DETAILED ACTION
This Office action is in response to the Application filed on May 9, 2024, which claims benefit of U.S. Provisional Application No. 63/500965, filed on May 9, 2023. An action on the merits follows. Claims 1-20 are pending on the application.
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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-4, 6-7, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over IWAI et al. (US PG Publication No. 2021/0004951 A1), hereafter referred to as IWAI, in view of LEE et al. (WO Publication No. 2021/049868 A1), hereafter referred to as LEE, in further view of Olm et al. (US PG Publication No. 2010/0155679 A1), hereafter referred to as Olm.
Regarding claim 1, IWAI discloses a method (Par. [0006]: inspection apparatus that performs image processing using an image processing algorithm… inspects presence of a foreign matter contained in the article based on an image processing result, including a storage unit configured to store the image processing algorithms) comprising:
(a) controlling, by one or more processors, an X-ray machine to capture an image of a good (Par. [0024-25]: in FIG. 1, an X-ray inspection apparatus (inspection apparatus) 1 includes an apparatus body 2… an X-ray irradiation unit 6, an X-ray detector 7, a display operating unit 8, and a controller 20… X-ray inspection apparatus 1 generates X-ray transmission images of articles; Par. [0031-35]: controller 20 is disposed inside the apparatus body 2. The controller 20 controls the operation of various units of the X-ray inspection apparatus 1. The controller 20 is made up of a central processing unit (CPU)… controller 20 can control the various operations of the X-ray inspection apparatus 1… controller 20, as illustrated in FIG. 3, includes a storage unit 21, and an acquisition unit 22, an image processor 23, an evaluation unit 24, a setting unit 25, and a foreign-matter determination unit 28, which are configured by reading in predetermined computer software on hardware such as the CPU, the RAM, and the like and being executed under the control of the CPU… acquisition unit 22 acquires a transmission image P11 (see FIG. 4) that is obtained by transmitting X-rays through the article A (see FIG. 4) that may contain a foreign matter F; Par. [0041-46]: acquisition unit 22 irradiates an article A with the X-rays, the article A containing foreign matters F… transported by the belt conveyor as the transport unit 5, and acquires the transmission image P11 by detecting the X-rays that were transmitted through the article A… As illustrated in FIG. 5A, the acquisition unit 22 acquires a transmission image PF of a plurality of foreign matters F; controlling, by one or more processors, an X-ray machine to capture an image of a good (e.g. X-ray inspection apparatus (i.e. system, machine, device, etc.) for inspecting presence of a foreign matter (i.e. a foreign material) to be detected from an article (i.e. a good, an item, a product, etc.) to be inspected includes a controller (i.e. a processor, CPU, computer, etc.) and an image processor (i.e. one or more processors) that control the operation (i.e. method, process, algorithm, steps, etc.) of the X-ray inspection apparatus (i.e. controlling, by one or more processors, an X-ray machine), for example, including acquiring (i.e. capturing, obtaining, etc.) a transmission image by detecting X-rays transmitted through the article (i.e. controlling, by one or more processors, an X-ray machine to capture an image of a good), as indicated above), for example);
(b) analyzing, by the one or more processors, the image captured by the X-ray machine by applying a foreign material detection algorithm to the image; and
(c) determining, by the one or more processors, whether the good includes a foreign material by detecting the foreign material when analyzing the image (Par. [0006-7]: inspection apparatus that performs image processing using an image processing algorithm… inspects presence of a foreign matter contained in the article based on an image processing result, including a storage unit configured to store the image processing algorithms… evaluate detection accuracy of the foreign matter for each of the image processing algorithms based on evaluation images obtained by processing the reference transmission image with the image processing algorithms stored in the storage unit… The foreign matter contained in the reference transmission image acquired by the acquisition unit includes a test piece (specimen) corresponding to the foreign matter. In the inspection apparatus of this configuration, on the basis of the evaluation images obtained by processing the reference transmission image by the respective image processing algorithms stored in the storage unit, the detection accuracy of the foreign matter for each of the image processing algorithms is evaluated; Par. [0032-37]: controller 20, as illustrated in FIG. 3, includes a storage unit 21, and an acquisition unit 22, an image processor 23… The storage unit 21 stores therein a plurality of image processing algorithms… acquisition unit 22 acquires a transmission image P11 (see FIG. 4) that is obtained by transmitting X-rays through the article A (see FIG. 4) that may contain a foreign matter F. The image processor 23 performs, on the transmission image P11, image processing using an image processing algorithm… and generates a processed image P2 that is a grayscale image… foreign-matter determination unit 28 performs inspection of the article A based on the processed image P2 that has been image-processed by the image processor 23. Specifically, the foreign-matter determination unit 28 determines the presence of the foreign matter F; Par. [0043-44]: acquisition unit 22 irradiates foreign matters F with the X-rays, the foreign matters F being transported by the belt conveyor as the transport unit 5, and acquires the foreign-matter transmission image PF by detecting the X-rays that were transmitted through the foreign matters F… image processing by each image processing algorithm can be performed… each image processing algorithm can be evaluated by whether the foreign matter can be properly detected from the image processing result of each image processing algorithm; analyzing, by the one or more processors, the image captured by the X-ray machine by applying a foreign material detection algorithm to the image; and
determining, by the one or more processors, whether the good includes a foreign material by detecting the foreign material when analyzing the image (e.g. X-ray inspection apparatus (i.e. system, machine, device, etc.) for inspecting presence of a foreign matter (i.e. a foreign material, substance, composition, etc.) to be detected from an article (i.e. a good, an item, a product, etc.) to be inspected includes a controller (i.e. a processor, CPU, computer, etc.) and an image processor (i.e. one or more processors) that control the operation of the X-ray inspection apparatus, for example, including inspecting (i.e. analyzing) presence of a foreign matter contained (i.e. included) in an article based on an image processing algorithm (i.e. a foreign material detection algorithm) result, for example, including acquiring (i.e. capturing, obtaining, etc.) a transmission image by detecting X-rays transmitted through the article (i.e. the image captured by the X-ray machine), for example, and performing, on the transmission image, image processing using the image processing algorithm (i.e. analyzing, by the one or more processors, the image captured by the X-ray machine by applying a foreign material detection algorithm to the image), in order to detect foreign matter from the image processing result of the image processing algorithm (i.e. determining, by the one or more processors, whether the good includes a foreign material by detecting the foreign material when analyzing the image), as indicated above), for example), but fails to teach the following as further recited in claim 1.
