6Notice 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 .
DETAILED ACTION
This action is responsive to applicant’s filing of 7/08/2024. Claims 1-20 are pending. Claims 1-2 and 7-20 are rejected. Claims 3-6 are objected to.
Priority
Applicant’s claim of priority to application 18221363 filed 7/12/2023, now Patent 12,033,399 is acknowledged.
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)(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, 7, 9-12, 14-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lee et al. (US20200234066) which discloses all the claimed elements including:
(re: cl 1) A method for predicting turning and braking actions of vehicles, comprising: at a first vehicle (⁋22- First vehicle ego) including one or more processors and memory (⁋26-multicore processors #102 with associated memory #118);
obtaining a plurality of images along a road (⁋21-sequence of images ⁋22-whilst travelling along a route);
for each image of the plurality of images: extracting, from the image, a plurality of image regions each corresponding to a respective vehicle light of a second vehicle positioned on the road near the first vehicle (⁋21, 22-framework established about taillights of ado vehicle; ⁋55-56-taillight image regions detected of ado vehicle);
generating, for each image of the plurality of images, a composite image that includes the plurality of image regions (⁋54-compares composite image using difference; ⁋18-composite images used);
and determining, for each image region, a probability that a respective vehicle light of the second vehicle changed its state (⁋50- recognizing flashing vs. static; fig. 7, #704 and #708 infer an intent of an ado vehicle);
predicting a vehicle action of the second vehicle based on the probability for each image region (⁋29-infering and predicting agent’s action; ⁋61-predicting vehicle action from its taillights);
and at least partially autonomously driving the first vehicle based on the predicted vehicle action of the second vehicle (⁋22-autonomous vehicle ego control from recognized images and their predictions of lead/second vehicle ado; fig. 7, #708, #710).
(re: cl 7) wherein the determining includes assigning, to a respective image region: a first probability value corresponding to a change in state of the respective vehicle light from a first state to a second state during a first predefined time interval (⁋21, 22-framework established about taillights; ⁋55-56-taillight image regions detected of ado (leading) vehicle); ⁋50- recognizing flashing vs. static; fig. 7, #704 and #708 infer an intent of an ado vehicle);
a second probability value corresponding to a change in state of the respective vehicle light from the second state to the first state during the first predefined time interval (⁋29-infering and predicting agent’s action; ⁋61-predicting vehicle action from its taillights);
and a third probability value corresponding to no change in state of the respective vehicle light during the first predefined time interval (⁋50-51-makes a temporal analysis to determine taillight flashing state; ⁋59-flashing).
(re: cl 9) further comprising: repeating, for each image of the plurality of images, the extracting, generating, determining, and predicting for one or more additional vehicles in the image.
(⁋21- continually updates sequences of images for continual taillight recognition on predicts vehicle behavior; ⁋44-temporally updates sequence of images for updated state of lead vehicle and anticipated behavior; ⁋50- temporally updates sequence of images for updated state of lead vehicle and anticipated behavior).
(re: cl 10) A first vehicle, comprising: a plurality of sensors;
a vehicle control system (⁋22 - First vehicle ego with ⁋29-control system for vehicle);
one or more processors memory (⁋26-multicore processors #102 with associated memory #118);
the one or more programs comprising instructions for: obtaining a plurality of images along a road (⁋21-sequence of images ⁋22-whilst travelling along a route);
extracting, from each image of the plurality of images, a plurality of image regions each corresponding to a respective vehicle light of a second vehicle positioned on the road near the first vehicle (⁋21, 22-framework established about taillights; ⁋55-56-taillight image regions detected of ado vehicle);
generating, for each image of the plurality of images, a composite image that includes the plurality of image regions (⁋54-compares composite image using difference ; ⁋18-composite images used );
and determining, for each image region, a probability that a respective vehicle light of the second vehicle changed its state (⁋50- recognizing flashing vs. static; fig. 7, #704 and #708 infer an intent of an ado vehicle);
predicting a vehicle action of the second vehicle based on the probability for each image region (⁋29-infering and predicting agent’s action; ⁋61-predicting vehicle action from its taillights);
and at least partially autonomously driving the first vehicle based on the predicted vehicle action of the second vehicle (⁋22-autonomous vehicle ago control from recognized images and their predictions of lead vehicle ado; fig. 7, #708, #710).
(re: cl 11) further comprising instructions for: applying a prediction model to predict the vehicle action of the second vehicle based on the probability of each image region (⁋29-infering and predicting agent’s action; ⁋61-predicting vehicle action from its taillights); (⁋21-temporal attended model; ⁋42-spatial & temp detection model integrate portions of the framework, Figs. 6A,B).
(re: cl 12) wherein the prediction model is a temporal convolution neural network (⁋21-temporal attended model; ⁋42-spatial & temp detection model integrate portions of the framework, Figs. 6A,B).
