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 § 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3-8, 10-16, 18-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Probst et al. US 20220315047 A1 (hereinafter Probst).
Regarding claim 1, Probst discloses A method of controlling an autonomous vehicle ([0022] …the ego-vehicle can be controlled to drive in accordance with the selected ego-trajectory…), the method comprising:
receiving, by one or more processors ([0012] …using software functioning in conjunction with at least one of a programmed microprocessor…), data identifying an object ([0017] …at least one traffic participant involved in the traffic situation is selected…);
generating, by one or more processors ([0012] …using software functioning in conjunction with at least one of a programmed microprocessor…), a first portion of a trajectory ([0027] The ego-trajectory can be selected…) using a first uncertainty distribution for the object ([0024] The uncertainty area can be determined by estimating at least one of inaccuracies of sensors… [0026] …uncertainty in predicting positions of the at least one traffic participant…), wherein the first portion of the trajectory enables the autonomous vehicle to make progress towards a destination of the autonomous vehicle ([0027] The ego-trajectory can be selected in order to reduce lateral distance between the selected position of the selected ego-trajectory and the selected position of the calculated ego-trajectory and to increase distance between the uncertainty areas.);
generating, by one or more processors ([0012] …using software functioning in conjunction with at least one of a programmed microprocessor…), a fallback portion of the trajectory ([0010] …generating at least one ego-trajectory alternative…) using a second uncertainty distribution for the object (Figs. 1a-1c show multiple uncertainty areas. [0037] As shown in FIG. 1B, the uncertainties grow over the future times from t1 to t2 and t2 to t3 along the trajectories.), wherein the fallback portion enables the autonomous vehicle to stop ([0022] …drive in accordance with the selected ego-trajectory by generating driving control signals controlling at least one of acceleration, braking and steering of the ego-vehicle. Examiner submits that the ego-trajectory dictates acceleration, braking and steering, and therefore “enables” the autonomous vehicle to stop), wherein the second uncertainty distribution is different from the first uncertainty distribution (Figs. 1a-1c show multiple uncertainty areas.) and the second uncertainty distribution is based on a predetermined uncertainty distribution ([0017] At least one future ego-trajectory alternative is generated by applying a lateral shift to the calculated ego-trajectory… Examiner submits that the lateral shift is a predetermined uncertainty distribution, as it accounts for uncertainty regarding inaccuracies in sensors and predictions: ([0020] With the uncertainty areas (offsets), position uncertainties are modeled, wherein the uncertainty models allow to incorporate continuous inaccuracies of signals (e.g., inaccuracies of sensors or prediction models)…)) if the autonomous vehicle loses a localization improvement process ([0020] …future lateral distance between the ego-vehicle and other vehicles can be increased in accordance with the evaluated potential risk. This not only increases distance/Time-To-Collision for lateral events but also increases the time to react (i.e. missing vehicle in blind spot at overtaking)… [0044] Such uncertainty can vary with … overall scene (possible occludes…)) Examiner submits that a vehicle missing in a blind spot/ occlusion is a loss of a localization improvement process.); and
controlling, by the one or more processors, the autonomous vehicle according to the trajectory. ([0022] …the ego-vehicle can be controlled to drive in accordance with the selected ego-trajectory…)
Regarding claim 2, Probst discloses The method of claim 1, further comprising:
determining the first uncertainty distribution as a convolution of a position control error uncertainty distribution ([0025] The inaccuracies in detecting the traffic situation can include inaccuracy in determining at least one of position of the ego-vehicle…) and a position perception error uncertainty distribution ([0024] The uncertainty area can be determined by estimating at least one of inaccuracies of sensors sensing the traffic situation…); and
determining the second uncertainty distribution (Figs. 1a-1c show multiple uncertainty areas. [0037] As shown in FIG. 1B, the uncertainties grow over the future times from t1 to t2 and t2 to t3 along the trajectories.) as a convolution of the position control error uncertainty distribution ([0025] The inaccuracies in detecting the traffic situation can include inaccuracy in determining at least one of position of the ego-vehicle…), the position perception error uncertainty distribution ([0024] The uncertainty area can be determined by estimating at least one of inaccuracies of sensors sensing the traffic situation…), and the predetermined uncertainty distribution. ([0017] At least one future ego-trajectory alternative is generated by applying a lateral shift to the calculated ego-trajectory… Examiner submits that the lateral shift is a predetermined uncertainty distribution, as it accounts for uncertainty regarding inaccuracies in sensors and predictions: ([0020] With the uncertainty areas (offsets), position uncertainties are modeled, wherein the uncertainty models allow to incorporate continuous inaccuracies of signals (e.g., inaccuracies of sensors or prediction models)…))
Regarding claim 3, Probst discloses The method of claim 1, further comprising, using the second uncertainty distribution to determine a buffer for avoiding the object. ([0027] The ego-trajectory can be selected in order to reduce lateral distance between the selected position of the selected ego-trajectory and the selected position of the calculated ego-trajectory and to increase distance between the uncertainty areas.)
