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 .
Notice to Applicants
This communication is in response to the Application filed on 1/17/2025.
Claims 1-16 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/17/2025 has been considered by the examiner.
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 1-3, 11, and 15-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., abstract idea – mental process) without significantly more. Claim 1 is used as an example. Claims 15 and 16 recite a device and device-readable recording medium, respectively, having a memory and a physical processor. The two-part test to identify claims that are directed to a judicial exception (Step 2A) and to then evaluate if additional elements of the claim provide an inventive concept (Step 2B) are:
(1) Are the claims directed to a process, machine, manufacture or composition of matter;
(2A) Prong One: Are the claims directed to a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea;
Prong Two: If the claims are directed to a judicial exception under Prong One, then is the judicial exception integrated into a practical application;
(2B) If the claims are directed to a judicial exception and do not integrate the judicial exception, do the claims provide an inventive concept.
Claim 1. A method of recognizing signal information for autonomous driving of a vehicle, which is performed by a computing device, the method comprising: (a) collecting a plurality of images generated by capturing images of a traffic light located in a predetermined region; (b) extracting a plurality of pieces of signal state information from each of the plurality of collected images; and (c) determining final signal information of the traffic light using the plurality of pieces of extracted signal state information. [emphasis added].
With regard to (1), the instant claims recite an apparatus and a method, therefore the answer is "yes".
With regard to (2A), Prong One: Yes. When viewed under the broadest most reasonable interpretation, the instant claims are directed to a Judicial Exception – an abstract idea belonging to the group of mental process – concepts that are practicably performed in the human mind (including an observation, evaluation, judgement, opinion). The steps of (b) and (c) (above in emphasized claim 1) are generically recited and nothing in these steps precludes the steps from practically being performed by a human equipped with an appropriate apparatus. It can be interpreted as merely looking at the data and determining a color/location of a region in the image. There is nothing in the claim that requires more than an operation that a human, armed with the appropriate apparatus, pen and a paper, can not perform. The determining and extracting, under its broadest reasonable interpretation, covers performance of the limitation in the mind. The claim encompasses the user looking at region of an image once the image is received, attribute such as a color, location or an orientation of a section of the image can be determined. This way, essentially one can present/output information about the section of an image that represents that color, location, orientation. Thus, these limitations are a mental process.
With regard to (2A), Prong Two: No. The instant claims do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception of (a) “collecting…by capturing”, and therefore does not integrate the judicial exception into a practical application.
The use of a device/memory/processor to collect images by capturing images (i.e., “data”) at a high level of generality such that said “data” can be used in the operation of the recited judicial exception (the mental step of “receiving”). Supplying “data” does not provide for “integration” of the abstract idea into a practical application, as said data do not change the way in which said system operates. There are no specifics on how the data is received/captured. This can be interpreted as “visualization”. Even if this step is by a “device/processor” that may be, for example, a camera. A camera/sensor is well known in the field, and receiving data from a camera/sensor is also well known.
This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. In conclusion, the claim as a whole does not provide for “integration” of the abstract idea into a practical application.
The claim is directed to the abstract idea.
With regard to (2B), as discussed with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The pending claims do not show what is more than a routine in the art presented in the claims, i.e., the additional elements are nothing more than routine and well-known steps. There is no improvement to technology here. There is only steps of (b) and (c), with additional elements of (a), and it has not been shown that the mental process allows the “technology” to do something that it previously was not able to do.
Therefore, the claims 1, 15, and 16 are ineligible.
With regard to dependent claims 2-3 and 11 similar analysis is applied and therefore does not integrate the judicial exception into a practical application – does not provide significant more than the judicial exception. These claims are similarly rejected for the same reasons discussed in view of steps recited in claim 1 and not repeated herewith.
Claim 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim is NOT directed to a process, machine, manufacture or composition of matter. The claimed “computer program” are non-structural per se, and the specification does not exclude the “computer program” from being software (see pages 16-19). Therefore, a reasonable interpretation in light of the specification leads to the conclusion that the claim encompasses pure software, which does not fall within the definition of a process, machine, manufacture or composition of matter.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1 and 15-16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2018/0112997 to Fasola et al. (hereafter, “Fasola”).