(re: cl 14) wherein the instructions for the determining include instructions for: generating, for the plurality of images, a plurality of composite images (⁋56-generates a composite sequence representing the image of each light);
and applying a light status change classifier *#312 fig. 3) to determine, based on the plurality of composite images, a respective probability that a respective vehicle light of the second vehicle changed its state (⁋50, ⁋48-vehicle taillight states determined using ⁋48-probability distribution predicted from images).
(re: cl 15) wherein the light status change classifier is obtained by training, using machine learning, a classification model using a plurality of training images labeled with vehicle light states (⁋42-trains using spatial and temporal detection model with, ⁋21-sequence of images, ⁋22)
(re: cl 16) A non-transitory computer-readable storage medium storing one or more programs configured for execution by one or more processors of a first vehicle (⁋7-“ A non-transitory computer-readable medium having program code recorded thereon”; cl8-“ A non-transitory computer-readable medium having program code recorded thereon for performing vehicle taillight recognition,”),
the first vehicle further including a plurality of sensors (⁋33-senssor module #302, ⁋41-second sensor #304) and a vehicle control system (⁋22-autonomous vehicle ado control from recognized images and their predictions of first vehicle ego),
the one or more programs comprising instructions for: obtaining a plurality of images along a road (⁋21-sequence of images ⁋22-whilst travelling along a route);
for each image of the plurality of images: extracting, from the image, a plurality of image regions each corresponding to a respective vehicle light of a second vehicle positioned on the road near the first vehicle (⁋21, 22-framework established about taillights; ⁋55-56-taillight image regions detected of ado vehicle);
generating, for each image of the plurality of images, a composite image that includes the plurality of image regions (⁋54-compares composite image using difference; ⁋18-composite images used);
and determining, for each image region, a probability that a respective vehicle light of the second vehicle changed its state (⁋50- recognizing flashing vs. static; fig. 7, #704 and #708 infer an intent of an ado vehicle);
predicting a vehicle action of the second vehicle based on the probability for each image region (⁋29-infering and predicting agent’s action; ⁋61-predicting vehicle action from its taillights);
and at least partially autonomously driving the first vehicle based on the predicted vehicle action of the second vehicle (⁋22-autonomous vehicle ago control from recognized images and their predictions of lead/second vehicle ado; fig. 7, #708, #710).
(re: cl 17) wherein the instructions for extracting further include instructions for: for each image of the plurality of images, applying a detection model to: detect the plurality of image regions corresponding to a plurality of vehicle lights of the second vehicle (⁋21, 22-framework established about taillights of ado vehicle; ⁋55-56-taillight image regions detected of ado vehicle); and generate a respective bounding box around each respective vehicle light of the plurality of vehicle lights (⁋44-bounding boxes generated about light regions).
(re: cl 18) wherein the instructions for generating the composite image include instructions for concatenating a plurality of bounding boxes corresponding to the plurality of vehicle lights (⁋56-generates a composite sequence representing the image of each light ⁋46-concatentate images ⁋57-58).
(re: cl 19) the one or more programs comprising instructions for: applying the detection model to classify each of the respective plurality of vehicle lights to have a classification (⁋21-temporal attended model; ⁋42-spatial & temp detection model integrate portions of the framework, Figs. 6A,B);
selected from the group consisting of: a left vehicle light, a right vehicle light, and a middle vehicle light (Fig. 5b-middle light on; Fig. 5g-left light on/right light on; Fig. 5a- all lights off).
(re: cl 20) wherein the detection model is obtained by training, using machine learning, an object detection model using a plurality of training images with labeled vehicle lights (⁋42-trains using spatial and temporal detection mode; ⁋21-plurality sequence of images ⁋22-whilst travelling along a route).
Claim Rejections - 35 USC § 103
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.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US20200234066) wherein Lee et al. teaches the elements previously discussed and
further suggests: (re: cl 2) wherein the composite image consists only of the plurality of image regions (⁋54-compares composite image using difference ; ⁋18-composite images used ).
It 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, for Lee et al. to perform the negative limitation of using the composite image only of the plurality of image regions as Lee et al. has already taught using the composite images in recognizing taillights and evaluating the tail light signals as disclosed simplifies the recognition task without the need for additional difficulty and complication as one of ordinary skill in the art would recognize.