However, LEE teaches applying a foreign material model to the image (Par. [1]: product quality management system and method capable of analyzing an image of a product to be inspected through machine learning based on a learning model to detect a quantity for each foreign substance… thereby determining the quality of a product to be inspected and informing a worker of the determination result; Par. [14-25]: the quality of a product is managed in such a manner that a worker directly checks whether there is a foreign object in a bag… an analysis engine unit configured to analyze the image based on the production information stored in the database table to detect a foreign object and a quantity of each of the objects from the product to be inspected … product quality management system according to an embodiment of the present disclosure may further include a learning model building unit configured to collect, as learning data, an image obtained by passing through X-ray equipment in a state in which real foreign substances are inserted into a normal product, and construct a learning model by performing machine learning based on the learning data and the production information, wherein the analysis engine unit may analyze the image based on the learning model… photographing, by an image capturing unit of the product quality management system, an image of the product to be inspected; and analyzing, by an analysis engine unit of the product quality management system, the image based on production information stored in the database table to detect a foreign object and a quantity of each of the objects from the product to be inspected… determining, by a quality management unit of the product quality management system, the quality of the product to be inspected as any one of a normal grade and a defect grade based on a result of detecting the quantity of the foreign matter; Par. [44-45]: image capturing unit 120 May capture an X-ray image of the product to be inspected. To this end, the image capturing unit 120 May be implemented as X-ray equipment. That is, the image capturing unit 120 May capture an X ray image of the product to be inspected through X-ray equipment and output the X-ray image to the analysis engine unit 140… learning model constructer 130 May collect the X-ray image obtained by passing through the X-ray equipment in a state in which the actual foreign substances are inserted into the normal product as training data; Par. [52-64]: analysis engine unit 140 May analyze the X-ray image by interworking with the learning model construction unit 130 in order to improve the analysis efficiency of the X-ray image… That is, the analysis engine unit 140 May analyze the X-ray image based on the learning model constructed by the learning model construction unit 130, and detect the number of foreign substances… from the inspection target product based on the analysis result of the X-ray image… The analysis engine unit 140 May image a detection result of the foreign object… and transmit the detected object image to the worker monitor 101… control unit 150 May generally control operations of the product quality management system 100, that is, the memory unit 110, the image photographing unit 120, the learning model construction unit 130, and the analysis engine unit 140… the quality manager 350 May determine the quality of the product to be inspected as a defect grade when the foreign object is detected (foreign object detection) or the number of detected objects is not matched with the normal quantity as a result of the detection of the amount of the foreign object; applying a foreign material model to the image (e.g. product quality management system and method for analyzing an image of a product (i.e. a good, an article, etc.) to be inspected through machine learning based on a learning model to detect a quantity for each foreign matter, substances, or objects (i.e. a foreign material model) inserted (i.e. included, contained, etc.) into a product, for example, include capturing an X-ray image of the product to be inspected through X-ray equipment and output the X-ray image to an analysis engine unit that analyzes the image based on the learning model (i.e. applying a foreign material model to the image) in order to detect a number of foreign matter, substances, or objects, as indicated above), for example).
IWAI and LEE are considered to be analogous art because they pertain to image processing applications. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the inspection apparatus for inspecting presence of a foreign matter to be detected from an article by using an image processing algorithm (as disclosed by IWAI) with applying a foreign material model to the image (as taught by LEE, Par. [1, 14-25, 44-45, 52-64]) to check whether there is a foreign object in a bag or a process, to provide a product quality management system and method capable of determining the quality of a product to be inspected and informing a worker of the result of the determination by analyzing an image of the product to be inspected through machine learning based on a learning model to detect the number of foreign substances, to improve the efficiency of product inspection, to improve the analysis efficiency of an X-ray image, and to enhance the ability to analyze the image of the product to be inspected, thereby improving the efficiency of a worker (LEE, Par. [12-15, 26-27, 52, 56 ]).
The combination of IWAI and LEE, as a whole, teaches the inspection apparatus for inspecting presence of a foreign matter to be detected from an article by using an image processing algorithm, as indicated above, but fails to teach the following as further recited in claim 1.
However, Olm teaches determining whether the good includes foreign material comprising a challenge card by detecting at least one marking component on the foreign material when analyzing the image, wherein the challenge card comprises the at least one marking component and a challenge material (Par. [0007]: method of authentication is the detection of specific reflective, absorptive, or emissive responses of marker materials. Emissive materials are common as security markers; Par. [0022-23]: invention provides an security marker material comprising emissive particles which can be grouped into… groups with different size distributions… a security system including a security marker material comprising emissive particles which can be grouped into… groups with different size distributions, placing the security marker in or on an item, exciting the security marker with electromagnetic radiation in one or more specified spectral bands, detecting electromagnetic radiation emitted by the security marker in a one or more specified spectral bands in an image-wise fashion, analyzing and characterizing attributes of the image, comparing the image attributes to preset authentication criteria; Par. [0035-39]: invention relates to emissive, particulate, security materials applied to an item and to the image-wise capture of light emanating from the marked item when it is irradiated with electromagnetic radiation of appropriate wavelength… Authentication of the item is contingent on evaluating the image of the emissive marker or markers and matching specific image characteristics to pre-determined criteria… marker particles are dispersed… or otherwise coated on or applied to the item to be authenticated. Examples of such items include labels, packaging materials, plastic laminates, and films… the presence of the security marker material on the item being authenticated is detected by illuminating the item with electromagnetic radiation in one or more spectral bands chosen specifically to excite the security marker, detecting electromagnetic radiation in a one or more spectral bands chosen to match the emission of the security marker material and detecting this emitted electromagnetic radiation in an image-wise fashion; Par. [0052]: the security marker material is chosen to contain at least two groups of emissive particles with different size distributions. The two particle groups are chosen so that the difference between their mean diameters is large enough to distinguish in the image of the particle emission as applied to the item to be authenticated. A useful figure-of-merit (FOM) has been empirically defined, which enables prediction of which groups of emissive marker particles can be combined to give a marker material with a unique particle emission image; determining whether the good includes foreign material comprising a challenge card by detecting at least one marking component on the foreign material when analyzing the image, wherein the challenge card comprises the at least one marking component and a challenge material (e.g. method of authentication provides security (i.e. challenge, authentication, interrogation, authorization, validation, identification, etc.) marker (i.e. challenge card, marking component, etc.) material (i.e. foreign material comprising a challenge card that comprises at least one marking component and a challenge material) applied to an item (i.e. good includes foreign material), for example, including detection of specific reflective, absorptive, or emissive responses of marker materials, for example, including detecting presence of a security marker material on an item being authenticated (i.e. determining whether the good includes foreign material comprising a challenge card by detecting at least one marking component on the foreign material), for example, by illuminating (i.e. radiating) the item with electromagnetic radiation (i.e. X-rays) in one or more spectral bands chosen specifically to excite the security marker, detecting electromagnetic radiation in a one or more spectral bands chosen to match the emission of the security marker material, and detecting this emitted electromagnetic radiation in an image-wise fashion (i.e. determining whether the good includes foreign material comprising a challenge card by detecting at least one marking component on the foreign material when analyzing the image, wherein the challenge card comprises the at least one marking component and a challenge material), for example, by detecting optical or magnetic properties of markers, as indicated above), for example).
