DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6, 8-13, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Patil (U.S. Patent No. 2014/0008440 A1, hereafter referred as Patil) in view of Yang et al. (U.S. Patent App. Pub No. 2023/0325618 A1, hereafter referred as Yang).
Regarding Claim 1:
Patil teaches a method (Patil: Title; method for finding target distance from barode imaging scanner), comprising: capturing, by a device utilizing a current focus setting (Patil: Par. [0027]; The first pixel data is then processed to estimate a feature-size (e.g., the width "w" or the height "h") in the image of the target object when the imaging lens arrangement is at a predetermined focus length and/or at a predetermined zoom setting. Using the value of the predetermined focus length and the predetermined zoom setting as indexes for the lookup table previously created during the calibration process, a controller in the imaging scanner 50 can select a sub-table from the lookup table that includes multiple entries each indentifying a paired relationship between a feature-size of the aiming cross-wire and a corresponding the distance "d" between the target object 45 and the imaging scanner 50.), a current image of an area (Patil: Par. [0018]; The imaging sensor 62 is operative to detect light captured by an imaging lens arrangement 60 along an optical path or axis 61 through the window 56. Generally, the imaging sensor 62 and the imaging lens arrangement 60 are designed to operate together for capturing light scattered or reflected from a barcode 40 as pixel data over a two-dimensional imaging field of view (FOV).), the area having one or more labels present therein (Patil: [0021]; In many embodiments, the controller 90 also includes a decoder for decoding one or more barcodes that are within the imaging field of view (FOV) of the imaging scanner 50. In some implementations, the barcode 40 can be decoded by digitally processing a captured image of the barcode with a microprocessor.); determining a first distance between the device and one or more labels present in the current image based on the current focus setting of the device during capture of the current image (Patil: Par. [0025]; The width "w" and the height "h" of the aiming cross-wire, however, generally remains at the same constant, if the distance "d" between the target object 45 and the imaging scanner 50 is kept at a constant, for the same the focus lengths "F" and the same zoom settings "Z" of the imaging lens arrangement 60. Consequently, for an imaging lens arrangement 60 that has variable focuses and/or more than one zoom setting, if the focus lengths "F" and the same zoom settings "Z" are known or predetermined, it would be possible to use the feature-size of the aiming cross-wire in the pixel data (e.g., the width "w" or the height "h") to determine the distance "d" between the target object 45 and the imaging scanner 50.); detecting, (Patil: Par. [0021-0022]; In many embodiments, the controller 90 also includes a decoder for decoding one or more barcodes that are within the imaging field of view (FOV) of the imaging scanner 50. In some implementations, the barcode 40 can be decoded by digitally processing a captured image of the barcode with a microprocessor; The captured image of the barcode 40 is transferred to the controller 90 as pixel data. Such pixel data is digitally processed by the decoder in the controller 90 to decode the barcode. The information obtained from decoding the barcode 40 is then stored in the memory 94 or sent to other devices for further processing.); (Patil: Par. [0025]; The width "w" and the height "h" of the aiming cross-wire, however, generally remains at the same constant, if the distance "d" between the target object 45 and the imaging scanner 50 is kept at a constant, for the same the focus lengths "F" and the same zoom settings "Z" of the imaging lens arrangement 60. Consequently, for an imaging lens arrangement 60 that has variable focuses and/or more than one zoom setting, if the focus lengths "F" and the same zoom settings "Z" are known or predetermined, it would be possible to use the feature-size of the aiming cross-wire in the pixel data (e.g., the width "w" or the height "h") to determine the distance "d" between the target object 45 and the imaging scanner 50.); determining whether the current label is in focus based on the first distance, the second distance of the current label, and at least one attribute of the device; and responsive to determining the current label is in focus, processing the current label (Patil: Par. [0019]; Some of the imaging scanners can include an auto-focus system to enable a barcode be more clearly imaged with the imaging sensor 62 based on the measured distance of this barcode. In some implementations of the auto-focus system, the focus length of the imaging lens arrangement 60 is adjusted based on the measured distance of the barcode. In some other implementations of the auto-focus system, the distance between the imaging lens arrangement 60 and the imaging sensor 62 is adjusted based on the measured distance of the barcode.).
