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 .
Response to Arguments
Applicant’s arguments filed on March 09, 2026 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's arguments filed March 09, 2026 with regard to the 35 USC 101 rejection have been fully considered but they are not persuasive since the amended limitations do not provide enough detail for providing a technical improvement and a "significantly more" for the limitation “generate a second feature map using one or more second neural networks based, at least in part, on a second set of image frames of the plurality of image frames that are preceding the first set of image frames, and one or more proximity metrics associated with a temporal distance between the second set of image frames and the first set of image frames”. Applicant is welcomed to point out where in the specification that the Examiner can find support for providing a technical improvement and a "significantly more" according to the limitations claimed in the claim 1, 8 and 15. In addition, human mind can generate proximity metrics associated with the temporal distance between two sets of image frames and humans naturally organize memories and visual experiences based on temporal proximity, where events occurring closer together in time are represented with higher similarity, forming a cognitive link. Therefore, the 35 USC 101 rejection is maintained.
Response to Amendment
The amendment to the claims received on March 09, 2026 has been entered.
The amendment of claims 1-8 and 15 is acknowledged.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112, first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 8 and 15 have been amended with a new limitation “generate a second feature map using one or more second neural networks based, at least in part, on a second set of image frames of the plurality of image frames that are preceding the first set of image frames, and one or more proximity metrics associated with a temporal distance between the second set of image frames and the first set of image frames”. However, the original specification does not explain or define anything about “generate a second feature map using one or more second neural networks based, at least in part, on a second set of image frames of the plurality of image frames that are preceding the first set of image frames, and one or more proximity metrics associated with a temporal distance between the second set of image frames and the first set of image frames”. Examiner will take the position that claim limitation defines an icon for the purpose of prior art consideration. Applicant is welcomed to point out where in the specification the Examiner can find support for this limitation, if Applicant believes otherwise.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a Judicial Exception in the form of an Abstract Idea, without significantly more:
Beginning with independent claim 1, a processor claim, which recites:
One or more processors, comprising: circuity to: generate a first feature map using one or more first neural networks based, at least in part, on a first set of image frames of a plurality of image frames that are temporally proximate to an image frame; generate a second feature map using one or more second neural networks based, at least in part, on a second set of image frames of the plurality of image frames that are preceding the first set of image frames, and one or more proximity metrics associated with a temporal distance between the second set of image frames and the first set of image frames; detect one or more objects in an environment based, at least in part, on the first feature map and the second feature map; and cause a device to move within the environment with respect to the one or more detected objects.
The claim recites abstract ideas:
“a processor, comprising one or more circuit to perform the “generating” steps, the “detecting” step and the “causing” step to move a device is considered being performed by a generic computer. Therefore, If the apparatus, processor and memory are removed from the claim, the method can be easily performed by a human being without the need of any of a computer component. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Human mind can generate proximity metrics associated with the temporal distance between two sets of image frames and humans naturally organize memories and visual experiences based on temporal proximity, where events occurring closer together in time are represented with higher similarity, forming a cognitive link.
A process that encompass a human performing the steps mentally with or without a physical aid in the form of the “causing” step to move a device, with the “generating” steps and the “detecting” step being pre-solution acts of processing information which could be performed visually and/or mentally; and
A method of organizing human behavior in the form of a social activity of following rules or instructions informing a person to perform the “generating” steps, the “detecting” step and the “causing” step to move a device.
These two abstract ideas will be considered together for analysis as a single abstract idea per MPEP 2106:
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This judicial exception is not integrated into a practical application because there are no recited additional elements that amount to a practical application, such as but no limited to the following as noted in MPEP 2106:
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Independent claim 8, a system claim, which recites:
A system, comprising: one or more processors to cause one or more circuits to: generate a first feature map using one or more first neural networks based, at least in part, on a first set of image frames of a plurality of image frames that are temporally proximate to an image frame; generate a second feature map using one or more second neural networks based, at least in part, on a second set of image frames of the plurality of image frames that are preceding the first set of image frames, and one or more proximity metrics associated with a temporal distance between the second set of image frames and the first set of image frames; detect one or more objects in an environment based, at least in part, on the first feature map and the second feature map; and cause a device to move within the environment with respect to the one or more objects.
