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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claim 1 is rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over claim 1 of U.S. Patent No. US 12205379. The conflicting claims are not identical because patent claim 1 requires the additional elements of “ using the camera image patch and a pedestrian countdown signal classifier model, a state of a pedestrian countdown signal”, not required by claim 1 of the instant application. However, the conflicting claims are not patentably distinct from each other because:
• Claims 1 of application '766 and claim 1 of patent '379 recite common subject matter;
• Whereby claim 1, which recites the open-ended transitional phrase “comprising”, does not preclude the additional elements recited by claim 1 of the patent, and
• Whereby the elements of claim 1 are fully anticipated by patent claim 1, and anticipation is “the ultimate or epitome of obviousness” (In re Kalm, 154 USPQ 10 (CCPA 1967), also In re Dailey, 178 USPQ 293 (CCPA 1973) and In re Pearson, 181 USPQ 641 (CCPA 1974)).
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wellington et al (US 2017/0262709) in view of Shwartz et al (US 20210162995)
As to claim 1, Wellington teaches a computer-implemented method comprising:
determining, by the computing system, a state of a pedestrian countdown signal (the state output 472 generated by the signal analysis system 400 can indicate to the AV control system 490 a timing feature (e.g., a countdown) to when the state for the pass-through action will change to a different state, paragraph [0076]);
determining, by the computing system based on the state of the pedestrian countdown signal, a prediction of whether a pedestrian will enter a crosswalk governed by the pedestrian countdown signal ( the generated output 472 can include an indication that the autonomous vehicle has a right of way through the intersection for the pass-through action. In further variations, the state output 472 can include an indication to the AV control system 490 to yield to vehicles having right-of-way, or to pedestrians, before passing through the intersection, paragraph [0067]; the generated output 472 can further provide a prediction of when the state for the pass-through action will change. The prediction can be based on timing characteristics of the traffic signaling system 500 indicated in the matching signal map 427 ( paragraph [0082])).
While Wellington teaches the limitation above, Wellington fails to teach “based on the prediction, causing, by the computing system, the vehicle to perform an invitation action that invites a pedestrian to enter the crosswalk. “
Shwartz et al teaches a navigational constraint relaxation factor may include any suitable indicator that one or more navigational constraints may be suspended, altered, or otherwise relaxed in at least one aspect. In some embodiments, the at least one navigational constraint relaxation factor may include a determination (based on image analysis) that the eyes of a pedestrian are looking in a direction of the host vehicle. In such cases, it may more safely be assumed that the pedestrian is aware of the host vehicle (paragraph[0311]) . Shwartz clearly teaches he at least one navigational constraint relaxation factor may include: a pedestrian determined to be not moving (e.g., one presumed to be less likely of entering a path of the host vehicle); or a pedestrian whose motion is determined to be slowing. The navigational constraint relaxation factor may also include more complicated actions, such as a pedestrian determined to be not moving after the host vehicle has come to a stop and then resumed movement ( paragraph [0311]). Shwartz teaches Where trained navigational systems are used, the availability of relaxed navigational constraints for certain navigational situations may represent a mode switching from a trained system response to an untrained system response. For example, a trained navigational network may determine an original navigational action for the host vehicle, based on the first navigational constraint. The action taken by the vehicle, however, may be one that is different from the navigational action satisfying the first navigational constraint. Rather, the action taken may satisfy the more relaxed second navigational constraint and may been action developed by a non-trained system (e.g., as a response to detection of a particular condition in the environment of the host vehicle, such as the presence of a navigational constraint relaxation factor ( paragraph [0314]). It would have been obvious to one skilled in the art before filing of the claimed invention to use the constraints of Shwartz in order to create a mathematical model for safety assurance and a design of a system that adheres to safety assurance requirements while being scalable to millions of car . Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
As to claim 2, Schwartz et al teaches the computer-implemented method of claim 1, wherein the invitation action comprises turning toward the crosswalk and then stopping before entering the crosswalk (The navigational constraint relaxation factor may also include more complicated actions, such as a pedestrian determined to be not moving after the host vehicle has come to a stop and then resumed movement. In such a situation, the pedestrian may be assumed to understand that the host vehicle has a right of way, and the pedestrian coming to a stop may suggest an intent of the pedestrian to give way to the host vehicle. Other situations that may cause one or more constraints to be relaxed include the type of curb stone (e.g., a low curb stone or one with a gradual slope might allow a relaxed distance constraint), lack of pedestrians or other objects on sidewalk, a vehicle with its engine not running may have a relaxed distance, or a a situation in which a pedestrian is facing away and/or is moving away from the area towards which the host vehicle is heading, paragraph [0311]).
