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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/24/2024 have been entered and considered. Initialed copies of the PTO-1449 by the Examiner are attached.
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, 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, 5-10, 12-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Panferov (Pub No.: 2015/0278593) in view of Burry et al (Pub No.: US20130272579A1).
Regarding independent claim 1, Panferov teaches a method (method and system for creating structure description for image of documents with fixed structure - see [p][0002]), comprising:
identifying, by a device (device – see [p][0108]), one or more shapes (see Fig 4A) defined by a plurality of edges (see Fig 4A) in a document (402 – see Fig4 A) depicted in image data based on using one or more computer vision techniques ([f]irst, document 402 within image 401 is identified – see Fig 4A and [p][0041]);
identifying, by the device and based on identifying the one or more shapes, a segment of interest within the image data ([t]he rotated image 405 may be manipulated until the image 405 is a geometrically proper rectangle with boundaries 408 as shown. The properly oriented image 410 of the document without distorted lines will be located in this rectangle 408 – see [p][0041]);
cropping, by the device, the segment of interest to obtain a portion of the image data and of the plurality of edges, that correspond to boxes or lines identified using one or more computer vision techniques to enable optical character recognition to be performed on the segment of interest ([t]his image may be manipulated, for example, by cropping (418) the identified distorted document (416) along the document's boundaries from its background (414) and then by subsequent compressing and stretching (424) the cropped document (422) along the corresponding coordinate axes (420). The result is a document with the distortions in the perspective corrected (426) – see [p][0042] and Fig 4B);
performing, by the device, the optical character recognition on the portion of the image data to generate a result based on the optical character recognition ([i]nitially, all the geometrically identical variations Ci of the image examined (and there may be more than one) are recognized (528) by means of OCR/ICR, and the information about the text, the font used in the document image, the text's coordinates, and the coordinates of the rectangles enclosing different words is obtained (530, 532, 534, 536) – see [p][0066] and Fig 5A); and
verifying, by the device, the result based on obtaining validation data ([r]ecognition of fields may be performed immediately after results of recognition (530, 532, 534, 536) are obtained (545). Then, the best data for the fields is selected from among the variations of recognition (548, FIG. 5). The best data for the field may be selected using various criteria. For example, the best data for the field may be based on an internal rating for recognition quality, as output by the OCR engine. Alternatively, the best data for the field may be determined by using a database for first names, surnames, names of states, and other data. In another alternative embodiment of the method 500, one can also select a field that best corresponds to a previously determined format. After this step, a list of fields and data that corresponds to the fields and data for each template in the group is obtained – see [p][0100]).
Panferov does not explicitly teach exclude edges.
Burry explicitly teaches exclude edges (horizontally cropping the license plate image to account for a noise source and an interfering artifact in the license plate image; at least one of removing a logo from the license plate sub-image, removing a symbol from the license plate sub-image, and flagging an artifact from the license plate sub-image before submitting the license plate sub-image for character recognition; identifying a license plate edge artifact utilizing the gradient-based classifier on the license plate sub-image to accurately determine a license plate border artifact for removal without incorrectly discarding a valid license plate character – see [p][0086]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Panferov a method/system, comprising: identifying, by a device with the teachings of Burry exclude edges.
Wherein having Panferov exclude edges.
The motivation behind the modification would have been accurately determine a license plate border artifact for removal without incorrectly discarding a valid license plate character thus efficiently capture data from images of documents with fixed structure in a mobile setting, without the necessity of setup pre-processing steps on an image of the document with fixed structure since both Panferov and Burry are directed to performing OCR, wherein Panferov efficiently data capture from images of documents with fixed structure in a mobile setting, without the necessity of setup pre-processing steps on an image of the document with fixed structure, while Buttram for accurately determine a license plate border artifact for removal without incorrectly discarding a valid license plate character (Please see Panferov (Pub No.: 2015/0278593), [p][0030] in view of Burry et al (Pub No.: US20130272579A1), [p][0086]).
