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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 1 and 2 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 7 of U.S. Patent No. 11783606. Although the claims at issue are not identical, they are not patentably distinct from each other because claim 7 of the patent contains all the elements of instant claims as well as additional elements.
Re claim 1 claim 7 of ‘606 discloses
A computing system for identifying a SKU associated with a package comprising: at least one processor; and at least one non-transitory computer-readable media storing: at least one machine learning model that has been trained with a plurality of images of packages; and instructions that, when executed by the at least one processor, cause the computing system to perform the following operations: (see claim 7 “A computing system for identifying SKUs in a stack of a plurality of packages comprising: at least one processor; and at least one non-transitory computer-readable media storing: at least one machine learning model that has been trained with a plurality of images of packages; and instructions that, when executed by the at least one processor, cause the computing system to perform the following operations:”)
a) receiving at least one image of the package; (see claim 7 “a) receiving a plurality of stack images of the stack of the plurality of packages”)
b) using the at least one machine learning model, inferring at least one classification based upon the at least one image (see claim 7 “e) using the at least one machine learning model, inferring at least one classification based upon each of the second package faces and inferring a plurality of classifications for each of the first package faces);”)
c) performing optical character recognition on the at least one image;(see claim 7 “f) performing optical character recognition on one of the plurality of first package faces”)
d) associating one of a plurality of SKUs with the package based upon operations b) and c) (see claim 7 “g) associating a SKU with the one of the plurality of first package faces based upon the inferred plurality of classifications for the one of the plurality of first packages faces and based upon the optical character recognition on the one of the plurality of first package faces”).
Re claim 2 claim 7 discloses wherein the at least one image of the package includes a plurality of images of the package, each of a different one of a plurality of package faces of the package. (see claim 7 “a) receiving a plurality of stack images of the stack of the plurality of packages, wherein each of the plurality of stack images is of a different face of the stack of the plurality of packages, the plurality of stack images including a first stack image of a first face of the stack of the plurality of packages and a second stack image of a second face of the stack of the plurality of packages, wherein the first face of the stack of the plurality of packages abuts the second face of the stack of the plurality of packages at a first second corner” “linking each of the plurality of right first package faces along the first second corner to a different one of the plurality of left second package faces along the first second corner as different faces of the same ones of the plurality of package” note some packages have multiple faces captured).
Claim 3, 7, 8, 11, 15, and 17 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 7 of U.S. Patent No. 11783606 in view of Martin Jr US 2020/0273131.
Re claim 3 does not expressly disclose wherein operation b) includes inferring at least one of a plurality of brands independently for each of the plurality of package faces. Martin Jr discloses wherein operation b) includes inferring at least one of a plurality of brands independently for each of the plurality of package faces (see paragraph 60-63 “The package type of each item 20 is identified by the computer, such as reusable beverage crate, corrugated tray with translucent plastic wrap, or fully enclosed cardboard or paperboard box. The branding of each item 20 is also identified by the computer (e.g. a specific flavor from a specific manufacturer), such as by reading the images/text on the packaging” “After individual items 20 are identified on each of the four sides of the loaded pallet 22, based upon the known dimensions of the items 20 and pallet 22, duplicates are removed, i.e. it is determined which items are visible from more than one side and appear in more than one image. If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used” note that brands are determined for each pack in each image and each side, subsequently if the item is identified on multiple sides the highest confidence side is used. The examiner notes that these operations are performed separately i.e. independently as they are generated with different confidences). The motivation to combine is “The individual items 20 are then identified on each of the four sides of the loaded pallet 22.” See paragraph 60 and “If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used.” (see paragraph 62). One of ordinary skill in the art could have easily used the method of Martin Jr to modify the teachings of claim 7 to determine brands of multiple faces as part of the recognition process. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing dates of the claimed invention to combine claim 7 and Martin Jr. to reach the aforementioned advantage.