With regard to claim 1 Fasola discloses a method of recognizing signal information for autonomous driving of a vehicle (), which is performed by a computing device, the method comprising: collecting a plurality of images generated by capturing images of a traffic light located in a predetermined region (paragraph [0071], at least one sensing device 40a visualizes the traffic light based on the control commands 108 and generates sensed data 106, generally in the form of images or video data of at least the traffic light; paragraph [0075], Referring to FIG. 5B, an example view from a front camera of the autonomous vehicle 10 included in the at least one sensing device 40a is shown. The view is representative of images taken by the front camera of an autonomous vehicle 10 located at anchor point 6000 of FIG. 5A. Two traffic lights labelled as 1105, 1106 in the semantic map 101 of FIG. 5A are visualized by the front camera); extracting a plurality of pieces of signal state information from each of the plurality of collected images (paragraph [0075], computer vision system 74 is configured to extract traffic light location data 124 included in the semantic map 101 and associated with the straight-ahead intersection lane and to configure a field of view of the front camera and/or to focus on the traffic light in the images obtained by the camera based on the traffic light location data 124; paragraph [0076], traffic light location data 124 for each traffic light 1105, 1106 can be extracted from the semantic map 101; paragraph [0085], extracts traffic light location data 124 associated with the labelled intersection lane in the semantic map 101; paragraph [0086], assesses the state thereof); and determining final signal information of the traffic light using the plurality of pieces of extracted signal state information (paragraph [0081], in the event of a green light state defined by the traffic light state data 107, vehicle control system 80 is configured to start going or to continue going through the intersection as prescribed by the route…In the event of a red light state defined by the traffic light state data 107, vehicle control system 80 is configured to stop the vehicle in advance of the traffic light).
With regard to claims 15-16, claims 15-16 are rejected same as claim 1 and the arguments similar to that presented above for claim 1 are equally applicable to claims 15-16. Fasola discloses a device and a computer including a processor, memory and a network interface as shown in Figs. 1-4, and all of the other limitations similar to claim 1 are not repeated herein, but incorporated by reference.
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 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 of this title, 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2-3 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over US 2018/0112997 to Fasola et al. (hereafter, “Fasola”) in combination with Behrendt et al. (“A deep learning approach to traffic lights: Detection, tracking, and classification”, IEEE, 2017) (hereafter, “Behrendt”).
With regard to claim 2, Fasola teaches the method of claim 1, wherein the extracting of the plurality of pieces of signal state information includes: identifying a traffic light region of each of the plurality of collected images based on location information of the traffic light and positioning information of an autonomous driving vehicle, which are recorded on precise map data corresponding to the predetermined region (paragraphs [0071, 0075-0076, 0081]); [cropping only the identified traffic light region from each of the plurality of collected images and generating a plurality of traffic light images;] and analyzing each of the plurality of generated traffic light images and extracting the plurality of pieces of signal state information (paragraphs [0075-0076, 0085, 0086]). However, Fasola does not expressly teach cropping only the identified traffic light region from each of the plurality of collected images and generating a plurality of traffic light images.
Behrendt teaches cropping only the identified traffic light region from each of the plurality of collected images and generating a plurality of traffic light images (page 1370, Fig. 1 bottom image is a crop image of only traffic light region, page 1372 left column 2nd full paragraph, section C, Fig. 4 on page 1373).
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 Fasola’s reference to have cropping of the traffic light image of Behrendt’s reference. The suggestion/motivation for doing so would have been to increase convergence speed and accuracy significantly, as suggested by Behrendt on page 1372.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Behrendt with Fasola to obtain the invention as specified in claim 2.
With regard to claim 3, Fasola teaches the method of claim 1, wherein the extracting of the plurality of pieces of signal state information includes analyzing a first image among the plurality of collected images [using a pre-trained signal classification model] and extracting first signal state information of the first image (paragraphs [0075-0076, 0085, 0086]).
However, Fasola does not expressly teach a pre-trained signal classification model. Behrendt teaches a pre-trained signal classification model (page 1372 section C. Classification, table IV).
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 Fasola’s reference to have classification model of Behrendt’s reference. The suggestion/motivation for doing so would have been to differentiate between the different traffic light states and additionally removes false positives, as suggested by Behrendt on page 1372.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Behrendt with Fasola to obtain the invention as specified in claim 3.
With regard to claim 11, Fasola teaches the method of claim 3, wherein the determining of the final signal information includes: generating input data using the plurality of pieces of extracted signal state information; and [inputting the generated input data into a pre-trained artificial intelligence model and] extracting the final signal information as result data (paragraphs [0071, 0075-0076, 0085, 0086]).
However, Fasola does not expressly teach inputting the generated input data into a pre-trained artificial intelligence model. Behrendt teaches inputting the generated input data into a pre-trained artificial intelligence model (Fig. 4, page 1372 section C. Classification, table IV).
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 Fasola’s reference to have classification model of Behrendt’s reference. The suggestion/motivation for doing so would have been to differentiate between the different traffic light states and additionally removes false positives, as suggested by Behrendt on page 1372.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Behrendt with Fasola to obtain the invention as specified in claim 11.
Allowable Subject Matter
Claims 4-10 and 12-14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHEFALI D. GORADIA whose telephone number is (571)272-8958. The examiner can normally be reached Monday-Thursday 8AM-6PM, Friday 8AM-12PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henok Shiferaw can be reached at 571-272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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SHEFALI D. GORADIA
Primary Patent Examiner
Art Unit 2676
/SHEFALI D GORADIA/Primary Patent Examiner, Art Unit 2676
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