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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/10/2026 has been entered.
Election/Restrictions
Applicant's election with traverse of the invention of GROUP 1 and also GROUP A which corresponds to claims 1, 2, 16, 21-29 and 32.
The traversal, basically, is on the ground(s) that:
[…..The Restriction Requirement is improper on its face as there has been a final rejection in the prosecution of the present application. Pursuant to M.P.E.P. § 811, "37 CFR 1.142(a), second sentence, indicates that a restriction requirement 'will normally be made before any action upon the merits; however, it may be made at any time before final action.' This means the examiner should make a proper requirement as early as possible in the prosecution, in the first action if possible, otherwise, as soon as the need for a proper requirement develops." Thus, Restriction Requirements must be made before a final office action. Because the Restriction Requirement was made after the Final Office Action mailed April 25, 2026, the Restriction Requirement is technically improper.]
This is not found persuasive because {M.P.E.P. § 811, "37 CFR 1.142(a), second sentence, indicates that a restriction requirement 'will normally be made before any action upon the merits; however, it may be made at any time before final action.} The phrase, “will normally be made before…” does not absolute bar restrictions from being made before final. In addition the phrase, “however, it may be made at any time before final action” indicates that it MAY BE MADE, not MUST NOT BE MADE.
In addition the present case has new claims that were not finally rejected, as such the new claims are not and were also not under final rejection.
Furthermore, contrary to appellants arguments, restrictions after final rejections are allowed under 37 CFR 1.142(b) and MPEP § 821.03. Restrictions are proper anytime during prosecution when there is demonstrated a burden. As such it believed that restrictions after final are allowed, contrary to appellant’s arguments.
Because the examiner would rather move the prosecution of case forward than engaging in arguments and disagreements the restriction requirement dated 5/1/2026 is hereby vacated.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 21-39 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In claim 21, the phrase, “the the” lacks antecedent basis, second occurrence emphasized.
The rest of the claims are rejected for depending on a rejected base claim.
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, 2, 8, 9, 15-17, 21-34 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…);
using, by one or more processors ([0012] …using software functioning in conjunction with at least one of a programmed microprocessor…), 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…), to generate a first portion of a trajectory ([0027] The ego-trajectory can be selected…), 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.);
using, by one or more processors ([0012] …using software functioning in conjunction with at least one of a programmed microprocessor…), 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.), to generate a fallback portion of the trajectory ([0010] …generating at least one ego-trajectory alternative…), 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 (sec 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 {i.e. how close a position of the autonomous vehicle is to a desired position according to a current trajectory of the autonomous vehicle; sec 0026, 0027, 0037, 0038-0045; in addition, sec 0020, 0024 discloses that the uncertainty area can be determined by estimating at least one of inaccuracies of sensors sensing the traffic situation……., implying that the (past , present , future or a combination thereof) accuracies or uncertainties of a plurality of sensors including perception and position uncertainties are mathematically estimated, predicted, extrapolated, combined or added or calculated or estimated, or a covariance thereof taken, incorporated as continuous inaccuracies of signals e.g., inaccuracies of sensors or prediction models…etc; sec 0050-61} and
determining the second uncertainty distribution (Figs. 1a-1c show multiple uncertainty areas. [sec 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 (sec 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 (sec 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. (sec 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: (sec 0020, 0024; 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 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…);
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…) to generate a first portion of a trajectory ([0027] The ego-trajectory can be selected…), 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 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 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 data identifying an object ([0017] …at least one traffic participant involved in the traffic situation is 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…), to generate a first portion of a trajectory ([0027] The ego-trajectory can be selected…), 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.);
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.) to generate a fallback portion of the trajectory ([0010] …generating at least one ego-trajectory alternative…), 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 21, Probst discloses the method of claim 1, wherein the first uncertainty distribution (a different uncertainty distribution is generated based on inaccuracies of each of the different sensors in the vehicle; figs. 1; sec 0020-0026, 0042, 0043, 0051, 0052, 0059-0061) is a perception uncertainty distribution (a perception uncertainty distribution for the object is generated based on inaccuracies of each of the different sensors in the vehicle; sec 0043, 0051, 0052, 0060, 0092) for the object which corresponds to an uncertainty in a distance between the vehicle and the object (sec 0020, 0044, 0045, 0052) at a time the object was last detected by a perception system of the autonomous vehicle (sec 0023, 0024, 0026, 0037, 0038-0045, 0059-0061).
