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 .
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 06/18/2025 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered if signed and initialed by the Examiner.
Status of Claims
This is a Final Rejection office action in response to application Serial No. 17/861,852. Claim(s) 1, 4-9 have been examined and fully considered.
Claim(s) 1 have been amended.
Claim(s) 9 is newly added.
Claim(s) 1, 4-9 are pending in Instant Application.
Response to Arguments
Applicant’s amendments and associated arguments, filed 06/18/2025, with respect to the rejection of the claims under 35 U.S.C. §103(a) have been considered but are moot because the arguments do not apply to all of the references being applied to the amended limitations in the current rejection.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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 for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, and 4-5 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cohen et al. (Pub. No.: US 2017 / 0371347; previous recorded), hereinafter, referred to as “Cohen” in view of Lavoie et al. (Pub. No.: US 2017/0072947; previous recorded), hereinafter, referred to as “Lavoie”, and in view of Niewiadomski et al. (Pub. No.: US 2021/0276553), hereinafter, referred to as “Niewiadomski”
Regarding [claim 1], Cohen discloses a vehicle behavior prediction device that predicts an entry of a target vehicle into a host lane on which a host vehicle travels, from a road region adjacent to the host lane, the device comprising:
a driving support electronic control unit (ECU) configured to:
(see Cohen [0077]: “FIG. 1 is a block diagram representation of a system 100 consistent with the exemplary disclosed embodiments. System 100 may include various components depending on the requirements of a particular implementation. In some embodiments, system 100 may include a processing unit 110, an image acquisition unit 120, a position sensor 130, one or more memory units 140, 150, a map database 160, a user interface 170, and a wireless transceiver 172”)
detect the target vehicle existing in the road region (see Cohen [0241]: “FIG. 16A depicts a parked car 1602 from a point-of-view of a system consistent with the disclosed embodiments” and [0245]: “At step 1704, processing unit 110 may analyze the at least one image to identify a target vehicle. For example, identifying a target vehicle may further include associating at least one bounding box with a shape of the side of the target vehicle”);
acquire a front distance that is a distance between the target vehicle (see Cohen [0182]: “processing unit 110 may execute stereo image analysis module 404 to detect candidate objects (e.g., vehicles, pedestrians, road marks, traffic lights, road hazards, etc.) within the first and second plurality of images, filter out a subset of the candidate objects based on various criteria, and perform multi-frame analysis, construct measurements, and determine a confidence level for the remaining candidate objects. .. For example, position, velocity, and/or acceleration relative to vehicle 200 may be determined based on trajectories, positions, movement characteristics, etc. of features associated with an object appearing in one or both of the image streams.” …;
predict whether or not the target vehicle is able to enter the host lane (see Cohen [0247]: “At step 1706, processing unit 110 may identify motion associated with the identified wheel(s).”; and [0250]: “Processing unit 110 may use indicators of rotation, positional changes of the at least one feature, and/or tracked points to determine a speed at which the target vehicle is moving.”; and
a storage unit configured to store information for each type of vehicle, the type of vehicle comprising a passenger car, a truck, and a van (see Cohen [0104]: “system 100 may transmit data to a server according to an “intermediate” privacy level and include additional information not included under a “high” privacy level, such as a make and/or model of a vehicle and/or a vehicle type (e.g., a passenger vehicle, sport utility vehicle, truck, etc.); see also [0075] communication between vehicles)”;
wherein the driving support ECU is further configured to:
extract [information] corresponding to a type of the target vehicle Cohen [0075]: “Additionally, an autonomous vehicle may also use stored information, such as information that provides a model of the vehicle's environment when navigating. ... Some vehicles can also be capable of communication among them, sharing information, altering the peer vehicle of hazards or changes in the vehicles' surroundings, etc.” and [0164]: “At step 546, processing unit 110 may construct a set of measurements for the detected objects. … In some embodiments, processing unit 110 may construct the measurements based on estimation techniques using a series of time-based observations such as Kalman filters or linear quadratic estimation (LQE), and/or based on available modeling data for different object types (e.g., cars, trucks, pedestrians, bicycles, road signs, etc.)”; and
wherein the target vehicle exits from a parallel parking state in the road region from a stopped state to enter the host lane (see Cohen [0251]: “At step 1708, processing unit 110 may cause a navigational change of the host vehicle. For example, navigational responses may include a change in a heading direction of the host vehicle (as depicted in FIG. 13 above), a lane shift, a change in acceleration (e.g., applying brakes of the host vehicle), and the like. Processing unit 110 may cause the one or more navigational responses based on the determination performed at step 1706. For example, processing unit 110 may move the host vehicle away from the target vehicle and/or decelerate the host vehicle in response to movement of the target vehicle”).