The claim recites abstract ideas:
“a system, comprising one or more processor to cause one or more circuit to perform the “generating” steps, the “detecting” step and the “causing” step to move a device is considered being performed by a generic computer. Therefore, If the apparatus, processor and memory are removed from the claim, the method can be easily performed by a human being without the need of any of a computer component. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Human mind can generate proximity metrics associated with the temporal distance between two sets of image frames and humans naturally organize memories and visual experiences based on temporal proximity, where events occurring closer together in time are represented with higher similarity, forming a cognitive link.
A process that encompass a human performing the steps mentally with or without a physical aid in the form of the “causing” step to move a device, with the “generating” steps and the “detecting” step being pre-solution acts of processing information which could be performed visually and/or mentally; and
A method of organizing human behavior in the form of a social activity of following rules or instructions informing a person to perform the “generating” steps, the “detecting” step and the “causing” step to move a device.
These two abstract ideas will be considered together for analysis as a single abstract idea per MPEP 2106:
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This judicial exception is not integrated into a practical application because there are no recited additional elements that amount to a practical application, such as but no limited to the following as noted in MPEP 2106:
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Independent claim 15, a processing claim, which recites:
A method, comprising: generating a first feature map using one or more first neural networks based, at least in part, on a first set of image frames of a plurality of image frames that are temporally proximate to an image frame; generating a second feature map using one or more second neural networks based, at least in part, on a second set of image frames of the plurality of image frames that are preceding the first set of image frames, and one or more proximity metrics associated with a temporal distance between the second set of image frames and the first set of image frames; detecting one or more objects in an environment based, at least in part, on the first feature map and the second feature map; and causing a device to move within the environment with respect to the one or more objects.
The claim recites abstract ideas:
Human mind can generate proximity metrics associated with the temporal distance between two sets of image frames and humans naturally organize memories and visual experiences based on temporal proximity, where events occurring closer together in time are represented with higher similarity, forming a cognitive link.
A process that encompass a human performing the steps mentally with or without a physical aid in the form of the “causing” step to move a device, with the “generating” steps and the “detecting” step being pre-solution acts of processing information which could be performed visually and/or mentally; and
A method of organizing human behavior in the form of a social activity of following rules or instructions informing a person to perform the “generating” steps, the “detecting” step and the “causing” step to move a device .
These two abstract ideas will be considered together for analysis as a single abstract idea per MPEP 2106:
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This judicial exception is not integrated into a practical application because there are no recited additional elements that amount to a practical application, such as but no limited to the following as noted in MPEP 2106:
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The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reason: There are not additional elements other than the abstract idea.
Independent claims 1, 8 and 15 are merely a generic computer implementation of the abstract ideas and likewise do not amount to significantly more. See MPEP 2106:
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Likewise, the following dependent claims have been analyzed and do not recite elements that recite a practical application or significantly more and remain rejected under 35 USC 101: Claims 2-7, 9-14 and 16-20.
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.
Claims 1-3, 5, 6, 8-10, 12-17, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over McCurrie’627 (US 2021/0114627), and further in view of Senay’383 (US 2019/0251383), Wu’064 (US 2020/0293064).
With respect to claim 1, McCurrie’627 teaches One or more processors (Fig.3, item 300), comprising:
circuitry to: generate a first feature map using one or more first neural networks based, at least in part, on a first set of image frames of a plurality of image frames that are temporally proximate to an image frame (Fig.7, step 704);
cause a device to move within the environment with respect to the one or more detected objects (Fig.7, step 712)
McCurrie’627 does not teach generate a second feature map using one or more second neural networks based, at least in part, on a second set of image frames of the plurality of image frames that are preceding the first set of image frames, and one or more proximity metrics associated with a temporal distance between the second set of image frames and the first set of image frames; detect one or more objects in an environment based, at least in part, on the first feature map and the second feature map.