Regarding claim 2, Panferov in view of Burry teach the method of claim 1, Panferov teaches wherein the shape includes a rectangular shape (for e.g. 100 – see Fig 1).
Regarding claim 3, Panferov in view of Burry teach the method of claim 1, Panferov teaches wherein verifying the result comprises: comparing portions of the validation data and portions of recognized text identified in the result (an image of document with fixed structure was obtained and at the output stage, one template from a set of templates was selected and matched, determining the type of document and the data in the fields (560, FIG. 5) that correspond to that type – see [p][0103]).
Regarding claim 5, Panferov in view of Burry teach the method of claim 1, Panferov teaches further comprising: obtaining the validation data based on generating the result (a list of fields and data that corresponds to the fields and data for each template in the group is obtained - see [p][0100] and selected best template may determine the type of the document – see [p][0101]).
Regarding claim 6, Panferov in view of Burry teach the method of claim 1, Panferov teaches further comprising: obtaining the validation data based on searching for recognized text identified in the result in a database or using a search engine (the best data for the field may be determined by using a database for first names, surnames, names of states, and other data. – see [p][0100]).
Regarding claim 7, Panferov in view of Burry teach the method of claim 1, Panferov teaches further comprising: selecting an optical character recognition model based on a type of text of interest identified in the segment of interest, wherein performing the optical character recognition on the portion of the image data is based on using the optical character recognition model ([t]he information obtained after recognition by means of OCR/ICR (530, 532, 534, and 536, FIG. 5) is used to matching of templates that correspond to various types of documents (538, FIG. 5) and to select the best quality Qi, (540, FIG. 5). In one embodiment, the quality of the matched template is characterized by the parameter Qi. This template parameter Qi may be computed in various ways. For example the Quality Qi of the matched template may be computed based on the coincidence of the recognized key words in the document image and corresponding words in the template. Here term “words in the template” may refer to the character, figure, word, regular expression, etc. As a result, it is possible to match template either individually based on the results of recognition of each image Ci, or based on the consolidated results of recognizing all the images or groups of images – see [p][0077]).
Regarding independent claim 8, Panferov teaches a device (method and system for creating structure description for image of documents with fixed structure – see [p][0002]), comprising: one or more memories (computer readable medium – see [p][0111]); and one or more processors (see [p][0110]), coupled to the one or more memories (see [p][0110 - 0111]), configured to:
identify one or more shapes (see Fig 4A) defined by a plurality of edges (see Fig 4A) in a document (402 – see Fig4 A) depicted in image data based on using one or more computer vision techniques (First, document 402 within image 401 is identified – see Fig 4A and [p][0041]);
identify based on identifying the one or more shapes, a segment of interest within the image data ([t]he rotated image 405 may be manipulated until the image 405 is a geometrically proper rectangle with boundaries 408 as shown. The properly oriented image 410 of the document without distorted lines will be located in this rectangle 408 – see [p][0041]);
crop the segment of interest to obtain a portion of the image data and of the plurality of edges, that correspond to boxes or lines identified using one or more computer vision techniques to enable optical character recognition to be performed on the segment of interest ([t]his image may be manipulated, for example, by cropping (418) the identified distorted document (416) along the document's boundaries from its background (414) and then by subsequent compressing and stretching (424) the cropped document (422) along the corresponding coordinate axes (420). The result is a document with the distortions in the perspective corrected (426) – see [p][0042] and Fig 4B);
perform the optical character recognition on the portion of the image data to generate a result based on the optical character recognition ([i]nitially, all the geometrically identical variations Ci of the image examined (and there may be more than one) are recognized (528) by means of OCR/ICR, and the information about the text, the font used in the document image, the text's coordinates, and the coordinates of the rectangles enclosing different words is obtained (530, 532, 534, 536) – see [p][0066] and Fig 5A); and
verify the result based on obtaining validation data ([r]ecognition of fields may be performed immediately after results of recognition (530, 532, 534, 536) are obtained (545). Then, the best data for the fields is selected from among the variations of recognition (548, FIG. 5). The best data for the field may be selected using various criteria. For example, the best data for the field may be based on an internal rating for recognition quality, as output by the OCR engine. Alternatively, the best data for the field may be determined by using a database for first names, surnames, names of states, and other data. In another alternative embodiment of the method 500, one can also select a field that best corresponds to a previously determined format. After this step, a list of fields and data that corresponds to the fields and data for each template in the group is obtained – see [p][0100]).
