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 .
Claim Interpretation - 35 USC § 101
The limitations “replacing each detected region of interest of each electronic document with the corresponding generated random image to create a modified plurality of electronic document images”, cannot be practically performed in the human mind or with the assistance of pen and paper, as the human mind is not equipped to perform replacing each detected region of interest of each electronic document with the corresponding generated random image to create a modified plurality of electronic document images and thus it is not an abstract idea.
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, 2, 6-10, 14-18 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 6-10, 14-18 of copending Application No. 18/733,263 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the notion of the claims does refer to the same invention and claim 1 of the current application corresponds with claim 1 of copending Application No. 18/733,263. Claim 1 of copending Application No. 18/733,263 anticipates claim 1 of the current application because it includes all of the limitations of claim 1 of the current application.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Below is a limitation mapping between claim 1 of the current application and claim 1 of copending Application No. 18/733,263
Current Application
18/733,263
1. A computer-implemented method of training a machine learning model for processing an electronic document, comprising: detecting a region of interest for each of a plurality of electronic documents using a bounding box detection mechanism;
generating a random replacement image for each region of interest of the plurality of electronic documents utilizing a script;
replacing each detected region of interest of each electronic document with the corresponding generated random image to create a modified plurality of electronic document images;
generating a training set comprising the modified plurality of electronic document images; and
training the machine learning model using the training set.
1. A computer-implemented method of training a machine learning model for processing an electronic document, comprising: detecting a magnetic ink character recognition (MICR) region for each of a plurality of electronic documents using a bounding box detection mechanism;
generating a random replacement image for each MICR region of the plurality of electronic documents utilizing a script;
replacing each detected MICR region of each electronic document with the corresponding generated random image to create a modified plurality of electronic document images;
generating a training set comprising the modified plurality of electronic document images; and
training the machine learning model using the training set.
Below is a claim mapping between the current application and copending Application No. 18/733,263.
Current Application
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Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 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, 2, 6-10, 14-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wu (US 2022/0284721)(Hereinafter referred to as Wu).
Regarding claim 1, Wu teaches A computer-implemented method of training a machine
learning model for processing an electronic document (A system and method for constructing a training dataset and training a neural network include obtaining a searchable portable document format (PDF) document, identifying a bounding box defining a region in a background image that is associated with an overlaying text object defined in the PDF document, determining an image crop of the PDF document according to the bounding box, and generating a training data sample for the training dataset, the training data sample comprising a data pair of the image crop and the associated text object. See abstract), comprising:
detecting a region of interest for each of a plurality of electronic documents using a bounding box detection mechanism (At 204, processing device 2 may identify a bounding box defining a region in a background image and an overlaying text layer according to the searchable PDF document. The searchable PDF document can be rendered as the background image having an array of pixels. The bounding box may define a rectangular region that may cover a portion of the image, where the height and width of the bounding box may be measured in terms of numbers of pixels. In particular, the content of a Portable Document Format (PDF) file may be organized according to document structure descriptors that may include an identifier ( e.g., Td or TD) for defining the beginning position of a text object with reference to the dimension of the background image presented on a display device. For example, Td (or TD) may specify the 2D coordinate values of the bounding box with reference to the background image. Further information defined according to the document structure descriptors may include the font information of the text object and a width information of the text object. Font information is defined as font dictionary objects in the PDF document which may include the font types and font sizes. The height of the bounding box may be calculated according to the font information. The width information may define a length of the text object on the background image. Other information that may be used to calculate the bounding box may include different text position shifts such as superscript and/or subscript. The text position shift information may be specified according to a text rise operator (Ts) which may be a factor for determining both the height and width of the text object on the background image. Processing device 2 may calculate the positions of the bounding box and its height and width in the background image of the searchable PDF document based on the beginning position, the font information, the width, and optionally the text position shift information of the text object. A single searchable PDF document may include one or more text objects and therefore, can result in one or more bounding boxes defining one or more training data samples. Each text object may include a word, a sentence, a paragraph, and/or an article composed from one or more natural languages. See paragraph [0022]);