While Cohen discloses the detection and identification of multiple vehicles and the distance to each vehicle (see mappings above), it does not explicitly recite the distance between the target vehicle and the obstacle in front of the target vehicle as claimed. In addition, while Cohen discloses the detection of a vehicle turning into the host lane (see mapping above), it does not explicitly recite the estimation of a turning radius and associated ability to enter the host lane based on collected information.
However, Lavoie teaches:
acquire a front distance that is a distance between the target vehicle and an obstacle existing in front of the target vehicle (see Lavoie [0016]: “Known techniques may be used to obtain a measurement of distance between the vehicle 10 and the parked vehicle. For example, a plenoptic lens camera technique, such is known, which employs a single camera with multiple lenses with differing depth of fields, can determine the distance between the vehicle 10 and the parked vehicle 14” and see also Lavoie [0018], Fig. 7);
estimate a turning radius of the target vehicle; and predict whether or not the target vehicle is able to enter the host lane while avoiding the obstacle, based on the acquired front distance and the estimated turning radius (see Lavoie [0023]–[0025]: disclosing estimates made using distances and steering angles; and Lavoie [0026]: “if the steering wheel has reached an extreme left limit and the wheel angle 15 is not sufficient for a single forward egress maneuver, the driver of the vehicle 10 may have to deploy additional maneuvering techniques. For example, the driver may have to back up the vehicle 10 while turning the steering wheel 30 in the opposite direction (to the right) to acquire more distance between the vehicle 10 and the vehicle 14”; see also fig. 7); and
a storage unit configured to store turning radius information including a minimum turning radius (see Lavoie [0023]: “Moreover, if the vehicle 10 has a steering system computer (SSC) (not shown), the SSC can assist the driver and turn the wheels 11 to an angle which allows the vehicle 10 to egress the parking spot, and/or the SSC can autonomously perform an egress maneuver for the vehicle 10 while monitoring the vehicle 10 position if the driver chooses an autonomous park exit mode”; and [0026]: “if the steering wheel has reached an extreme left limit and the wheel angle 15 is not sufficient for a single forward egress maneuver, the driver of the vehicle 10 may have to deploy additional maneuvering techniques” Examiner Note: the turning radius information must be stored in order to be recalled in an autonomous driving environment)….
wherein the driving support ECU is further configured to:
extract the minimum turning radius corresponding to … the determined type of the target vehicle based on the minimum turning radius of the … the target vehicle stored in the storage unit, and estimate the turning radius of the target vehicle based on the extracted minimum turning radius (see Lavoie [0023]: “Moreover, if the vehicle 10 has a steering system computer (SSC) (not shown), the SSC can assist the driver and turn the wheels 11 to an angle which allows the vehicle 10 to egress the parking spot, and/or the SSC can autonomously perform an egress maneuver for the vehicle 10 while monitoring the vehicle 10 position if the driver chooses an autonomous park exit mode” and see [0026]: “However, if the steering wheel has reached an extreme left limit and the wheel angle 15 is not sufficient for a single forward egress maneuver, the driver of the vehicle 10 may have to deploy additional maneuvering techniques. For example, the driver may have to back up the vehicle 10 while turning the steering wheel 30 in the opposite direction (to the right) to acquire more distance between the vehicle 10 and the vehicle 14”; see also Fig. 2-4, 7 for determinations of turning radius), and
wherein the target vehicle exits from a parallel parking state in the road region from a stopped state to enter the host lane based on the minimum turning radius (see Lavoie (Fig. 8. [0051] disclosing vehicle moving away from parking space)).
Cohen and Lavoie are each directed to autonomous driving systems making determinations associated with parallel parking park-out process in a shared driving space. As Cohen discloses the use of stored information associated with multiple vehicle types and the sharing of information between vehicles (see [0075]), it would have been obvious to incorporate the park out assist considerations (information gathering and processing) as taught by Lavoie in the data collection and analysis for the host vehicle control of Cohen for the purpose of improving the decision making and response of the autonomous features of both host and target vehicles operating in a shared driving space (see Cohen [0002]–[0003], Lavoie [0001]–[0002]).
