DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Preliminary Amendment
A Preliminary Amendment was filed on October 16, 2023 amending claims 1-10 and adding new claims 11-18, as well as amending the specification and drawings. No new matter was added. The pending claims and those subject to examination are therefore claims 1-18.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 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 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.
(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 1-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kuno et al. (US2021/0370924 A1).
Regarding claim 1, Kuno discloses:
A method for ascertaining a temporal parameter the method comprising (see Kuno Figs. 6 (attached below) and 10):
ascertaining a movement parameter that is dependent on a movement of at least one of the ego vehicle [[(10)]] and the at least one further object [[(18)]] (in the present published disclosure, Aguirre Mehlhorn (US2024/0034310 A1), paragraph 0027 teaches that “the movement parameter can be a relative speed between the ego vehicle and the object”. Claim 2 explicitly defines the movement parameter as such. With that in mind, see Kuno Fig. 6. See paragraph 0058 for the X-Y coordinate system being at the front center of the host vehicle 10. See paragraph 0065 for obtaining the “moving speed of the three-dimensional target.” This means the moving speed of the “other vehicle 62” as it is called in paragraph 0103 and elsewhere. Since the moving speed is in the coordinate system of the host vehicle it is a relative speed between the ego vehicle and the other vehicle.);
ascertaining a current and/or possible occupied region [[(20)]] for at least one of the ego vehicle [[(10)]] and the at least one further object [[(18)]] (see Fig. 6 and paragraph 0108 for the host vehicle 10 and the other vehicle 62 having a possible occupied region, called the “ ‘intersecting region S’” which is the hatched area of Fig. 6, if both vehicles continue on their current trajectories.); and
ascertaining the temporal parameter based on the basis of the movement parameter and the occupied region [[(20)]] (in a broad reasonable interpretation, the temporal parameter can be a TTC. With that in mind, see Kuno Fig. 6 and paragraph 0114 for TTC2 defined as a distance divided by the relative velocity between the ego vehicle and the other vehicle. See also paragraph 0115),
wherein the occupied region [[(20)]] is ascertained based on the basis of a vicinity model of the ego vehicle [[(10)]] (since the term “vicinity model” is not common in the vehicle control art, the specification should be consulted for what the term “vicinity model” means. This accords with the MPEP 2111 which states that “the meaning given to a claim term…must be consistent with the use of the claim term in the specification and drawings.” So what is a “vicinity model” according to the present published disclosure? Paragraphs 0010-0019 discuss its virtues but are rather vague about the specifics. It seems the vicinity model is at least mostly based on sensor data but may be used to perform calculations that do not deal “directly” with pure or individual data from a sensor, but include a multiplicity of sensors perhaps resolved into a common coordinate system. Paragraph 0010 states “It is known to monitor the vehicle vicinity of an ego vehicle using sensors and also to generate based thereon vicinity models, for example.” These can include “other transportation vehicles” and “also static objects such as, for example, that traffic infrastructure” that are “detected in the vehicle vicinity.” Thus a “vicinity model” is largely, at least, “based” on sensor data. This is in a background section. Paragraph 0018 begins teaching what the present disclosure proposes and states that “it is proposed to use two-dimensional (and/or geometric) considerations as a basis and/or to assess possible collisions” and that “a vicinity model (or environment model) of the transportation vehicle…are thus not necessarily dependent directly, but merely indirectly on direct sensor recordings of the environment.” Paragraph 0019 adds that the vicinity model “goes beyond pure (individual) sensor measurement values.” How? Paragraph 0026 states that “the occupied region may be ascertained here on the basis of a vicinity model of the ego vehicle” such that “All distances or other parameters required for assessing a risk of collision can also be derived from the vicinity model and do not necessarily correspond to direct (individual) sensor measurement values.” This could mean that the sensor