generating a random replacement image for each region of interest of the plurality of electronic documents utilizing a script (At 206, processing device 2 may perform a crop operation on the background image of the searchable PDF document according to the bounding box to produce a potential training data sample. See paragraph [0023])( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]);
replacing each detected region of interest of each electronic document with the corresponding generated random image to create a modified plurality of electronic document images ( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]);
generating a training set comprising the modified plurality of electronic document images (The training data can be used to train the unified OCR model (referred to as the "training dataset"). The unified OCR model may combine the characteristics of an image model and a natural language model into one neural network that is characterized by parameters learned using a hybrid training data. The hybrid training data can be a set of data samples that each may be composed of a data pair of <image crop, the corresponding text present in the image crop>. An image crop can be an array of pixels ( e.g., 1 0x20 pixels) cropped from a document image. The image crop may exhibit certain texts therein. The document image can be a grey level or color image of the document obtained, for example, by scanning Thus, each pixel can be represented by one channel of a certain number of bits ( e.g., eight bits) or three channels ( e.g., red, green, blue) each including a certain number of bits. The training of a proper OCR model that can be used in real-world applications usually requires hundred million of such training data samples. See paragraph [0016])( generate hybrid training data that can be a data sample including both an image segment and the text therein. See paragraph [0015]) ( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]); and
training the machine learning model using the training set (Neural network training program 102 may, at 106, further train a unified OCR model. The trained OCR model may be used to accurately recognize the texts in document images. See paragraph [0015]).
Regarding claim 2, Wu teaches The computer-implemented method of claim 1, wherein the generating the random replacement image comprises: selecting one or more parameters for each region of interest at random ( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]); determining a size of each detected region of interest (At 206, processing device 2 may perform a crop operation on the background image of the searchable PDF document according to the bounding box to produce a potential training data sample. See paragraph [0023]); and assembling a replacement image for each region of interest based on the selected parameters and the size of each detected region of interest ( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]).
Regarding claim 6, Wu teaches The computer-implemented method of claim 1, wherein the creating the training set comprises combining the modified plurality of electronic documents with a second plurality of unmodified electronic documents from a database ( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]).
Regarding claim 7, Wu teaches The computer-implemented method of claim 1, further comprising: applying a destructive technique to each modified electronic document ( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]).
Regarding claim 8, Wu teaches The computer-implemented method of claim 7, wherein the destructive technique comprises at least one of the following: inverting the colors of the modified electronic document ( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]);
applying a grain filter to the modified electronic document ( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]);
adding a synthetic ink streak to the modified electronic document; and removing standard sections of the modified electronic document ( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]).
Regarding claim 9, Wu teaches A system (A system and method for constructing a training dataset and training a neural network include obtaining a searchable portable document format (PDF) document, identifying a bounding box defining a region in a background image that is associated with an overlaying text object defined in the PDF document, determining an image crop of the PDF document according to the bounding box, and generating a training data sample for the training dataset, the training data sample comprising a data pair of the image crop and the associated text object. See abstract), comprising:
one or more memories (In a further aspect, the computer system 500 may include a processing device 502, a volatile memory 504 (e.g., random access memory (RAM)), a non-volatile memory 506 ( e.g., read-only memory (ROM) or electricallyerasable programmable ROM (EEPROM)), and a data storage device 516, which may communicate with each other via a bus 508. See paragraph [0040]);
at least one processor each coupled to at least one of the memories and configured to perform operations (In a further aspect, the computer system 500 may include a processing device 502, a volatile memory 504 (e.g., random access memory (RAM)), a non-volatile memory 506 ( e.g., read-only memory (ROM) or electrically erasable programmable ROM (EEPROM)), and a data storage device 516, which may communicate with each other via a bus 508. See paragraph [0040]) (Processing device 502 may be provided by one or more processors such as a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor). See paragraph [041]) comprising:
detecting a region of interest for each of a plurality of electronic documents using a bounding box detection mechanism (At 204, processing device 2 may identify a bounding box defining a region in a background image and an overlaying text layer according to the searchable PDF document. The searchable PDF document can be rendered as the background image having an array of pixels. The bounding box may define a rectangular region that may cover a portion of the image, where the height and width of the bounding box may be measured in terms of numbers of pixels. In particular, the content of a Portable Document Format (PDF) file may be organized according to document structure descriptors that may include an identifier ( e.g., Td or TD) for defining the beginning position of a text object with reference to the dimension of the background image presented on a display device. For example, Td (or TD) may specify the 2D coordinate values of the bounding box with reference to the background image. Further information defined according to the document structure descriptors may include the font information of the text object and a width information of the text object. Font information is defined as font dictionary objects in the PDF document which may include the font types and font sizes. The height of the bounding box may be calculated according to the font information. The width information may define a length of the text object on the background image. Other information that may be used to calculate the bounding box may include different text position shifts such as superscript and/or subscript. The text position shift information may be specified according to a text rise operator (Ts) which may be a factor for determining both the height and width of the text object on the background image. Processing device 2 may calculate the positions of the bounding box and its height and width in the background image of the searchable PDF document based on the beginning position, the font information, the width, and optionally the text position shift information of the text object. A single searchable PDF document may include one or more text objects and therefore, can result in one or more bounding boxes defining one or more training data samples. Each text object may include a word, a sentence, a paragraph, and/or an article composed from one or more natural languages. See paragraph [0022]);
generating a random replacement image for each region of interest of the plurality of electronic documents utilizing a script (At 206, processing device 2 may perform a crop operation on the background image of the searchable PDF document according to the bounding box to produce a potential training data sample. See paragraph [0023])( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]);
replacing each detected region of interest of each electronic document with the corresponding generated random image to create a modified plurality of electronic document images ( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]);
generating a training set comprising the modified plurality of electronic document images (The training data can be used to train the unified OCR model (referred to as the "training dataset"). The unified OCR model may combine the characteristics of an image model and a natural language model into one neural network that is characterized by parameters learned using a hybrid training data. The hybrid training data can be a set of data samples that each may be composed of a data pair of <image crop, the corresponding text present in the image crop>. An image crop can be an array of pixels ( e.g., 1 0x20 pixels) cropped from a document image. The image crop may exhibit certain texts therein. The document image can be a grey level or color image of the document obtained, for example, by scanning Thus, each pixel can be represented by one channel of a certain number of bits ( e.g., eight bits) or three channels ( e.g., red, green, blue) each including a certain number of bits. The training of a proper OCR model that can be used in real-world applications usually requires hundred million of such training data samples. See paragraph [0016])( generate hybrid training data that can be a data sample including both an image segment and the text therein. See paragraph [0015]) ( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]); and
training the machine learning model using the training set (Neural network training program 102 may, at 106, further train a unified OCR model. The trained OCR model may be used to accurately recognize the texts in document images. See paragraph [0015]).
Regarding claim 10, Wu teaches The system of claim 9, wherein the generating the random replacement image comprises: selecting one or more parameters for each region of interest at random ( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]);
determining a size of each detected region of interest (At 206, processing device 2 may perform a crop operation on the background image of the searchable PDF document according to the bounding box to produce a potential training data sample. See paragraph [0023]); and
assembling a replacement image for each region of interest based on the selected parameters and the size of each detected region of interest ( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]).
Regarding claim 14, Wu teaches the system of claim 9, wherein the creating the training set comprises combining the modified plurality of electronic documents with a second plurality of unmodified electronic documents from a database ( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]).
Regarding claim 15, Wu teaches The system of claim 9, the operations further comprising: applying a destructive technique to each modified electronic document ( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]).
Regarding claim 16, Wu teaches The system of claim 15, wherein the destructive technique comprises at least one of the following: inverting the colors of the modified electronic document ( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]);
applying a grain filter to the modified electronic document ( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]);
adding a synthetic ink streak to the modified electronic document; and removing standard sections of the modified electronic document ( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]).