Regarding claim 4, Probst discloses The method of claim 3, wherein determining the buffer for the object includes using a risk assessment value to identify a value from the second uncertainty distribution. ([0018] Information on the closeness of uncertainty areas is used to evaluate a potential risk of an ego-trajectory, wherein overlapping areas represent a high risk.)
Regarding claim 5, Probst discloses The method of claim 4, wherein the risk assessment value indicates how risk-averse the autonomous vehicle should be. (Since a lateral offset reduces spatio-temporal event criticality, the future lateral distance between the ego-vehicle and other vehicles can be increased in accordance with the evaluated potential risk.)
Regarding claim 6, Probst discloses The method of claim 1, further comprising:
planning, by the one or more processors ([0012] …using software functioning in conjunction with at least one of a programmed microprocessor…), a second trajectory without using the predetermined uncertainty distribution ([0030] A plurality of ego-trajectory alternatives can be generated by applying a plurality of different lateral shifts to the calculated ego-trajectory. Examiner notes that one of the plurality of ego-trajectory alternatives can be considered a “second trajectory”. Further, a “different” lateral shift applied to the trajectory indicates that the trajectory was planned without using “the” predetermined uncertainty distribution, as the different shift is a different predetermined uncertainty distribution.); and
selecting ([0018] …an ego-trajectory is selected from the plurality of ego-trajectories based on the evaluated spatio-temporal closeness.), by the one or more processors ([0012] …using software functioning in conjunction with at least one of a programmed microprocessor…), the trajectory from the trajectory and the second trajectory based on a determination of whether the autonomous vehicle is able to use the localization improvement process ([0020] …future lateral distance between the ego-vehicle and other vehicles can be increased in accordance with the evaluated potential risk. This not only increases distance/Time-To-Collision for lateral events but also increases the time to react (i.e. missing vehicle in blind spot at overtaking)… [0044] Such uncertainty can vary with … overall scene (possible occludes…)) Examiner submits that a vehicle missing in a blind spot/ occlusion is a loss of a localization improvement process.), and wherein the controlling is in response to the selecting. ([0022] …the ego-vehicle can be controlled to drive in accordance with the selected ego-trajectory…)
Regarding claim 7, Probst discloses The method of claim 1, wherein generating the first portion ([0027] The ego-trajectory can be selected…) includes using a first risk assessment value ([0018] Information on the closeness of uncertainty areas is used to evaluate a potential risk of an ego-trajectory, wherein overlapping areas represent a high risk.) and generating the fallback portion ([0010] …generating at least one ego-trajectory alternative…) includes using a second risk assessment value ([0038] Since, in the illustrated example, the risk increases with the time, the velocity of the ego-vehicle 1 can be reduced and/or the lateral distance to the trajectory 9 can be increased in order to reduce the risk at the times t2 and t3. Examiner submits that the risk at t2 and t3 are “second risk assessment values”.), the first risk assessment value being different from the second risk assessment value. ([0038] …the risk increases with the time…)
Regarding claim 8, Probst discloses A system for controlling an autonomous vehicle ([0022] …the ego-vehicle can be controlled to drive in accordance with the selected ego-trajectory…), the system comprising:
one or more processors ([0012] …using software functioning in conjunction with at least one of a programmed microprocessor…) configured to: receive data identifying an object ([0017] …at least one traffic participant involved in the traffic situation is selected…);
generate a first portion of a trajectory ([0027] The ego-trajectory can be selected…) using a first uncertainty distribution for the object ([0024] The uncertainty area can be determined by estimating at least one of inaccuracies of sensors… [0026] …uncertainty in predicting positions of the at least one traffic participant…), wherein the first portion of the trajectory enables the autonomous vehicle to make progress towards a destination of the autonomous vehicle ([0027] The ego-trajectory can be selected in order to reduce lateral distance between the selected position of the selected ego-trajectory and the selected position of the calculated ego-trajectory and to increase distance between the uncertainty areas.);