Re claim 7 claim 7 of the patent discloses all of the features of claim 2 claim 7 does not expressly disclose wherein operation b) includes inferring the at least one classification for each of the plurality of images of the packages independently of one another. Martin Jr discloses wherein operation b) includes inferring the at least one classification for each of the plurality of images of the packages independently of one another (see paragraph 60-63 “The package type of each item 20 is identified by the computer, such as reusable beverage crate, corrugated tray with translucent plastic wrap, or fully enclosed cardboard or paperboard box. The branding of each item 20 is also identified by the computer (e.g. a specific flavor from a specific manufacturer), such as by reading the images/text on the packaging” “After individual items 20 are identified on each of the four sides of the loaded pallet 22, based upon the known dimensions of the items 20 and pallet 22, duplicates are removed, i.e. it is determined which items are visible from more than one side and appear in more than one image. If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used” note that brands are determined for each pack in each image and each side, subsequently if the item is identified on multiple sides the highest confidence side is used. The examiner notes that these operations are performed separately i.e. independently as they are generated with different confidences). The motivation to combine is “The individual items 20 are then identified on each of the four sides of the loaded pallet 22.” See paragraph 60 and “If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used.” (see paragraph 62). One of ordinary skill in the art could have easily used the method of Martin Jr to modify the teachings of claim 7 to determine brands of multiple faces as part of the recognition process. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing dates of the claimed invention to combine claim7 and Martin Jr. to reach the aforementioned advantage.
Re claim 8, claim 7 of the patent discloses and wherein the at least one machine learning model is trained on a plurality of images of packages. (see claim 7 “at least one machine learning model that has been trained with a plurality of images of packages”). Claim 7 does not expressly disclose wherein the package contains a plurality of beverage containers. Martin Jr discloses wherein the package contains a plurality of beverage containers (See paragraph 2 “for example, the products may be cases of beverage containers (e.g. cartons of cans and beverage crates containing bottles or cans, etc.). There are many different permutations of flavors, sizes, and types of beverage containers delivered to each store.” The motivation to combine is “The improved delivery system facilitates order accuracy from the warehouse to the store by combining machine learning and computer vision software” of the beverage containers of paragraph 2. One of ordinary skill in the art could have easily applied the method of claim 7 to the beverage containers of Martin Jr to recognize cases of beverages. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine claim 7 and Martin Jr. to reach the aforementioned advantage.
Re claim 11 claim 7 discloses all of the features of claim 2 claim 7 does not expressly disclose wherein operation b) includes inferring at least one of a plurality of package types independently for each of the plurality of package faces. Martin Jr discloses w wherein operation b) includes inferring at least one of a plurality of package types independently for each of the plurality of package faces. (See paragraph 60-63 “The package type of each item 20 is identified by the computer, such as reusable beverage crate, corrugated tray with translucent plastic wrap, or fully enclosed cardboard or paperboard box. The branding of each item 20 is also identified by the computer (e.g. a specific flavor from a specific manufacturer), such as by reading the images/text on the packaging” “After individual items 20 are identified on each of the four sides of the loaded pallet 22, based upon the known dimensions of the items 20 and pallet 22, duplicates are removed, i.e. it is determined which items are visible from more than one side and appear in more than one image. If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used” note that brands are determined for each pack in each image and each side, subsequently if the item is identified on multiple sides the highest confidence side is used. The examiner notes that these operations are performed separately i.e. independently as they are generated with different confidences). The motivation to combine is “The individual items 20 are then identified on each of the four sides of the loaded pallet 22.” See paragraph 60 and “If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used.” (see paragraph 62). One of ordinary skill in the art could have easily used the method of Martin Jr to modify the teachings of claim 7 to determine brands of multiple faces as part of the recognition process. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing dates of the claimed invention to combine claim 7 and Martin Jr. to reach the aforementioned advantage.
Re claim 15 claim 7 discloses all the features of claim 1 claim 7 does not expressly disclose wherein the operations further include: e) receiving an expected SKU; and f) comparing the associated one of the plurality of SKUs with the expected SKU. Martin Jr. discloses the operations further include: e) receiving an expected SKU; and f) comparing the associated one of the plurality of SKUs with the expected SKU (see paragraph 50 “The SKUs of the products 20 on the pallet 22 are compared to the pick sheet 64 by the DC computer 26 in step 160, to ensure that all the SKUs associated with the pallet id of the pallet 22 on the pick sheet 64 are present on the correct pallet 22”). The motivation to combine is to ensure that all the SKUs associated with the pallet id of the pallet 22 on the pick sheet 64 are present on the correct pallet 22”. One of ordinary skill in the art could have easily modified claim 7 to compare the detected SKU to an expected listed as described in Martin Jr. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine claim 7 and Martin Jr to reach the aforementioned advantage.