Regarding claim 17, Wu teaches A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, causes the at least one computing device to perform operations (Data storage device 516 may include a non-transitory computer-readable storage medium 524 on which may store instructions 526 encoding any one or more of the methods or functions described herein, including instructions of the neural network training application 102 of FIG. 1. See paragraph [0043]) comprising:
detecting a region of interest for each of a plurality of electronic documents using a bounding box detection mechanism (At 204, processing device 2 may identify a bounding box defining a region in a background image and an overlaying text layer according to the searchable PDF document. The searchable PDF document can be rendered as the background image having an array of pixels. The bounding box may define a rectangular region that may cover a portion of the image, where the height and width of the bounding box may be measured in terms of numbers of pixels. In particular, the content of a Portable Document Format (PDF) file may be organized according to document structure descriptors that may include an identifier ( e.g., Td or TD) for defining the beginning position of a text object with reference to the dimension of the background image presented on a display device. For example, Td (or TD) may specify the 2D coordinate values of the bounding box with reference to the background image. Further information defined according to the document structure descriptors may include the font information of the text object and a width information of the text object. Font information is defined as font dictionary objects in the PDF document which may include the font types and font sizes. The height of the bounding box may be calculated according to the font information. The width information may define a length of the text object on the background image. Other information that may be used to calculate the bounding box may include different text position shifts such as superscript and/or subscript. The text position shift information may be specified according to a text rise operator (Ts) which may be a factor for determining both the height and width of the text object on the background image. Processing device 2 may calculate the positions of the bounding box and its height and width in the background image of the searchable PDF document based on the beginning position, the font information, the width, and optionally the text position shift information of the text object. A single searchable PDF document may include one or more text objects and therefore, can result in one or more bounding boxes defining one or more training data samples. Each text object may include a word, a sentence, a paragraph, and/or an article composed from one or more natural languages. See paragraph [0022]);
generating a random replacement image for each region of interest of the plurality of electronic documents utilizing a script (At 206, processing device 2 may perform a crop operation on the background image of the searchable PDF document according to the bounding box to produce a potential training data sample. See paragraph [0023])( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]);
replacing each detected region of interest of each electronic document with the corresponding generated random image to create a modified plurality of electronic document images ( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]);
generating a training set comprising the modified plurality of electronic document images; and training the machine learning model using the training set (The training data can be used to train the unified OCR model (referred to as the "training dataset"). The unified OCR model may combine the characteristics of an image model and a natural language model into one neural network that is characterized by parameters learned using a hybrid training data. The hybrid training data can be a set of data samples that each may be composed of a data pair of <image crop, the corresponding text present in the image crop>. An image crop can be an array of pixels ( e.g., 1 0x20 pixels) cropped from a document image. The image crop may exhibit certain texts therein. The document image can be a grey level or color image of the document obtained, for example, by scanning Thus, each pixel can be represented by one channel of a certain number of bits ( e.g., eight bits) or three channels ( e.g., red, green, blue) each including a certain number of bits. The training of a proper OCR model that can be used in real-world applications usually requires hundred million of such training data samples. See paragraph [0016])( generate hybrid training data that can be a data sample including both an image segment and the text therein. See paragraph [0015]) ( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]).
Regarding claim 18, Wu teaches The non-transitory computer-readable medium of claim 17, wherein the generating the random replacement image comprises: selecting one or more parameters for each region of interest at random ( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]);
determining a size of each detected region of interest (At 206, processing device 2 may perform a crop operation on the background image of the searchable PDF document according to the bounding box to produce a potential training data sample. See paragraph [0023]); and
assembling a replacement image for each region of interest based on the selected parameters and the size of each detected region of interest ( At 208, processing device 2 may further augment the background image to produce an augmented training data sample. The augmentation may add variations that may happen in practice, thus producing more useful training data samples. Implementations of the disclosure may support two types of augmentations to the crop image and the associated text object. The first type of augmentation may include adding random variations to the pixel values in the background image crop to allow the learning of variations in the image. In the second type, the processing device may perform customization of color schema, random rotation, random blurry, random torsion, random transparency generation to the background image. In one example, to crop two images to the same size to merge these two images, usually a stamp would be always in the center of the line data. Applying the roll operation (available in the NumPy library of Python) enables the stamp image to be stamped more randomly in the crop image. After augmenting, processing device 2 may add the augmented training data sample to the training dataset, thus enrich the training dataset to include variations to the images of searchable PDF documents. See paragraph [0024]).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 3, 4, 11, 12, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wu (US 2022/0284721)(Hereinafter referred to as Wu) in view of Wang et al. (US 2017/0185833)(Hereinafter referred to as Wang).