As to claim 3, Schwartz et al teaches the computer-implemented method of claim 1, wherein the invitation action comprises illuminating a light source ( based on image analysis a particular traffic light may be determined to be facing the crosswalk in a direction generally perpendicular to a lane of travel of a vehicle. In some cases, the detected traffic light may display special symbols (e.g., a symbol of a person walking, a symbol of a person standing, a symbol of a palm indicating a stop request, and the like). These features may analyze to identify the traffic light's relevancy to a crosswalk. This determination may be made in real time as a host vehicle navigates and intersection to assist the host vehicle in navigating relative to a detected crosswalk, or it may be analyzed in a post-capture process where drive information from multiple previous drives are collected, analyzed, and used to generate a map that indicates traffic light relevancy, paragraph [0911]).
As to claim 4, Schwartz et al teaches the computer-implemented method of claim 1, wherein the invitation action comprises displaying a message (Based on the output from the driving policy module 803, control module 805, which may also be implemented using processing unit 110, may develop control instructions for one or more actuators or controlled devices associated with the host vehicle. Such actuators and devices may include an accelerator, one or more steering controls, a brake, a signal transmitter, a display, or any other actuator or device that may be controlled as part of a navigation operation associated with a host vehicle. Aspects of control theory may be used to generate the output of control module 805. Control module 805 may be responsible for developing and outputting instructions to controllable components of the host vehicle in order to implement the desired navigational goals or requirements of driving policy module 803, paragraph [0216])
As to claim 5, Schwartz et al teaches the computer-implemented method of claim 1, wherein the invitation action comprises outputting a sound ( The wireless communication may include one or more devices configured to exchange transmissions over an air interface to one or more networks (e.g., cellular, the Internet, etc.) using an electromagnetic field at the radio frequency, infrared frequency, or ultraviolet frequency. Additionally, or alternatively, wireless communication may use magnetic fields, electric fields, or sound. Such transmissions can include communication between a traffic light and an autonomous vehicle in the proximity of the traffic light, and/or in some cases, such communication may include communication between the traffic light and the distributed navigation system and between the distributed navigation system and the vehicle navigation system of an autonomous vehicle, paragraph [0903]).
As to claim 6, Schwartz et al teaches the computer-implemented method of claim 1, further comprising detecting, by the computing system, a transition of the pedestrian countdown signal from: i) a do not walk state to ii) a walk state or a countdown state, wherein the prediction is further based on the detecting of the transition (The navigation system may be configured to execute a planned navigational action that allows the host vehicle to traverse the pedestrian crosswalk (e.g., crosswalk 5451) without a pedestrian-induced adjustment if the detected traffic light (e.g., traffic light 5430) is in a green state and the determined proximity of a pedestrian relative to the detected pedestrian crosswalk, paragraph [0926-0927]).
As to claim 7, Schwartz et al teaches the computer-implemented method of claim 1, further comprising determining a length of the crosswalk, wherein the prediction is further based on the length of the crosswalk (distance information may be provided by onboard radar and/or lidar systems. Alternatively or additionally, distance information may be derived from analysis of one or more images captured from the environment of the host vehicle. For example, numbers of pixels of a recognized object represented in an image may be determined and compared to known field of view and focal length geometries of the image capture devices to determine scale and distances. Velocities and accelerations may be determined, for example, by observing changes in scale between objects from image to image over known time intervals. This analysis may indicate the direction of movement toward or away from the host vehicle along with how fast the object is pulling away from or coming toward the host vehicle. Crossing velocity may be determined through analysis of the change in an object's X coordinate position from one image to another over known time periods, paragraph [0291].
As to claim 8, Schwartz et al teaches the computer-implemented method of claim 1, wherein: the state of the pedestrian countdown signal is a walk state or a countdown state, the method further comprises determining that the state of the pedestrian countdown signal is governed by a pedestrian control, and the prediction is further based on the determining that the state of the pedestrian countdown signal is governed by the pedestrian control(processing unit 110 may execute navigational response module 408 to cause one or more navigational responses in vehicle 200 based on the analysis performed at step 520 and the techniques as described above in connection with FIG. 4, paragraph [0181].
As to claim 9, Schwartz et al teaches the computer-implemented method of claim 8, wherein the determining the prediction comprises determining that an occluded pedestrian’s intent is to enter the crosswalk based on the state of the pedestrian countdown signal and the determining that the pedestrian countdown signal is governed by the pedestrian control(processing unit 110 may execute navigational response module 408 to cause one or more navigational responses in vehicle 200 based on the analysis performed at step 520 and the techniques as described above in connection with FIG. 4, paragraph [0181]..