generate a fallback portion of the trajectory ([0010] …generating at least one ego-trajectory alternative…) using a second uncertainty distribution for the object (Figs. 1a-1c show multiple uncertainty areas. [0037] As shown in FIG. 1B, the uncertainties grow over the future times from t1 to t2 and t2 to t3 along the trajectories.), wherein the fallback portion enables the autonomous vehicle to stop ([0022] …drive in accordance with the selected ego-trajectory by generating driving control signals controlling at least one of acceleration, braking and steering of the ego-vehicle. Examiner submits that the ego-trajectory dictates acceleration, braking and steering, and therefore “enables” the autonomous vehicle to stop), wherein the second uncertainty distribution is different from the first uncertainty distribution (Figs. 1a-1c show multiple uncertainty areas.) and the second uncertainty distribution is based on a predetermined uncertainty distribution ([0017] At least one future ego-trajectory alternative is generated by applying a lateral shift to the calculated ego-trajectory… Examiner submits that the lateral shift is a predetermined uncertainty distribution, as it accounts for uncertainty regarding inaccuracies in sensors and predictions: ([0020] With the uncertainty areas (offsets), position uncertainties are modeled, wherein the uncertainty models allow to incorporate continuous inaccuracies of signals (e.g., inaccuracies of sensors or prediction models)…)) if the autonomous vehicle loses a localization improvement process ([0020] …future lateral distance between the ego-vehicle and other vehicles can be increased in accordance with the evaluated potential risk. This not only increases distance/Time-To-Collision for lateral events but also increases the time to react (i.e. missing vehicle in blind spot at overtaking)… [0044] Such uncertainty can vary with … overall scene (possible occludes…)) Examiner submits that a vehicle missing in a blind spot/ occlusion is a loss of a localization improvement process.); and
control the autonomous vehicle according to the trajectory. ([0022] …the ego-vehicle can be controlled to drive in accordance with the selected ego-trajectory…)
Regarding claim 9, Probst discloses The system of claim 8, wherein the one or more processors are further configured to: determine the first uncertainty distribution as a convolution of a position control error uncertainty distribution ([0025] The inaccuracies in detecting the traffic situation can include inaccuracy in determining at least one of position of the ego-vehicle…) and a position perception error uncertainty distribution ([0024] The uncertainty area can be determined by estimating at least one of inaccuracies of sensors sensing the traffic situation…); and
determine the second uncertainty distribution (Figs. 1a-1c show multiple uncertainty areas. [0037] As shown in FIG. 1B, the uncertainties grow over the future times from t1 to t2 and t2 to t3 along the trajectories.) as a convolution of the position control error uncertainty distribution ([0025] The inaccuracies in detecting the traffic situation can include inaccuracy in determining at least one of position of the ego-vehicle…), the position perception error uncertainty distribution ([0024] The uncertainty area can be determined by estimating at least one of inaccuracies of sensors sensing the traffic situation…), and the predetermined uncertainty distribution. ([0017] At least one future ego-trajectory alternative is generated by applying a lateral shift to the calculated ego-trajectory… Examiner submits that the lateral shift is a predetermined uncertainty distribution, as it accounts for uncertainty regarding inaccuracies in sensors and predictions: ([0020] With the uncertainty areas (offsets), position uncertainties are modeled, wherein the uncertainty models allow to incorporate continuous inaccuracies of signals (e.g., inaccuracies of sensors or prediction models)…))
Regarding claim 10, Probst discloses The system of claim 8, wherein the one or more processors are further configured to use the second uncertainty distribution to determine a buffer for avoiding the object. ([0027] The ego-trajectory can be selected in order to reduce lateral distance between the selected position of the selected ego-trajectory and the selected position of the calculated ego-trajectory and to increase distance between the uncertainty areas.)