Lovie teaches determinations of turning radius for egress. Nowakowski more explicitly teaches the amended limitations directed to associating turning radius with an identified type of target vehicle, including:
…wherein the driving support ECU is further configured to: determine a type of the target vehicle; extract the minimum turning radius corresponding to the determined type of the target vehicle based on the minimum turning radius of the determined type of the target vehicle stored in the storage unit (see, Paragraphs [0059]: “The vehicle computer 110 can be programmed to identify target vehicle 106 parameters, e.g., dimensions (e.g., height, length, width), a turning radius, a wheelbase, etc. For example, the classifier can be further trained with data known to represent various types, e.g., makes and/or models, of vehicles. Thus, in addition to identifying the target vehicle 106, the classifier can output a type of the target vehicle 106. Once trained, the classifier can accept as input host vehicle sensor 115 data, e.g., an image including the target vehicle 106, and then provide as output an identification of the type of the target vehicle 106 in the image. As another example, the vehicle computer 110 can determine a type of the target vehicle 106 based on image data, e.g., by using image recognition techniques. The vehicle computer 110 can then determine one or more target vehicle 106 parameters based on the type of the target vehicle 106. For example, the vehicle computer 110 may store, e.g., in a memory, a look-up table or the like that associates target vehicle 106 parameters with a type of target vehicle 106.”; [0063]: “The vehicle computer 110 can predict whether a future location of the target vehicle 106 will intersect the path P of the host vehicle 105. The future location of the target vehicle 106 is defined at least in part by a predicted path of the target vehicle 106. For example, the vehicle computer 110 can predict a path of the target vehicle 106 based on the travel direction, turning direction, and target vehicle 106 parameters (e.g., turning radius, wheelbase…” and [0085]: “Additionally, the vehicle computer 110 can determine one or more target vehicle 106 parameters (e.g., turning radius, wheelbase, dimensions, etc.) of the target vehicle 106 based on object classification techniques, as discussed above. For example, the classifier can be further trained to output the type, e.g., make and model, of the target vehicle 106 based on the image data. The vehicle computer 110 can then determine one or more target vehicle 106 parameters based on the type of the target vehicle 106. For example, the vehicle computer 110 may store, e.g., in a memory, a look-up table or the like that associates target vehicle 106 parameters with a type of target vehicle 106.”)
…
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention classifying extract the minimum turning radius corresponding to the determined type of the target vehicle as taught by Nowakowski. One would be motivated to make this modification in order to, while operating a host vehicle in an area, identify a target vehicle in a sub-area based on detecting a visual cue from the target vehicle, and update operations of the target vehicle path based on operating parameters associated with the target vehicle (see, Paragraph [0008]).
As to [claim 4], the combination Cohen, Lavoie and Nowakowski teaches the vehicle behavior prediction device according to claim 1. Cohen and Lavoie teaches wherein the driving support ECU is further configured to predict whether or not the target vehicle is able to enter the host lane, based on a lateral position of the target vehicle in the road region (see at least Paragraph [0055]: “FIG. 17 is a flowchart showing an exemplary process for causing one or more navigational responses based on detection of a target vehicle entering the host vehicle's lane”; and [0232]-[0233]: “Systems and methods that identify road homography and identify wheel components of the vehicles may allow for targeted monitoring for movement of the vehicles. By targeting the monitoring, the system may identify and react to motion into a host vehicle's lane, either from another lane or from a parked position, with a shorter reaction time, at least under certain circumstances, than traditional motion detection. Embodiments of the present disclosure described below relate to systems and methods for detecting a vehicle entering a host vehicle's lane using targeted monitoring” and [0251]; see also Lavoie, Fig. 2-7, disclosing position of target vehicle relative to object in front of the target vehicle and associated lateral distances and relative positioning associated with each estimated turning radius).
As to [claim 5], the combination Cohen, Lavoie and Nowakowski teaches the vehicle behavior prediction device according to claim 1. Cohen and Lavoie discloses wherein the driving support ECU is further configured to predict whether or not the target vehicle is able to enter the host lane, based on an inclination of the target vehicle with respect to an extension direction of the host lane (see Cohen [0055]: “FIG. 17 is a flowchart showing an exemplary process for causing one or more navigational responses based on detection of a target vehicle entering the host vehicle's lane”; and [0232]-[0233]: “Systems and methods that identify road homography and identify wheel components of the vehicles may allow for targeted monitoring for movement of the vehicles. By targeting the monitoring, the system may identify and react to motion into a host vehicle's lane, either from another lane or from a parked position, with a shorter reaction time, at least under certain circumstances, than traditional motion detection. Embodiments of the present disclosure described below relate to systems and methods for detecting a vehicle entering a host vehicle's lane using targeted monitoring” and [0251]; see also Lavoie, Fig. 2-7, disclosing position of target vehicle relative to host lane and relative positioning associated with each estimated turning radius).
