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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 9/2/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-8 and 35 U.S.C. 102 and 103 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 9/2/2026 with respect to 35 U.S.C. 112 have been fully considered but they are not persuasive. Applicant merely argues that amendments to the claims have resolved the issues. While applicant has deleted some of the subject matter which formed the grounds for the rejection thew newly amended subject matter creates additional issues as indicated in the new grounds of rejection below.
Claim Objections
Claim 2 objected to because of the following informalities: “a license plate” should read the license plate as the element has already been introduced. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—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 or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 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.
Claim 1-8 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), 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 or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Re claim 1 The examiner notes that the specification does not disclose “create, based on the first detection rectangle, a first shape comprising a rendered region of the first character and first location information, and, based on the second detection rectangle, a second shape comprising a another rendered region of the second character”. The examiner notes that it is unclear how the specification discloses creating a shape comprising a rendered region for characters using a rectangle. The only element that appears similar to a shape of a rendered region of the character is the rectangle itself which is claimed as a separate element. It’s not clear what element of the specification this element refers to and thus the examiner cannot find support for this feature.
Re claim 2-6 these claims contain similar elements.
Re claims 7, claims 7 contains the language “creating, based on the first detection rectangle, a first shape comprising a rendered region of the first character and first location information, and, based on the second detection rectangle, a second shape comprising a rendered region of the second character”. This is similar to the language of claim 1 and contains similar issues.
Re claim 8, claim 8 contains the language “creating, based on the first detection rectangle, a first shape comprising a rendered region of the first character and a first location information, and, based on the second detection rectangle, a second shape comprising a rendered region of the second character”. This is similar to the language of claim 1 and contains similar issues.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 2 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Re claim 2 the claim since claim 1 claims “wherein the target is a license plate” while claim 2 which depends from claim 1 claims “with at least one of a license plate or a sign set as the target.” This appears to broaden claim 1 rather than further limit the claim. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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.
Claim(s) 1, 3, 5 7 and 8 is/are rejected under 35 U.S.C. 102(A)(1) as being anticipated by Li et al “Component-Based License Plate Detection Using Conditional Random Field Model.” IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL.14, NO.4, DECEMBER 2013.
Re claim 1
Li discloses A recognition device comprising: a memory; and at least one processor coupled to the memory, the at least one processor being configured to (see section IV D “All experiments are performed on one PC with 2.66-GHz Intel Core 2 Quad CPU and 2-GB memory. The main framework of the algorithm is finished in MATLAB, whereas some time consuming modules such as MSER extraction and BP inference are implemented by the combination of C++ and OpenCV.” Note that the method is implemented by a processor and a memory):
acquire a time-series image acquired in an environment in which a vehicle travels (see section IV section A);
detect, by the at least one processor on a pixel-by-pixel basis, a first character and a second character of a predetermined character string from the time-series image (see section III part a candidate character detection second paragraph “MSER detection is similar to the watershed algorithm. A sequence of intensity thresholds ranging from 0 to 255 are applied to obtain a series of binary images. In this sequential binarization, MSERs are defined as the connected components” note that connected components are used to determine candidate letters) using a first detection rectangle and a second detection rectangle respectively, (see section III part a candidate character detection third paragraph “After MSER extraction, all the connected components are filtered by some geometric constraints, such as height, width, area, and height width ratio. The remaining blob regions are considered as candidate characters. The results of candidate characters detection are shown in Figs. 5(a) and 6(a). Bounding boxes of bright and dark MSERs are marked by red and blue rectangular boxes, respectively” note that characters are detected and bounding boxes for these characters are determined, note that the blobs are the connected components i.e. characters)
wherein the first detection rectangle represents, by coordinates, a range of the first character, and the second detection rectangle represents, by coordinates, a range of the second character (see table 1 note that candidate characters are defied by the coordinate center and the height of the bounding boxes see also section III B and section III A last paragraph “After MSER extraction, all the connected components are filtered by some geometric constraints, such as height, width, area, and height–width ratio”);
create, based on the first detection rectangle, a first shape comprising a rendered region of the first character and first location information ,and, based on the second detection rectangle, a second shape comprising a rendered region of the second character (see table 1 note that candidate characters are defied by the coordinate center and the height of the bounding boxes see also section III B)
identify a region in the time-series image in which a target is drawn (see section III first paragraph note that the analysis of the letters is used to identify a bounding box of the license plate see also section III D first paragraph “Based on prior knowledge of the character’s relative position to a rectangle license plate frame, the bounding boxes of license plates are estimated.” Note that that a bounding box of the license plate is estimated) based on a positional relationship between the first detection rectangle and the second detection rectangle (see table I note that Euclidian distance for example is determined between bounding boxes of letters) , one or more predetermined conditions for recognizing a predetermined shape as the target(see section III D first paragraph “Based on prior knowledge of the character’s relative position to a rectangle license plate frame, the bounding boxes of license plates are estimated.” Note the license plate region is determined based on relative position), wherein the target is a license plate;( see abstract)
and a relationship, in each of the first detection rectangle and the second detection rectangle, between a pixel of the corresponding character and a pixel other than the corresponding character (see section III A second paragraph “Bright MSERs mean that intensities of pixels inside the blobs are higher than those boundary pixels. Dark MSERs are reverse.