Regarding claim 11, Probst discloses The system of claim 10, wherein the one or more processors are further configured to determine the buffer for the object ([0027] The ego-trajectory can be selected in order to reduce lateral distance between the selected position of the selected ego-trajectory and the selected position of the calculated ego-trajectory and to increase distance between the uncertainty areas.) by using a risk assessment value to identify a value from the second uncertainty distribution. ([0018] Information on the closeness of uncertainty areas is used to evaluate a potential risk of an ego-trajectory, wherein overlapping areas represent a high risk.)
Regarding claim 12, Probst discloses The system of claim 11, wherein the risk assessment value indicates how risk-averse the autonomous vehicle should be. (Since a lateral offset reduces spatio-temporal event criticality, the future lateral distance between the ego-vehicle and other vehicles can be increased in accordance with the evaluated potential risk.)
Regarding claim 13, Probst discloses The system of claim 8, wherein the one or more processors ([0012] …using software functioning in conjunction with at least one of a programmed microprocessor…) are further configured to:
plan a second trajectory without using the predetermined uncertainty distribution ([0030] A plurality of ego-trajectory alternatives can be generated by applying a plurality of different lateral shifts to the calculated ego-trajectory. Examiner notes that one of the plurality of ego-trajectory alternatives can be considered a “second trajectory”. Further, a “different” lateral shift applied to the trajectory indicates that the trajectory was planned without using “the” predetermined uncertainty distribution, as the different shift is a different predetermined uncertainty distribution.); and
select the trajectory from the trajectory and the second trajectory ([0018] …an ego-trajectory is selected from the plurality of ego-trajectories based on the evaluated spatio-temporal closeness.) based on a determination of whether the autonomous vehicle is able to use the localization improvement process ([0020] …future lateral distance between the ego-vehicle and other vehicles can be increased in accordance with the evaluated potential risk. This not only increases distance/Time-To-Collision for lateral events but also increases the time to react (i.e. missing vehicle in blind spot at overtaking)… [0044] Such uncertainty can vary with … overall scene (possible occludes…)) Examiner submits that a vehicle missing in a blind spot/ occlusion is a loss of a localization improvement process.), and wherein the controlling is in response to the selecting. ([0022] …the ego-vehicle can be controlled to drive in accordance with the selected ego-trajectory…)
Regarding claim 14, Probst discloses The system of claim 8, wherein the one or more processors are further configured to generate the first portion ([0027] The ego-trajectory can be selected…) by using a first risk assessment value ([0018] Information on the closeness of uncertainty areas is used to evaluate a potential risk of an ego-trajectory, wherein overlapping areas represent a high risk.) and generating the fallback portion ([0010] …generating at least one ego-trajectory alternative…) includes using a second risk assessment value ([0038] Since, in the illustrated example, the risk increases with the time, the velocity of the ego-vehicle 1 can be reduced and/or the lateral distance to the trajectory 9 can be increased in order to reduce the risk at the times t2 and t3. Examiner submits that the risk at t2 and t3 are “second risk assessment values”.), the first risk assessment value being different from the second risk assessment value. ([0038] …the risk increases with the time…)
Regarding claim 15, Probst discloses The system of claim 8, further comprising the autonomous vehicle. ([0001] …a system for assisting a driver in driving a vehicle…)
