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 .
Status of Claims
This Office Action is in response to the application filed on 02/17/2026. Claims 1-7 were cancelled. Claims 10-11 are amended. Claims 8-14 are presently pending and are presented for examination.
Response to Amendments
In response to Applicant's Amendments dated 02/17/2026, Examiner withdraws the previous objections to the claims, and maintains the previous prior art rejections.
Response to Arguments
Applicant's arguments filed 02/17/2026 have been fully considered but they are not persuasive.
Applicant argues that the combination of Didcock, Sannodo, and Kean fails to disclose a "target speed" or a low-pass filter configured to filter a signal characteristic of a "target speed" to provide a signal as the "target speed" (see response). In response to applicant’s arguments:
In considering the disclosure of a reference, it is proper to take into account not only the specific teachings but also the inferences which one of ordinary skill in the art would reasonably be expected to draw therefrom (see MPEP § 2144.01). Didcock discloses an acceleration prediction model based on a "chronological sequence of speed values". One of ordinary skill in the art would understand that such a sequence utilized within a predictive model inherently informs or defines the speeds the vehicle is intended to reach, making it functionally equivalent to a "target speed". Similarly, while Sannodo filters an acceleration sensor value, acceleration is the rate of change of speed; therefore, removing high-frequency noise from an acceleration signal inherently and predictably affects the stability of the related speed signal.
The recitations of the speed value as a "target speed" and the configuration of the filter to provide a "target speed" are considered statements of intended use, a recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim.
It would have been obvious to one of ordinary skill in the art to utilize a speed value derived from an acceleration model as a "target speed" and to apply known low-pass filtering techniques to that target speed signal. Such modifications represent the routine optimization of vehicle control systems to ensure smooth operation and fall within the scope of applying known elements to yield predictable results (see MPEP § 2144.05 (II)).
For these reasons, the rejection is maintained.
The remaining arguments are essentially the same as those addressed above and/or below and are unpersuasive for at least the same reasoning.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to ATA 35 U.S.C. 102 and 103 is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 8-11 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 20230236088 (hereinafter, "Didcock"; previously of record) in further view of U.S. Pub. No. 20150212107 (hereinafter, "Sannodo"; previously of record).
Regarding claim 8, Didcock discloses a device for predicting a future actual speed of a motor vehicle, the device comprising:
an acceleration governor, wherein the acceleration governor is configured to predetermine a target acceleration of the motor vehicle in a time interval depending at least on the target speed of the motor vehicle (“at least one speed value associated with a current time interval and/or at least one past time interval, and using the state vector and a probability-based acceleration prediction model to determine an acceleration value for the current time interval” (para 0014)); and
a model, wherein the model is configured to predict the future actual speed depending at least on the target acceleration (“means for determining an acceleration value in consideration of probabilities resulting from the acceleration prediction model and the state vector, means for integrating the selected acceleration value over the current time interval in order to obtain a predicted speed value for a next future time interval” (para 0044)).
However, Didcock does not explicitly teach
a low-pass filter, wherein the low-pass filter is configured to filter a signal which is characteristic of a target speed of the motor vehicle and to provide the signal as the target speed of the motor vehicle;
Sannodo, in the same field of endeavor, teaches
a low-pass filter, wherein the low-pass filter is configured to filter a signal which is characteristic of a target speed of the motor vehicle and to provide the signal as the target speed of the motor vehicle (“when the detection value of the acceleration sensor 11 is used, the deceleration factor estimation apparatus 1 preferably uses a value obtained by correcting the detection value using a low pass filter as the acceleration” (para 0104));
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Didcock with the teachings of Sannodo in order to remove a high frequency component value using a low pass filter; see Sannodo at least at [0105].
Regarding claim 9, Didcock discloses the device as claimed in claim 8. Additionally, Didcock discloses wherein: the acceleration governor is configured to predetermine the target acceleration of the motor vehicle depending additionally on an actual speed of the motor vehicle and a gain factor (“at least one speed value associated with a current time interval and/or at least one past time interval, and using the state vector and a probability-based acceleration prediction model to determine an acceleration value for the current time interval” (para 0014)), and/or
the model is configured to predict the future actual speed depending additionally on the actual speed (“multiple predicted speed values are in each case obtained for the same future time intervals based on the past speed curve so that statistical speed distributions are obtained for future time intervals” (para 0021)).
Regarding claim 10, Didcock discloses the device as claimed in claim 9. Additionally, Didcock discloses wherein the device is configured:
to store as information each of the target speed, the actual speed, and the target acceleration for at least two time intervals, to select a first subset of the information, to train the model depending on the first subset, to select a second subset of the information, and to adjust the gain factor depending on the second subset, the model, and the acceleration governor (“the acceleration prediction model is based on a statistical evaluation of measured driving data of at least one real vehicle, wherein preferably the measured driving data of the at least one real vehicle only comprises a chronological sequence of speed values. Preferably, the driving data of the real vehicle measured under real driving conditions is used for model training, particularly for the determining of model parameters” (para 0033) and “A further aspect of the invention relates to a computer-readable medium on which a computer program product according to one of the cited embodiments is stored” (para 0043)).