Regarding claim 3, Wu teaches The computer-implemented method of claim 2, but is silent to wherein the region of interest comprises a date section.
However, Wu teaches that OCR technology may be used to recognize many types of documents including bank checks which generally have a date (The OCR technology may be used to recognize characters ( e.g., linguistic, numerical, and mathematical symbols) in document images. Examples of document images may include images of bank checks, receipts, legal documents etc. See paragraph [003])
Wang teaches utilizing machine learning in an OCR system to recognize (The card is typically a plastic card containing the account information and other data on the card. In many card embodiments, the customer name, expiration date, and card numbers are physically embossed on the card. See paragraph [0015])( The OCR algorithm may represent any process, program, method, or other manner of recognizing the digits represented on the card image. The OCR system extracts the digits and may display the extracted digits on the user interface of the user computing device. The OCR system may categorize groups of digits into categories such as account numbers, user name, expiration date, card issuer, or other suitable data. The OCR system may categorize the groups of digits by comparing the formats of groups of digits to a database of formats. For example, if the results of the OCR algorithm on a group of digits is “10/15”, then the OCR system may interpret the format as being associated with an expiration date. See paragraph [0030])( Any manner of assessing a confidence level may be used. For example, the OCR application 115 may use a machine learning algorithm to determine the likelihood that a digit is correct. The machine learning algorithm may be updated with some or all of the verifications or revisions of the results by the user 101, or an operator of the OCR system 120, or any suitable person. See paragraph [0082])
Wu and Wang teach of utilizing OCR technology and machine learning on image documents Wang teaches that the date can be recognized and the system can associate it with an expiration date, therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the system of Wu with the OCR data analysis of Wang such that the system can make informed decisions about the date data associated with a particular document.
Regarding claim 4, Wu in view of Wang teaches the computer-implemented method of claim 3, wherein the one or more parameters comprises at least a date value and a date format (Wang; The OCR algorithm may represent any process, program, method, or other manner of recognizing the digits represented on the card image. The OCR system extracts the digits and may display the extracted digits on the user interface of the user computing device. The OCR system may categorize groups of digits into categories such as account numbers, user name, expiration date, card issuer, or other suitable data. The OCR system may categorize the groups of digits by comparing the formats of groups of digits to a database of formats. For example, if the results of the OCR algorithm on a group of digits is “10/15”, then the OCR system may interpret the format as being associated with an expiration date. See paragraph [0030]).
Regarding claim 11, Wu teaches The system of claim 10, wherein the region of interest comprises a date section.
However, Wu teaches that OCR technology may be used to recognize many types of documents including bank checks which generally have a date (The OCR technology may be used to recognize characters ( e.g., linguistic, numerical, and mathematical symbols) in document images. Examples of document images may include images of bank checks, receipts, legal documents etc. See paragraph [003])
Wang teaches utilizing machine learning in an OCR system to recognize (The card is typically a plastic card containing the account information and other data on the card. In many card embodiments, the customer name, expiration date, and card numbers are physically embossed on the card. See paragraph [0015])( The OCR algorithm may represent any process, program, method, or other manner of recognizing the digits represented on the card image. The OCR system extracts the digits and may display the extracted digits on the user interface of the user computing device. The OCR system may categorize groups of digits into categories such as account numbers, user name, expiration date, card issuer, or other suitable data. The OCR system may categorize the groups of digits by comparing the formats of groups of digits to a database of formats. For example, if the results of the OCR algorithm on a group of digits is “10/15”, then the OCR system may interpret the format as being associated with an expiration date. See paragraph [0030])( Any manner of assessing a confidence level may be used. For example, the OCR application 115 may use a machine learning algorithm to determine the likelihood that a digit is correct. The machine learning algorithm may be updated with some or all of the verifications or revisions of the results by the user 101, or an operator of the OCR system 120, or any suitable person. See paragraph [0082])
Wu and Wang teach of utilizing OCR technology and machine learning on image documents Wang teaches that the date can be recognized and the system can associate it with an expiration date, therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the system of Wu with the OCR data analysis of Wang such that the system can make informed decisions about the date data associated with a particular document.