Regarding claim 16, Probst discloses A non-transitory recording medium on which instructions are stored ([0012] …using software functioning in conjunction with at least one of a programmed microprocessor…), the instructions, when executed by one or more processors ([0012] …using software functioning in conjunction with at least one of a programmed microprocessor…), cause the one or more processors to perform method of controlling an autonomous vehicle ([0022] …the ego-vehicle can be controlled to drive in accordance with the selected ego-trajectory…), the method comprising:
receiving data identifying an object ([0017] …at least one traffic participant involved in the traffic situation is selected…);
generating a first portion of a trajectory ([0027] The ego-trajectory can be selected…) using a first uncertainty distribution for the object ([0024] The uncertainty area can be determined by estimating at least one of inaccuracies of sensors… [0026] …uncertainty in predicting positions of the at least one traffic participant…), wherein the first portion of the trajectory enables the autonomous vehicle to make progress towards a destination of the autonomous vehicle ([0027] The ego-trajectory can be selected in order to reduce lateral distance between the selected position of the selected ego-trajectory and the selected position of the calculated ego-trajectory and to increase distance between the uncertainty areas.);
generating a fallback portion of the trajectory ([0010] …generating at least one ego-trajectory alternative…) using a second uncertainty distribution for the object (Figs. 1a-1c show multiple uncertainty areas. [0037] As shown in FIG. 1B, the uncertainties grow over the future times from t1 to t2 and t2 to t3 along the trajectories.), wherein the fallback portion enables the autonomous vehicle to stop ([0022] …drive in accordance with the selected ego-trajectory by generating driving control signals controlling at least one of acceleration, braking and steering of the ego-vehicle. Examiner submits that the ego-trajectory dictates acceleration, braking and steering, and therefore “enables” the autonomous vehicle to stop), wherein the second uncertainty distribution is different from the first uncertainty distribution (Figs. 1a-1c show multiple uncertainty areas.) and the second uncertainty distribution is based on a predetermined uncertainty distribution ([0017] At least one future ego-trajectory alternative is generated by applying a lateral shift to the calculated ego-trajectory… Examiner submits that the lateral shift is a predetermined uncertainty distribution, as it accounts for uncertainty regarding inaccuracies in sensors and predictions: ([0020] With the uncertainty areas (offsets), position uncertainties are modeled, wherein the uncertainty models allow to incorporate continuous inaccuracies of signals (e.g., inaccuracies of sensors or prediction models)…)) if the autonomous vehicle loses a localization improvement process ([0020] …future lateral distance between the ego-vehicle and other vehicles can be increased in accordance with the evaluated potential risk. This not only increases distance/Time-To-Collision for lateral events but also increases the time to react (i.e. missing vehicle in blind spot at overtaking)… [0044] Such uncertainty can vary with … overall scene (possible occludes…)) Examiner submits that a vehicle missing in a blind spot/ occlusion is a loss of a localization improvement process.); and
controlling the autonomous vehicle according to the trajectory. ([0022] …the ego-vehicle can be controlled to drive in accordance with the selected ego-trajectory…)
Regarding claim 17, Probst discloses The medium of claim 16, wherein the method further comprises:
determining the first uncertainty distribution as a convolution of a position control error uncertainty distribution ([0025] The inaccuracies in detecting the traffic situation can include inaccuracy in determining at least one of position of the ego-vehicle…) and a position perception error uncertainty distribution ([0024] The uncertainty area can be determined by estimating at least one of inaccuracies of sensors sensing the traffic situation…); and