Regarding claim 22, Probst discloses the method of claim 21, wherein the first portion of the trajectory is generated further based on a third uncertainty distribution (a different uncertainty distribution generated based on inaccuracies of each of the different sensors in the vehicle; figs. 1; sec 0020-0026, 0042, 0043, 0051, 0050-0061), the third uncertainty distribution being a position control uncertainty distribution which corresponds to how close a position of the autonomous vehicle is to a desired position according to a current trajectory of the autonomous vehicle (sec 0026, 0027, 0037, 0038-0045, 0050-0061).
Regarding claim 23, Probst discloses the method of claim 22, further comprising, combining the first uncertainty distribution and the third uncertainty distribution to determine a fourth uncertainty distribution for the object (a different uncertainty distribution is generated based on inaccuracies of each of the different sensors in the vehicle; figs. 1; sec 0020-0026, 0042, 0043, 0050, 0051, 0052, 0059-0061), wherein the fourth uncertainty distribution represents likelihood of the autonomous vehicle coming closer to the object than intended (sec 0059-0066).
Regarding claim 24, Probst discloses the method of claim 23, wherein the combining involves using an assumption that each of the first uncertainty distribution and the third uncertainty distribution (sec 0020-0026, 0042, 0043, 0050, 0051, 0052, 0059-0061), are bell-shaped curves (Gaussian uncertainty distribution, Kalman filter uncertainty distribution; sec 0040).
Regarding claim 25, Probst discloses the method of claim 23, wherein the object is a road user object (sec 0020-0026, 0042, 0043, 0051, 0050-0061), and the method further comprises:
determining a risk assessment value for interactions with the object, wherein determining the risk assessment value is based on a type of the road user object (collision, coming close, weather, etc; sec 0020-0026, 0042, 0043, 0051, 0050-0061), and wherein generating the first portion of the trajectory further includes using the risk assessment value to query the third uncertainty distribution for an uncertainty value for the object (sec 0018-0020, 0032, 0038, 0063-0065).
Regarding claim 26, Probst discloses the method of claim 25, wherein generating the first portion of the trajectory further includes using the uncertainty value to define a buffer for the trajectory, the buffer defining an around a predicted future location of the road user object that the trajectory will avoid (figs. 1, 2, 6, 8, 12-20; sec 0020-0026, 0042, 0043, 0051, 0050-0061).
Regarding claim 27, Probst discloses the method of claim 26, wherein defining the buffer includes adding the uncertainty value to a minimum buffer value (sec 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; also see figs. 1, 2, 6, 8, 12-20; sec 0020-0026, 0037, 0038, 0042, 0043, 0051, 0050-0061, 0080-0087).
Regarding claim 28, Probst discloses the method of claim 25, wherein the determining the risk assessment value is further based on a severity of collisions with the type of road user object (collision, coming close, weather, etc; sec 0020-0026, 0042, 0043, 0051, 0050-0061).
Regarding claim 29, Probst discloses the method of claim 25, wherein the determining the risk assessment value is further based on a speed of the autonomous vehicle (sec 0043, 0045, 0050-0054, 0058-0060, 0067),.
Regarding claim 30, Probst discloses the method of claim 1, wherein the object is a road user object, and the method further comprises selecting the first uncertainty distribution from a plurality of pre-stored uncertainty distributions, wherein the selection is based on a type of the road user object (figs. 1, 2, 6, 8, 12-20; sec 0020-0026, 0042, 0043, 0051, 0050-0061).