As to claim 10, Schwartz et al teaches the computer-implemented method of claim 1, further comprising: obtaining an audio signal; and determining that the audio signal is indicative of the state of the pedestrian countdown signal being a walk state or a countdown state, wherein the prediction is further based on the determining that the audio signal is indicative of the state of the pedestrian countdown signal being the walk state or the countdown state (The navigational action may include actions that may not be directly related to the motion of a vehicle. For example, such navigational actions may include turning on/off headlights, engaging/disengaging antilock brakes, switching transmission gears, adjusting parameters of a vehicle suspension, turning on/off vehicle warning lights, turning on/off vehicle turning lights or brake lights, producing audible signals and the like. In various embodiments, the navigational actions are based on navigational data available to the navigation system, paragraph [0896], The machine learning models may be trained using training data collected from various sensors (e.g., image sensors for collecting images of an environment of an example vehicle, sensors for collecting audio data relate to the environment of the vehicle, and the like. Additionally, the training data may include navigational actions executed by human drivers, or by trained machine-learning modes that correspond to the images of the environment of the example vehicle[0917]).
As to claim 11, Schwartz et al teaches the computer-implemented method of claim 1, wherein the pedestrian countdown signal classifier model is a classification convolutional neural network that is configured to classify the state of the pedestrian countdown signal as being one of multiple states( FIG. 54 shows that various pedestrians may have different abilities, velocities, expected trajectories, and the like. For example, pedestrian 5422 may be determined to enter crosswalk 5451 and traverse crosswalk 5451 before traffic light 5432 has a red light (i.e., the light prohibiting the pedestrians traversing crosswalk 5451). Pedestrian 5420, on the other hand, appears to be stationary and may be unlikely to enter crosswalk 5451. Pedestrian 5424 may be a young person on a skateboard, whose motion may be unpredictable. Pedestrian 5421 may be an old gentleman who traverses crosswalk 5453 slowly and predictably. Similarly, pedestrian 5423 is a person in a wheelchair who has limited mobility (e.g., unlikely to rapidly change direction when traversing crosswalk 5453). Any or all of these detected characteristics may be relied upon in determining a navigational action for the host vehicle, including, for example, a velocity at which the host vehicle is allowed to approach or traverse a crosswalk, paragraph [0921-0922]).
As to clam 12, Schwartz teaches a computer-implemented method of claim 1, wherein determining the state of the pedestrian countdown signal comprises: obtaining an image using a camera coupled to the vehicle (the navigation system may include a machine-learning model that can take, as an input, images obtained by one or more image capture devices (e.g., devices 5460, 5461, and 5462), and return an age of a pedestrian, a motion vector of the pedestrian, a location of the pedestrian, an expected trajectory of the pedestrian, an uncertainty trajectory envelop (e.g., a trajectory envelop 5471 for pedestrian 5424, or a trajectory envelop 5472 for pedestrian 5422), and the like, paragraph [922]); and using a mapped position of the pedestrian countdown signal to select from within the image a camera image patch in which the pedestrian countdown signal is visible (Training of the system using reinforcement learning may involve learning a driving policy in order to map from sensed states to navigational actions ; paragraph [0218][0945])
As to clam 13, Wellington teaches a computer-implemented method of claim 12, further comprising: determining that an orientation of the pedestrian countdown signal with respect to an orientation of the vehicle satisfies a threshold condition; and obtaining the image based on the determining that the orientation of the pedestrian countdown signal with respect to the orientation of the vehicle satisfies the threshold condition (The signal analysis system 400 can identify a traffic signaling system 500 in the image data 407 (705), determine a position and orientation of the autonomous vehicle (i.e., pose information) (710), paragraph [0080]; when a clear matching threshold is met (e.g., a brightness threshold) for a particular state (e.g., a green light state), the signal analysis system 400 can generate and transmit a state output 472 to the AV control system 490 (760). In many examples, the probabilistic matching operation is performed dynamically as the autonomous vehicle approaches the intersection. Accordingly, the state output 472 may also be generated dynamically. Furthermore, in some aspects, the generated output 472 can further provide a prediction of when the state for the pass-through action will change, paragraph [0082])
The limitation of claims 14-20 has been addressed above.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NANCY BITAR whose telephone number is (571)270-1041. The examiner can normally be reached Mon-Friday from 8:00 am to 5:00 p.m..
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NANCY . BITAR
Examiner
Art Unit 2664
/NANCY BITAR/Primary Examiner, Art Unit 2664
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