readings are resolved into a shared local coordinate system and are thus not “direct” measurements but augmented. This interpretation is supported by paragraph 0028 which adds that “to ascertain the occupied region, the ego vehicle can determine its own spatial coordinates, for example, in the (optionally, at least, two-dimensional) vicinity model. For example, if the dimensions of the ego vehicle are known, its outlines can be at least roughly approximated”. Thus, the vicinity model does not have to be in two-dimensions, although it could be. The paragraph could reasonably mean that the origin of a local coordinate system is set somewhere on the ego vehicle and then the boundaries of the ego vehicle are then taken into consideration from that origin. Paragraph 0047 states that the “movement corridor” of the ego vehicle and the possible occupied region is ascertained (apparently from sensor data) and a TTC determined but that “All these parameters can be derived from and/or modeled in the vicinity model.” This might mean that all distances and times and therefore the TTC can be derived based on sensor data and the local coordinate system whose origin could be somewhere on the host vehicle. Paragraph 0052 teaches that there may be top views of the ego vehicle and object and that “these views furthermore reflect information that is stored in a vicinity model of the ego vehicle 10 and/or are derivable therefrom. This vicinity model can generally be created from a totality of available information that may have been collected only in part by way of sensor or at least by various sensor devices. Known approaches from the prior art may be used herefor.” This could broadly and reasonably mean that the “views” are not the model itself but “reflect information that is stored” in the model and are derivable from the model. So lidar data and GPS data, for example, might be part of a vicinity model from which a top view can be derived. Paragraph 0064 and others refer to the “occupied region” having a “coordinate set” in the vicinity model.
Based on all of this, for examination purposes, a vicinity model will be interpreted as, in a sense, just that, a model (or a not-necessarily-visual representation or description, such as an description) of the vicinity of the host vehicle. It can include data from more than one sensor, and resolved into a coordinate system. In a broad reasonable interpretation, therefore, a “vicinity model” is the resolution of data from more than one sensor into a coordinate system.
The examiner submits that Kuno teaches a vicinity model in at least as much detail as the present disclosure. See Kuno paragraph 0037 for a vehicle speed sensor 32. In paragraph 0039 there is a front camera 31. Paragraph 0059 teaches that the host vehicle’s ECU 21 “prestores various ‘templates’ corresponding to other vehicles and pedestrians”. Paragraph 0063 teaches that the ECU 21 resolves sensor data into the X-Y coordinate system shown in Fig. 1, as mentioned in paragraph 0058. Paragraph 0035 states that the ECU 21 can reference “lookup tables (maps)”. Note that the ECU 21 in Kuno extensively breaks down sensor data, such as distances and velocities, into X and Y coordinates. See Fig. 6 and paragraphs 0063 and 0066 for just a few examples of this. Thus, Kuno is generating a two-dimensional model of the sensor data based on a local coordinate system in order to calculate a TTC. Thus, Kuno teaches generating what the present disclosure calls a vicinity model.).
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Regarding claim 2, Kuno discloses the method of claim 1.
Kuno further discloses:
The method of claim 1, wherein
the temporal parameter based on see Kuno Fig. 6 and paragraph 0114 for TTC2 defined as a distance divided by the relative velocity between the ego vehicle and the other vehicle. See also paragraph 0115.),
wherein the temporal parameter based on see Kuno Fig. 6 and paragraph 0114 for TTC2 defined as a distance divided by the relative velocity between the ego vehicle and the other vehicle. See also paragraph 0115.), and
wherein the movement parameter is a relative speed between the ego vehicle [[L(10)]] and the object [[(18)]] (in a broad reasonable interpretation this claim defines the temporal parameter as the TTC and defines the TTC as the distance between a vehicle and object divided by their relative speed. With that in mind, see Kuno Fig. 6 and paragraph 0114 for TTC2 defined as a distance divided by the relative velocity between the ego vehicle and the other vehicle. See also paragraph 0115.).
Regarding claim 3, Kuno discloses the method of claim 1.
