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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 8 January 2025 and 13 November 2025 are being 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.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 20 recites “computer-readable storage medium.” The broadest reasonable interpretation of a claim drawn to a computer readable medium (also called machine readable medium and other such variations) typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. See MPEP 2111.01.
The USPTO recognizes that applicants may have claims directed to computer readable media that cover signals per se, which the USPTO must reject under 35 U.S.C. § 101 as covering both non-statutory subject matter and statutory subject matter. In an effort to assist the patent community in overcoming a rejection or potential rejection under 35 U.S.C. § 101 in this situation, the USPTO suggests the following approach. A claim drawn to such a computer readable medium that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 U.S.C. § I01 by adding the limitation "non-transitory" to the claim. Cf. Animals -Patentability, 1 077 0ff. Gaz. Pat. Office 24 (April 21, 1987) (suggesting that applicants add the limitation "non-human" to a claim covering a multi-cellular organism to avoid a rejection under 35 U.S.C. § 101). Such an amendment would typically not raise the issue of new matter, even when the specification is silent because the broadest reasonable interpretation relies on the ordinary and customary meaning that includes signals per se. The limited situations in which such an amendment could raise issues of new matter occur, for example, when the specification does not support a non-transitory embodiment because a signal per se is the only viable embodiment such that the amended claim is impermissibly broadened beyond the supporting disclosure. See, e.g., Gentry Gallery, Inc. v. Berkline Corp., 134 F.3d 1473 (Fed. Cir. 1998).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (CN 113 673 513 A) in view of Rosas-Maxemin et al. (US 2022/0237713).
Regarding claim 1, Chang et al. disclose a method of recognizing a vehicle license plate, comprising:
performing vehicle license plate detection on an obtained image to obtain a vehicle license plate region (Figure 3, step S301 and paragraph [0065].);
enlarging the vehicle license plate region based on all pixels of the vehicle license plate region to obtain a deformed image of the vehicle license plate in response to the vehicle license plate region being in a non-reference direction, wherein a resolution of the deformed image of the vehicle license plate is higher than a resolution of the vehicle license plate region (Figure 3, steps S303 and paragraph [0070], width/height expansion is performed before perspective transformation, i.e. enlargement, which results in a higher resolution.); and
performing reference direction correction on the deformed image of the vehicle license plate to obtain a to-be-detected image (Paragraph [0072].); and
recognizing the to-be-detected image to obtain an output character corresponding to the vehicle license plate region (Paragraphs [0072]-[0074], particularly the last sentence of paragraphs [0072]-[0073].).
Chang et al. fail to explicitly teach wherein the image is a fisheye image.
Rosas-Maxemin et al. disclose wherein an image is a fisheye image (Paragraph [0052].).
Hence the prior art includes each element claimed although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of the actual combination of the elements in a single prior art reference. In combination Chang et al. performs the same function as it does separately of providing a method of recognizing a vehicle license plate, and Rosas-Maxemin et al. performs the same function as it does separately of providing a fisheye image.
Therefore, one of ordinary skill in the art before the effective filing date of the claimed invention could have combined the elements as claimed by known methods, and that in combination, each element merely performed the same function as it does separately. The results of the combination would have been predictable and resulted in a method of recognizing a vehicle license plate using a fisheye image.
Therefore, 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.
Regarding claim 17, this claim is rejected under the same rationale as claim 1, and furthermore Chang et al. also disclose a memory, a processor, and a computer program stored in the memory (Figure 10, processor 1001 and memory 1003, and paragraph [0150].).
Regarding claim 20, this claim is rejected under the same rationale as claim 17, where the memory of claim 17 is a computer-readable storage medium.
Claims 2 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (CN 113 673 513 A) in view of Rosas-Maxemin et al. (US 2022/0237713) and further in view of Matsuhira (US 2006/0257039).
Regarding claim 2, Chang et al. and Rosas-Maxemin et al. disclose the method according to claim 1.
Chang et al. and Rosas-Maxemin et al. fail to teach wherein the enlarging the vehicle license plate region based on all pixels of the vehicle license plate region to obtain a deformed image of the vehicle license plate, comprises: performing interpolation based on the all pixels in the vehicle license plate region to generate at least one interpolated point; performing weighted fusion on pixel values of all pixels in a predetermined range corresponding to each of the at least one interpolated point to obtain a pixel value of each of the at least one interpolated point; and generating the deformed image of the vehicle license plate based on each of the at least one interpolated point and the pixels.