Patil fails to teach detecting, utilizing a trained machine learning model, the one or more labels present in the current image, each label having one or more identifiers; determining whether the one or more labels are assessed; responsive to determining the one or more labels are not assessed.
Yang, like Patil, is directed to decoding barcodes. Yang does teach detecting, utilizing a trained machine learning model, the one or more labels present in the current image, each label having one or more identifiers (Yang: Par. [0069]; The decoding module 528 may comprise a convolutional neural network (CNN), for example. As shown in FIG. 5B, the decoding module 528 may include a CNN feature extractor 528a.); determining whether the one or more labels are assessed; responsive to determining the one or more labels are not assessed (Yang: Par. [0073]; In block 606, the image may be evaluated (e.g., region by region) to determine if a bounding box (e.g., a barcode) has been detected. If a bounding box has not been detected, the process 600 may return to block 604 to continue processing the image.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Patil to utilize the machine learning technique, as taught by Yang, to arrive at the claimed invention discussed above. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. As taught by Yang, the proposed modification would help decode barcodes that are blurred, warped, faded, or that may be partially occluded by other overlaid markings, labels, or further obscured by plastic wrapping (Yang: Par. [0004]).
In regards to Claim 2, Patil as modified by Yang further teaches the method of claim 1, wherein the current focus setting is one of a fixed focus setting of the device or an autofocus setting of the device (Patil: Par. [0019]; Some of the imaging scanners can include an auto-focus system to enable a barcode be more clearly imaged with the imaging sensor 62 based on the measured distance of this barcode. In some implementations of the auto-focus system, the focus length of the imaging lens arrangement 60 is adjusted based on the measured distance of the barcode. In some other implementations of the auto-focus system, the distance between the imaging lens arrangement 60 and the imaging sensor 62 is adjusted based on the measured distance of the barcode.).
In regards to Claim 3, Patil as modified by Yang further teaches the method of claim 1, wherein detecting, utilizing the trained machine learning model, the one or more labels present in the current image comprises: generating a bounding box corresponding to each label; and determining a pixel size of each label based on a pixel length and/or a pixel height of the bounding box (Patil: Par. [0025] and Fig. 5C; The pixel data, as shown in FIG. 5B or FIG. 5C, includes the image of the aiming cross-wire with a width "w" and a height "h"; the pixel data has a horizontal resolution "X" and vertical resolution "Y").
In regards to Claim 4, Patil as modified by Yang further teaches the method of claim 1, further comprising training a machine learning model to detect the one or more labels based on at least one of historical data including one or more previously detected labels or datasets including images of one or more label types, each label type having a known size (Patil: Par. [0027]; The first pixel data is then processed to estimate a feature-size (e.g., the width "w" or the height "h") in the image of the target object when the imaging lens arrangement is at a predetermined focus length and/or at a predetermined zoom setting. Using the value of the predetermined focus length and the predetermined zoom setting as indexes for the lookup table previously created during the calibration process, a controller in the imaging scanner 50 can select a sub-table from the lookup table that includes multiple entries each indentifying a paired relationship between a feature-size of the aiming cross-wire and a corresponding the distance "d" between the target object 45 and the imaging scanner 50.).
In regards to Claim 5, Patil as modified by Yang further teaches the method of claim 1, wherein the device is one of a mobile computer, a head-mounted display, a tablet, a smartphone, a camera, or a wearable computing device (Patil: Par. [0034]; an embodiment can be implemented as a computer-readable storage medium having computer readable code stored thereon for programming a computer (e.g., comprising a processor) to perform a method as described and claimed herein; Yang: Par. [0061]; In some aspects, the image may be supplied via a lower cost/lower resolution (e.g., less than 12 megapixel) camera such as a mobile phone camera, for instance.); and the at least one attribute of the device is a depth of field (Patil: Par. [0018]; Generally, the imaging sensor 62 and the imaging lens arrangement 60 are designed to operate together for capturing light scattered or reflected from a barcode 40 as pixel data over a two-dimensional imaging field of view (FOV).).