determining the second uncertainty distribution (Figs. 1a-1c show multiple uncertainty areas. [0037] As shown in FIG. 1B, the uncertainties grow over the future times from t1 to t2 and t2 to t3 along the trajectories.) as a convolution of the position control error uncertainty distribution ([0025] The inaccuracies in detecting the traffic situation can include inaccuracy in determining at least one of position of the ego-vehicle…), the position perception error uncertainty distribution ([0024] The uncertainty area can be determined by estimating at least one of inaccuracies of sensors sensing the traffic situation…), and the predetermined uncertainty distribution. ([0017] At least one future ego-trajectory alternative is generated by applying a lateral shift to the calculated ego-trajectory… Examiner submits that the lateral shift is a predetermined uncertainty distribution, as it accounts for uncertainty regarding inaccuracies in sensors and predictions: ([0020] With the uncertainty areas (offsets), position uncertainties are modeled, wherein the uncertainty models allow to incorporate continuous inaccuracies of signals (e.g., inaccuracies of sensors or prediction models)…))
Regarding claim 18, Probst discloses The medium of claim 16, wherein the method further comprises using the second uncertainty distribution to determine a buffer for avoiding the object. ([0027] The ego-trajectory can be selected in order to reduce lateral distance between the selected position of the selected ego-trajectory and the selected position of the calculated ego-trajectory and to increase distance between the uncertainty areas.)
Regarding claim 19, Probst discloses The medium of claim 18, wherein the method includes determining the buffer for the object ([0027] The ego-trajectory can be selected in order to reduce lateral distance between the selected position of the selected ego-trajectory and the selected position of the calculated ego-trajectory and to increase distance between the uncertainty areas.) further by using a risk assessment value to identify a value from the second uncertainty distribution. ([0018] Information on the closeness of uncertainty areas is used to evaluate a potential risk of an ego-trajectory, wherein overlapping areas represent a high risk.)
Regarding claim 20, Probst discloses The medium of claim 16, wherein the method further comprises:
planning a second trajectory without using the predetermined uncertainty distribution ([0030] A plurality of ego-trajectory alternatives can be generated by applying a plurality of different lateral shifts to the calculated ego-trajectory. Examiner notes that one of the plurality of ego-trajectory alternatives can be considered a “second trajectory”. Further, a “different” lateral shift applied to the trajectory indicates that the trajectory was planned without using “the” predetermined uncertainty distribution, as the different shift is a different predetermined uncertainty distribution.); and
selecting the trajectory from the trajectory and the second trajectory ([0018] …an ego-trajectory is selected from the plurality of ego-trajectories based on the evaluated spatio-temporal closeness.) based on a determination of whether the autonomous vehicle is able to use the localization improvement process ([0020] …future lateral distance between the ego-vehicle and other vehicles can be increased in accordance with the evaluated potential risk. This not only increases distance/Time-To-Collision for lateral events but also increases the time to react (i.e. missing vehicle in blind spot at overtaking)… [0044] Such uncertainty can vary with … overall scene (possible occludes…)) Examiner submits that a vehicle missing in a blind spot/ occlusion is a loss of a localization improvement process.), and wherein the controlling is in response to the selecting. ([0022] …the ego-vehicle can be controlled to drive in accordance with the selected ego-trajectory…)
Allowable Subject Matter
Claims 2, 9, 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The prior art does not disclose:
2. (original) The method of claim 1, further comprising: determining the first uncertainty distribution as a convolution of a position control error uncertainty distribution and a position perception error uncertainty distribution; and determining the second uncertainty distribution as a convolution of the position control error uncertainty distribution, the position perception error uncertainty distribution, and the predetermined uncertainty distribution.