Panferov does not explicitly teach exclude edges.
Burry explicitly teaches exclude edges (horizontally cropping the license plate image to account for a noise source and an interfering artifact in the license plate image; at least one of removing a logo from the license plate sub-image, removing a symbol from the license plate sub-image, and flagging an artifact from the license plate sub-image before submitting the license plate sub-image for character recognition; identifying a license plate edge artifact utilizing the gradient-based classifier on the license plate sub-image to accurately determine a license plate border artifact for removal without incorrectly discarding a valid license plate character – see [p][0086]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Panferov a method/system, comprising: identifying, by a device with the teachings of Burry exclude edges.
Wherein having Panferov exclude edges.
The motivation behind the modification would have been accurately determine a license plate border artifact for removal without incorrectly discarding a valid license plate character thus efficiently capture data from images of documents with fixed structure in a mobile setting, without the necessity of setup pre-processing steps on an image of the document with fixed structure since both Panferov and Burry are directed to performing OCR, wherein Panferov efficiently data capture from images of documents with fixed structure in a mobile setting, without the necessity of setup pre-processing steps on an image of the document with fixed structure while Buttram for accurately determine a license plate border artifact for removal without incorrectly discarding a valid license plate character (Please see Panferov (Pub No.: 2015/0278593), [p][0030] in view of Burry et al (Pub No.: US20130272579A1), [p][0086]).
Regarding claim 9, which corresponds to claim 2 except for reciting a different statutory category of a device. Therefore, the rejection analysis of claim 2 is fully applicable to claim 9.
Regarding claim 10, which corresponds to claim 3 except for reciting a different statutory category of a device. Therefore, the rejection analysis of claim 3 is fully applicable to claim 10.
Regarding claim 12, which corresponds to claim 5 except for reciting a different statutory category of a device. Therefore, the rejection analysis of claim 5 is fully applicable to claim 12.
Regarding claim 13, which corresponds to claim 6 except for reciting a different statutory category of a device. Therefore, the rejection analysis of claim 6 is fully applicable to claim 13.
Regarding claim 14, which corresponds to claim 7 except for reciting a different statutory category of a device. Therefore, the rejection analysis of claim 7 is fully applicable to claim 14.