Regarding claim 12, Wu in view of Wang teaches The system of claim 11, wherein the one or more parameters comprises at least a date value and a date format(Wang; The OCR algorithm may represent any process, program, method, or other manner of recognizing the digits represented on the card image. The OCR system extracts the digits and may display the extracted digits on the user interface of the user computing device. The OCR system may categorize groups of digits into categories such as account numbers, user name, expiration date, card issuer, or other suitable data. The OCR system may categorize the groups of digits by comparing the formats of groups of digits to a database of formats. For example, if the results of the OCR algorithm on a group of digits is “10/15”, then the OCR system may interpret the format as being associated with an expiration date. See paragraph [0030]).
Regarding claim 19, Wu teaches The non-transitory computer-readable medium of claim 18, wherein the region of interest comprises a date section.
However, Wu teaches that OCR technology may be used to recognize many types of documents including bank checks which generally have a date (The OCR technology may be used to recognize characters ( e.g., linguistic, numerical, and mathematical symbols) in document images. Examples of document images may include images of bank checks, receipts, legal documents etc. See paragraph [003])
Wang teaches utilizing machine learning in an OCR system to recognize (The card is typically a plastic card containing the account information and other data on the card. In many card embodiments, the customer name, expiration date, and card numbers are physically embossed on the card. See paragraph [0015])( The OCR algorithm may represent any process, program, method, or other manner of recognizing the digits represented on the card image. The OCR system extracts the digits and may display the extracted digits on the user interface of the user computing device. The OCR system may categorize groups of digits into categories such as account numbers, user name, expiration date, card issuer, or other suitable data. The OCR system may categorize the groups of digits by comparing the formats of groups of digits to a database of formats. For example, if the results of the OCR algorithm on a group of digits is “10/15”, then the OCR system may interpret the format as being associated with an expiration date. See paragraph [0030])( Any manner of assessing a confidence level may be used. For example, the OCR application 115 may use a machine learning algorithm to determine the likelihood that a digit is correct. The machine learning algorithm may be updated with some or all of the verifications or revisions of the results by the user 101, or an operator of the OCR system 120, or any suitable person. See paragraph [0082])
Wu and Wang teach of utilizing OCR technology and machine learning on image documents Wang teaches that the date can be recognized and the system can associate it with an expiration date, therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the system of Wu with the OCR data analysis of Wang such that the system can make informed decisions about the date data associated with a particular document.
Regarding claim 20, Wu in view of Wang teaches the non-transitory computer-readable medium of claim 19, wherein the one or more parameters comprises at least a date value and a date format (Wang; The OCR algorithm may represent any process, program, method, or other manner of recognizing the digits represented on the card image. The OCR system extracts the digits and may display the extracted digits on the user interface of the user computing device. The OCR system may categorize groups of digits into categories such as account numbers, user name, expiration date, card issuer, or other suitable data. The OCR system may categorize the groups of digits by comparing the formats of groups of digits to a database of formats. For example, if the results of the OCR algorithm on a group of digits is “10/15”, then the OCR system may interpret the format as being associated with an expiration date. See paragraph [0030]).
Allowable Subject Matter
Claims 5 and 13 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 prior art of record alone or in combination is silent to the limitations “wherein the assembling the replacement image comprises: retrieving a random handwritten character image from a database for each character of the selected date value; determining a random kerning for each character image based on the size of the detected date section and the selected date; and joining the character images sequentially based on the selected date and the random kerning for each character image.” Of claim 5 when read in light of the rest of the limitations in claim 5 and the claims to which claim 5 depends and thus claim 5 contains allowable subject matter.
The prior art of record alone or in combination is silent to the limitations “wherein the assembling the replacement image comprises: retrieving a random handwritten character image from a database for each character of the selected date value; determining a random kerning for each character image based on the size of the detected date section and the selected date; and joining the character images sequentially based on the selected date and the random kerning for each character image. ” Of claim 13 when read in light of the rest of the limitations in claim 13 and the claims to which claim 13 depends and thus claim 13 contains allowable subject matter.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS R WILSON whose telephone number is (571)272-0936. The examiner can normally be reached M-F 7:30-5:00PM.
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/NICHOLAS R WILSON/Primary Examiner, Art Unit 2611