These bright and dark attributes will contribute to our CRF construction in the next section” note that for the candidate character it is determined if the pixels are a bright character or dark character by comparing to the pixels on the boundary),
and recognize, based on the identified region, a shape of the target (see section III first paragraph note that the analysis of the letters is used to identify a bounding box of the license plate)
Re claim 3 Li discloses wherein the processor is configured to evaluate positional relationship to determine the relationship between respective pixels of the first character and the second character (see for example table 1 note that the Euclidian distance between the center of the character is determined see also section III part A note that the character blobs are composed of pixels).
Re claim 5 Li discloses to detect [the first detection rectangle representing a range of the first character defined by coordinates of the first character(see figure 3 and table one note that the character is defined by a bounding bx and coordinates ) and evaluate [the relationship between the pixel of the first character and pixel of the object other than the first character in the detection rectangle (see section III a penultimate paragraph “Bright MSERs mean that intensities of pixels inside the blobs are higher than those boundary pixels. Dark MSERs are reverse. These bright and dark attributes will contribute to our CRF construction in the next section” note that the blobs which are the characters are evaluated to determine of the are lighter or darker than boundary pixels).
Re claim 7
Li discloses A recognition method for causing a computer to execute processing, the processing comprising (see section IV D “All experiments are performed on one PC with 2.66-GHz Intel Core 2 Quad CPU and 2-GB memory. The main framework of the algorithm is finished in MATLAB, whereas some time consuming modules such as MSER extraction and BP inference are implemented by the combination of C++ and OpenCV.” Note that the method is implemented by a processor and a memory):
acquiring a time-series image acquired in an environment in which a vehicle travels (see section IV section A);
detecting, by at least one processor on a pixel-by-pixel basis, a first character and a second character of a predetermined character string from the time-series image (see section III part a candidate character detection second paragraph “MSER detection is similar to the watershed algorithm. A sequence of intensity thresholds ranging from 0 to 255 are applied to obtain a series of binary images. In this sequential binarization, MSERs are defined as the connected components” note that connected components are used to determine candidate letters) using a first detection rectangle and a second detection rectangle respectively, (see section III part a candidate character detection third paragraph “After MSER extraction, all the connected components are filtered by some geometric constraints, such as height, width, area, and height width ratio. The remaining blob regions are considered as candidate characters. The results of candidate characters detection are shown in Figs. 5(a) and 6(a). Bounding boxes of bright and dark MSERs are marked by red and blue rectangular boxes, respectively” note that characters are detected and bounding boxes for these characters are determined, note that the blobs are the connected components i.e. characters)
wherein the first detection rectangle represents, by coordinates, a range of the first character, and the second detection rectangle represents, by coordinates, a range of the second character (see table 1 note that candidate characters are defied by the coordinate center and the height of the bounding boxes see also section III B and section III A last paragraph “After MSER extraction, all the connected components are filtered by some geometric constraints, such as height, width, area, and height–width ratio”);
creating, based on the first detection rectangle, a first shape comprising a rendered region of the first character and first location information ,and, based on the second detection rectangle, a second shape comprising a rendered region of the second character (see table 1 note that candidate characters are defied by the coordinate center and the height of the bounding boxes see also section III B)
identifying a region in the time-series image in which a target is drawn (see section III first paragraph note that the analysis of the letters is used to identify a bounding box of the license plate see also section III D first paragraph “Based on prior knowledge of the character’s relative position to a rectangle license plate frame, the bounding boxes of license plates are estimated.” Note that that a bounding box of the license plate is estimated) based on a positional relationship between the first detection rectangle and the second detection rectangle (see table I note that Euclidian distance for example is determined between bounding boxes of letters) , one or more predetermined conditions for recognizing a predetermined shape as the target(see section III D first paragraph “Based on prior knowledge of the character’s relative position to a rectangle license plate frame, the bounding boxes of license plates are estimated.” Note the license plate region is determined based on relative position), wherein the target is a license plate;( see abstract)