9. (original) The system of claim 8, wherein the one or more processors are further configured to: determine the first uncertainty distribution as a convolution of a position control error uncertainty distribution and a position perception error uncertainty distribution; and determine the second uncertainty distribution as a convolution of the position control error uncertainty distribution, the position perception error uncertainty distribution, and the predetermined uncertainty distribution.
17. (original) The medium of claim 16, wherein the method further comprises: determining the first uncertainty distribution as a convolution of a position control error uncertainty distribution and a position perception error uncertainty distribution; and determining the second uncertainty distribution as a convolution of the position control error uncertainty distribution, the position perception error uncertainty distribution, and the predetermined uncertainty distribution.
Emphasis added.
Response to Arguments
Applicant's arguments filed 2/22/2024 have been fully considered but they are not persuasive.
Applicant argues that Probst does not, “describe generation of a {“first portion” or a “fallback portion” of the same “ego-trajectory” let alone “generating, by one or more processors, a first portion of a trajectory” and “generating, by one or more processors, a fallback portion of the [same] trajectory” as presented in claim 1.}
The examiner respectfully disagrees. The terms in the claims are interpreted according to the manner described in applicant’s disclosure.
The examiner respectfully submits that by applicant’s definition, “….the first portion of the trajectory enables the autonomous vehicle to make progress towards a destination of the autonomous vehicle”. In addition, applicant’s disclosure indicates that, “wherein the fallback portion enables the autonomous vehicle to stop”.
As such Probst anticipates, “generating, by one or more processors ([0012] …using software functioning in conjunction with at least one of a programmed microprocessor…), a first portion of a trajectory ([0027] The ego-trajectory can be selected…) using a first uncertainty distribution for the object ([0024] The uncertainty area can be determined by estimating at least one of inaccuracies of sensors… [0026] …uncertainty in predicting positions of the at least one traffic participant…), wherein the first portion of the trajectory enables the autonomous vehicle to make progress towards a destination of the autonomous vehicle ([0027].
In addition, Probst anticipates, “generating, by one or more processors ([0012] …using software functioning in conjunction with at least one of a programmed microprocessor…), a fallback portion of the trajectory ([0010] …generating at least one ego-trajectory alternative…) using a second uncertainty distribution for the object (Figs. 1a-1c show multiple uncertainty areas. [0037] As shown in FIG. 1B, the uncertainties grow over the future times from t1 to t2 and t2 to t3 along the trajectories.), wherein the fallback portion enables the autonomous vehicle to stop ([0022] …drive in accordance with the selected ego-trajectory by generating driving control signals controlling at least one of acceleration, braking and steering of the ego-vehicle. Examiner submits that the ego-trajectory dictates acceleration, braking and steering, and therefore “enables” the autonomous vehicle to stop), wherein the second uncertainty distribution is different from the first uncertainty distribution (Figs. 1a-1c show multiple uncertainty areas.) and the second uncertainty distribution is based on a predetermined uncertainty distribution ([0017]”.
Further, applicant argues that the prior art does not disclose, “generating, by one or more processors, a fallback portion of the trajectory using a second uncertainty distribution for the object”. The examiner respectfully disagrees. Probst discloses, “generating, by one or more processors ([0012] …using software functioning in conjunction with at least one of a programmed microprocessor…), a fallback portion of the trajectory ([0010] …generating at least one ego-trajectory alternative…) using a second uncertainty distribution for the object (Figs. 1a-1c show multiple uncertainty areas. [0037] As shown in FIG. 1B, the uncertainties grow over the future times from t1 to t2 and t2 to t3 along the trajectories.)”
Applicant’s argument with respect to claims 2, 9, 17 are convincing. The rejection therein is vacated,
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.
Communication
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RONNIE MANCHO whose telephone number is (571)272-6984. The examiner can normally be reached Mon-Thurs.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Adam Mott can be reached on 571 270 5376. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RONNIE M MANCHO/Primary Examiner, Art Unit 3664
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