Senay’383 teaches generate a second feature map using one or more second neural networks based, at least in part, on a second set of image frames of the plurality of image frames that are preceding the first set of image frames [The feature map for the past frame and current frame are being generated with different CNNs (paragraph 4)];
detect one or more objects in an environment based, at least in part, on the first feature map and the second feature map [The feature map for the past frame and current frame are being generated with different CNNs and the one or more objects in an environment are being detected (paragraph 4)].
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of McCurrie’627 according to the teaching of Senay’383 to include a second CNN to generate a second feature map from the preceding inputted images of a scene because this will allow the objects in the scene to be determined more effective and to control the vehicle to drive more effectively and safely.
The combination of McCurrie’627 and Senay’383 does not teach generate a second feature map using one or more second neural networks based, at least in part, on a second set of image frames of the plurality of image frames that are preceding the first set of image frames, and one or more proximity metrics associated with a temporal distance between the second set of image frames and the first set of image frames.
Wu’064 teaches generate a second feature map using one or more second neural networks based, at least in part, on a second set of image frames of the plurality of image frames that are preceding the first set of image frames, and one or more proximity metrics associated with a temporal distance between the second set of image frames and the first set of image frames (paragraph 58 and Fig.5A).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of McCurrie’627 and Senay’383 according to the teaching of Wu’064 to incorporate context from previous, or "preceding," frames rather than analyzing only the current frame because this will improve the accuracy of video analysis to control the vehicle to bed drove more effectively and safely.
With respect to claim 2, which further limits claim 1, McCurrie’627 teaches wherein the first set of image frames are sequentially proximate to the image frame (Fig.2B and paragraph 40), and
wherein the first feature map further comprises using a third set of image frames of the plurality of image frames that is sequentially adjacent the first set of image frame (Fig.2B and paragraph 40).
With respect to claim 3, which further limits claim 1, McCurrie’627 does not teach wherein the second feature map is based, at least in part, on temporal proximity of each image frame in the second set of image frames to a current image frame.
Senay’383 teaches wherein the second feature map is based, at least in part, on temporal proximity of each image frame in the second set of image frames to a current image frame [as shown in Fig.2, the feature map for the past frame is being generated by the CNN 122 and the feature map for the current frame is being generated by the CNN 121. The feature map for the current frame is considered based, at least in part, on temporal proximity of current frame (each image frame in the second set of image frames) to a past image frame].
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of McCurrie’627 and Wu’064 according to the teaching of Senay’383 to include a second CNN to generate a second feature map from the preceding inputted images of a scene because this will allow the objects in the scene to be determined more effective and to control the vehicle to drive more effectively and safely.
With respect to claim 5, which further limits claim 1, McCurrie’627 teaches wherein the one or more second neural networks is a three-dimensional ("3D") convolutional neural network [a three-dimensional ("3D") convolutional neural network is considered being used to generated the 3D feature map 502 shown in Fig.5]
With respect to claim 6, which further limits claim 1, McCurrie’627 does not teach wherein the first feature map and the second feature map are combined into a combined feature map to be used to detect one or more objects.
Senay’383 teaches wherein the first feature map and the second feature map are combined into a combined feature map to be used to detect one or more objects (Fig.8).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of McCurrie’627 and Wu’064 according to the teaching of Senay’383 to include a second CNN to generate a second feature map from the preceding inputted images of a scene because this will allow the objects in the scene to be determined more effective and to control the vehicle to drive more effectively and safely.
With respect to claim 8, McCurrie’627 teaches a system (Fig.3), comprising:
one or more processors to cause one or more circuits (Fig.3, item 300) to:
generate a first feature map using one or more first neural networks based, at least in part, on a first set of image frames of a plurality of image frames that are temporally proximate to an image frame (Fig.7, step 704);
cause a device to move within the environment with respect to the one or more detected objects (Fig.7, step 712)
McCurrie’627 does not teach generate a second feature map using one or more second neural networks based, at least in part, on a second set of image frames of the plurality of image frames that are preceding the first set of image frames, and one or more proximity metrics associated with a temporal distance between the second set of image frames and the first set of image frames; detect one or more objects in an environment based, at least in part, on the first feature map and the second feature map.