Regarding claim 31, Probst discloses the method of claim 30, wherein the road user object is a bicyclist (sec 0084) and the method further comprises:
receiving, by one or more processors, data identifying a second vehicle figs. 1, 2, 6, 8, 12-20; sec 0020-0026, 0037, 0038, 0042, 0043, 0051); and
selecting a third uncertainty distribution for the second vehicle from a plurality of uncertainty distributions for vehicles, the third uncertainty distribution being different from the first uncertainty distribution and the second uncertainty distribution (a different uncertainty distribution for an object is generated based on inaccuracies of each of the different sensors in the vehicle; figs. 1; sec 0020-0026, 0042, 0043, 0051, 0052, 0059-0061), and wherein the first portion of the trajectory is generated further based on the third uncertainty distribution (figs. 1; sec 0020-0026, 0042, 0043, 0051, 0052, 0059-0061).
Regarding claim 32, Probst discloses the method of claim 1, further comprising selecting the first uncertainty distribution from a plurality of pre-stored uncertainty distributions, wherein the selection is based on a distance between the autonomous vehicle and the object at a time when the object was last detected by a perception system of the autonomous vehicle (figs. 1; sec 0020-0026, 0042, 0043, 0051, 0052, 0059-0061).
Regarding claim 33, Probst discloses the method of claim 1, wherein the first uncertainty distribution (a different uncertainty distribution is generated based on inaccuracies of each of the different sensors in the vehicle; figs. 1; sec 0020-0026, 0042, 0043, 0051, 0052, 0059-0061) is a perception uncertainty distribution (a perception uncertainty distribution for the object is generated based on inaccuracies of each of the different sensors in the vehicle; sec 0043, 0051, 0052, 0060, 0092) for the object which corresponds to an uncertainty in a distance between the autonomous vehicle and the object (sec 0020, 0044, 0045, 0052) at a time the object was last detected by a perception system of the autonomous vehicle (sec 0023, 0024, 0026, 0037, 0038-0045, 0059-0061), and the method further comprises:
identifying a third uncertainty distribution (a different uncertainty distribution generated based on inaccuracies of each of the different sensors in the vehicle; figs. 1; sec 0020-0026, 0042, 0043, 0051, 0050-0061), the third uncertainty distribution being a position control uncertainty distribution which corresponds to how close a position of the autonomous vehicle is to a desired position according to a current trajectory of the autonomous vehicle (sec 0026, 0027, 0037, 0038-0045, 0050-0061); and
combining the first uncertainty distribution, the second uncertainty distribution, and the third uncertainty distribution to generate a fourth uncertainty distribution (a different uncertainty distribution is generated based on inaccuracies of each of the different sensors in the vehicle; figs. 1; sec 0020-0026, 0042, 0043, 0050, 0051, 0052, 0059-0061), the fourth uncertainty distribution representing likelihood of the autonomous vehicle coming closer to the object than intended (sec 0059-0066) if there is a total loss of localization improvement process {sec 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)… sec 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 fallback portion of the trajectory is generated further based on the fourth uncertainty distribution (a plurality greater that four different uncertainty distributions for an object is generated based on inaccuracies of each of the different plurality of sensors in the vehicle for generating a fall back portion of a trajectory; figs. 1; sec 0020-0026, 0042, 0043, 0051, 0052, 0059-0061).
Regarding claim 34, Probst discloses the method of claim 33, wherein the combining involves using an assumption that each of the first uncertainty distribution, the second uncertainty distribution, and the third uncertainty distribution (sec 0020-0026, 0042, 0043, 0050, 0051, 0052, 0059-0061), are bell-shaped curves (Gaussian uncertainty distribution, Kalman filter uncertainty distribution; sec 0040).
Conclusion
The prior art, US 20230192077 A1 (see sec 0013, 0020, 0023, 0024, 0040, 0044); WO 2021222375 A1 (see sec 0013-0017) made of record and not relied upon is considered pertinent to applicant's disclosure.
Response to Arguments
Applicant's arguments filed 02/10/2026 have been fully considered but they are not persuasive.
Applicant has reworded the claim language by shifting phrases from one spot to another of the claim and traverses the decision of the Board of Patent appeals. If applicant believes that the presently amended independent claims are patentably distinct from the independent claims that were appealed then applicant has no possession of the presently amended claims because the wording in the claims are not supported as originally filed.
Applicant’s arguments in the remarks are not persuasive because the Board addressed all the arguments and found that applicant was persuasive in demonstrating that the prior does not read on the claims. The examiner defers to the decision of the Board. The prior art reads on the claims.
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 3657
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