In regards to Claim 6, Patil as modified by Yang further teaches the method of claim 1, wherein processing the current label comprises: decoding an identifier corresponding to a predetermined symbology and/or barcode data structure; or utilizing character recognition to recognize an identifier corresponding to a predetermined character string structure (Yang: Par. [0079]; Fig. 7 is a table 700 of example barcode types that may be detected and decoded, in accordance with aspects of the present disclosure. As shown in table 700, one-dimensional barcodes (e.g., as a uniform product code (UPC) or a European article number (EAN)), as well as two-dimensional barcodes (e.g., quick response (QR) codes) may be detected and decoded. The barcodes may vary in length and pattern or shape. However, the example barcodes in table 700 is not an exhaustive list of barcodes that may be detected and decoded. Rather, aspects of the present disclosure are extensible such that different formats including different lengths and composed of alphanumeric and other characters, for example, in accordance with current and future barcode formats (of any dimension number) may be detected and decoded; Par. [0064]; The barcode decoding module 506 may decode the input 502 at the location of the one or more bounding boxes to generate an output 508 including a set of characters corresponding to the one or more barcodes included in the input 502. A character may, for example, refer to a digit, an alpha-numeric character, a special character, a mathematical symbol, or the like.).
Regarding Claim 8:
Patil as modified by Yang further teaches a device, comprising: an imaging assembly; one or more processors; and a non-transitory computer-readable memory coupled to the one or more processors, the memory storing instructions thereon that, when executed by the one or more processors, cause the one or more processors to: receive a current image (Yang: Par. [0009]; a non-transitory computer-readable medium having program code recorded thereon is disclosed. The program code is executed by a processor and includes program code to receive, by an artificial neural network (ANN), an image.), captured by the imaging assembly utilizing a current focus setting (Patil: Par. [0027]; The first pixel data is then processed to estimate a feature-size (e.g., the width "w" or the height "h") in the image of the target object when the imaging lens arrangement is at a predetermined focus length and/or at a predetermined zoom setting. Using the value of the predetermined focus length and the predetermined zoom setting as indexes for the lookup table previously created during the calibration process, a controller in the imaging scanner 50 can select a sub-table from the lookup table that includes multiple entries each indentifying a paired relationship between a feature-size of the aiming cross-wire and a corresponding the distance "d" between the target object 45 and the imaging scanner 50.), of area (Patil: Par. [0018]; The imaging sensor 62 is operative to detect light captured by an imaging lens arrangement 60 along an optical path or axis 61 through the window 56. Generally, the imaging sensor 62 and the imaging lens arrangement 60 are designed to operate together for capturing light scattered or reflected from a barcode 40 as pixel data over a two-dimensional imaging field of view (FOV).), of an area (Patil: Par. [0018]; The imaging sensor 62 is operative to detect light captured by an imaging lens arrangement 60 along an optical path or axis 61 through the window 56. Generally, the imaging sensor 62 and the imaging lens arrangement 60 are designed to operate together for capturing light scattered or reflected from a barcode 40 as pixel data over a two-dimensional imaging field of view (FOV).), the area having one or more labels present therein (Patil: [0021]; In many embodiments, the controller 90 also includes a decoder for decoding one or more barcodes that are within the imaging field of view (FOV) of the imaging scanner 50. In some implementations, the barcode 40 can be decoded by digitally processing a captured image of the barcode with a microprocessor.); determine a first distance between the device and one or more labels present in the current image based on the current focus setting of the imaging assembly during capture of the current image (Patil: Par. [0025]; The width "w" and the height "h" of the aiming cross-wire, however, generally remains at the same constant, if the distance "d" between the target object 45 and the imaging scanner 50 is kept at a constant, for the same the focus lengths "F" and the same zoom settings "Z" of the imaging lens arrangement 60. Consequently, for an imaging lens arrangement 60 that has variable focuses and/or more than one zoom setting, if the focus lengths "F" and the same zoom settings "Z" are known or predetermined, it would be possible to use the feature-size of the aiming cross-wire in the pixel data (e.g., the width "w" or the height "h") to determine the distance "d" between the target object 45 and the imaging scanner 50.); detect, utilizing a trained machine learning model (Yang: Par. [0069]; The decoding module 528 may comprise a convolutional neural network (CNN), for example. As shown in FIG. 5B, the decoding module 528 may include a CNN feature extractor 528a.), the one or more labels present in the current image, each label having one or more identifiers (Patil: Par. [0021-0022]; In many embodiments, the controller 90 also includes a decoder for decoding one or more barcodes that are within the imaging field of view (FOV) of the imaging scanner 50. In some implementations, the barcode 40 can be decoded by digitally processing a captured image of the barcode with a microprocessor; The captured image of the barcode 40 is transferred to the controller 90 as pixel data. Such pixel data is digitally processed by the decoder in the controller 90 to decode the barcode. The information obtained from decoding the barcode 40 is then stored in the memory 94 or sent to other devices for further processing.); determine whether the one or more labels are assessed; responsive to determining the one or more labels are not assessed (Yang: Par. [0073]; In block 606, the image may be evaluated (e.g., region by region) to determine if a bounding box (e.g., a barcode) has been detected. If a bounding box has not been detected, the process 600 may return to block 604 to continue processing the image.), determine, for a current label among the one or more labels, a second distance between the device and the current label based on a known size of the current label and/or a feature of the current label (Patil: Par. [0025]; The width "w" and the height "h" of the aiming cross-wire, however, generally remains at the same constant, if the distance "d" between the target object 45 and the imaging scanner 50 is kept at a constant, for the same the focus lengths "F" and the same zoom settings "Z" of the imaging lens arrangement 60. Consequently, for an imaging lens arrangement 60 that has variable focuses and/or more than one zoom setting, if the focus lengths "F" and the same zoom settings "Z" are known or predetermined, it would be possible to use the feature-size of the aiming cross-wire in the pixel data (e.g., the width "w" or the height "h") to determine the distance "d" between the target object 45 and the imaging scanner 50.); determine whether the current label is in focus based on the first distance, the second distance of the current label, and at least one attribute of the imaging assembly; and responsive to determining the current label is in focus, process the current label (Patil: Par. [0019]; Some of the imaging scanners can include an auto-focus system to enable a barcode be more clearly imaged with the imaging sensor 62 based on the measured distance of this barcode. In some implementations of the auto-focus system, the focus length of the imaging lens arrangement 60 is adjusted based on the measured distance of the barcode. In some other implementations of the auto-focus system, the distance between the imaging lens arrangement 60 and the imaging sensor 62 is adjusted based on the measured distance of the barcode.).
In regards to Claim 9, Patil as modified by Yang further teaches the device of claim 8, wherein the current focus setting is one of a fixed focus setting of the imaging assembly or an autofocus setting of the imaging assembly (Patil: Par. [0019]; Some of the imaging scanners can include an auto-focus system to enable a barcode be more clearly imaged with the imaging sensor 62 based on the measured distance of this barcode. In some implementations of the auto-focus system, the focus length of the imaging lens arrangement 60 is adjusted based on the measured distance of the barcode. In some other implementations of the auto-focus system, the distance between the imaging lens arrangement 60 and the imaging sensor 62 is adjusted based on the measured distance of the barcode.).
In regards to Claim 10, Patil as modified by Yang further teaches the device of claim 8, wherein the instructions, when executed, further cause the one or more processors to detect, utilizing the trained machine learning model, the one or more labels present in the current image by: generating a bounding box corresponding to each label (Yang: Par. [0069]; The CNN feature extractor 528a may extract features of the detected bounding boxes. That is, the CNN feature extractor 528a processes the bounding boxes (e.g., 526a 526b), extracting features of the image 522 at the location of the bounding boxes (e.g., 526a 526b); Par. [0064]; The barcode decoding module 506 may extract features of the input 502 at the location of the one or more bounding boxes. The barcode decoding module 506 may decode the input 502 at the location of the one or more bounding boxes to generate an output 508 including a set of characters corresponding to the one or more barcodes included in the input 502. A character may, for example, refer to a digit, an alpha-numeric character, a special character, a mathematical symbol, or the like); and determining a pixel size of each label based on a pixel length and/or a pixel height of the bounding box (Patil: Par. [0025] and Fig. 5C; The pixel data, as shown in FIG. 5B or FIG. 5C, includes the image of the aiming cross-wire with a width "w" and a height "h"; the pixel data has a horizontal resolution "X" and vertical resolution "Y").