Regarding independent claim 15, Panferov teaches a non-transitory computer-readable medium (computer readable medium – see [p][0111]) storing a set of instructions (computer program instructions – see [p][0111]), the set of instructions comprising: one or more instructions that, when executed by one or more processors (see [p][0110]) of a device (see [p][0110]), cause the device to:
identify one or more shapes (see Fig 4A) defined by a plurality of edges (see Fig 4A) in a document (402 – see Fig4 A) depicted in image data based on using one or more computer vision techniques (First, document 402 within image 401 is identified – see Fig 4A and [p][0041]);
identify based on identifying the one or more shapes, a segment of interest within the image data ([t]he rotated image 405 may be manipulated until the image 405 is a geometrically proper rectangle with boundaries 408 as shown. The properly oriented image 410 of the document without distorted lines will be located in this rectangle 408 – see [p][0041]);
crop the segment of interest to obtain a portion of the image data and of the plurality of edges, that correspond to boxes or lines identified using one or more computer vision techniques to enable optical character recognition to be performed on the segment of interest ([t]his image may be manipulated, for example, by cropping (418) the identified distorted document (416) along the document's boundaries from its background (414) and then by subsequent compressing and stretching (424) the cropped document (422) along the corresponding coordinate axes (420). The result is a document with the distortions in the perspective corrected (426) – see [p][0042] and Fig 4B);
perform the optical character recognition on the portion of the image data to generate a result based on the optical character recognition ([i]nitially, all the geometrically identical variations Ci of the image examined (and there may be more than one) are recognized (528) by means of OCR/ICR, and the information about the text, the font used in the document image, the text's coordinates, and the coordinates of the rectangles enclosing different words is obtained (530, 532, 534, 536) – see [p][0066] and Fig 5A); and
verify the result based on obtaining validation data ([r]ecognition of fields may be performed immediately after results of recognition (530, 532, 534, 536) are obtained (545). Then, the best data for the fields is selected from among the variations of recognition (548, FIG. 5). The best data for the field may be selected using various criteria. For example, the best data for the field may be based on an internal rating for recognition quality, as output by the OCR engine. Alternatively, the best data for the field may be determined by using a database for first names, surnames, names of states, and other data. In another alternative embodiment of the method 500, one can also select a field that best corresponds to a previously determined format. After this step, a list of fields and data that corresponds to the fields and data for each template in the group is obtained – see [p][0100]).
Panferov does not explicitly teach exclude edges.
Burry explicitly teaches exclude edges (horizontally cropping the license plate image to account for a noise source and an interfering artifact in the license plate image; at least one of removing a logo from the license plate sub-image, removing a symbol from the license plate sub-image, and flagging an artifact from the license plate sub-image before submitting the license plate sub-image for character recognition; identifying a license plate edge artifact utilizing the gradient-based classifier on the license plate sub-image to accurately determine a license plate border artifact for removal without incorrectly discarding a valid license plate character – see [p][0086]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Panferov a method/system, comprising: identifying, by a device with the teachings of Burry exclude edges.
Wherein having Panferov exclude edges.
The motivation behind the modification would have been accurately determine a license plate border artifact for removal without incorrectly discarding a valid license plate character thus efficiently capture data from images of documents with fixed structure in a mobile setting, without the necessity of setup pre-processing steps on an image of the document with fixed structure since both Panferov and Burry are directed to performing OCR, wherein Panferov efficiently data capture from images of documents with fixed structure in a mobile setting, without the necessity of setup pre-processing steps on an image of the document with fixed structure while Buttram accurately determine a license plate border artifact for removal without incorrectly discarding a valid license plate character (Please see Panferov (Pub No.: 2015/0278593), [p][0030] in view of Burry et al (Pub No.: US20130272579A1), [p][0086]).
Regarding claim 16, which corresponds to claim 3 except for reciting a different statutory category of a non-transitory computer-readable medium. Therefore, the rejection analysis of claim 3 is fully applicable to claim 16.
Regarding claim 18, which corresponds to claim 5 except for reciting a different statutory category of a non-transitory computer-readable medium. Therefore, the rejection analysis of claim 5 is fully applicable to claim 18.
Regarding claim 19, which corresponds to claim 6 except for reciting a different statutory category of a non-transitory computer-readable medium. Therefore, the rejection analysis of claim 6 is fully applicable to claim 19.
Regarding claim 20, which corresponds to claim 7 except for reciting a different statutory category of a non-transitory computer-readable medium. Therefore, the rejection analysis of claim 7 is fully applicable to claim 20.
Claims 4, 11 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Panferov (Pub No.: 2015/0278593) in view of Burry et al (Pub No.: US20130272579A1) applied to claims 1, 8 and 15 further in view of Tzadok et al (Pub No.: US20090220175).
Regarding claim 4, Panferov in view of Burry teach the method of claim 1, Panferov in view of Burry does not explicitly teach further comprising: retraining an optical character recognition model based on verifying the result.