and a relationship, in each of the first detection rectangle and the second detection rectangle, between a pixel of the corresponding character and a pixel other than the corresponding character (see section III A second paragraph “Bright MSERs mean that intensities of pixels inside the blobs are higher than those boundary pixels. Dark MSERs are reverse.
These bright and dark attributes will contribute to our CRF construction in the next section” note that for the candidate character it is determined if the pixels are a bright character or dark character by comparing to the pixels on the boundary),
and recognizing, based on the identified region, a shape of the target (see section III first paragraph note that the analysis of the letters is used to identify a bounding box of the license plate).
Re claim 8
Li discloses A non-transitory, computer-readable storage medium storing a recognition program for causing a computer to execute processing, the processing comprising (see section IV D “All experiments are performed on one PC with 2.66-GHz Intel Core 2 Quad CPU and 2-GB memory. The main framework of the algorithm is finished in MATLAB, whereas some time consuming modules such as MSER extraction and BP inference are implemented by the combination of C++ and OpenCV.” Note that the method is implemented by a processor and a memory):
acquiring a time-series image acquired in an environment in which a vehicle travels (see section IV section A);
detecting, by at least one processor on a pixel-by-pixel basis, a first character and a second character of a predetermined character string from the time-series image (see section III part a candidate character detection second paragraph “MSER detection is similar to the watershed algorithm. A sequence of intensity thresholds ranging from 0 to 255 are applied to obtain a series of binary images. In this sequential binarization, MSERs are defined as the connected components” note that connected components are used to determine candidate letters) using a first detection rectangle and a second detection rectangle respectively, (see section III part a candidate character detection third paragraph “After MSER extraction, all the connected components are filtered by some geometric constraints, such as height, width, area, and height width ratio. The remaining blob regions are considered as candidate characters. The results of candidate characters detection are shown in Figs. 5(a) and 6(a). Bounding boxes of bright and dark MSERs are marked by red and blue rectangular boxes, respectively” note that characters are detected and bounding boxes for these characters are determined, note that the blobs are the connected components i.e. characters)
wherein the first detection rectangle represents, by coordinates, a range of the first character, and the second detection rectangle represents, by coordinates, a range of the second character (see table 1 note that candidate characters are defied by the coordinate center and the height of the bounding boxes see also section III B and section III A last paragraph “After MSER extraction, all the connected components are filtered by some geometric constraints, such as height, width, area, and height–width ratio”);
creating, based on the first detection rectangle, a first shape comprising a rendered region of the first character and first location information ,and, based on the second detection rectangle, a second shape comprising a rendered region of the second character (see table 1 note that candidate characters are defied by the coordinate center and the height of the bounding boxes see also section III B)
identifying a region in the time-series image in which a target is drawn (see section III first paragraph note that the analysis of the letters is used to identify a bounding box of the license plate see also section III D first paragraph “Based on prior knowledge of the character’s relative position to a rectangle license plate frame, the bounding boxes of license plates are estimated.” Note that that a bounding box of the license plate is estimated) based on a positional relationship between the first detection rectangle and the second detection rectangle (see table I note that Euclidian distance for example is determined between bounding boxes of letters) , one or more predetermined conditions for recognizing a predetermined shape as the target(see section III D first paragraph “Based on prior knowledge of the character’s relative position to a rectangle license plate frame, the bounding boxes of license plates are estimated.” Note the license plate region is determined based on relative position), wherein the target is a license plate;( see abstract)
and a relationship, in each of the first detection rectangle and the second detection rectangle, between a pixel of the corresponding character and a pixel other than the corresponding character (see section III A second paragraph “Bright MSERs mean that intensities of pixels inside the blobs are higher than those boundary pixels. Dark MSERs are reverse.