In regards to Claim 11, Patil as modified by Yang further teaches the device of claim 8, wherein the instructions, when executed, further cause the one or more processors to train a machine learning model to detect the one or more labels based on at least one of historical data including one or more previously detected labels or datasets including images of one or more label types, each label type having a known size (Patil: Par. [0027]; The first pixel data is then processed to estimate a feature-size (e.g., the width "w" or the height "h") in the image of the target object when the imaging lens arrangement is at a predetermined focus length and/or at a predetermined zoom setting. Using the value of the predetermined focus length and the predetermined zoom setting as indexes for the lookup table previously created during the calibration process, a controller in the imaging scanner 50 can select a sub-table from the lookup table that includes multiple entries each indentifying a paired relationship between a feature-size of the aiming cross-wire and a corresponding the distance "d" between the target object 45 and the imaging scanner 50.).
In regards to Claim 12, Patil as modified by Yang further teaches the device of claim 8, wherein the device is one of a mobile computer, a head-mounted display, a tablet, a smartphone, a camera, or a wearable computing device (Patil: Par. [0034]; an embodiment can be implemented as a computer-readable storage medium having computer readable code stored thereon for programming a computer (e.g., comprising a processor) to perform a method as described and claimed herein; Yang: Par. [0061]; In some aspects, the image may be supplied via a lower cost/lower resolution (e.g., less than 12 megapixel) camera such as a mobile phone camera, for instance.); and the at least one attribute of the imaging assembly is a depth of field (Patil: Par. [0018]; Generally, the imaging sensor 62 and the imaging lens arrangement 60 are designed to operate together for capturing light scattered or reflected from a barcode 40 as pixel data over a two-dimensional imaging field of view (FOV).).
In regards to Claim 13, Patil as modified by Yang further teaches the device of claim 8, wherein the instructions, when executed, cause the one or more processors to process the current label by: decoding an identifier corresponding to a predetermined symbology and/or barcode data structure; or utilizing character recognition to recognize an identifier corresponding to a predetermined character string structure (Yang: Par. [0079]; Fig. 7 is a table 700 of example barcode types that may be detected and decoded, in accordance with aspects of the present disclosure. As shown in table 700, one-dimensional barcodes (e.g., as a uniform product code (UPC) or a European article number (EAN)), as well as two-dimensional barcodes (e.g., quick response (QR) codes) may be detected and decoded. The barcodes may vary in length and pattern or shape. However, the example barcodes in table 700 is not an exhaustive list of barcodes that may be detected and decoded. Rather, aspects of the present disclosure are extensible such that different formats including different lengths and composed of alphanumeric and other characters, for example, in accordance with current and future barcode formats (of any dimension number) may be detected and decoded; Par. [0064]; The barcode decoding module 506 may decode the input 502 at the location of the one or more bounding boxes to generate an output 508 including a set of characters corresponding to the one or more barcodes included in the input 502. A character may, for example, refer to a digit, an alpha-numeric character, a special character, a mathematical symbol, or the like.).