Tzadok explicitly teaches further comprising: retraining an optical character recognition model based on verifying the result (some OCR errors 205 may be found as well for cases where OCR result doesn't match the word in question. This information (regarding correct and incorrect recognition results) can be used in order to train the OCR engine at 207 (for instance, if OCR is based on the template matching approach, character images can be used in order to learn appropriate character templates valid to the specific book in question). Retrained OCR at 207 can be used for repeated recognition at 208 followed by another verification at 209 -see [p][0013]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Panferov a method/system, comprising: identifying, by a device with the teachings of Tzadok further comprising: retraining an optical character recognition model based on verifying the result
Wherein having Panferov further comprising: retraining an optical character recognition model based on verifying the result.
The motivation behind the modification would have been retraining an OCR model thus improving quality of the OCR results an iterative manner thereby efficiently capturing data from images of documents with fixed structure in a mobile setting, without the necessity of setup pre-processing steps on an image of the document with fixed structure since both Panferov and Tzadok are directed to performing OCR, wherein Panferov efficiently data capture from images of documents with fixed structure in a mobile setting, without the necessity of setup pre-processing steps on an image of the document with fixed structure while Tzadok retrains an OCR model thus improving quality of the OCR results an iterative manner (Please see Panferov (Pub No.: 2015/0278593), [p][0030] and Tzadok et al (Pub No.: US20090220175), [p][0013]).
Regarding claim 11, which corresponds to claim 4 except for reciting a different statutory category of a device. Therefore, the rejection analysis of claim 4 is fully applicable to claim 11.
Regarding claim 17, which corresponds to claim 4 except for reciting a different statutory category of a non-transitory computer-readable medium. Therefore, the rejection analysis of claim 4 is fully applicable to claim 17.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 8 and 15 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 9 and 16 of U.S. Patent No. 10095925 (herein referred to as Patent’925) in view of Burry et al (Pub No.: US20130272579A1).
Instant claims
US Patent No.: 10095925
1. A method, comprising:
16. A method, comprising:
identifying, by a device, one or more shapes defined by a plurality of edges in a document depicted in image data based on using one or more computer vision techniques;
receiving, by a device, image data representing a document, the document including: text, and a plurality of edges;
receiving, by the device, data identifying text of interest associated with the image data; identifying, by the device and based on the plurality of edges, a plurality of segments within the image data;
identifying, by the device and, a segment of interest within the image data;
identifying, by the device and from the plurality of segments, a segment of interest, the segment of interest including the text of interest;
cropping, by the device, the segment of interest to obtain a portion of the image data and exclude edges, of the plurality of edges, that correspond to boxes or lines identified using one or more computer vision techniques to enable optical character recognition to be performed on the segment of interest;
cropping, by the device, the segment of interest to obtain a portion of the image data; performing, by the device, optical character recognition on the portion of the image data, the optical character recognition producing recognized text, the recognized text including the text of interest;
performing, by the device, the optical character recognition on the portion of the image data to generate a result based on the optical character recognition; and
verifying, by the device, the result based on obtaining validation data
obtaining, by the device, based on the recognized text, validation data, the validation data including verification text;
determining, by the device, whether the recognized text is verified based on the verification text, the recognized text including a recognized account identifier, the recognized account identifier being compared to a corresponding account from a database, information from the corresponding account being compared to other information recognized in the image data to determine whether the recognized account identifier is accurate;
and performing, by the device, an action based on the recognized text.
Patent’925 does not explicitly teach based on identifying the one or more shapes and exclude edges.
Burry explicitly teaches based on identifying the one or more shapes ([t]he license plate is then localized, as illustrated in block 202 – see [p][0057] and Fig 11) and exclude edges (horizontally cropping the license plate image to account for a noise source and an interfering artifact in the license plate image; at least one of removing a logo from the license plate sub-image, removing a symbol from the license plate sub-image, and flagging an artifact from the license plate sub-image before submitting the license plate sub-image for character recognition; identifying a license plate edge artifact utilizing the gradient-based classifier on the license plate sub-image to accurately determine a license plate border artifact for removal without incorrectly discarding a valid license plate character – see [p][0086]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Patent’925 a method/system, comprising: identifying, by a device with the teachings of Burry based on identifying the one or more shapes and exclude edges.