Regarding claim 11, Didcock discloses the device as claimed in claim 11. Additionally, Didcock discloses further comprising:
an acceleration prediction unit, wherein the acceleration prediction unit is configured to determine a correction acceleration depending on the target speed (“the checking in step 203 show that the previously predicted speed values and corresponding time intervals do not meet the criteria of the RDE guidelines for the duration of highway driving at increased speed, the probability for an acceleration scenario is increased by the correction and the probability for a deceleration scenario is correspondingly reduced in step 204” (para 0065) and “In step 207, the corrected acceleration value a′.sub.t is integrated over the current time interval t in order to obtain a next predicted speed value v.sub.t+1 for a next time interval t+1 in the future” (para 0068)),
wherein the model is configured to predict the future actual speed depending additionally on the correction acceleration (“In step 207, the corrected acceleration value a′.sub.t is integrated over the current time interval t in order to obtain a next predicted speed value v.sub.t+1 for a next time interval t+1 in the future” (para 0068)).
Regarding claim 13, Didcock discloses the device as claimed in claim 11. Additionally, Didcock discloses further comprising:
wherein the acceleration governor is configured to predetermine the target acceleration of the motor vehicle depending additionally on the filtered target speed of the motor vehicle (“at least one speed value associated with a current time interval and/or at least one past time interval, and using the state vector and a probability-based acceleration prediction model to determine an acceleration value for the current time interval” (para 0014)).
However, Didcock does not explicitly teach
a reference filter, wherein the reference filter is configured to determine a filtered target speed depending on the target speed,
Sannodo, in the same field of endeavor, teaches
a reference filter, wherein the reference filter is configured to determine a filtered target speed depending on the target speed (“when the detection value of the acceleration sensor 11 is used, the deceleration factor estimation apparatus 1 preferably uses a value obtained by correcting the detection value using a low pass filter as the acceleration” (para 0104)),
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Didcock with the teachings of Sannodo in order to remove a high frequency component value using a low pass filter; see Sannodo at least at [0105].
Regarding claim 14, Didcock discloses the device as claimed in claim 13. However, Didcock does not explicitly teach wherein the device is configured to automatically set the reference filter as a product of a transfer function of the acceleration prediction unit and a transfer function of the model.
Sannodo, in the same field of endeavor, teaches
wherein the device is configured to automatically set the reference filter as a product of a transfer function of the acceleration prediction unit and a transfer function of the model (“The deceleration factor estimation apparatus 1 can calculate a relationship between the driving force, the acceleration, and the speed during travel accurately by increasing the precision of the deceleration factor terms in Equation 1. As a result, when travel behavior of the vehicle is predicted during travel assistance, the travel behavior can be predicted accurately. By predicting the travel behavior accurately, travel assistance can be executed more appropriately. Here, when the detection value of the acceleration sensor 11 is used, the deceleration factor estimation apparatus 1 preferably uses a value obtained by correcting the detection value using a low pass filter as the acceleration” (para 0103-0104)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Didcock with the teachings of Sannodo in order to remove a high frequency component value using a low pass filter; see Sannodo at least at [0105].
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 20230236088 (hereinafter, "Didcock"; previously of record) in view of U.S. Pub. No. 20150212107 (hereinafter, "Sannodo"; previously of record), as applied to claim 11 above, and in further view of U.S. Pub. No. 20190100205 (hereinafter, "Kean"; previously of record).
Regarding claim 12, Didcock discloses the device as claimed in claim 11. However, Didcock does not explicitly teach wherein the device is configured to automatically define the acceleration prediction unit as a product of an inversion of a transfer function of the model and a causality factor.
Kean, in the same field of endeavor, teaches
wherein the device is configured to automatically define the acceleration prediction unit as a product of an inversion of a transfer function of the model and a causality factor (“The average expected motor acceleration 326 with or without the time delay 328 affect is averaged with the predicted acceleration 340 and sent to the IMU 240 to be combined or processed with the IMU reading 344 as an estimated acceleration 350” (para 0064)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Didcock with the teachings of Kean in order to apply a time delay to the average expected motor acceleration to correlate the average expected motor acceleration; see Kean at least at [0061].
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM ALHARBI whose telephone number is 313-446-6621. The examiner can normally be reached on M-F 10am-6:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abby Flynn can be reached on (571) 272-9855. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8406.
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/ADAM M ALHARBI/Primary Examiner, Art Unit 3663