Re claim 17, claim 7 of the patent discloses and wherein the at least one machine learning model is trained on a plurality of images of packages. (see claim 7 “at least one machine learning model that has been trained with a plurality of images of packages”). Claim 7 does not expressly disclose wherein the package contains a plurality of beverage containers. Martin Jr discloses wherein the package contains a plurality of beverage containers (See paragraph 2 “for example, the products may be cases of beverage containers (e.g. cartons of cans and beverage crates containing bottles or cans, etc.). There are many different permutations of flavors, sizes, and types of beverage containers delivered to each store.” The motivation to combine is “The improved delivery system facilitates order accuracy from the warehouse to the store by combining machine learning and computer vision software” of the beverage containers of paragraph 2. One of ordinary skill in the art could have easily applied the method of claim 7 to the beverage containers of Martin Jr to recognize cases of beverages. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine claim 7 and Martin Jr. to reach the aforementioned advantage.
Claims 18 and 19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 8 of U.S. Patent No. 11783606. Although the claims at issue are not identical, they are not patentably distinct from each other because claim 8 of the patent contains all the elements of instant claims as well as additional elements.
Re claim 18, claim 8 discloses A computer method for determining a classification of a plurality of classifications of a package including: (see claim 8 “A computing system for identifying SKUs in a stack of a plurality of packages comprising: at least one processor; and at least one non-transitory computer-readable media storing: at least one machine learning model that has been trained with a plurality of images of packages; and instructions that, when executed by the at least one processor, cause the computing system to perform the following operations” note that function of the computing system of claim 8 corresponds to the computing method of claim 18)
a) receiving in at least one computer at least one image of the package; (see claim 8 “a) receiving a plurality of stack images of the stack of the plurality of packages, wherein each of the plurality of stack images is of a different face of the stack of the plurality of packages)”
b) the at least one computer using at least one machine learning model to infer at least one inferred classification based upon each of the at least one image; (see claim 8 “c) using the at least one machine learning model, inferring a plurality of brands based upon each of the first package faces;)” note that the brand could correspond to the classification)
c) the at least one computer performing optical character recognition on the at least one image; (see claim 8 “d) performing optical character recognition on one of the plurality of first package faces;” )
and d) the at least one computer determining the classification of the package based upon steps b) and c).( see claim 8 “associating one of the plurality of brands with the one of the plurality of first package faces based upon the inferred plurality of brands for the one of the plurality of first packages faces and based upon the optical character recognition on the one of the plurality of first package faces.” note that the brand could correspond to a classification).
Re claim 19, claim 8 discloses wherein the at least one image of the package includes a plurality of images of the package, each of a different one of a plurality of package faces of the package. (See claim 8 “receiving a plurality of stack images of the stack of the plurality of packages, wherein each of the plurality of stack images is of a different face of the stack of the plurality of packages, the plurality of stack images including a first stack image of a first face of the stack of the plurality of packages” note some packages have multiple faces captured if they are captured multiple faces of the pallet are captured).
Claim 20, 21, 25, 28 and 31 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 8 of U.S. Patent No. 11783606 in view of Martin Jr US 2020/0273131.
Re claim 20 claim 8 discloses and wherein the at least one machine learning model is trained on a plurality of images of packages. (see claim 8 “at least one machine learning model that has been trained with a plurality of images of packages”). Claim 8 does not expressly disclose wherein the package contains a plurality of beverage containers. Martin Jr discloses wherein the package contains a plurality of beverage containers (See paragraph 2 “for example, the products may be cases of beverage containers (e.g. cartons of cans and beverage crates containing bottles or cans, etc.). There are many different permutations of flavors, sizes, and types of beverage containers delivered to each store.” The motivation to combine is “The improved delivery system facilitates order accuracy from the warehouse to the store by combining machine learning and computer vision software” of the beverage containers of paragraph 2. One of ordinary skill in the art could have easily applied the method of claim 8 to the beverage containers of Martin Jr to recognize cases of beverages. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine claim 8 and Martin Jr. to reach the aforementioned advantage.
Re claim 21 claim 8 discloses all of the features of claim 20, claim 8 does not expressly disclose wherein operation b) includes inferring at least one of a plurality of brands independently for each of the plurality of package faces. Martin Jr discloses wherein operation b) includes inferring at least one of a plurality of brands independently for each of the plurality of package faces (see paragraph 60-63 “The package type of each item 20 is identified by the computer, such as reusable beverage crate, corrugated tray with translucent plastic wrap, or fully enclosed cardboard or paperboard box. The branding of each item 20 is also identified by the computer (e.g. a specific flavor from a specific manufacturer), such as by reading the images/text on the packaging” “After individual items 20 are identified on each of the four sides of the loaded pallet 22, based upon the known dimensions of the items 20 and pallet 22, duplicates are removed, i.e. it is determined which items are visible from more than one side and appear in more than one image. If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used” note that brands are determined for each pack in each image and each side, subsequently if the item is identified on multiple sides the highest confidence side is used. The examiner notes that these operations are performed separately i.e. independently as they are generated with different confidences). The motivation to combine is “The individual items 20 are then identified on each of the four sides of the loaded pallet 22.” See paragraph 60 and “If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used.” (see paragraph 62). One of ordinary skill in the art could have easily used the method of Martin Jr to modify the teachings of claim 8 to determine brands of multiple faces as part of the recognition process. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing dates of the claimed invention to combine claim 8 and Martin Jr. to reach the aforementioned advantage.
