Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on September 16, 2025 has been entered.
Status of Claims
Claims 7, 10 and 19 have been canceled.
Claims 1, 8, 9, 11, 20 and 21 are amended
Claims 1 – 6, 8, 9, 11 – 18, 20 and 21 remain pending.
Response to Arguments
Applicant's arguments filed September 16, 2025 with respect to claims 1 – 6, 8, 9, 11 – 18, 20 and 21 have been considered but are moot because the new grounds of rejection do not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1 – 6, 8, 9, 11 – 18, 20 and 21 are rejected under 35 U.S.C 103 as being unpatentable over Balakrishnan et al. US Patent Application Publication No. US-20210124919-A1 (hereinafter Vasanth) in view of Allen US Patent Application No. US-20220004755-A1 (hereinafter Allen) and Van Deventer US Patent Publication No. US-9373030-B2 (hereinafter Jorgen).
Regarding claim 1, Vasanth discloses a method comprising using at least one hardware processor to (Vasanth in [0038] discloses, “Embodiments of the invention may take the form of a hardware implemented embodiment”): in each of a plurality of iterations of a document-recognition session (Vasanth in [0088] discloses, “If the generated score does not satisfy a desired threshold level ... then re-scoring with more attributes specific to the most likely template (if one has been identified) and iterating the processing by performing the image transformation estimation step(s)”), receive an image, wherein the image is an image of multiple input images that comprise the same document (Vasanth in [0066] discloses; “Identify and/extract data or images from the subject document to compare with the attributes and requirements of the template for document content”), locate the document in the image and attempt to identify one or more of a plurality of templates that match the document (Vasanth in [0122] discloses, “The extracted invariable attributes are compared against the attributes for a set of templates, with each template representing a type or class of documents (such as a driver's license issued by state A, a passport from country B, etc.”), when one or more templates that match the document are identified, for each of the one or more templates, for each of a plurality of zones in the template (In [0009] Vasanth discloses about template matching. Furthermore, Vasanth in [0063] discloses about having plurality of zones, “one or more data fields 108 containing alphanumeric characters (identified as Header, Field 1 , and Field 2 in the figure)” and in [0088] Vasanth discloses about extracting sub-image, “Identifying/extracting one or more fields, data, content, images, or other elements from the subject document image”), wherein a plurality of objects are extracted from at least one of the extracted sub-images (Vasanth in [0088] discloses about plurality of sub-image disclosed in Fig. 1(a) and [0063]. And each sub-image obviously have different object relative to one another. For example, sub-image of Logos or hologram contains multiple characters wherein extracting multiple characters or symbols from a logo or hologram equates to extracting plurality of objects), and, for each extracted object, perform object recognition on the object (In [0135] Vasanth discloses, “The extracted information may be checked or compared to information available in a database or data record as part of verifying the information, and hence the subject document (as suggested by database checks 168)”, and perform document recognition based on the one or more templates and results of the object recognition performed for each extracted object (Vasanth in [0135] discloses, “After the subject document has been associated with a template with a sufficient degree of confidence, other aspects of the subject document may be identified/extracted and subject to verification (step or stage 166). This may include content such as a person's name, address, date of birth, driver's license number, or other information that is expected to be unique to a particular subject document. The extracted information may be checked or compared to information available in a database or data record as part of verifying the information, and hence the subject document (as suggested by database checks 168)”); accumulate, in non-persistent storage of the document-recognition session, a result of the document recognition performed in the iteration with a result of the document recognition performed in one or more prior iterations (Vasanth in [0088] discloses, “If the generated score does not satisfy a desired threshold level or confidence value, or a heat map indicates a lower than desirable confidence level, then re-scoring with more attributes specific to the most likely template (if one has been identified) and iterating the processing by performing the image transformation estimation step(s)”), and output a final result based on the accumulated result of the document recognition in the at least one iteration plurality of iterations (Vasanth in [0088] discloses if the generated score or heat map indicates a sufficient reliability or confidence in the authenticity of the document, then accepting the subject document and the information it contains as accurate for purposes of what the document discloses and for identification of the person presenting the subject document (step or stage 146 ).
Vasanth doesn’t disclose the limitation as further recited in the claim.
Allen discloses about determine whether any of the plurality of zones in the template satisfy a zone-level stopping condition in which all objects in the zone have satisfied an object-level stopping condition, that has not satisfied the zone- level stopping condition, and skip a zone that has satisfied the zone-level stopping condition in one or more prior iterations, of the zone that has not satisfied the object-level stopping condition, and skip an object that has satisfied the object-level stopping condition in one or more prior iterations (Allen in [0052] discloses, “In block 414, the page flow model 132 determines whether the percentage of inaccuracy for the zone exceeds (is greater than) an inaccuracy threshold. If so, processing continues to block 416, otherwise, processing continues to block 418. In certain embodiments, the page flow model 132 adjusts accuracy for the zone for a text area and other zones in the document if the expected size of the zone is determined to be over a pixel distance threshold from the historical size of the zone. In certain embodiments, the page flow model 132 estimates the length of text values in a text area from historical data and multiplies that by the font size to get a pixel size for the expected size of the zone, and this pixel size is compared to a pixel distance threshold” wherein texts are the objects within the zone).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Allen into the system of Vasanth because it would allow the system to reduce the computational processing by not processing the already fully processed zones while processing the more complicated zones.