Kuno further discloses:
The method of claim 1,wherein
the current occupied region [[(20)]] of the ego vehicle [[(10)]] is ascertained as the occupied region [[(20)]] taking into account dimensions of the ego vehicle [[(10)]] (see Fig. 6 and paragraph 0108 for the host vehicle 10 and the other vehicle 62 having a possible occupied region, called the “ ‘intersecting region S’” which is the hatched area of Fig. 6, if both vehicles continue on their current trajectories. See Fig. 1 for the dimensions of the ego vehicle being known in relation to the local coordinate system shown. See Fig. 6 for the dimensions of the other vehicle 62 also being known. See paragraph 0116 for there being a “three-dimensional target” which is other vehicle 62 but these dimensions are processed in at least a 2D coordinate system as shown in Fig. 6.).
Regarding claim 4, Kuno discloses the method of claim 1.
Kuno further discloses:
The method of claim 1 [[and 2]], wherein
a braking corridor [[(23)]] of the ego vehicle [[(10)]] is ascertained as the possible occupied region [[(20)]] (see Fig. 6 for the width of the ego vehicle and various braking distances along the lines marked Lr7 and Lr8 described in paragraph 0104 as the “driver’s vehicle passing region”.),
wherein the braking corridor [[(23)]] is ascertained based on see Fig. 6 for the width of the ego vehicle and various braking distances along the lines marked Lr7 and Lr8 described in paragraph 0104 as the “driver’s vehicle passing region”. See also Fig. 7).
Regarding claim 5, Kuno discloses the method of claim 1.
Kuno further discloses:
The method of claim 1 [[and 2]], wherein
at least one turning circle [[(22)]] of the ego vehicle [[(10)]] is ascertained as the possible occupied region [[(20)]] (see Fig. 3. The ego vehicle may occupy Lr5 and Lr6 according to a turning circle.).
Regarding claim 6, Kuno discloses the method of claim 1.
Kuno further discloses:
The method of claim 1 [[and 2]], wherein
a movement corridor [[(21)]] of the object [[(18)]] is ascertained as the possible occupied region [[(20)]] (see Fig. 6 for the extrapolated occupied region 62a and the movement coordinate bounded by Lr9 and Lr10).
Regarding claim 7, Kuno discloses the method of claim 2.
Kuno further discloses:
The method of claim 2 [[and 7]], wherein
the temporal parameter based on in response to the ego vehicle assuming a position that is reachable by performing an avoidance maneuver (see Fig. 3. The ego vehicle 10 can make various avoidance maneuvers, such as one along turning radius Rs, but there still can be a TTC with other vehicle 61a. Fig. 3 clearly shows this and Fig. 4 describes the speeds and distances. See also paragraphs 0082-0085. Paragraph 0082 teaches determining when the avoidance maneuver must start to avoid a collision.).
Regarding claim 8, Kuno discloses the method of claim 1.
Kuno further discloses:
The method of claim 1 wherein
a movement corridor [[(21)]] of the ego vehicle [[(10)]] as the possible occupied region [[(20)]] and see Fig. 6 for the width of the ego vehicle and various braking distances along the lines marked Lr7 and Lr8 described in paragraph 0104 as the “driver’s vehicle passing region”. The hatched region is the region of overlap while the current location of vehicle 10 shown in Fig. 6 it is current occupied region.), and[[and]]
wherein the temporal parameter see Fig. 6 and Fig. 7 for all the distances and times of the occupied regions being determined. See Fig. 6 and paragraph 0114 for TTC2 defined as a distance divided by the relative velocity between the ego vehicle and the other vehicle. See also paragraph 0115.).
Regarding claim 9, Kuno discloses the method of claim 1.
Kuno further discloses:
The method of claim 1,wherein
the occupied region [[(20)]] is at least two-dimensional (see Fig. 6).