Matsuhira disclose wherein enlarging a region based on all pixels of the region, comprises:
performing interpolation based on the all pixels in the region to generate at least one interpolated point (Paragraph [0179]);
performing weighted fusion on pixel values of all pixels in a predetermined range corresponding to each of the at least one interpolated point to obtain a pixel value of each of the at least one interpolated point (Paragraph [0179]); and
generating the image based on each of the at least one interpolated point and the pixels (Figure 18).
Hence the prior art includes each element claimed although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of the actual combination of the elements in a single prior art reference. In combination, the combination of Chang et al. and Rosas-Maxemin et al. performs the same function as it does separately of enlarging a region for license plate recognition, and Matsuhira performs the same function as it does separately of enlarging a region based on interpolation.
Therefore, one of ordinary skill in the art before the effective filing date of the claimed invention could have combined the elements as claimed by known methods, and that in combination, each element merely performed the same function as it does separately. The results of the combination would have been predictable and resulted in enlarging the region using interpolation for license plate recognition.
Therefore, 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.
Regarding claim 18, this claim is rejected under the same rationale as claim 2.
Claims 3 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (CN 113 673 513 A) in view of Rosas-Maxemin et al. (US 2022/0237713) and further in view of Matsuhira (US 2006/0257039) and Englard et al. (US 2019/0180502).
Regarding claim 3, Chang et al., Rosas-Maxemin et al. and Matsuhira disclose the method according to claim 2.
Chang et al., Rosas-Maxemin et al. and Matsuhira wherein the performing weighted fusion on pixel values of all pixels in a predetermined range corresponding to each of the at least one interpolated point to obtain a pixel value of each of the at least one interpolated point, comprises: for each of the at least one interpolated point, performing weighted fusion of a distance weight and an angle weight corresponding to each of the all pixels within the predetermined range corresponding to the interpolated point to determine a pixel value of the corresponding interpolated point.
Englard et al. disclose wherein for each of at least one interpolated point, performing weighted fusion of a distance weight and an angle weight corresponding to each of the all pixels within the predetermined range corresponding to the interpolated point to determine a pixel value of the corresponding interpolated point (Paragraph [0044].).
Therefore, it would have been obvious to “one of ordinary skill” in the art before the effective filing date of the claimed invention to use the weighted fusion teachings of Englard et al. in the interpolation taught by the combination of Chang et al., Rosas-Maxemin et al. and Matsuhira. The motivation to combine would have been in order to improve the interpolation process by using a distance weight and an angle weight resulting in the enlargement providing improved image quality.
Regarding claim 19, this claim is rejected under the same rationale as claim 3.
Claims 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (CN 113 673 513 A) in view of Rosas-Maxemin et al. (US 2022/0237713) and further in view of Schmer (US 2021/0097300).
Regarding claim 8, Chang et al. and Rosas-Maxemin et al. disclose the method according to claim 1.
Chang et al. and Rosas-Maxemin et al. fail to teach wherein the recognizing the to-be-detected image to obtain an output character corresponding to the vehicle license plate region, comprises: performing character detection on the to-be-detected image to obtain character information corresponding to each character in the vehicle license plate region; extracting a region image containing the character from the to-be-detected image, wherein the region image containing the character is a sub-image of the to-be-detected image; determining an output character corresponding to the vehicle license plate region based on the character information and the region image containing the character.
Schmer discloses recognizing a to-be-detected image to obtain an output character corresponding to a vehicle license plate region, comprises:
performing character detection on the to-be-detected image to obtain character information corresponding to each character in the vehicle license plate region (Paragraph [0034], OCR, where character detection occurs in OCR.);
extracting a region image containing the character from the to-be-detected image, wherein the region image containing the character is a sub-image of the to-be-detected image (Paragraph [0034], OCR, where region extraction occurs in OCR.);
determining an output character corresponding to the vehicle license plate region based on the character information and the region image containing the character (Paragraph [0034], OCR, where an output character is determined in OCR.).