IWAI, LEE, and Olm are considered to be analogous art because they pertain to image processing applications. Therefore, the combined teachings of IWAI, LEE, and Olm, as a whole, would have rendered obvious the invention recited in claim 1 with a reasonable expectation of success in order to modify the inspection apparatus for inspecting presence of a foreign matter to be detected from an article by using an image processing algorithm (as disclosed by IWAI) with determining whether the good includes foreign material comprising a challenge card by detecting at least one marking component on the foreign material when analyzing the image, wherein the challenge card comprises the at least one marking component and a challenge material (as taught by Olm, Abstract, Par. [0007, 22-23, 52]) to detect optical or magnetic properties of markers, in situ, without the need to alter or destroy the object on which they reside based on detection of specific reflective, absorptive, or emissive responses of marker materials, and to provide authentication of an item contingent on evaluating an image of an emissive marker or markers and matching specific image characteristics to pre-determined criteria (Olm, Abstract, Par. [0001-7, 19-23, 35, 52]).
Regarding claim 3, claim 1 is incorporated and the combination of IWAI, LEE, and Olm, as a whole, teaches the method (IWAI, Par. [0006]), wherein the at least one marking component is recognizable by the X-ray machine as an identifier of the challenge card or the challenge material (Olm, Par. [0001]: invention generally relates to emissive security markers and a method of authenticating these markers. It is specifically concerned with security markers applied at very low levels to an object which, when excited with light of appropriate wavelengths, emit radiation which produce a unique image, for authenticating and identifying the object. The marker image is related to the size, and size distribution of particulate security marker as applied to the object; Par. [0035-39]: invention relates to emissive, particulate, security materials applied to an item and to the image-wise capture of light emanating from the marked item when it is irradiated with electromagnetic radiation of appropriate wavelength… Authentication of the item is contingent on evaluating the image of the emissive marker or markers and matching specific image characteristics to pre-determined criteria… marker particles are dispersed… or otherwise coated on or applied to the item to be authenticated. Examples of such items include labels, packaging materials, plastic laminates, and films… the presence of the security marker material on the item being authenticated is detected by illuminating the item with electromagnetic radiation in one or more spectral bands chosen specifically to excite the security marker, detecting electromagnetic radiation in a one or more spectral bands chosen to match the emission of the security marker material and detecting this emitted electromagnetic radiation in an image-wise fashion; wherein the at least one marking component is recognizable by the X-ray machine as an identifier of the challenge card or the challenge material (e.g. method of authentication provides security (i.e. challenge, authentication, interrogation, authorization, validation, identification, etc.) marker (i.e. challenge card, marking component, etc.) material (i.e. foreign material comprising a challenge card that comprises at least one marking component and a challenge material) applied to an item (i.e. good includes foreign material), for example, including detection of specific reflective, absorptive, or emissive responses of marker materials, for example, including detecting presence of a security marker material (i.e. at least one marking component is recognizable as an identifier) on an item being authenticated, for example, by illuminating (i.e. radiating) the item with electromagnetic radiation (i.e. X-rays) in one or more spectral bands chosen specifically to excite the security marker, detecting electromagnetic radiation in a one or more spectral bands chosen to match the emission of the security marker material, and detecting this emitted electromagnetic radiation in an image-wise fashion (i.e. wherein the at least one marking component is recognizable by the X-ray machine as an identifier of the challenge card or the challenge material), for example, by detecting optical or magnetic properties of markers, as indicated above), for example).
The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1.
Regarding claim 4, claim 1 is incorporated and the combination of IWAI, LEE, and Olm, as a whole, teaches the method (IWAI, Par. [0006]), wherein the at least one marking component comprises at least one of:
(a) at least two marking components positioned in a particular arrangement;
(b) a particular shape of the at least one marking component;
(c) a particular material comprising different X-ray absorption properties than the challenge material; and
(d) at least two marking components (Olm, Par. [0018]: approach used to increase the security of marked items is to combine multiple markers, in specific ratios; Par. [0035]: emissive, particulate, security materials applied to an item and to the image-wise capture of light emanating from the marked item when it is irradiated with electromagnetic radiation of appropriate wavelength. (While the word "light" is used herein, the term is not meant to exclude wavelengths outside the visible spectrum.) Authentication of the item is contingent on evaluating the image of the emissive marker or markers and matching specific image characteristics to pre-determined criteria; at least two marking components (e.g. method of authentication provides security (i.e. challenge, authentication, interrogation, authorization, validation, identification, etc.) marker (i.e. challenge card, marking component, etc.) material applied to an item (i.e. good includes foreign material), for example, including detection of specific reflective, absorptive, or emissive responses of marker materials, for example, in which authentication of an item is contingent on evaluating the image of the emissive marker or markers (i.e. at least two marking components) and matching specific image characteristics to pre-determined criteria as indicated above), for example).
The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1.