Vasanth and Allen in the combination doesn’t disclose the limitation as further recited in the claim.
Jorgen discloses about extract a sub-image of the zone from the image, for each extracted sub-image, extract one or more objects from the sub-image (Jorgen in Fig. 6 discloses about identifying the sub-image 612 and plurality of zones in the image 610).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Jorgen into the system of Vasanth in view of Allen because it would allow the system to extract objects and use object specific preprocessing for higher accuracy.
Summary of Citations (Allen)
Paragraph [0052]; “In block 414, the page flow model 132 determines whether the percentage of inaccuracy for the zone exceeds (is greater than) an inaccuracy threshold. If so, processing continues to block 416, otherwise, processing continues to block 418. In certain embodiments, the page flow model 132 adjusts accuracy for the zone for a text area and other zones in the document if the expected size of the zone is determined to be over a pixel distance threshold from the historical size of the zone. In certain embodiments, the page flow model 132 estimates the length of text values in a text area from historical data and multiplies that by the font size to get a pixel size for the expected size of the zone, and this pixel size is compared to a pixel distance threshold”.
Summary of Citations (Vasanth)
Paragraph [0009]; “access a set of document templates, wherein each template represents an example of a type of document and includes information regarding a set of invariable attributes associated with each type of document; identify a template in the set of document templates representing a document of the type of the subject document”.
Paragraph [0038]; “Embodiments of the invention may take the form of a hardware implemented embodiment, a software implemented embodiment, or an embodiment combining software and hardware aspects”.
Paragraph [0063]; “FIG. 1(a ) is a diagram illustrating an example document 100 that might be a subject of the authentication/verification processing ... the document may include elements or features such as a logo 102 , a photo or similar image 104 , a hologram of other specific form of “watermark” or marker 106 , one or more data fields 108 containing alphanumeric characters (identified as Header, Field 1 , and Field 2 in the figure), and additional text 110”.
Paragraph [0066]; “Identify and/extract data or images from the subject document to compare with the attributes and requirements of the template for document content”.
Paragraph [0066 – 0068]; “At a high-level, the processing and authenticating of a subject document involves one or more of the following steps, stages, functions, methods or operations: Receive or access an image of a subject document (step or stage 121); Identify and/or extract invariable attributes of the subject document (step 122)”.
Paragraph [0088]; “Identifying/extracting one or more fields, data, content, images, or other elements from the subject document image for use in further comparisons and authentication or verification processing ... if the generated score or heat map indicates a sufficient reliability or confidence in the authenticity of the document, then accepting the subject document and the information it contains as accurate for purposes of what the document discloses and for identification of the person presenting the subject document (step or stage 146 ); If the generated score does not satisfy a desired threshold level or confidence value, or a heat map indicates a lower than desirable confidence level, then re-scoring with more attributes specific to the most likely template (if one has been identified) and iterating the processing by performing the image transformation estimation step(s)”.
Paragraph [0122]; “The extracted invariable attributes are compared against the attributes for a set of templates, with each template representing a type or class of documents (such as a driver's license issued by state A, a passport from country B, etc.). This typically means that an initial set of invariable attributes are used to determine one or more templates that might correspond to the subject document being processed. In most cases, a small set of invariable attributes, for which there is a relatively high level of confidence with regards to their identification, are used to find one or more templates that contain those attributes”.
Paragraph [0124]; “Common attributes that are present in a number of templates (for example, the text “Driver's”, “US”, “License” etc.) may be assigned lower confidence levels, while more unique attributes (for example, seals, logos, a state name such as “UTAH”, country codes etc.) are given higher confidence levels. In this way, the confidence level represents a measure of the commonness of an attribute among a group of templates and results in giving less weight to the most common attributes when deciding which template or templates best represent a subject document”.
Paragraph [0135]; “After the subject document has been associated with a template with a sufficient degree of confidence, other aspects of the subject document may be identified/extracted and subject to verification (step or stage 166). This may include content such as a person's name, address, date of birth, driver's license number, or other information that is expected to be unique to a particular subject document. The extracted information may be checked or compared to information available in a database or data record as part of verifying the information, and hence the subject document (as suggested by database checks 168)”.
Regarding claim 2 – 6, the grounds of rejection from the last Office Action with respect to Vasanth in view of Allen and Jorgen in the combination apply in here.