Hence the prior art includes each element claimed although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of the actual combination of the elements in a single prior art reference. In combination, the combination of Chang et al. and Rosas-Maxemin et al. performs the same function as it does separately of recognizing a to-be-detected image to obtain an output character corresponding to a vehicle license plate region, and Schmer performs the same function as it does separately of using OCR for license plate recognition.
Therefore, one of ordinary skill in the art before the effective filing date of the claimed invention could have combined the elements as claimed by known methods, and that in combination, each element merely performed the same function as it does separately. The results of the combination would have been predictable and resulted in recognizing a to-be-detected image to obtain an output character corresponding to a vehicle license plate region using OCR.
Therefore, 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.
Regarding claim 9, Chang et al., Rosas-Maxemin et al. and Schmer disclose the method according to claim 8, wherein the character information comprises at least one candidate character and at least one confidence level, the at least one candidate character and the at least one confidence level are in one-to-one correspondence; and the recognizing the to-be-detected image to obtain an output character corresponding to the vehicle license plate region, comprises:
comparing the at least one confidence level with a predetermined confidence level (Schmer: Paragraph [0065].);
in response to at least one of the at least one confidence level exceeding the predetermined confidence level, determining a candidate character corresponding to the at least one confidence level exceeding the predetermined confidence level as the output character of the corresponding character (Schmer: Paragraph [0034].).
Regarding claim 10, Chang et al., Rosas-Maxemin et al. and Schmer disclose the method according to claim 9, wherein the recognizing the to-be-detected image to obtain an output character corresponding to the vehicle license plate region, comprises: in response to each of the at least one confidence level of the at least one candidate character corresponding to the character not exceeding the predetermined confidence level, determining the output character corresponding to the character based on the character information and the region image containing the character (Schmer: Paragraph [0065], a person performs review, however, its ”based on” the character information and the region image containing the character.).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (CN 113 673 513 A) in view of Rosas-Maxemin et al. (US 2022/0237713) and further in view of Schmer (US 2021/0097300) and Crary et al. (US 2021/0097306).
Regarding claim 11, Chang et al., Rosas-Maxemin et al. and Schmer disclose the method according to claim 10.
Chang et al., Rosas-Maxemin et al. and Schmer fail to teach wherein the determining the output character corresponding to the character based on the character information and the region image containing the character, comprises: calculating a similarity between the region image containing the character and each of the at least one candidate character; and selecting a candidate character having a greatest similarity with the region image containing the character as the output character of the character.
Crary et al. disclose calculating a similarity between a region image containing the character and each of the at least one candidate character; and selecting a candidate character having a greatest similarity with the region image containing the character as the output character of the character (Paragraph [0064], where comparing the similarity is calculating a similarity.).
Therefore, it would have been obvious to “one of ordinary skill” in the art before the effective filing date of the claimed invention to use the similarity teachings of Crary et al. in the license plate recognition taught by the combination of Chang et al., Rosas-Maxemin et al. and Schmer. The motivation to combine would have been in order to improve the accuracy of the results of the OCR function (See paragraph [0007]-[0008] of Crary et al.).
Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (CN 113 673 513 A) in view of Rosas-Maxemin et al. (US 2022/0237713) and further in view of Schmer (US 2021/0097300) and Georgis (US 2020/0074211).
Regarding claim 12, Chang et al., Rosas-Maxemin et al. and Schmer disclose the method according to claim 8,
wherein a ML model can be used (Schmer: Paragraph [0050] and Figure 2, 122), wherein the ML model is trained based on a first training sample set, etc. (Paragraph [0053] and Figure 2, 203).
Chang et al., Rosas-Maxemin et al. and Schmer fail to teach wherein the disclosed ML model is used for character recognition of the license plate, and thus fails to explicitly teach the determining an output character corresponding to the vehicle license plate region based on the character information and the region image containing the character, comprises: inputting the at least one candidate character and the region image containing the character into a vehicle license plate recognition model to be recognized to obtain the output character corresponding to the character; wherein the vehicle license plate recognition model is trained based on a first training sample set and a template sample set, the first training sample set comprises a plurality of first sample images, each of the plurality of first sample images comprises one vehicle license plate character, the template sample set comprises a plurality of vehicle license plate templates, each alphabet corresponds to one of the plurality of vehicle license plate templates, and each number corresponds to one of the plurality of vehicle license plate templates.