Senay’383 teaches generate a second feature map using one or more second neural networks based, at least in part, on a second set of image frames of the plurality of image frames that are preceding the first set of image frames [The feature map for the past frame and current frame are being generated with different CNNs (paragraph 4)];
detect one or more objects in an environment based, at least in part, on the first feature map and the second feature map [The feature map for the past frame and current frame are being generated with different CNNs and the one or more objects in an environment are being detected (paragraph 4)].
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of McCurrie’627 according to the teaching of Senay’383 to include a second CNN to generate a second feature map from the preceding inputted images of a scene because this will allow the objects in the scene to be determined more effective and to control the vehicle to drive more effectively and safely.
The combination of McCurrie’627 and Senay’383 does not teach generate a second feature map using one or more second neural networks based, at least in part, on a second set of image frames of the plurality of image frames that are preceding the first set of image frames, and one or more proximity metrics associated with a temporal distance between the second set of image frames and the first set of image frames.
Wu’064 teaches generate a second feature map using one or more second neural networks based, at least in part, on a second set of image frames of the plurality of image frames that are preceding the first set of image frames, and one or more proximity metrics associated with a temporal distance between the second set of image frames and the first set of image frames (paragraph 58 and Fig.5A).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of McCurrie’627 and Senay’383 according to the teaching of Wu’064 to incorporate context from previous, or "preceding," frames rather than analyzing only the current frame because this will improve the accuracy of video analysis to control the vehicle to bed drove more effectively and safely.
With respect to claim 9, which further limits claim 8, McCurrie’627 teaches wherein the first set of image frames are sequentially proximate to the image frame (Fig.2B and paragraph 40), and
wherein the first feature map further comprises using a third set of image frames of the plurality of image frames that is sequentially adjacent the first set of image frame (Fig.2B and paragraph 40).
With respect to claim 10, which further limits claim 8, McCurrie’627 does not teach wherein the second feature map is based, at least in part, on temporal proximity of each image frame in the second set of image frames to a current image frame.
Senay’383 teaches wherein the second feature map is based, at least in part, on temporal proximity of each image frame in the second set of image frames to a current image frame [as shown in Fig.2, the feature map for the past frame is being generated by the CNN 122 and the feature map for the current frame is being generated by the CNN 121. The feature map for the current frame is considered based, at least in part, on temporal proximity of current frame (each image frame in the second set of image frames) to a past image frame].
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of McCurrie’627 and Wu’064 according to the teaching of Senay’383 to include a second CNN to generate a second feature map from the preceding inputted images of a scene because this will allow the objects in the scene to be determined more effective and to control the vehicle to drive more effectively and safely.
With respect to claim 12, which further limits claim 8, McCurrie’627 teaches wherein the one or more second neural networks is a three-dimensional ("3D") convolutional neural network [a three-dimensional ("3D") convolutional neural network is considered being used to generated the 3D feature map 502 shown in Fig.5]
With respect to claim 13, which further limits claim 8, McCurrie’627 does not teach wherein the first feature map and the second feature map are combined into a combined feature map to be used to detect one or more objects.
Senay’383 teaches wherein the first feature map and the second feature map are combined into a combined feature map to be used to detect one or more objects (Fig.8).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of McCurrie’627 and Wu’064 according to the teaching of Senay’383 to include a second CNN to generate a second feature map from the preceding inputted images of a scene because this will allow the objects in the scene to be determined more effective and to control the vehicle to drive more effectively and safely.