Re claim 25 claim 8 discloses all of the features of claim 20, claim 8 does not expressly disclose wherein step b) includes inferring the at least one classification for each of the plurality of images of the packages independently of one another. Martin Jr discloses wherein step b) includes inferring the at least one classification for each of the plurality of images of the packages independently of one another (see paragraph 60-63 “The package type of each item 20 is identified by the computer, such as reusable beverage crate, corrugated tray with translucent plastic wrap, or fully enclosed cardboard or paperboard box. The branding of each item 20 is also identified by the computer (e.g. a specific flavor from a specific manufacturer), such as by reading the images/text on the packaging” “After individual items 20 are identified on each of the four sides of the loaded pallet 22, based upon the known dimensions of the items 20 and pallet 22, duplicates are removed, i.e. it is determined which items are visible from more than one side and appear in more than one image. If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used” note that brands are determined for each pack in each image and each side, subsequently if the item is identified on multiple sides the highest confidence side is used. The examiner notes that these operations are performed separately i.e. independently as they are generated with different confidences). The motivation to combine is “The individual items 20 are then identified on each of the four sides of the loaded pallet 22.” See paragraph 60 and “If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used.” (see paragraph 62). One of ordinary skill in the art could have easily used the method of Martin Jr to modify the teachings of claim 8 to determine brands of multiple faces as part of the recognition process. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing dates of the claimed invention to combine claim 8 and Martin Jr. to reach the aforementioned advantage.
Re claim 28 claim 8 discloses all of the features of claim 20, claim 8 does not expressly disclose wherein operation b) includes inferring at least one of a plurality of package types independently for each of the plurality of package faces. Martin Jr discloses w wherein operation b) includes inferring at least one of a plurality of package types independently for each of the plurality of package faces. (see paragraph 60-63 “The package type of each item 20 is identified by the computer, such as reusable beverage crate, corrugated tray with translucent plastic wrap, or fully enclosed cardboard or paperboard box. The branding of each item 20 is also identified by the computer (e.g. a specific flavor from a specific manufacturer), such as by reading the images/text on the packaging” “After individual items 20 are identified on each of the four sides of the loaded pallet 22, based upon the known dimensions of the items 20 and pallet 22, duplicates are removed, i.e. it is determined which items are visible from more than one side and appear in more than one image. If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used” note that brands are determined for each pack in each image and each side, subsequently if the item is identified on multiple sides the highest confidence side is used. The examiner notes that these operations are performed separately i.e. independently as they are generated with different confidences). The motivation to combine is “The individual items 20 are then identified on each of the four sides of the loaded pallet 22.” See paragraph 60 and “If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used.” (see paragraph 62). One of ordinary skill in the art could have easily used the method of Martin Jr to modify the teachings of claim 8 to determine brands of multiple faces as part of the recognition process. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing dates of the claimed invention to combine claim 8 and Martin Jr. to reach the aforementioned advantage.
Re claim 31 claim 8 discloses all the features of claim 18, claim 8 does not expressly disclose e) receiving an expected classification; and f) comparing the classification of the package determined in step d) with the expected classification. Martin Jr. d e) receiving an expected classification; and f) comparing the classification of the package determined in step d) with the expected classification (see paragraph 50 “The SKUs of the products 20 on the pallet 22 are compared to the pick sheet 64 by the DC computer 26 in step 160, to ensure that all the SKUs associated with the pallet id of the pallet 22 on the pick sheet 64 are present on the correct pallet 22”). The motivation to combine is to ensure that all the SKUs associated with the pallet id of the pallet 22 on the pick sheet 64 are present on the correct pallet 22”. One of ordinary skill in the art could have easily modified claim 8, to compare the detected SKU to an expected listed as described in Martin Jr. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine claim 8 and Martin Jr to reach the aforementioned advantage.