Regarding Claim 15:
Patil as modified by Yang further teaches a non-transitory computer-readable medium storing instructions thereon that, when executed by one or more processors, cause the one or more processors to: receive a current image (Yang: Par. [0009]; a non-transitory computer-readable medium having program code recorded thereon is disclosed. The program code is executed by a processor and includes program code to receive, by an artificial neural network (ANN), an image.), captured by an imaging assembly utilizing a current focus setting (Patil: Par. [0027]; The first pixel data is then processed to estimate a feature-size (e.g., the width "w" or the height "h") in the image of the target object when the imaging lens arrangement is at a predetermined focus length and/or at a predetermined zoom setting. Using the value of the predetermined focus length and the predetermined zoom setting as indexes for the lookup table previously created during the calibration process, a controller in the imaging scanner 50 can select a sub-table from the lookup table that includes multiple entries each indentifying a paired relationship between a feature-size of the aiming cross-wire and a corresponding the distance "d" between the target object 45 and the imaging scanner 50.), of area (Patil: Par. [0018]; The imaging sensor 62 is operative to detect light captured by an imaging lens arrangement 60 along an optical path or axis 61 through the window 56. Generally, the imaging sensor 62 and the imaging lens arrangement 60 are designed to operate together for capturing light scattered or reflected from a barcode 40 as pixel data over a two-dimensional imaging field of view (FOV).), the area having one or more labels present therein (Patil: [0021]; In many embodiments, the controller 90 also includes a decoder for decoding one or more barcodes that are within the imaging field of view (FOV) of the imaging scanner 50. In some implementations, the barcode 40 can be decoded by digitally processing a captured image of the barcode with a microprocessor.); determine a first distance between the device and one or more labels present in the current image based on the current focus setting of the imaging assembly during capture of the current image (Patil: Par. [0025]; The width "w" and the height "h" of the aiming cross-wire, however, generally remains at the same constant, if the distance "d" between the target object 45 and the imaging scanner 50 is kept at a constant, for the same the focus lengths "F" and the same zoom settings "Z" of the imaging lens arrangement 60. Consequently, for an imaging lens arrangement 60 that has variable focuses and/or more than one zoom setting, if the focus lengths "F" and the same zoom settings "Z" are known or predetermined, it would be possible to use the feature-size of the aiming cross-wire in the pixel data (e.g., the width "w" or the height "h") to determine the distance "d" between the target object 45 and the imaging scanner 50.); detect, utilizing a trained machine learning model (Yang: Par. [0069]; The decoding module 528 may comprise a convolutional neural network (CNN), for example. As shown in FIG. 5B, the decoding module 528 may include a CNN feature extractor 528a.), the one or more labels present in the current image, each label having one or more identifiers (Patil: Par. [0021-0022]; In many embodiments, the controller 90 also includes a decoder for decoding one or more barcodes that are within the imaging field of view (FOV) of the imaging scanner 50. In some implementations, the barcode 40 can be decoded by digitally processing a captured image of the barcode with a microprocessor; The captured image of the barcode 40 is transferred to the controller 90 as pixel data. Such pixel data is digitally processed by the decoder in the controller 90 to decode the barcode. The information obtained from decoding the barcode 40 is then stored in the memory 94 or sent to other devices for further processing.); determine whether the one or more labels are assessed; responsive to determining the one or more labels are not assessed (Yang: Par. [0073]; In block 606, the image may be evaluated (e.g., region by region) to determine if a bounding box (e.g., a barcode) has been detected. If a bounding box has not been detected, the process 600 may return to block 604 to continue processing the image.), determine, for a current label among the one or more labels, a second distance between the device and the current label based on a known size of the current label and/or feature of the current label (Patil: Par. [0025]; The width "w" and the height "h" of the aiming cross-wire, however, generally remains at the same constant, if the distance "d" between the target object 45 and the imaging scanner 50 is kept at a constant, for the same the focus lengths "F" and the same zoom settings "Z" of the imaging lens arrangement 60. Consequently, for an imaging lens arrangement 60 that has variable focuses and/or more than one zoom setting, if the focus lengths "F" and the same zoom settings "Z" are known or predetermined, it would be possible to use the feature-size of the aiming cross-wire in the pixel data (e.g., the width "w" or the height "h") to determine the distance "d" between the target object 45 and the imaging scanner 50.); determine whether the current label is in focus based on the first distance, the second distance of the current label, and at least one attribute of the imaging assembly; and responsive to determining the current label is in focus, process the current label (Patil: Par. [0019]; Some of the imaging scanners can include an auto-focus system to enable a barcode be more clearly imaged with the imaging sensor 62 based on the measured distance of this barcode. In some implementations of the auto-focus system, the focus length of the imaging lens arrangement 60 is adjusted based on the measured distance of the barcode. In some other implementations of the auto-focus system, the distance between the imaging lens arrangement 60 and the imaging sensor 62 is adjusted based on the measured distance of the barcode.).