Regarding claim 6, claim 4 is incorporated and the combination of IWAI, LEE, and Olm, as a whole, teaches the method (IWAI, Par. [0006]), wherein determining that the good includes the challenge card comprises determining, by the one or more processors, that the particular arrangement of the plurality of marking components matches a predefined challenge arrangement (Olm, Par. [0007]: method of authentication is the detection of specific reflective, absorptive, or emissive responses of marker materials. Emissive materials are common as security markers; Par. [0022-23]: invention provides an security marker material comprising emissive particles which can be grouped into… groups with different size distributions… a security system including a security marker material comprising emissive particles which can be grouped into… groups with different size distributions, placing the security marker in or on an item, exciting the security marker with electromagnetic radiation in one or more specified spectral bands, detecting electromagnetic radiation emitted by the security marker in a one or more specified spectral bands in an image-wise fashion, analyzing and characterizing attributes of the image, comparing the image attributes to preset authentication criteria; Par. [0035-39]: invention relates to emissive, particulate, security materials applied to an item and to the image-wise capture of light emanating from the marked item when it is irradiated with electromagnetic radiation of appropriate wavelength… Authentication of the item is contingent on evaluating the image of the emissive marker or markers and matching specific image characteristics to pre-determined criteria… marker particles are dispersed… or otherwise coated on or applied to the item to be authenticated. Examples of such items include labels, packaging materials, plastic laminates, and films… the presence of the security marker material on the item being authenticated is detected by illuminating the item with electromagnetic radiation in one or more spectral bands chosen specifically to excite the security marker, detecting electromagnetic radiation in a one or more spectral bands chosen to match the emission of the security marker material and detecting this emitted electromagnetic radiation in an image-wise fashion; Par. [0052]: the security marker material is chosen to contain at least two groups of emissive particles with different size distributions. The two particle groups are chosen so that the difference between their mean diameters is large enough to distinguish in the image of the particle emission as applied to the item to be authenticated. A useful figure-of-merit (FOM) has been empirically defined, which enables prediction of which groups of emissive marker particles can be combined to give a marker material with a unique particle emission image; wherein determining that the good includes the challenge card comprises determining, by the one or more processors, that the particular arrangement of the plurality of marking components matches a predefined challenge arrangement (e.g. method of authentication provides security (i.e. challenge, authentication, interrogation, authorization, validation, identification, etc.) marker (i.e. challenge card, marking component, etc.) material applied to an item (i.e. good includes foreign material), for example, including detection of specific reflective, absorptive, or emissive responses of marker materials, for example, in which authentication of an item is contingent on evaluating the image of the emissive marker or markers (i.e. at least two marking components) and matching specific image characteristics to pre-determined criteria (i.e. wherein determining that the good includes the challenge card comprises determining, by the one or more processors, that the particular arrangement of the plurality of marking components matches a predefined challenge arrangement), as indicated above), as indicated above), for example).
The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1.
Regarding claim 7, claim 1 is incorporated and the combination of IWAI, LEE, and Olm, as a whole, teaches the method (IWAI, Par. [0006]), wherein each of the at least one marking components has different X-ray absorption properties than the challenge material,
wherein each of the at least one marking components comprises a material that absorbs a greater percentage of X-rays emitted by the X-ray machine than the challenge material (Olm, Par. [0007]: method of authentication is the detection of specific reflective, absorptive, or emissive responses of marker materials. Emissive materials are common as security markers; Par. [0022-23]: invention provides an security marker material comprising emissive particles which can be grouped into… groups with different size distributions… a security system including a security marker material comprising emissive particles which can be grouped into… groups with different size distributions, placing the security marker in or on an item, exciting the security marker with electromagnetic radiation in one or more specified spectral bands, detecting electromagnetic radiation emitted by the security marker in a one or more specified spectral bands in an image-wise fashion, analyzing and characterizing attributes of the image, comparing the image attributes to preset authentication criteria; Par. [0035-39]: invention relates to emissive, particulate, security materials applied to an item and to the image-wise capture of light emanating from the marked item when it is irradiated with electromagnetic radiation of appropriate wavelength… Authentication of the item is contingent on evaluating the image of the emissive marker or markers and matching specific image characteristics to pre-determined criteria… marker particles are dispersed… or otherwise coated on or applied to the item to be authenticated. Examples of such items include labels, packaging materials, plastic laminates, and films… the presence of the security marker material on the item being authenticated is detected by illuminating the item with electromagnetic radiation in one or more spectral bands chosen specifically to excite the security marker, detecting electromagnetic radiation in a one or more spectral bands chosen to match the emission of the security marker material and detecting this emitted electromagnetic radiation in an image-wise fashion; Par. [0052]: the security marker material is chosen to contain at least two groups of emissive particles with different size distributions. The two particle groups are chosen so that the difference between their mean diameters is large enough to distinguish in the image of the particle emission as applied to the item to be authenticated. A useful figure-of-merit (FOM) has been empirically defined, which enables prediction of which groups of emissive marker particles can be combined to give a marker material with a unique particle emission image; wherein each of the at least one marking components has different X-ray absorption properties than the challenge material, wherein each of the at least one marking components comprises a material that absorbs a greater percentage of X-rays emitted by the X-ray machine than the challenge material (e.g. method of authentication provides security (i.e. challenge, authentication, interrogation, authorization, validation, identification, etc.) marker (i.e. challenge card, marking component, etc.) material applied to an item (i.e. good includes foreign material), for example, including detection of specific reflective, absorptive, or emissive responses of marker materials, for example, including detecting presence of a security marker material on an item being authenticated, for example, by illuminating (i.e. radiating) the item with electromagnetic radiation (i.e. X-rays) in one or more spectral bands chosen specifically to excite the security marker (i.e. wherein each of the at least one marking components has different X-ray absorption properties than the challenge material), detecting electromagnetic radiation in a one or more spectral bands chosen to match the emission of the security marker material (i.e. wherein each of the at least one marking components comprises a material that absorbs a greater percentage of X-rays emitted by the X-ray machine than the challenge material), and detecting this emitted electromagnetic radiation in an image-wise fashion, as indicated above), for example).
The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1.
Regarding claim 19, is a corresponding apparatus claim rejected as applied to the method claim 1 above.
Regarding claim 20, is a corresponding computer readable medium claim rejected as applied to the method claim 1 above. The recited steps of claim 20 correspond to claim 1 when executed.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over IWAI, in view, in further view of Olm, as applied to claim 1, and in further view of Kabumoto et al. (US PG Publication No. 2010/0135459 A1), hereafter referred to as Kabumoto.
Regarding claim 2, claim 1 is incorporated and the combination of IWAI, LEE, and Olm, as a whole, teaches the method (IWAI, Par. [0006]), further comprising:
determining, by the one or more processors and based on the analyzing of the image, that the good includes the foreign material, but fails to teach the following as further recited in claim 2.