Wherein having Patent’925 based on identifying the one or more shapes and exclude edges.
The motivation behind the modification would have been accurately determine a license plate border artifact for removal without incorrectly discarding a valid license plate character and determine whether the recognized text is verified based on the verification text, the recognized text including a recognized account identifier, the recognized account identifier being compared to a corresponding account from a database, information from the corresponding account being compared to other information recognized in the image data to determine whether the recognized account identifier is accurate; and perform an action based on the recognized text since both Patent’925 and Burry are directed to performing OCR, wherein Patent’925 determine whether the recognized text is verified based on the verification text, the recognized text including a recognized account identifier, the recognized account identifier being compared to a corresponding account from a database, information from the corresponding account being compared to other information recognized in the image data to determine whether the recognized account identifier is accurate; and perform an action based on the recognized text while Buttram for accurately determine a license plate border artifact for removal without incorrectly discarding a valid license plate character (Please see Patent’925 (U.S. Patent No. 10095925), claim 1 in view of Burry et al (Pub No.: US20130272579A1), [p][0086]).
Claims 1, 8 and 15 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 8 and 15 of U.S. Patent No. 10943106 (herein referred to as Patent’106) in view of Burry et al (Pub No.: US20130272579A1).
Instant claims
US Patent No.: 10943106
1. A method, comprising:
8. A method, comprising:
identifying, by a device, one or more shapes defined by a plurality of edges in a document depicted in image data based on using one or more computer vision techniques;
receiving, by a device, image data representing a document, the document including:
text, and a plurality of edges within the document;
identifying, by the device and based on identifying the one or more shapes, a segment of interest within the image data;
identifying, by the device and based on the plurality of edges, a segment of interest within the image data;
cropping, by the device, the segment of interest to obtain a portion of the image data and exclude edges, of the plurality of edges, that correspond to boxes or lines identified using one or more computer vision techniques to enable optical character recognition to be performed on the segment of interest;
cropping, by the device, the segment of interest to obtain a portion of the image data, the segment of interest being defined by one or more edges, of the plurality of edges, forming a rectangular shape;
performing, by the device, the optical character recognition on the portion of the image data to generate a result based on the optical character recognition; and
verifying, by the device, the result based on obtaining validation data
performing, by the device, optical character recognition on the portion of the image data, the optical character recognition producing recognized text;
obtaining, by the device and based on the recognized text, validation data, the validation data including verification text; determining, by the device, whether the recognized text is verified based on the verification text; and
performing, by the device, an action based on a result of the determination of whether the recognized text is verified, the action including retraining, based on the image data and the result, the one or more optical character recognition models to recognize text included in another segment of interest that is similar to the segment of interest.
Patent’106 does not explicitly teach based on identifying the one or more shapes and exclude edges.
Burry explicitly teaches based on identifying the one or more shapes ([t]he license plate is then localized, as illustrated in block 202 – see [p][0057] and Fig 11) and exclude edges (horizontally cropping the license plate image to account for a noise source and an interfering artifact in the license plate image; at least one of removing a logo from the license plate sub-image, removing a symbol from the license plate sub-image, and flagging an artifact from the license plate sub-image before submitting the license plate sub-image for character recognition; identifying a license plate edge artifact utilizing the gradient-based classifier on the license plate sub-image to accurately determine a license plate border artifact for removal without incorrectly discarding a valid license plate character – see [p][0086]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Patent’106a method/system, comprising: identifying, by a device with the teachings of Burry based on identifying the one or more shapes and exclude edges.
Wherein having Patent’106 based on identifying the one or more shapes and exclude edges.