In regards to Claim 16, Patil as modified by Yang further teaches the non-transitory computer-readable medium of claim 15, wherein the instructions, when executed, further cause the one or more processors to detect, utilizing the trained machine learning model, the one or more labels present in the current image by: generating a bounding box corresponding to each label (Yang: Par. [0069]; The CNN feature extractor 528a may extract features of the detected bounding boxes. That is, the CNN feature extractor 528a processes the bounding boxes (e.g., 526a 526b), extracting features of the image 522 at the location of the bounding boxes (e.g., 526a 526b); Par. [0064]; The barcode decoding module 506 may extract features of the input 502 at the location of the one or more bounding boxes. The barcode decoding module 506 may decode the input 502 at the location of the one or more bounding boxes to generate an output 508 including a set of characters corresponding to the one or more barcodes included in the input 502. A character may, for example, refer to a digit, an alpha-numeric character, a special character, a mathematical symbol, or the like); and determining a pixel size of each label based on a pixel length and/or a pixel height of the bounding box (Patil: Par. [0025] and Fig. 5C; The pixel data, as shown in FIG. 5B or FIG. 5C, includes the image of the aiming cross-wire with a width "w" and a height "h"; the pixel data has a horizontal resolution "X" and vertical resolution "Y").
In regards to Claim 17, Patil as modified by Yang further teaches the non-transitory computer-readable medium of claim 15, wherein the instructions, when executed, further cause the one or more processors to train a machine learning model to detect the one or more labels based on at least one of historical data including one or more previously detected labels or datasets including images of one or more label types, each label type having a known size (Patil: Par. [0027]; The first pixel data is then processed to estimate a feature-size (e.g., the width "w" or the height "h") in the image of the target object when the imaging lens arrangement is at a predetermined focus length and/or at a predetermined zoom setting. Using the value of the predetermined focus length and the predetermined zoom setting as indexes for the lookup table previously created during the calibration process, a controller in the imaging scanner 50 can select a sub-table from the lookup table that includes multiple entries each indentifying a paired relationship between a feature-size of the aiming cross-wire and a corresponding the distance "d" between the target object 45 and the imaging scanner 50.).
In regards to Claim 18, Patil as modified by Yang further teaches the non-transitory computer-readable medium of claim 15, wherein the instructions, when executed, further cause the one or more processors to process the current label by: decoding an identifier corresponding to a predetermined symbology and/or barcode data structure; or utilizing character recognition to recognize an identifier corresponding to a predetermined character string structure (Yang: Par. [0079]; Fig. 7 is a table 700 of example barcode types that may be detected and decoded, in accordance with aspects of the present disclosure. As shown in table 700, one-dimensional barcodes (e.g., as a uniform product code (UPC) or a European article number (EAN)), as well as two-dimensional barcodes (e.g., quick response (QR) codes) may be detected and decoded. The barcodes may vary in length and pattern or shape. However, the example barcodes in table 700 is not an exhaustive list of barcodes that may be detected and decoded. Rather, aspects of the present disclosure are extensible such that different formats including different lengths and composed of alphanumeric and other characters, for example, in accordance with current and future barcode formats (of any dimension number) may be detected and decoded; Par. [0064]; The barcode decoding module 506 may decode the input 502 at the location of the one or more bounding boxes to generate an output 508 including a set of characters corresponding to the one or more barcodes included in the input 502. A character may, for example, refer to a digit, an alpha-numeric character, a special character, a mathematical symbol, or the like.).
Claims 7, 14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Patil (U.S. Patent No. 2014/0008440 A1, hereafter referred as Patil) in view of Yang et al. (U.S. Patent App. Pub No. 2023/0325618 A1, hereafter referred as Yang) and Patel et al. (U.S. Patent App. Pub No. 2005/0006477 A1, hereafter referred as Patel).