However, Kabumoto teaches and one or more of
(i) outputting, by the one or more processors, a signal to a notification device indicating that the good should be removed from production (Par. [0031]: FIG. 2B, a sorting device 30 is provided downstream from the X-ray inspection device 10. The X-ray inspection device 10 detects the presence of foreign material or other errors, and any end product M regarded as a failure or test piece P; Par. [0079-80]: determining part 12C retrieves the third threshold value from the third storage part 13E. When portions exceeding the third threshold value are not present in the X-ray transmission image of the end product M, the determining part 12C determines that the end product M to be an acceptable article that is not contaminated with foreign matter… Conversely, when portions that exceed the third threshold value are present in the X-ray transmission image, the end product M is determined to be an unacceptable article that is contaminated with foreign matter. The determining part 12C transmits a sorting signal corresponding to the unacceptable product M to the sorting device 30 via the local control device 25, and stores the captured X-ray transmission image of the unacceptable product M in the NG image storage part 13B. Upon receiving the sorting signal, the sorting device 30 removes the unacceptable end product M corresponding to the sorting signal from the production line; outputting, by the one or more processors, a signal to a notification device indicating that the good should be removed from production (e.g. X-ray inspection device detects presence of foreign material or other errors, and any end product regarded as a failure or test piece, for example, and if an article (i.e. god, item, product, etc.) is determined to be unacceptable by being contaminated with foreign matter, for example, transmit (i.e. output, send, etc.) a sorting signal corresponding to the unacceptable product to the sorting device via the local control device (i.e. outputting, by the one or more processors, a signal to a notification device indicating that the good should be removed from production), as indicated above), for example) and
(ii) controlling, by the one or more processors, a transport system to automatically remove the good from production (Par. [0031]: FIG. 2B, a sorting device 30 is provided downstream from the X-ray inspection device 10. The X-ray inspection device 10 detects the presence of foreign material or other errors, and any end product M regarded as a failure or test piece P; Par. [0079-83]: determining part 12C retrieves the third threshold value from the third storage part 13E. When portions exceeding the third threshold value are not present in the X-ray transmission image of the end product M, the determining part 12C determines that the end product M to be an acceptable article that is not contaminated with foreign matter… Conversely, when portions that exceed the third threshold value are present in the X-ray transmission image, the end product M is determined to be an unacceptable article that is contaminated with foreign matter. The determining part 12C transmits a sorting signal corresponding to the unacceptable product M to the sorting device 30 via the local control device 25, and stores the captured X-ray transmission image of the unacceptable product M in the NG image storage part 13B. Upon receiving the sorting signal, the sorting device 30 removes the unacceptable end product M corresponding to the sorting signal from the production line… determining part 12C stores the X-ray transmission image of the test piece P in the NG image storage part 13B, and causes the sorting device 30 to remove the test piece P from the production line; and controlling, by the one or more processors, a transport system to automatically remove the good from production (e.g. X-ray inspection device detects presence of foreign material or other errors, and any end product regarded as a failure or test piece, for example, and if an article (i.e. god, item, product, etc.) is determined to be unacceptable by being contaminated with foreign matter, for example, transmit (i.e. output, send, etc.) a sorting signal corresponding to the unacceptable product to the sorting device via the local control device (i.e. outputting, by the one or more processors, a signal to a notification device indicating that the good should be removed from production), for example, and upon receiving the sorting signal, the sorting device removes the unacceptable product corresponding to the sorting signal from the production line (i.e. and controlling, by the one or more processors, a transport system to automatically remove the good from production), as indicated above), for example).
IWAI, LEE, Olm, and Kabumoto are considered to be analogous art because they pertain to image processing applications. Therefore, the combined teachings of IWAI, LEE, Olm, and Kabumoto, as a whole, would have rendered obvious the invention recited in claim 2 with a reasonable expectation of success in order to modify the inspection apparatus for inspecting presence of a foreign matter to be detected from an article by using an image processing algorithm (as disclosed by IWAI) with and one or more of outputting, by the one or more processors, a signal to a notification device indicating that the good should be removed from production and controlling, by the one or more processors, a transport system to automatically remove the good from production (as taught by Kabumoto, Abstract, Par. [0031, 79-83]) to provide an X-ray inspection device and a production system that includes the X-ray inspection device in which the state of the X-ray inspection device can be assessed without stopping the production of the products by removing an unacceptable product from a production line without disrupting the flow of end products (Kabumoto, Abstract, Par. [0003-5, 31, 79-84]).
Claim 5, 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over IWAI, in view, in further view of Olm, as applied to claim 1, and in further view of DIAO et al. (US PG Publication No. 2019/0362480 A1), hereafter referred to as DIAO.
Regarding claim 5, claim 4 is incorporated and the combination of IWAI, LEE, and Olm, as a whole, teaches the method (IWAI, Par. [0006]), but fails to teach the following as further recited in claim 5.