Regarding claim 10, Kuno discloses:
A control device [[(12)]] for a transportation vehicle, which is configured to ascertain a temporal parameter for describing a possible collision of an ego vehicle with at least one further object by:
ascertaining a movement parameter that is dependent on a movement of at least one of the ego vehicle and the at least one further object (see Kuno Fig. 6. See paragraph 0058 for the X-Y coordinate system being at the front center of the host vehicle 10. See paragraph 0065 for obtaining the “moving speed of the three-dimensional target.” This means the moving speed of the “other vehicle 62” as it is called in paragraph 0103 and elsewhere. Since the moving speed is in the coordinate system of the host vehicle it is a relative speed between the ego vehicle and the other vehicle.);
ascertaining a current and/or possible occupied region for at least one of the ego vehicle and the at least one further object (see Fig. 6 and paragraph 0108 for the host vehicle 10 and the other vehicle 62 having a possible occupied region, called the “ ‘intersecting region S’” which is the hatched area of Fig. 6, if both vehicles continue on their current trajectories.); and
ascertaining the temporal parameter based on the movement parameter and the occupied region (see Kuno Fig. 6 and paragraph 0114 for TTC2 defined as a distance divided by the relative velocity between the ego vehicle and the other vehicle. See also paragraph 0115),
wherein the occupied region is ascertained based on a vicinity model of the ego vehicle (see Kuno, who teaches a vicinity model in at least as much detail as the present disclosure. See Kuno paragraph 0037 for a vehicle speed sensor 32. In paragraph 0039 there is a front camera 31. Paragraph 0059 teaches that the host vehicle’s ECU 21 “prestores various ‘templates’ corresponding to other vehicles and pedestrians”. Paragraph 0063 teaches that the ECU 21 resolves sensor data into the X-Y coordinate system shown in Fig. 1, as mentioned in paragraph 0058. Paragraph 0035 states that the ECU 21 can reference “lookup tables (maps)”. Note that the ECU 21 in Kuno extensively breaks down sensor data, such as distances and velocities, into X and Y coordinates. See Fig. 6 and paragraphs 0063 and 0066 for just a few examples of this. Thus, Kuno is generating a two-dimensional model of the sensor data based on a local coordinate system in order to calculate a TTC. Thus, Kuno teaches generating what the present disclosure calls a vicinity model.).
Regarding claims 11-17, they are respectively analogous to claims 2-9. Please see the rejections of those claims.
Additional Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Nister et al. (US2019/0243371 A1) teaches in paragraph 0286 that sensors “may expand the field of view” to detect turning vehicles. But this “expand the field of view” is in references to the other sensors on the vehicle which have narrower views. Nister does not teach expanding the field of view upon detecting that another vehicle is performing a U-turn. See the images below.
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Kamiya et al. (US2021/0114591 A1) extensively teaches TTC and “ECT”. See the images below. Kamiya makes a distinction between a “collision point K1” and the time and distance to it, as seen in Fig. 5 and paragraph 0044; and an “expected crossing point K2 and the time and distance to it, as seen in Fig. 5 and paragraphs 0048-0049 (emphases added). Point K1 is where the trajectories of vehicles intersect. Point K1 is associated with a “time to collision” or “TTC”, as discussed in paragraph 0044. The “deterministic indicator for collision” determines collision point K1 and times and distances associated with it. Point K2 is the “the closest point” between vehicles as they travel on their trajectories. Point K2 is associated with an “expected time to crossing” or “ECT,” as discussed in paragraph 0047. The “deterministic indicator for crossing” determines the crossing point K2 and the times and distances associated with it. (Emphases added.) As shown in Fig. 8, Fig. 3 with a NO out of S140, and paragraph 0052, there are cases in which the ECT is small yet the TTC is large. In those cases, the vehicle may be able to swerve out of the path of an oncoming vehicle rather than brake to avoid or minimize a collision. Therefore, the system uses both TTC and ECT as well as associated thresholds to determine what to do out of Fig. 3, S140. See the images below.
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Shalev-Shwartz et al. (US2021/0110484 A1) teaches making predictions and then refining them based on the actions of other actors. Shalev-Shwartz also teaches in paragraph 0289 that an autonomous vehicle can detect eye-contact of a pedestrian and interpret that as a recognition that the pedestrian sees the vehicle and that the pedestrian will therefore not cross in front of it. Therefore, the system relaxes a previous constraint. In other words, there are at least two actions that were contemplated, one before and one after the eye-contact. See the images below.
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Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL M. ROBERT whose telephone number is (571)270-5841. The examiner can normally be reached M-F 7:30-4:30 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hunter Lonsberry can be reached at 571-272-7298. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL M. ROBERT/Primary Examiner, Art Unit 3665