Georgis discloses inputting at least one candidate character and region image containing the character into a vehicle license plate recognition model to be recognized to obtain an output character corresponding to the character (Paragraph [0029]); wherein the vehicle license plate recognition model is trained based on a first training sample set and a template sample set, the first training sample set comprises a plurality of first sample images, each of the plurality of first sample images comprises one vehicle license plate character, the template sample set comprises a plurality of vehicle license plate templates, each alphabet corresponds to one of the plurality of vehicle license plate templates, and each number corresponds to one of the plurality of vehicle license plate templates (Paragraph [0062] and Figures 4A-4B).
Therefore, it would have been obvious to “one of ordinary skill” in the art before the effective filing date of the claimed invention to use the neural network model teachings of Georgis for the determining of the output character in the method taught by the combination of Chang et al., Rosas-Maxemin et al. and Schmer. The motivation to combine would have been in order to perform license plate recognition in a faster and more accurate manner (See paragraph [0029] of Georgis.).
Regarding claim 13, Chang et al., Rosas-Maxemin et al., Schmer and Georgis disclose the method according to claim 12, wherein the vehicle license plate recognition model is trained by performing the operations of:
obtaining the first training sample set and the template sample set, wherein the first training sample set comprises a plurality of first sample images, each of the plurality of first sample images comprises one vehicle license plate character, the template sample set comprises a plurality of vehicle license plate templates, each alphabet corresponds to one of the plurality of vehicle license plate templates, and each number corresponds to one of the plurality of vehicle license plate templates (Georgis: Paragraph [0062] and Figures 4A-4B); and
iteratively training the vehicle license plate recognition model based on an error value between a predetermined similarity and a similarity between each of the plurality of first sample images and a corresponding one of the plurality of vehicle license plate templates (Georgis: Paragraph [0062] and Figures 4A-4B, where step 426 says “retrain”, for example, meaning that the process of training is iterative as claimed.).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (CN 113 673 513 A) in view of Rosas-Maxemin et al. (US 2022/0237713) and further in view of Schmer (US 2021/0097300), Georgis (US 2020/0074211) and Challa (WO 2021/179035 A1).
Regarding claim 14, please refer to the rejection of claim 13, and furthermore Chang et al., Rosas-Maxemin et al., Schmer and Georgis fail to explicitly teach inputting the plurality of first sample images and the corresponding plurality of vehicle license plate templates are input to the vehicle license plate recognition model, and weighted fusion is performed on the input images and templates to obtain a corresponding feature map.
Challa disclose inputting a plurality of first sample images and corresponding plurality of vehicle license plate templates are input to the vehicle license plate recognition model, and weighted fusion is performed on the input images and templates to obtain a corresponding feature map (Paragraph [0090]).
Hence the prior art includes each element claimed although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of the actual combination of the elements in a single prior art reference. In combination, the combination of Chang et al., Rosas-Maxemin et al., Schmer and Georgis performs the same function as it does separately of using a vehicle license plate recognition model, and Challa performs the same function as it does separately of performing weighted fusion to obtain a corresponding feature map.
Therefore, one of ordinary skill in the art before the effective filing date of the claimed invention could have combined the elements as claimed by known methods, and that in combination, each element merely performed the same function as it does separately. The results of the combination would have been predictable and resulted in the vehicle license plate recognition model performing weighted fusion to obtain a corresponding feature map.
Therefore, 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.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (CN 113 673 513 A) in view of Rosas-Maxemin et al. (US 2022/0237713) and further in view of Wilbert et al. (US 2017/0193320).
Regarding claim 15, Chang et al. and Rosas-Maxemin et al. disclose the method according to claim 1, wherein the performing vehicle license plate detection on an obtained fisheye image to obtain a vehicle plate region, comprises: obtaining the fisheye image (See claim 1).
Chang et al. and Rosas-Maxemin et al. fail to teach:
performing detection of a corner point of the vehicle license plate on the fisheye image to obtain information of the corner point of the vehicle license plate; and
determining the vehicle license plate region corresponding to each vehicle license plate in the fisheye image based on the information of the corner point of the vehicle license plate.