Regarding claim 8, Vasanth in the combination further discloses the method of Claim 1, further comprising using the at least one hardware processor to add another iteration to the plurality of iterations until a recognition-level stopping condition is satisfied (Vasanth in [0118 – 0119] discloses, “If the generated score does not satisfy a desired threshold level or confidence value, or a heat map indicates a lower than desirable confidence level, then re-scoring with more attributes specific to the most likely template (if one has been identified) and iterating the processing by performing the image transformation estimation step(s) (step or stage 137) forward (step or stage 147); and if the score or evaluation still fails to satisfy the threshold, then rejecting the document and possibly requiring human intervention and other forms of analysis or evaluation”).
Summary of Citations (Vasanth)
Paragraph [0118 - 0119]; “If the generated score does not satisfy a desired threshold level or confidence value, or a heat map indicates a lower than desirable confidence level, then re-scoring with more attributes specific to the most likely template (if one has been identified) and iterating the processing by performing the image transformation estimation step(s) (step or stage 137) forward (step or stage 147); and if the score or evaluation still fails to satisfy the threshold, then rejecting the document and possibly requiring human intervention and other forms of analysis or evaluation”.
Regarding claim 9, Vasanth in the combination further discloses the method of Claim 1, integrate a result of the object recognition performed for that object in the iteration with a result of the object recognition performed for that object in one or more prior iterations (Vasanth in [0134] discloses, “If the score or metric is not sufficient to meet a threshold of reliability or confidence level, then the transformation, the assumed correct template or both may be subject to further review (step or stage 162 ). This may lead to a re-estimation of the transformation, generation of a revised standardized image, and a re-scoring of the subject document with regards to one or more templates in the set of templates”. Here the recognition result are re-scored and revised based on the previous score of the subject document implies to integrating the one or more prior iteration result to improve accuracy).
Jorgen further discloses further comprising using the atleast one hardware processor to, in each of the plurality of iterations, when one or more templates that match the document are identified in the frame, (Jorgen in [Column – 3; Line 25 – 28] discloses about receiving a video with multiple frames, “The video stream from the camera may be transmitted to a system for automated document recognition, identification, and data extraction” and in [Column – 8; Line 39 – 41] Jorgen discloses, “To determine the document type, the layout of the document 410 may be compared to layouts of document templates stored in the database”), for each of the one or more templates, for at least one extracted object on which object recognition is performed (Jorgen in [Column – 6; Line 8 – 12] discloses about using OCR to extract data for object recognition (“it may be determined that the image 145 depicts a California driver's license. OCR can be used to extract data from the image 145. The extracted data can be associated with fields of the document based on known locations of such fields for this document type”).
Summary of Citations (Vasanth)
Paragraph [0134]; “If the score or metric is not sufficient to meet a threshold of reliability or confidence level, then the transformation, the assumed correct template or both may be subject to further review (step or stage 162 ) to identify additional possible attributes for extraction and consideration (step or stage 164 ). This may lead to a re-estimation of the transformation, generation of a revised standardized image, and a re-scoring of the subject document with regards to one or more templates in the set of templates”.
Summary of Citations (Jorgen)
[Column – 3; Line 25 – 28]; “The video stream from the camera may be transmitted to a system for automated document recognition, identification, and data extraction”.
[Column – 6; Line 8 – 12]; “it may be determined that the image 145 depicts a California driver's license. OCR can be used to extract data from the image 145. The extracted data can be associated with fields of the document based on known locations of such fields for this document type”.
[Column – 8; Line 39 – 41]; “To determine the document type, the layout of the document 410 may be compared to layouts of document templates stored in the database”.
Regarding claim 11, Jorgen in the combination further discloses the method of Claim 7, wherein at least one object represents an optically variable device (In [Column – 8; Line 39 – 42] Jorgen discloses about Hologram which is an optically variable device, “The comparison may be based on location of data fields, photos, holograms, and so forth”).
Summary of Citations (Jorgen)
[Column – 8; Line 39 – 42]; “To determine the document type, the layout of the document 410 may be compared to layouts of document templates stored in the database. The comparison may be based on location of data fields, photos, holograms, and so forth”.
Regarding claim 12 – 18, the ground of rejection from the last Office Action with respect to Vasanth in view of Allen and Jorgen in the combination apply in here.
Regarding claim 20, apparatus claim 20 is corresponds to method claim 1. Therefore,
the rejection of claim 1 is applicable to claim 20.
Regarding claim 21, is a non-transitory computer readable storage medium claim
corresponds to method claim 1. Therefore, the rejection analysis of claim 1 is applied in claim 21. See also Fig. 4 and [0316] and [0039] for additional context.
Conclusion
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/ZAID MUHAMMAD SALEH/
Examiner, Art Unit 2668
11/15/2025
/VU LE/Supervisory Patent Examiner, Art Unit 2668