Claims 33 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 8 of U.S. Patent No. 11783606. Although the claims at issue are not identical, they are not patentably distinct from each other because claim 18 of the patent contains all the elements of instant claims as well as additional elements.
Re claim 33 claim 8 of the patent disclose A computing system for identifying a classification associated with a package comprising, wherein the classification is one of a plurality of classifications: at least one processor; and at least one non-transitory computer-readable media storing: at least one machine learning model that has been trained with a plurality of images of packages; and instructions that, when executed by the at least one processor, cause the computing system to perform the following operations: (see claim 8 “A computing system for identifying SKUs in a stack of a plurality of packages comprising: at least one processor; and at least one non-transitory computer-readable media storing: at least one machine learning model that has been trained with a plurality of images of packages; and instructions that, when executed by the at least one processor, cause the computing system to perform the following operations”)
a) receiving at least one image of the package; (see claim 8 “a) receiving a plurality of stack images of the stack of the plurality of packages, wherein each of the plurality of stack images is of a different face of the stack of the plurality of packages”)
b) using the at least one machine learning model, inferring at least one inferred classification based upon the at least one image; (see claim 8 “c) using the at least one machine learning model, inferring a plurality of brands based upon each of the first package faces;”) c) performing optical character recognition on the at least one image; (see claim 8 “d) performing optical character recognition on one of the plurality of first package faces;” Note that a brand could be a classification) and d) determining the classification of the package based upon operations b) and c). (see claim 8 “associating one of the plurality of brands with the one of the plurality of first package faces based upon the inferred plurality of brands for the one of the plurality of first packages faces and based upon the optical character recognition on the one of the plurality of first package faces.” Note that a brand could be a classification).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1-33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Re claim 1
The limitation at least one model that has been trained with a plurality of images of packages, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a trained model in the context of this claim encompasses the user having a mental model trained on packages.
The limitation receiving at least one image of the package, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, receiving and image in the context of this claim encompasses the user mentally looking at an image to receive it in the brain.
The limitation using the at least one model, inferring at least one classification based upon the at least one image, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, using a model to infer in the context of this claim encompasses the user mentally inferring based on a mental model.
The limitation performing character recognition on the at least one image, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, performing character recognition in the context of this claim encompasses the user mentally performing character recognition on the object.
The limitation associating one of a plurality of SKUs with the package based upon operations b) and c), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, associated in the context of this claim encompasses the user mentally making the association
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements –
at least one processor; and at least one non-transitory computer-readable media storing at least one processor; and at least one non-transitory computer-readable media storing: at least one machine learning model at least one processor; and at least one non-transitory computer-readable media storing: at least one machine learning model; and instructions that, when executed by the at least one processor, cause the computing system to perform the method; and Optical Character recognition.
The processor and non-transitory computer-readable media are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function and a generic computer readable medium storing instructions for performing generic processing functions)) such that it amounts no more than mere instructions to apply the exception using generic computer components. The machine learning model is merely a generic learning model that only limits the judicial exception to the field of machine learning. Optical character recognition is a well-known function of using a computer to recognize text in images simply used as a tool to perform the abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor and non-transitory computer readable media to perform steps amounts to no more than mere instructions to apply the exception using a generic computer component. The machine learning model is merely a generic learning model that only limits the judicial exception to the field of machine learning. Optical character recognition is a well-known function of using a computer to recognize text in images simply used as a tool to perform the abstract idea. Mere instructions to apply an exception using generic computer components combined with the generic concepts of OCR and machine learning cannot provide an inventive concept. The claim is not patent eligible.