Wilbert et al. disclose obtaining an image, performing detection of a corner point of a vehicle license plate on the image to obtain information of the corner point of the vehicle license plate (Paragraph [0094]); and
determining the vehicle license plate region corresponding to each vehicle license plate in the image based on the information of the corner point of the vehicle license plate (Paragraph [0094]).
Therefore, it would have been obvious to “one of ordinary skill” in the art before the effective filing date of the claimed invention to use the corner detection teachings of Wilbert et al. and apply them to the method taught by the combination of Chang et al. and Rosas-Maxemin et al. The motivation to combine would have been in order to provide better and more accurate recognition of the license plate region.
Allowable Subject Matter
Claims 4-7 and 16 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.
The following is a statement of reasons for the indication of allowable subject matter:
The primary reasons for indicating allowance subject matter in claim 4 is the inclusion of the limitations reciting “wherein before the performing weighted fusion of a distance weight and an angle weight corresponding to each of the all pixels within the predetermined range corresponding to the interpolated point to determine a pixel value of the corresponding interpolated point, the method further comprises: for each of the at least one interpolated point, calculating a distance between each of the all pixels within the predetermined range corresponding to the interpolated point and the interpolated point to obtain a pixel distance corresponding to each of the all pixels; in response to a plurality of pixels being on one straight line with the interpolated point, determining a ratio of distance weights of the plurality of pixels based on a ratio of pixel distance corresponding to the plurality of pixels; based on a distance weight of one of the plurality of pixels located on the one straight line and the ratio of distance weights of the plurality of pixels located on the one straight line, determining a distance weight of any pixel other than the one of the plurality of pixels located on the one straight line” which, in combination with the other recited features, is not taught and/or suggested either singularly or in combination within the prior art.
Claim 5 is objected to due to its dependency from claim 4.
The primary reasons for indicating allowance subject matter in claim 6 is the inclusion of the limitations reciting “wherein before the performing weighted fusion of a distance weight and an angle weight corresponding to each of the all pixels within the predetermined range corresponding to the interpolated point to determine a pixel value of the corresponding interpolated point, the method further comprises: for each of the at least one interpolated point, connecting the interpolated point to each of the all pixels within the predetermined range to obtain a pixel vector corresponding to each of the all pixels; determining a vehicle license plate vector corresponding to the vehicle license plate region based on position information of the vehicle license plate region in the fisheye image, wherein the vehicle license plate vector is non-parallel to the reference direction; obtaining angle information corresponding to each of the all pixels based on the pixel vector of each of the all pixels and the vehicle license plate vector; and determining an angle weight of each of the all pixels based on the angle information” which, in combination with the other recited features, is not taught and/or suggested either singularly or in combination within the prior art.
Claim 7 is objected to due to its dependency from claim 6.
The primary reasons for indicating allowance subject matter in claim 6 is the inclusion of the limitations reciting “wherein, the performing vehicle license plate detection on an obtained fisheye image to obtain a vehicle license plate region, comprises: applying a vehicle license plate detection model to perform the vehicle license plate detection on the fisheye image to obtain the vehicle license plate region; and the vehicle license plate detection model is trained by performing the operations of: obtaining a second training sample set, wherein the second training sample set comprises a plurality of fisheye sample images, each of the plurality of fisheye sample images comprises one vehicle license plate, each of the plurality of fisheye sample images has the true number of corner points of the comprised vehicle license plate, a labeled category of each of the corner points, and a labeled position of each of the corner points;performing, by the vehicle license plate detection model, the vehicle license plate detection on each of the plurality of fisheye sample images, to obtain the predicted number of corner points corresponding to the comprised vehicle license plate, a predicted position of each of the corner points corresponding to the comprised vehicle license plate, and a predicted category of each of the corner points corresponding to the comprised vehicle license plate;iteratively training the vehicle license plate detection model based on an error value between the true number of corner points and the predicted number of corner points corresponding to one vehicle license plate, an error value between the predicted position and the labeled position corresponding to one corner point, and an error value between the labeled category and the predicted category corresponding to one corner point” which, in combination with the other recited features, is not taught and/or suggested either singularly or in combination within the prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Broggi (US 2021/0142055) discloses of a surveillance camera system the looks at passing cars, where license plates can be recognized (See Paragraphs [0028] and [0146], for example.).
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/STEPHEN G SHERMAN/Primary Examiner, Art Unit 2621
25 August 2026
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