation is at least one of a pitch estimation and a roll estimation (Par. 14 “pitch” “roll”).
In claim 10, Yang further discloses wherein the inertial sensor device is at least one of an accelerometer and a gyroscope (Fig. 1, 110 120).
In claim 11, Yang further discloses a system for inertial navigation applying Extended Kalman Filter (EKF) (Par. 14 “extended Kalman filter”), the system comprising: at least one inertial sensor device (Fig. 1, 110 120 130); and a computing device (Par. 20 “processing device”) configured to perform an EKF calculation (Par. 20) based on inertial sensor data received from the at least one inertial sensor device (Par. 19), the computing device that, when executing program code stored on memory (Par. 162 “memory”), is configured to: detect a degraded quality of inertial sensor data (Par. 15 “an estimated error of an inertial device”) provided by an inertial sensor device (Fig. 1, 110, 120, 130), the inertial sensor data relating to at least one inertial axis (Par. 102 “3-axis”), and the degraded quality relating to at least one of a detecting of an error flag (Par. 108 “error”) indicating an error of at least one type of inertial sensor data (Par. 108 “small angle errors”) and a detecting of an absence of at least one type of incoming inertial sensor data; in response to the detecting of the degraded quality, temporarily increasing a noise estimate used by the EKF for the inertial sensor data from a first noise estimate value to a second noise estimate value (Par. 85 “measurement noise of the accelerometer and magnetic compass”), the noise estimate relating to the at least one inertial axis or the inertial sensor associated with the inertial sensor data (Par. 85 “measurement noise of the accelerometer and magnetic compass” “analysis of the process noise of the gyroscope”), and the second noise estimate value being greater than the first noise estimate value (Par. 85-90 examiner notes that the second value being greater would be required to show an increase in noise); detecting a normalization of quality of the at least one type of inertial sensor data (Fig. 4 435 450 455, Par. 117); and in response to the detecting of the normalization of quality, resetting the temporarily increased noise estimate value used by the EKF back to the first noise estimate value (Par. 107 “attitude may be periodically re-initialized”) after a predetermined delay period after detecting the normalization of quality (Par. 107 “at certain appropriate times”).
In claim 12, Yang further discloses wherein the degraded quality indicates an error causing unreliable or missing inertial sensor data (Par. 85 “process noise of the gyroscope and its associated drift”).
In claim 13, Yang further discloses temporarily replacing the unreliable or missing inertial sensor data used by the EKF with replacement data having a constant value (Par. 128 “constant speed”); and resuming using the inertial sensor data by the EKF after detecting that quality of the at least one type of inertial data is normalized (Par. 128).
In claim 14, Yang further discloses wherein the replacement data is at least one of: i) a zero value of the unreliable or missing inertial sensor data, ii) an estimate of a system state parameter determined by the EKF immediately before the detecting of the unreliable or missing inertial sensor data, the system state parameter corresponding to the unreliable or missing inertial sensor data (Par. 118), and iii) inertial sensor data obtained immediately before the detecting of the respective inertial sensor data to be unreliable or missing.
In claim 15, Yang further discloses wherein the at least one type of inertial sensor data comprises at least one of acceleration data relating to acceleration along at least one of three different axes (Par. 111 “acceleration”), and angular velocity data relating to a rotation about any one of the three different axes (Par. 53 “real time attitude rotation matrix, latitude, height, and velocity”).
In claim 16, Yang further discloses wherein the noise estimate is an observation noise covariance (Par. 53 “process noise covariance matrix”).
In claim 17, Yang further discloses upon detecting the error, resetting the inertial sensor providing the degraded quality inertial sensor data (Par. 107 “attitude may be periodically re-initialized”); detecting the inertial sensor resuming a normal operation after the resetting (Fig. 4 435 450 455, Par. 117); and resuming use of the inertial sensor data from the inertial sensor by the EKF before the resetting of the temporarily increased noise estimate (Par. 128).
In claim 18, Yang further discloses comprising implementing, by the EKF, a position estimation (Par. 19 “updated attitude”).
In claim 19, Yang further discloses wherein the position estimation is at least one of a pitch estimation and a roll estimation (Par. 14 “pitch” “roll”).
In claim 20, Yang further discloses wherein the inertial sensor device is at least one of an accelerometer and a gyroscope (Fig. 1, 110 120).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US 7643939 B2, Methods And Systems For Implementing An Iterated Extended Kalman Filter Within A Navigation System; US 20090048779 A1, SYSTEMS AND METHODS FOR GYROCOMPASS ALIGNMENT USING DYNAMICALLY CALIBRATED SENSOR DATA AND AN ITERATED EXTENDED KALMAN FILTER WITHIN A NAVIGATION SYSTEM.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON J BECKER whose telephone number is (571)431-0689. The examiner can normally be reached M-F 9:30-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby Turner can be reached at (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/B.J.B/ Examiner, Art Unit 2857
/SHELBY A TURNER/ Supervisory Patent Examiner, Art Unit 2857
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