However, DIAO teaches wherein the particular arrangement of the at least two marking components comprises one or more of:
(a) a placement of the at least two marking components in a ring around the challenge material at known rotations around the challenge material and in known ratios of relative size and distance,
(b) an arrangement of the at least two marking components on a first end of the challenge card, and wherein the challenge component is placed on a second end of the challenge card (Par. [0004-9]: detecting and using location and orientation of reference markers… method of performing automated object inspection, comprising: at a computing device (e.g., server system 152, FIG. 1B; server system 200, FIG. 2) having one or more processors and memory, obtaining a plurality of test images, each test image capturing a respective one of a plurality of composite objects that have been assembled in accordance with a predefined common layout for the plurality of composite objects. The predefined common layout specifies respective positions for two or more components of a respective composite object, and a predefined location and orientation for a reference marker, in the respective composite object… For each of the plurality of test images, the method comprises quantitatively determining a respective transformation from a predefined contour of the reference marker in the predefined common layout to a respective anchor contour corresponding to the reference marker as captured in the test image… the reference markers captured in the respective regularized versions of the plurality of test images share an image-independent location and orientation; Par. [0117-122]: transformation to at least a portion of the test image to obtain a respective regularized version of the corresponding portion of the test image, such that the reference markers captured in the respective regularized versions of the plurality of test images share an image-independent location and orientation… identifying the respective anchor contour from among the plurality of contours extracted from the test image further comprises: selecting the anchor contour from the two or more candidate contours based on relative closeness between the respective shape feature of each of the two or more candidate contours and a shape feature of the reference mark in the predefined common layout… the shape feature of the reference marker is learned by the computer using training images… and the portions of test images containing the candidate contours are cropped out and used as input for shape recognition. The candidate contour that is recognized as the reference marker by the computer is selected as the anchor contour… shape distances are computed between each candidate contour and the reference marker in the predefined common layout, and the candidate contour that has the smallest shape distance from the reference marker is selected as the anchor contour. Regardless of the technique that is used to identify the anchor contour from the candidate contour, the technique must be designed such that the identification can occur even when the reference markers in the test images are shifted and/or rotated relative to the position and orientation as indicated in the predefined common layout; an arrangement of the at least two marking components on a first end of the challenge card, and wherein the challenge component is placed on a second end of the challenge card (e.g. method of performing automated object inspection includes a predefined common layout that specifies respective positions for two or more components of a respective composite object, and a predefined location and orientation for a reference (i.e. challenge, authentication, interrogation, authorization, validation, identification, etc.) marker (i.e. challenge component), in the respective composite object, for example, including reference markers captured in test images that share an image-independent location and orientation (i.e. an arrangement of the at least two marking components), for example, and for each of the test images, the method comprises quantitatively determining a respective transformation from a predefined contour of the reference marker (i.e. a first end of the challenge card) in the predefined common layout to a respective anchor contour corresponding to the reference marker (i.e. a second end of the challenge card) as captured in the test image, for example, and the reference markers captured in the respective regularized versions of the plurality of test images share an image-independent location and orientation (i.e. an arrangement of the at least two marking components on a first end of the challenge card, and wherein the challenge component is placed on a second end of the challenge card), as indicated above), for example), and
(c) any geometrical shape around the challenge component (Par. [0083]: server system further extracts (438) an anchor contour (e.g., anchor contour 416, FIG. 4B) of the reference marker at position 402 in the standard sample image 412 using any suitable imaging processing technique. After determining the anchor contour 416, the server system also calculates (440) the acreage (A) of the anchor (e.g., the area enclosed by the anchor contour of the reference marker). Additionally, the server system determines and saves (442) shape features, such as the number of local maximum distances from the center point 420 of the anchor contour 416 to each point along the anchor contour 416. In some embodiments as illustrated in FIG. 4C, the server system extracts (444) a minimal enclosing rectangle 406 that encloses the anchor contour of the reference marker; Par. [0117-122]: transformation to at least a portion of the test image to obtain a respective regularized version of the corresponding portion of the test image, such that the reference markers captured in the respective regularized versions of the plurality of test images share an image-independent location and orientation… identifying the respective anchor contour from among the plurality of contours extracted from the test image further comprises: selecting the anchor contour from the two or more candidate contours based on relative closeness between the respective shape feature of each of the two or more candidate contours and a shape feature of the reference mark in the predefined common layout… the shape feature of the reference marker is learned by the computer using training images… and the portions of test images containing the candidate contours are cropped out and used as input for shape recognition. The candidate contour that is recognized as the reference marker by the computer is selected as the anchor contour… shape distances are computed between each candidate contour and the reference marker in the predefined common layout, and the candidate contour that has the smallest shape distance from the reference marker is selected as the anchor contour. Regardless of the technique that is used to identify the anchor contour from the candidate contour, the technique must be designed such that the identification can occur even when the reference markers in the test images are shifted and/or rotated relative to the position and orientation as indicated in the predefined common layout; and any geometrical shape around the challenge component (e.g. method of performing automated object inspection includes a predefined common layout that specifies respective positions for two or more components of a respective composite object, and a predefined location and orientation for a reference (i.e. challenge, authentication, interrogation, authorization, validation, identification, etc.) marker (i.e. challenge component), in the respective composite object, for example, in which shape features of reference markers are learned by a computer using training images and extracted by extracting a minimal enclosing rectangle (i.e. any geometrical shape) that encloses the anchor contour of the reference markers (i.e. and any geometrical shape around the challenge component) as indicated above), for example).
IWAI, LEE, Olm, and DIAO are considered to be analogous art because they pertain to image processing applications. Therefore, the combined teachings of IWAI, LEE, Olm, and DIAO, as a whole, would have rendered obvious the invention recited in claim 5 with a reasonable expectation of success in order to modify the inspection apparatus for inspecting presence of a foreign matter to be detected from an article by using an image processing algorithm (as disclosed by IWAI) with wherein the particular arrangement of the at least two marking components comprises one or more of: an arrangement of the at least two marking components on a first end of the challenge card, and wherein the challenge component is placed on a second end of the challenge card and any geometrical shape around the challenge component (as taught by DIAO, Abstract, Par. [0004-9, 83, 117-122]) to perform efficient and accurate inspection of a complex product including multiple component, and each component may include different types of defects, to improve defect detection accuracy, to identify product defects and report to the system to improve the production process (DIAO, Abstract, Par. [0002-9, 48, 83, 117-122]).
Regarding claim 10, claim 1 is incorporated and the combination of IWAI, LEE, and Olm, as a whole, teaches the method (IWAI, Par. [0006]), but fails to teach the following as further recited in claim 10.