Claim(s) 8, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US20200234066) in view of Huang et al. (WO2018059585), with citations per the machine translation. wherein Lee et al. teaches the elements previously discussed and
Huang et al. teaches any elements Lee et al. lacks including:
(re: cl 8) wherein the first predefined time interval is determined based on a standard frequency of vehicle light changes of vehicles (L382-384-“ For example, the state of the rear light of the vehicle, particularly the state of the turn signal, can be identified based on the color of the rear light in the light recognition area, the blinking frequency, or the blinking sequence.”; L397-400-”For example, the state of the rear light of the vehicle, particularly the state of the turn signal, can be identified based on the color of the rear light in the light recognition area, the blinking frequency, or the blinking sequence.”; L426-429-“ The color, flicker frequency, or flashing sequence of the taillights of the car, thereby determining the row and column coordinates of the taillights of the flashing car. It is set to finalize the left turn signal, right turn or a double flash lamp recognition of warning lights.“; L430-435-“ , a time micro-molecule image corresponding to a vehicle light recognition area, a continuously blinking taillight sub-image is highlighted in the micro-molecule image at that time, and the taillight sub-image is determined to be located in the light recognition by recognizing coordinates. On the left side of the area, the flicker frequency is once per second. For example, it can be determined that the target vehicle is currently playing a left turn signal.”).
It 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, for Lee et al. to use a prediction model is a rule-based system that applies one or more predefined rules based on a standard turn frequency to predict the vehicle action of the second as turn signals and emergency lights blink and using a frequency analysis can distinguish flashing lights and type distinguish as taught by Huang et al..
Huang et al. teaches any elements Lee et al. lacks including:
(re: cl 13 wherein the prediction model is a rule-based system that applies one or more predefined rules based on a standard turn frequency to predict the vehicle action of the second vehicle (L382-384-“For example, the state of the rear light of the vehicle, particularly the state of the turn signal, can be identified based on the color of the rear light in the light recognition area, the blinking frequency, or the blinking sequence.”; L397-400-” For example, the state of the rear light of the vehicle, particularly the state of the turn signal, can be identified based on the color of the rear light in the light recognition area, the blinking frequency, or the blinking sequence.”; L426-429-“The color, flicker frequency, or flashing sequence of the taillights of the car, thereby determining the row and column coordinates of the taillights of the flashing car. It is set to finalize the left turn signal, right turn or a double flash lamp recognition of warning lights.“; L430-435-“a time micro-molecule image corresponding to a vehicle light recognition area, a continuously blinking taillight sub-image is highlighted in the micro-molecule image at that time, and the taillight sub-image is determined to be located in the light recognition by recognizing coordinates. On the left side of the area, the flicker frequency is once per second. For example, it can be determined that the target vehicle is currently playing a left turn signal.”).
It 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, for Lee et al. to use a prediction model as a rule-based system that applies one or more predefined rules based on a standard turn frequency to predict the vehicle action of the second as turn signals and emergency lights blink and using a frequency analysis can distinguish flashing lights and type distinguish as taught by Huang et al..
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 § 2146 et seq. 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 filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual 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/apply/applying-online/eterminal-disclaimer.
Claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1, as well as 1’s further dependent claims 2-11 of U.S. Patent No. 18221363
to Subramanian et al..
Claim 3 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 5, as well as 1’s further dependent claims 6-8 of U.S. Patent No. 18221363 to Subramanian et al..
Claim 4 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 6 of U.S. Patent No. 18221363 to Subramanian et al..
Claim 5 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 4, as well as 4’s further dependent claim 6 of U.S. Patent No. 18221363 to Subramanian et al..
Claim 6 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 8 of U.S. Patent No. 18221363 to Subramanian et al..
Claim 9 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 8 of U.S. Patent No. 18221363 to Subramanian et al..
Claim 10 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 12, as well as 1’s further dependent claims 13-15 of U.S. Patent No. 18221363 to Subramanian et al..
Claim 11 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 13 of U.S. Patent No. 18221363 to Subramanian et al..
Claim 13 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 14 of U.S. Patent No. 18221363 to Subramanian et al..
Claim 16 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 15, as well as 15’s further dependent claim 16-18 of U.S. Patent No. 18221363 to Subramanian et al..
Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are obviated as broadened variants of cited claims from the parent patent with the claim elements of the instant application located at the cited locations with the claims of the parent patent below:
Claim Elements in Current Application
Claim Elements in Patent
12,033,399
CL 1 A method for predicting turning and braking actions of vehicles, comprising: at a first vehicle including one or more processors and memory:
CL 1
obtaining a plurality of images along a road;
CL 1
extracting, from each image of the plurality of images, a plurality of image regions each corresponding to a respective vehicle light of a second vehicle positioned on the road near the first vehicle;
CL 1-
Narrower
“Extracting”
Vs. “detecting”
generating, for each image of the plurality of images, a composite image that includes the plurality of image regions;
CL 1
determining, for each image region, a probability that a respective vehicle light of the second vehicle changed its state;
CL 1
predicting a vehicle action of the second vehicle based on the probability for each image region;
CL 1
and at least partially autonomously driving the first vehicle based on the predicted vehicle action of the second vehicle.