The motivation behind the modification would have been accurately determine a license plate border artifact for removal without incorrectly discarding a valid license plate character and d perform an action based on a result of the determination of whether the recognized text is verified, the action including retraining, based on the image data and the result, the one or more optical character recognition models to recognize text included in another segment of interest that is similar to the segment of interest since both Patent’106and Burry are directed to performing OCR, wherein Patent’106 determine whether the recognized text is verified based on the verification text, the recognized text including a recognized account identifier, the recognized account identifier being compared to a corresponding account from a database, information from the corresponding account being compared to other information recognized in the image data to determine whether the recognized account identifier is accurate; and perform an action based on the recognized text while Buttram for accurately determine a license plate border artifact for removal without incorrectly discarding a valid license plate character (Please see Patent’106 (U.S. Patent No. 10943106), claim 1 in view of Burry et al (Pub No.: US20130272579A1), [p][0086]).
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 5-10, 12-17 and 19-20 of U.S. Patent No. 12019675 (herein referred to as Patent’675) in view of Burry et al (Pub No.: US20130272579A1).
Instant claims
US Patent No.: 12019675
1. A method, comprising:
12. A method, comprising:
identifying, by a device, one or more shapes defined by a plurality of edges in a document depicted in image data based on using one or more computer vision techniques;
identifying, by a device, a plurality of edges within a document depicted in image data, wherein identifying the plurality of edges comprises:
determining artificial edges within the document, and determining the plurality of edges based on the artificial edges;
identifying, by the device and based on identifying the one or more shapes, a segment of interest within the image data;
identifying, by the device and based on the plurality of edges, a segment of interest within the image data;
cropping, by the device, the segment of interest to obtain a portion of the image data and exclude edges, of the plurality of edges, that correspond to boxes or lines identified using one or more computer vision techniques to enable optical character recognition to be performed on the segment of interest;
cropping, by the device, the segment of interest to obtain a portion of the image data and exclude edges, of the plurality of edges, that correspond to boxes or lines identified using one or more computer vision techniques to enable optical character recognition to be performed on the segment of interest unhindered by the boxes or the lines;
performing, by the device, the optical character recognition on the portion of the image data to generate a result based on the optical character recognition; and
verifying, by the device, the result based on obtaining validation data
4 The method of claim 1, further comprising: retraining an optical character recognition model based on verifying the result.
performing, by the device, the optical character recognition on the portion of the image data via one or more optical character recognition models to generate a result based on the optical character recognition;
comparing, by the device, the result of the optical character recognition and validation data to verify the result of the optical character recognition based on using the result of the optical character recognition to identify the validation data;
and retraining, by the device and based on the image data and comparing the result of the optical character recognition and the validation data, the one or more optical character recognition models to recognize text included in another segment of interest that is similar to the segment of interest.
Patent’675 does not explicitly teach exclude edges.
Burry explicitly teaches exclude edges (horizontally cropping the license plate image to account for a noise source and an interfering artifact in the license plate image; at least one of removing a logo from the license plate sub-image, removing a symbol from the license plate sub-image, and flagging an artifact from the license plate sub-image before submitting the license plate sub-image for character recognition; identifying a license plate edge artifact utilizing the gradient-based classifier on the license plate sub-image to accurately determine a license plate border artifact for removal without incorrectly discarding a valid license plate character – see [p][0086]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Patent’675 a method/system, comprising: identifying, by a device with the teachings of Burry based on identifying the one or more shapes and exclude edges.
Wherein having Patent’675 exclude edges.