In regards to Claim 7, Patil as modified by Yang fails to further teach the method of claim 1, further comprising: responsive to determining the current label is not in focus, adding the current label and the second distance of the current label to a list of unprocessed labels; determining whether the one or more labels are assessed; responsive to determining the one or more labels are assessed, determining and setting, based on the list of unprocessed labels, another focus setting of the device for a next image capture.
Patel, like Patil, is directed to decoding barcodes. Patel in combination with Yang does teach responsive to determining the current label is not in focus, adding the current label and the second distance of the current label to a list of unprocessed labels (Yang: Par. [0073]; In block 606, the image may be evaluated (e.g., region by region) to determine if a bounding box (e.g., a barcode) has been detected. If a bounding box has not been detected, the process 600 may return to block 604 to continue processing the image.); determining whether the one or more labels are assessed; responsive to determining the one or more labels are assessed, determining and setting, based on the list of unprocessed labels, another focus setting of the device for a next image capture (Patel: Par. [0026]; If the barcode symbol is not successfully processed, the actuator 114 is actuated to obtain a different positional setting of the carrier 105 along the optical axis 108, in an effort to accurately or substantially focus the optical code or target onto the image sensor array 104. The actuator 114 is manually actuated by the operator, e.g., pressing a trigger button on a barcode imager (see FIG. 4), or automatically by a processor upon realizing the barcode symbol was not successfully processed.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Patil to utilize the refocusing technique, as taught by Patel, to arrive at the claimed invention discussed above. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. As taught by Patel, the proposed modification allows for a plurality of focal planes traversing an optical axis and along an extended working range of the imaging arrangement (Patel: Par. [0007]).
In regards to Claim 14, Patil as modified by Yang and Patel further teaches the device of claim 8, wherein responsive to determining the current label is not in focus, the instructions, when executed, further cause the one or more processors to: add the current label and the second distance of the current label to a list of unprocessed labels (Yang: Par. [0073]; In block 606, the image may be evaluated (e.g., region by region) to determine if a bounding box (e.g., a barcode) has been detected. If a bounding box has not been detected, the process 600 may return to block 604 to continue processing the image.); determine whether the one or more labels are assessed; responsive to determining the one or more labels are assessed, determine and set, based on the list of unprocessed labels, another focus setting of the device for a next image capture (Patel: Par. [0026]; If the barcode symbol is not successfully processed, the actuator 114 is actuated to obtain a different positional setting of the carrier 105 along the optical axis 108, in an effort to accurately or substantially focus the optical code or target onto the image sensor array 104. The actuator 114 is manually actuated by the operator, e.g., pressing a trigger button on a barcode imager (see FIG. 4), or automatically by a processor upon realizing the barcode symbol was not successfully processed.).
In regards to Claim 19, Patil as modified by Yang and Patel further teaches the non-transitory computer-readable medium of claim 15, wherein responsive to determining the current label is not in focus, the instructions, when executed, further cause the one or more processors to: add the current label and the second distance of the current label to a list of unprocessed labels (Yang: Par. [0073]; In block 606, the image may be evaluated (e.g., region by region) to determine if a bounding box (e.g., a barcode) has been detected. If a bounding box has not been detected, the process 600 may return to block 604 to continue processing the image.); determine whether the one or more labels are assessed; responsive to determining the one or more labels are assessed, determine and set, based on the list of unprocessed labels, another focus setting of the device for a next image capture (Patel: Par. [0026]; If the barcode symbol is not successfully processed, the actuator 114 is actuated to obtain a different positional setting of the carrier 105 along the optical axis 108, in an effort to accurately or substantially focus the optical code or target onto the image sensor array 104. The actuator 114 is manually actuated by the operator, e.g., pressing a trigger button on a barcode imager (see FIG. 4), or automatically by a processor upon realizing the barcode symbol was not successfully processed.).
Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dahari (U.S. Patent App. Pub No. 2012/0048937 A1) teaches a barcode detection method and system which is capable of scanning an area including one or more barcodes, detecting and decoding the barcodes in the scanned area and generating a batch of barcodes all in a single scanning operation.
Conclusion
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/RENAE A BITOR/Examiner, Art Unit 2663
/GREGORY A MORSE/Supervisory Patent Examiner, Art Unit 2698
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