As to [claim 8], the combination Cohen, Lavoie and Nowakowski teaches the vehicle behavior prediction device according to claim 4. Cohen discloses wherein the driving support ECU is further configured to predict whether or not the target vehicle is able to enter the host lane, based on an inclination of the target vehicle with respect to an extension direction of the host lane (see Cohen Paragraph [0055]: “FIG. 17 is a flowchart showing an exemplary process for causing one or more navigational responses based on detection of a target vehicle entering the host vehicle's lane”; and [0232]-[0233]: “Systems and methods that identify road homography and identify wheel components of the vehicles may allow for targeted monitoring for movement of the vehicles. By targeting the monitoring, the system may identify and react to motion into a host vehicle's lane, either from another lane or from a parked position, with a shorter reaction time, at least under certain circumstances, than traditional motion detection. Embodiments of the present disclosure described below relate to systems and methods for detecting a vehicle entering a host vehicle's lane using targeted monitoring” and [0251]; see also Lavoie, Fig. 2-7, disclosing position of target vehicle relative to host lane and relative positioning associated with each estimated turning radius).
Claim(s) 6 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cohen, Lavoie and Nowakowski, and in view of Pfadler et al. (Pub. No.: US 2020/0324762; previous recorded), hereinafter, referred to as “Pfadler”.
As to [claim 6], the combination Cohen, Lavoie and Nowakowski teaches the vehicle behavior prediction device according to claim 1. Cohen and Lavoie discloses wherein when it is not possible for the target vehicle to enter the host lane while avoiding the obstacle, the driving support ECU is further configured to predict … protrusion of the target vehicle into the host lane (see Cohen [0055]: “FIG. 17 is a flowchart showing an exemplary process for causing one or more navigational responses based on detection of a target vehicle entering the host vehicle's lane”). The navigational response of Lavoie strongly suggests a predicted amount of target vehicle protrusion into the host lane. Lavoie further teaches a multi-maneuver egress, which results in a protrusion (see [0026] if the steering wheel has reached an extreme left limit and the wheel angle 15 is not sufficient for a single forward egress maneuver, the driver of the vehicle 10 may have to deploy additional maneuvering techniques; Fig. 2-3, specifically the point where the left wheel will be when the right wheel reaches the impact point with the vehicle 14). And Nowakowski (see, [0023]: “The vehicle computer 110 is further programmed to predict that a future location of the target vehicle 106 and a path P of the host vehicle 105 will intersect. The vehicle computer 110 is further programmed to determine a stop position S of the host vehicle 105 based on the future
location of the target vehicle 106”)
Niether Cohen, Lavoie or Nowakowski teaches … predict an amount of protrusion of the target vehicle into the host lane.
However, Pfadler more explicitly teaches:
predict an amount of protrusion of the target vehicle into the host lane (see [0008], [0017]: disclosing detecting a risk of protrusion and outputting a warning message to other road users)
Cohen, Lavoie, Nowakowski and Pfadler are each directed to driving systems associated with maneuvering in a shared driving space. Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to predict an amount of protrusion of the target vehicle into the host lane as taught by Pfadler. One would be motivated to make this modification in order to convey assistance systems can be used to avoid the risk of collision between transportation vehicles. This is beneficial with regard to identifying when vehicles will exceed lane boundaries (see Pfadler Paragraph [0007]).
As to [claim 9], recites analogous limitations that are present in claim 6, therefore claim 9 would be rejected for the same/similar premise above.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cohen, Lavoie and Nowakowski, and in view of Minemura (Pub. No.: US 2018/0312163; previously recorded).
As to [claim 7], the combination Cohen, Lavoie and Nowakowski teaches the vehicle behavior prediction device according to claim 1. Neither Cohen nor Lavoie expressly disclose wherein the driving support ECU is further configured to determine the type of target vehicle by pattern matching based on camera images.
As Nowakowski teaches wherein the driving support ECU is further configured to determine the type of target vehicle… based on camera images (see, Paragraphs [0037], [0047]-[0048], [0059]).
Additionally, Minemura teaches:
wherein the driving support ECU is further configured to determine the type of target vehicle by pattern matching based on camera images (see Paragraph [0039]: “…a HOG image, generated from an image region with a high probability of including a projection of a vehicle, can be matched to predetermined HOG patterns for a set of poses for a vehicle (e.g., generic vehicle, specific vehicle make and model, etc.), wherein the pose associated with the matched predetermined pattern can be assigned to the detected vehicle. In a second example, the external-facing cameras are a stereo camera pair, wherein the object distance from the vehicle can be determined based on the disparity between the images recorded by the stereo camera pair. However, the object pose, or elements thereof, can be otherwise determined.”).
Cohen, Lavoie and Nowakowski discloses the use of stored information associated with multiple vehicle types and the sharing of information between vehicles. Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to implement a pattern matching for the purpose of identifying vehicle type to detect vehicle in any situation as taught by Minemura. One would be motivated to make this modification in order improve the ability to identify and characterize a target object and the behavior thereof in a region of a vehicle ([0003], [0005]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
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/B.U./Examiner, Art Unit 3663
/ABBY J FLYNN/Supervisory Patent Examiner, Art Unit 3663