CL 1
CL 3 wherein the determining further comprises: comparing a first composite image generated from a first road image taken at a first time to a second composite image generated from a second road image taken at a second time,
CL 5
wherein the second time is subsequent to the first time;
CL 5
and determining whether there are any differences in the first and second composite images,
CL 5
wherein the differences are used to predict the vehicle action of the second vehicle.
CL 5
CL 4 wherein determining whether there are any differences in the first and second composite images includes: determining differences associated with each image region,
CL 5
corresponding to a respective vehicle light of the second vehicle, in the first and second composite images.
CL 6
CL 5 wherein a time difference between the first time and the second time falls within a range of about 0.2 seconds to about 0.5 seconds.
CL 7
CL 6 wherein determining whether there are any differences in the first and second composite images includes: subtracting pixel values of the second composite image from corresponding pixel values of the first composite image, for one or more of: a red component, a green component, or a blue component of the first and second composite images.
CL 8
CL 9 further comprising: repeating, for each image of the plurality of images, the extracting, generating, determining, and predicting for one or more additional vehicles in the image.
CL 11
CL 10 A first vehicle, comprising: a plurality of sensors;
CL 12
a vehicle control system; one or more processors; and memory storing one or more programs configured for execution by the one or more processors,
CL 12
the one or more programs comprising instructions for: obtaining a plurality of images along a road;
CL 12
extracting, from each image of the plurality of images, a plurality of image regions each corresponding to a respective vehicle light of a second vehicle positioned on the road near the first vehicle;
CL 12
Narrower
“Extracting”
Vs. “detecting”
generating, for each image of the plurality of images, a composite image that includes the plurality of image regions; determining, for each image region, a probability that a respective vehicle light of the second vehicle changed its state;
CL 12
predicting a vehicle action of the second vehicle based on the probability for each image region;
CL 12
and at least partially autonomously driving the first vehicle based on the predicted vehicle action of the second vehicle.
CL 12
CL 11 the one or more programs further comprising instructions for: applying a prediction model to predict the vehicle action of the second vehicle based on the probability of each image region.
CL 13
CL 13 wherein the prediction model is a rule-based system that applies one or more predefined rules based on a standard turn frequency to predict the vehicle action of the second vehicle.
CL 14
CL 16 A non-transitory computer-readable storage medium storing one or more programs configured for execution by one or more processors of a first vehicle,
CL 15
the first vehicle further including a plurality of sensors and a vehicle control system,
CL 15
the one or more programs comprising instructions for: obtaining a plurality of images along a road;
CL 15
extracting, from each image of the plurality of images, a plurality of image regions each corresponding to a respective vehicle light of a second vehicle positioned on the road near the first vehicle;
CL 15
Narrower
“Extracting”
Vs. “detecting”
generating, for each image of the plurality of images, a composite image that includes the plurality of image regions;
CL 15
determining, for each image region, a probability that a respective vehicle light of the second vehicle changed its state;
CL 15
predicting a vehicle action of the second vehicle based on the probability for each image region;
CL 15
and at least partially autonomously driving the first vehicle based on the predicted vehicle action of the second vehicle.
CL 15
Claim 2 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1, as well as 1’s further dependent claims 2-11 of U.S. Patent No. 18221363
to Subramanian et al. with cited locations per the claims of the parent application below:
CL 2 wherein the composite image consists only of the plurality of image regions.
CL 1
It 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, for Subramanian et al. to perform the negative limitation of using the composite image only of the plurality of image regions as Subramanian et al. has already taught using the composite images in recognizing taillights and evaluating the tail light signals as disclosed simplifies the recognition task without the need for additional difficulty and complication as one of ordinary skill in the art would recognize.
Allowable Subject Matter
Claims 3-6 are objected to as being dependent claims premised upon a rejected base claim but would be allowed if the re-written in independent form or if the limitations of an allowable claim were incorporated within the independent base claim from which this claims depend or if re-written premised upon dependence from an otherwise allowable base claim and the double patenting rejections were overcome.
The prior art neither discloses nor suggest as in claim 3 in combination with the base claim limitations, comparing differences in composite images to predict a vehicle action.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled "Comments on Statement of Reasons for Allowance."
The following is an examiner's statement of reasons for allowance: The prior art neither discloses nor suggest as in claim 6 in combination with the base claim limitations, normalizing taillight intensity images prior to generating a composite image.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicants’
disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL E BUTLER whose telephone number is (571)272-6937. The examiner can normally be reached on Tuesday, Wednesday, or Thursday.
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/M.E.B/ Examiner, Art Unit
/JACOB S. SCOTT/ Supervisory Patent Examiner, Art Unit 3655
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