These bright and dark attributes will contribute to our CRF construction in the next section” note that for the candidate character it is determined if the pixels are a bright character or dark character by comparing to the pixels on the boundary),
and recognizing, based on the identified region, a shape of the target (see section III first paragraph note that the analysis of the letters is used to identify a bounding box of the license plate).
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 Li et al “Component-Based License Plate Detection Using Conditional Random Field Model.” IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL.14, NO.4, DECEMBER 2013 in view of Fan US 2012/0269398.
Re claim 2 Li discloses all of the elements of claim 1 and the recognize operation further comprises recognizing a shape of the target by identifying a region in an image in which the target is drawn with at least one of a license plate (see abstract note that a license plate bounding box is detected) or a sign set as the target. Li does not expressly disclose to obtain the time-series image by capturing the time-series image by an in-vehicle camera. Fan discloses obtain the image by capturing the image by an in-vehicle camera ( see paragraph 43 “The image capturing unit 315 can be operated as a hand-held device and/or a vehicle-mounted device” note that the capture unit may be mounted in the vehicle). One of ordinary skill in the art could have easily substituted the camera of Li with an in vehicle camera and the results would be the same, i.e. license plates are detected and therefore predictable. The camera serves the same purpose of capturing an image of the license plate Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Li and Fan to reach the aforementioned advantage.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al “Component-Based License Plate Detection Using Conditional Random Field Model.” IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL.14, NO.4, DECEMBER 2013 in view of Wan US 2014/0111542.
Re claim 4 Li discloses all of the elements of claim 1 Li does not expressly disclose determine whether fonts of respective characters of the character string are same, and evaluate positional relationship between fonts determined to be same. Wan discloses determine whether fonts of respective characters of the character string are same, and evaluate positional relationship between fonts determined to be same (see paragraph 30 “detecting the presence of text for text markers that are aligned relative to a single imaginary straight line, with substantially equal spacing between individual characters and substantially equal spacing between groups of characters, and with the substantially the same font” note that text which as equal spacing and the same font is detected). The motivation to combine “The OCR engine 32 of the platform 10 detects non-cursive script, and the text to be detected generally conforms to a particular typeface. (see paragraph 105). That is that a target typically contain the same typeface (i.e. font). One or ordinary skill in the art could have easily applied the principles of detecting text with equal spacing and the same font as likely to be attributed to a target to the invention of LI. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Li and Wan to reach the aforementioned advantage.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al “Component-Based License Plate Detection Using Conditional Random Field Model.” IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL.14, NO.4, DECEMBER 2013 in view of Popov US 2020/0293794.
Re claim 6 Li discloses all the elements of claim 1. Li does not expressly disclose detect characters of the character string using a model learned in advance to detect a character of a specific font. Popov discloses detect characters of the character string using a model learned in advance to detect a character of a predetermined font (see paragraph 38 note that the character recognition model is trained on a plurality of fonts.) The motivation to combine is “the character recognition unit 208 takes into account the lighting and visibility conditions while performing character recognition” see paragraph 38. One of ordinary skill in the art could have easily used the character recognition model of Popov to perform the character recognition of Li to reach the aforementioned advantage. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Li and Popov to reach the aforementioned advantage.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEAN T MOTSINGER whose telephone number is (571)270-1237. The examiner can normally be reached 9AM-5PM.
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/SEAN T MOTSINGER/Primary Examiner, Art Unit 2673
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