With respect to claim 14, which further limits claim 8, McCurrie’627 teaches wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine (abstract); a first system for performing simulation operations; a second system for performing deep learning operations; a third system implemented using an edge device; a fourth system implemented using a robot; a fifth system incorporating one or more virtual machines (VMs); a sixth system implemented at least partially in a data center; a seventh system for performing digital twin operations; an eighth system for performing light transport simulation; a nineth system for performing collaborative content creation for 3D assets; a tenth system for performing conversational Artificial Intelligence operations; an eleventh system for generating synthetic data; a twelfth system for implementing a web-hosted service for detecting program workload inefficiencies; an application as an application programming interface ("API"); a thirteenth system implemented at least partially using cloud computing resources; a fourteenth system for presenting one or more of virtual reality content, augmented reality content, or mixed reality content; or a fifteenth system implementing one or more large language models (LLMs).
With respect to claims 15-17 and 19-20, they are a method claims that claim how the processor of claims 1-3 and 5-6 to control a device to move within the environment according to the received image frames. Claims 15-17 and 19-20 are obvious in view of McCurrie’627, Senay’383 and Wu’064 because the claimed combination operates at the same manner as described in the rejected claims 1-3 and 5-6. In addition, the reference has disclosed a processor to control a device to move within the environment according to the received image frames, the process (method) to control a device to move within the environment according to the received image frames is inherent disclosed to be performed by a processor when processor performs the operation to control a device to move within the environment according to the received image frames.
Claims 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over McCurrie’627 (US 2021/0114627), Senay’383 (US 2019/0251.383), Wu’064 (US 2020/0293064) and further in view of Haynes’395 (US 2023/0339395).
With respect to claim 4, which further limits claim 1, the combination of McCurrie’627, Senay’383 and Wu’064 does not teach wherein the one or more first neural networks is a transformer attention mechanism.
Haynes’395 teaches wherein the one or more first neural networks is a transformer attention mechanism (paragraph 140 and 144).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of McCurrie’627, Senay’383 and Wu’064 according to the teaching of Haynes’395 to utilize the transformers as the neural network to use a self-attention mechanism to process sequential data because this will allow the system to attend to different parts of the sequence images to generate predictions.
With respect to claim 11, which further limits claim 8, the combination of McCurrie’627, Senay’383 and Wu’064 does not teach wherein the one or more first neural networks is a transformer attention mechanism.
Haynes’395 teaches wherein the one or more first neural networks is a transformer attention mechanism (paragraph 140 and 144).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of McCurrie’627 and Senay’383 according to the teaching of Haynes’395 to utilize the transformers as the neural network to use a self-attention mechanism to process sequential data because this will allow the system to attend to different parts of the sequence images to generate predictions.
With respect to claim 18, it is a method claim that claims how the processor of claim 4 to control a device to move within the environment according to the received image frames. Claim 18 is obvious in view of McCurrie’627, Senay’383, Wu’064 and Haynes’395 because the claimed combination operates at the same manner as described in the rejected claims 4. In addition, the reference has disclosed a processor to control a device to move within the environment according to the received image frames, the process (method) to control a device to move within the environment according to the received image frames is inherent disclosed to be performed by a processor when processor performs the operation to control a device to move within the environment according to the received image frames.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over McCurrie’627 (US 2021/0114627), Senay’383 (US 2019/0251383), Wu’064 (US 2020/0293064) and further in view of Park’953 (US 2024/0020953).
With respect to claim 7, which further limits claim 1, the combination of McCurrie’627, Senay’383 and Wu’064 does not teach does not teach generating a first set of birds- eye-view ("BEV") feature maps using the first set of image frames; and generating a second set of BEV feature maps using the second set of image frames.
Park’953 teaches generating a first set of birds- eye-view ("BEV") feature maps using the first set of image frames [the feature values of an image is considered part of the image (Fig.5, step B502)]; and
generating a second set of BEV feature maps using the second set of image frames [the feature values of an image is considered part of the image (Fig.5, step B502)].
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of McCurrie’627, Senay’383 and Wu’064 according to the teaching of Park’953 to generate BEV feature maps from the received image data because this will allow the enhance the safety of autonomous driving.
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 extension fee 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 date of this final action.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUO LONG CHEN whose telephone number is (571)270-3759. The examiner can normally be reached on M-F 9am - 5pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tieu, Benny can be reached on (571) 272-7490. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HUO LONG CHEN/Primary Examiner, Art Unit 2682