However, DIAO teaches wherein each of the at least one marking components further has a particular shape (Par. [0083]: server system further extracts (438) an anchor contour (e.g., anchor contour 416, FIG. 4B) of the reference marker at position 402 in the standard sample image 412 using any suitable imaging processing technique. After determining the anchor contour 416, the server system also calculates (440) the acreage (A) of the anchor (e.g., the area enclosed by the anchor contour of the reference marker). Additionally, the server system determines and saves (442) shape features, such as the number of local maximum distances from the center point 420 of the anchor contour 416 to each point along the anchor contour 416. In some embodiments as illustrated in FIG. 4C, the server system extracts (444) a minimal enclosing rectangle 406 that encloses the anchor contour of the reference marker; Par. [0117-122]: transformation to at least a portion of the test image to obtain a respective regularized version of the corresponding portion of the test image, such that the reference markers captured in the respective regularized versions of the plurality of test images share an image-independent location and orientation… identifying the respective anchor contour from among the plurality of contours extracted from the test image further comprises: selecting the anchor contour from the two or more candidate contours based on relative closeness between the respective shape feature of each of the two or more candidate contours and a shape feature of the reference mark in the predefined common layout… the shape feature of the reference marker is learned by the computer using training images… and the portions of test images containing the candidate contours are cropped out and used as input for shape recognition. The candidate contour that is recognized as the reference marker by the computer is selected as the anchor contour… shape distances are computed between each candidate contour and the reference marker in the predefined common layout, and the candidate contour that has the smallest shape distance from the reference marker is selected as the anchor contour. Regardless of the technique that is used to identify the anchor contour from the candidate contour, the technique must be designed such that the identification can occur even when the reference markers in the test images are shifted and/or rotated relative to the position and orientation as indicated in the predefined common layout; wherein each of the at least one marking components further has a particular shape (e.g. method of performing automated object inspection includes a predefined common layout that specifies respective positions for two or more components of a respective composite object, and a predefined location and orientation for a reference (i.e. challenge, authentication, interrogation, authorization, validation, identification, etc.) marker (i.e. challenge component), in the respective composite object, for example, in which shape features of reference markers (i.e. wherein each of the at least one marking components further has a particular shape) are learned by a computer using training images and extracted by extracting a minimal enclosing rectangle that encloses the anchor contour of the reference markers, as indicated above), for example).
The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 5.
Regarding claim 17, claim 1 is incorporated and the combination of IWAI, LEE, and Olm, as a whole, teaches the method (IWAI, Par. [0006]), but fails to teach the following as further recited in claim 17.
However, DIAO teaches further comprising:
(a) downloading, by the one or more processors, a universally updated foreign material model from a server device; and
(b) replacing, by the one or more processors, the updated foreign material model with the universally updated foreign material model (Par. [0004-9]: detecting and using location and orientation of reference markers to standardize captured images before performing visual inspection of the components of the composite object or the assessor pack has the advantage of high degree of an automatic process, thus more efficient and accurate. Moreover, the system can be easily modified, e.g., by updating the blueprint, or constantly performing machine learning to incorporate any updates to the model, and timely identifying defects that have been intentionally or inadvertently introduced to the assembly lines… method of performing automated object inspection, comprising: at a computing device (e.g., server system 152, FIG. 1B; server system 200, FIG. 2) having one or more processors and memory… system supports a cloud computing framework connecting the edge devices and the server system, thus the system can handle multiple tasks simultaneously… the system has the flexibility of using more training data from multiple assembly lines to train the model to adapt the model to different situations, or using training data from a certain assembly line to improve accuracy to target at a specific assembly line… a method of performing automated object inspection, comprising: at a computing device (e.g., server system 152, FIG. 1B; server system 200, FIG. 2) having one or more processors and memory, obtaining a plurality of test images, each test image capturing a respective one of a plurality of composite objects that have been assembled in accordance with a predefined common layout for the plurality of composite objects; Par. [0045]: server system trains a respective model using training data obtained from a corresponding edge device and uses the respective model to analyze the test data obtained from the corresponding edge device to determine whether the product contains a certain defect… the server system updates the respective models based on the identified defects detected during testing; downloading, by the one or more processors, a universally updated foreign material model from a server device; and
replacing, by the one or more processors, the updated foreign material model with the universally updated foreign material model (e.g. method of performing automated object inspection includes a computing device (e.g., server system 152, FIG. 1B; server system 200, FIG. 2) having one or more processors (i.e. one or more processors) and memory, for example, including a server system that trains a respective model (i.e. downloading, by the one or more processors, a model from a server device) using training data obtained from a corresponding edge device and uses the respective model to analyze test data obtained from the corresponding edge device to determine whether a product contains a certain defect (i.e. downloading, by the one or more processors, a foreign material model from a server device), for example, and the server system updates the respective models based on identified defects detected during testing (i.e. downloading, by the one or more processors, a universally updated foreign material model from a server device and replacing, by the one or more processors, the updated foreign material model with the universally updated foreign material model), for example, by constantly performing machine learning to incorporate any updates to the model, and timely identifying defects that have been intentionally or inadvertently introduced to the assembly lines, as indicated above). for example).
The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 5.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over IWAI, in view, in further view of Olm, as applied to claim 1, and in further view of GROF et al. (US PG Publication No. 2019/0156075 A1), hereafter referred to as GROF.
Regarding claim 9, claim 1 is incorporated and the combination of IWAI, LEE, and Olm, as a whole, teaches the method (IWAI, Par. [0006]), but fails to teach the following as further recited in claim 9.
However, GROF teaches wherein the X-ray machine comprises a photon counting X-ray machine, and
wherein the image comprises a photon X-ray image (Par. [0001]: present invention is in the field of X-Ray Fluorescence (XRF) and particularly relates to XRF marking of electronic systems; Par. [0016]: coding system associated with the marking may also include localized marking… such that the configuration of the locations of the marking constitute part of the code. Namely, the specific locations of the markings would be incorporated in the code word associated with the marking… method/system (e.g. XRF reader) for reading the marking includes operating means, such as an imager and/or image recognition means) for identifying and determining the location of the marking on the marked component (on the marked circuit board), and for determining the code word read from the marking based on both (i) the XRF signal obtained from the marking; and (ii) the location of the marking on the marked component; Par. [0031-32]: providing plurality of samples of including samples of various substrate material and various XRF marking compositions of different concentrations of XRF marker elements on the various substrate materials… interrogating the plurality of samples of the particular substrate material by an XRF analyzer to determine for each sample a counts per second (CPS) value indicative of photons of a certain energy range(s) associated with the XRF marking elements; Par. [0125-128]: method 500 provides for generating a calibration data (e.g. in the form of, or indicative of, a curve/plot) associating the counts collected in a time period (e.g. counts per second (CPS)) of x-ray photons within certain energy range(s) that are received from a component (e.g. C1) of the electronic system 100 with the concentration of a corresponding XRF marker element/material (which emits XRF in the same energy range(s)) that is included in the component (e.g. C1)… providing plurality of samples/standards of a plurality of substrate materials whereby for each substrate there may be several samples with XRF marking compositions having different predetermined concentration(s) of XRF marker element(s)… Operation 530 includes interrogating the plurality of samples/standards by an XRF analyzer to determine counts per second (CPS) value for each standard/sample having the predetermined concentration(s) of XRF marking elements, whereby the CPS is indicative of photons of a certain energy range(s) corresponding the XRF emission from those marking elements arriving from the standard/sample in response to the interrogation; wherein the X-ray machine comprises a photon counting X-ray machine, and wherein the image comprises a photon X-ray image (e.g. method/system for reading X-Ray Fluorescence (XRF) marking includes operating means, such as an imager (i.e. a photon X-ray imager) and/or image recognition means for identifying and determining a location of a plurality of marking elements (XRF markings) on a marked component, for example, and for determining a code word read from each marking based on XRF signals obtained from the markings and the location of the markings on the marked component, for example, by interrogating (i.e. challenging, identifying, etc.) the plurality XRF markings to determine counts per second (CPS) value for each standard/sample having a predetermined concentration(s) of XRF marking elements (i.e. wherein the X-ray machine comprises a photon counting X-ray machine, and wherein the image comprises a photon X-ray image), to determine counts per second (CPS) value for each standard/sample having predetermined concentration(s) of XRF marking elements, whereby the CPS is indicative of photons of a certain energy range(s) corresponding XRF emission from marking elements arriving from the standard/sample in response to the interrogation, as indicated above), for example.