The motivation behind the modification would have been accurately determine a license plate border artifact for removal without incorrectly discarding a valid license plate character and retrain, based on the image data and comparing the result of the optical character recognition and the validation data, the one or more optical character recognition models to recognize text included in another segment of interest that is similar to the segment of interest since both Patent’675 and Burry are directed to performing OCR, wherein Patent’675 retrain, based on the image data and comparing the result of the optical character recognition and the validation data, the one or more optical character recognition models to recognize text included in another segment of interest that is similar to the segment of interest while Buttram for accurately determine a license plate border artifact for removal without incorrectly discarding a valid license plate character (Please see Patent’675 (U.S. Patent No. 12019675), claim 1 in view of Burry et al (Pub No.: US20130272579A1), [p][0086]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yeah et al (Pub No.: 20190197307) discloses a graphical user interfaces based on new data obtained from electronic documents comprises a computing system and an image capturing system of a user. The computing device receives a first digital image comprising a first set of data and extracts the first set of data from the first digital image. The computing device populates a list with a first set of data. The computing device receives a second digital image comprising a second set of data and extracts the second set of data from the second digital image. The computing device then modifies the list based on the second set of data. The computing device searches for third party data to associate with items on the list and takes appropriate action based on the association.
Loginov et al (Pub No.: 20180157907) discloses a method of determining a digital document suitability for OCR processing, the method executable by a user electronic device, the user electronic device configured for capturing a digital image of a document. The method comprises: acquiring by the user electronic device, the digital image of the document; determining, by a classifier executed by the user electronic device, an OCR suitability parameter associated with the digital image, the OCR suitability parameter indicative of whether the digital image is suitable for producing an output of the OCR processing of an acceptable quality, the classifier having been trained to determine the OCR suitability parameter at least partially based on a level of noise associated with the digital image; in response to the OCR suitability parameter being below a pre-determined threshold, causing the user electronic device to re-acquire the digital image.
Lai et al (Pub No.: 20170064035) discloses a method for automatically creating online accounts based on digital images, such as digital images of business cards. In one technique, multiple data items that have been extracted from a digital image of a business card are identified. A particular data item is contact information of a user associated with (or identified by) the business card. A verification code is sent, based on the particular data item, to a computing device of the user. The verification code is received from the computing device of the user. In response to receiving the verification code an account is created for the user and the account is modified to include a least some of the multiple data items.
Tripuraneni et al (US Patent No.: 10095925) discloses a device may receive image data representing a document, the document including: text, and edges. Based on the edges, the device may identify, a segment of interest within the image data and crop the segment of interest to obtain a portion of the image data. In addition, the device may perform optical character recognition on the portion of the image data, the optical character recognition producing recognized text. The device may obtain, based on the recognized text, validation data that includes verification text, and determine whether the recognized text is verified based on the verification text. Based on a result of the determination, the device may perform an action.
Verma et al (Pub No.: 20160358268) discloses a computerized method for detecting anomalies anomalies in expense reports of an enterprise includes the step, of implementing a semantic analysis algorithm on an expense report data submitted by an employee, wherein the expense report data is provided in a computer-readable format. The method includes the step of, with one or more machine learning algorithms, detecting an anomaly expense report data. The method includes the step of obtaining an augmentation of the expense report data with a se of web scale data. The method includes the step of verifying receipts associated with an expense report. The method includes the step of determining that the employee or any employee has previously claimed an expense in the expense report data. The method includes the step of identifying an inappropriate expense in the expense report data.
Wang et al (Pub No.: 20150003666) discloses a method for extracting financial card information with relaxed alignment comprises a method to receive an image of a card, determine one or more edge finder zones in locations of the image, and identify lines in the one or more edge finder zones. The method further identifies one or more quadrilaterals formed by intersections of extrapolations of the identified lines, determines an aspect ratio of the one or more quadrilateral, and compares the determined aspect ratios of the quadrilateral to an expected aspect ratio. The method then identifies a quadrilateral that matches the expected aspect ratio and performs an optical character recognition algorithm on the rectified model. A similar method is performed on multiple cards in an image. The results of the analysis of each of the cards are compared to improve accuracy of the data.
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/ANDRAE S ALLISON/Primary Examiner, Art Unit 2673 June 16, 2026
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