IWAI, LEE, Olm, and GROF are considered to be analogous art because they pertain to image processing applications. Therefore, the combined teachings of IWAI, LEE, Olm, and GROF, as a whole, would have rendered obvious the invention recited in claim 9 with a reasonable expectation of success in order to modify the inspection apparatus for inspecting presence of a foreign matter to be detected from an article by using an image processing algorithm (as disclosed by IWAI) with wherein the X-ray machine comprises a photon counting X-ray machine, and wherein the image comprises a photon X-ray image (as taught by GROF, Abstract, Par. [0001,16, 31-32, 125-128]) to determine counts per second (CPS) value for each standard/sample having predetermined concentration(s) of XRF marking elements, whereby the CPS is indicative of photons of a certain energy range(s) corresponding XRF emission from marking elements arriving from the standard/sample in response to the interrogation, to verify using XRF marking elements that components are getting to the ‘right’ destination in order to prevent unauthorized trade of his components, and using marker composition for marking one or more components of a system that includes a plurality of XRF marker elements, each being present in different concentrations or form, to provide a unique signature of a marking composition with spectral features characteristic not only of specific elements in the combination but also of their concentrations or relative concentrations, and to provide/output suitable indication of a compatibility of the components in a system that includes an indication that the system and/or the components are authentic and not counterfeit system/components (GROF, Abstract, Par. [0009-13,16, 31-32, 58, 96, 106, 125-128]).
Allowable Subject Matter
Claim 8, 11-12, 14-16, and 18 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 fails to anticipate or render obvious the following limitations as claimed:
In view of claim 1 in its entirety, the further limitations of “… wherein the challenge card further comprises an RFID tag, and wherein the method further comprises:
(a) receiving, by the one or more processors and via an RFID reader, additional descriptive information of the challenge material from the RFID tag; and
(b) updating, by the one or more processors, the foreign material model with the additional descriptive information of the challenge material” as recited in claim 8.
In view of claim 1 in its entirety, the further limitations of “… wherein the challenge card further comprises one or more of a barcode and a serial number, and wherein the method further comprises:
(a) controlling, by the one or more processors, a camera to capture an image of the one or more of the barcode and the serial number;
(b) determining, by the one or more processors, additional descriptive information of the challenge material based on the one or more of the barcode and the serial number; and
(c) updating, by the one or more processors, the foreign material model with the additional descriptive information of the challenge material” as recited in claim 11.
In view of claim 1 in its entirety, the further limitations of “… in response to determining that the foreign material is the challenge card, updating, by the one or more processors, the foreign material model to include information descriptive of the challenge material” as recited in claim 12.
In view of claim 1 in its entirety, the further limitations of “… wherein the challenge card further comprises a radio frequency identification (RFID) tag, and wherein the method further comprises:
(a) failing to detect, by the one or more processors, that the good includes the foreign material comprising the challenge card; and
(b) in response to failing to detect that the good includes the foreign material comprising the challenge card, receiving, by the one or more processors via a radio frequency identification (RFID) reader, a signal including information stored on the RFID tag on the challenge card” as recited in claim 14.
In view of claim 1 in its entirety, the further limitations of “… wherein the challenge card comprises a first challenge card, and wherein a second challenge card comprises a secondary identification tag and a challenge material for the second challenge card,
wherein the method further comprises:
(a) receiving, by the one or more processors, the secondary identification tag;
(b) extracting, by the one or more processors, a unique signature for the challenge material for the second challenge card from the secondary identification tag; and
(c) updating, by the one or more processors, the foreign material model to include the unique signature for the challenge material for the second challenge card, wherein receiving the secondary identification tag comprises one or more of:
(i) receiving, by the one or more processors via a radio frequency identification (RFID) reader, the secondary identification tag from an RFID tag on the second challenge card, and
(ii) receiving, by the one or more processors via a barcode scanner, the secondary identification tag from a barcode on the second challenge card” as recited in claim 15.
Claim 16 is dependent upon claim 15.
In view of claim 1 in its entirety, the further limitations of “… wherein the good comprises a first good, wherein the challenge material comprises a first instance of the challenge material, wherein controlling the X-ray machine to capture the image of the first good comprises controlling, by the one or more processors, the X-ray machine to capture the image of the first good while in a scan mode, and wherein the method further comprises:
(a) controlling, by the one or more processors, the X-ray machine to capture an image of a second good while in the scan mode;
(b) analyzing, by the one or more processors, the image of the second good captured by the X-ray machine by applying the foreign material detection algorithm and the foreign material model to the image;
(c) determining, by the one or more processors, that a second instance of the challenge material has a same signature as the first instance of the challenge material of the challenge card;
(d) determining, by the one or more processors, that the at least one marking component is not present in the image; and
(e) outputting, by the one or more processors, an alert that the second good includes the second instance of the challenge material and that the second good should be removed from production” as recited in claim 18.
Examiner was not able to find prior art similar to aforementioned feature limitations.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to GUILLERMO M RIVERA-MARTINEZ whose telephone number is (571) 272-4979. The examiner can normally be reached on 9 am to 5 pm.
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/GUILLERMO M RIVERA-MARTINEZ/ Primary Examiner, Art Unit 2677
Prosecution Insights