Re claim 2 the limitation “wherein the at least one image of the package includes a plurality of images of the package, each of a different one of a plurality of package faces of the package”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, received images in the context of this claim encompasses the user mentally receiving a plurality of images by looking at them.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 3 the limitation” wherein operation b) includes inferring at least one of a plurality of brands independently for each of the plurality of package faces”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, inferring in the context of this claim encompasses the user mentally making an inference.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 4 the limitation “wherein the plurality of package faces includes a first package face and a second package face, wherein operation b) includes inferring a first plurality of brands based upon the first package face and inferring a second plurality of brands based upon the second package face, wherein the inference based upon the first package face is performed independently of the inference of the second package face.”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, inferring in the context of this claim encompasses the user mentally making the inferences.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 5 the limitation wherein operation b) includes inferring at least one of a plurality of package types independently for each of the plurality of package faces, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, inferring in the context of this claim encompasses the user mentally making the inferences.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 6 the limitation of the limitation wherein operation b) includes inferring a first plurality of package types based upon the first package face and inferring a second plurality of package types based upon the second package face, wherein the inference of the first plurality of package types is performed independently of the inference of the second plurality of package types, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, inferring in the context of this claim encompasses the user mentally making the inferences.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 7 the limitation wherein operation b) includes inferring the at least one classification for each of the plurality of images of the packages independently of one another, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, inferring in the context of this claim encompasses the user mentally making the inferences.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 8 the limitation wherein the package contains a plurality of beverage containers and wherein the at least one model is trained on a plurality of images of packages of beverage containers., as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, training a model in the context of this claim encompasses the user mentally training a mental model using the images.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 9 the limitation determining a best classification independently for each of the plurality of images based upon the inference of the at least one classification and the optical character recognition of the image, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, determining a classification in the context of this claim encompasses the user mentally determining the best classification.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 10 the limitation wherein operation d) is performed based upon the best classifications of the plurality of images, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, performing operation d in the context of this claim encompasses the user mentally associating a SKU with the package.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 11 the limitation wherein operation b) includes inferring at least one of a plurality of package types independently for each of the plurality of package faces, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, inferring package types in the context of this claim encompasses the user mentally making inferences.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 12 the limitation wherein the plurality of package faces includes a first package face and a second package face, wherein operation b) includes inferring a first plurality of package types based upon the first package face and inferring a second plurality of package types based upon the second package face, wherein the inference based upon the first package face is performed independently of the inference of the second package face, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, inferring in the context of this claim encompasses the user mentally making inferences.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 13 the limitation wherein the at least one classification is a plurality of classifications and wherein operation b) includes inferring each of the plurality of classifications at a confidence level, and wherein the operations further include augmenting at least one of the confidence levels associated with the plurality of classifications based upon operation c) and wherein operation d) is performed based upon the at least one augmented confidence level, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, inferring classification, augmenting classification and associating an SKU in the context of this claim encompasses the user mentally inferring a classification mentally changing a classification and mentally determining an SKU.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 14 the limitation wherein the package contains a plurality of beverage containers and wherein the at least one machine model is trained on a plurality of images of packages of beverage containers, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, training a model in the context of this claim encompasses the user mentally training a mentally model based on the claimed images.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 15 the limitation e) receiving an expected SKU; and f) comparing the associated one of the plurality of SKUs with the expected SKU., as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, receiving a expected SKU and comparing it in the context of this claim encompasses the user mentally learning of an expected SKU and comparing it to the inferred one.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 16 the limitation wherein the operations further include: g) comparing results of operation c) with the inferred at least one classification; and h) comparing the results of operation c) with the expected SKU., as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, comparing in the context of this claim encompasses the user mentally making comparisons.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 17 the limitation wherein the package contains a plurality of beverage containers and wherein the at least one model is trained on a plurality of images of packages of beverage containers., as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, training a model in the context of this claim encompasses the user mentally training a mental model using the claimed images.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 18
the limitation “a) receiving in at least one computer at least one image of the package”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, receiving and image in the context of this claim encompasses the user mentally looking at an image to receive it in the brain.
The limitation using at least one learning model to infer at least one inferred classification based upon each of the at least one image, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, using a model to infer in the context of this claim encompasses the user mentally inferring based on a mental model.
The limitation performing optical character recognition on the at least one image, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, performing character recognition in the context of this claim encompasses the user mentally performing character recognition on the object.
The limitation determining the classification of the package based upon steps b) and c), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, associated in the context of this claim encompasses the user mentally making classification.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements –
A computer to perform the method; at least one machine learning model and Optical Character recognition.
The computer is recited at a high-level of generality (i.e., a generic computer performing generic processing functions)) such that it amounts no more than mere instructions to apply the exception using generic computer components. The machine learning model is merely a generic learning model that only limits the judicial exception to the field of machine learning. Optical character recognition is a well-known function of using a computer to recognize text in images simply used as a tool to perform the abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to perform steps amounts to no more than mere instructions to apply the exception using a generic computer component. The machine learning model is merely a generic learning model that only limits the judicial exception to the field of machine learning. Optical character recognition is a well-known function of using a computer to recognize text in images simply used as a tool to perform the abstract idea. Mere instructions to apply an exception using generic computer components combined with the generic concepts of OCR and machine learning cannot provide an inventive concept. The claim is not patent eligible.
Re claim 19 the limitation “wherein the at least one image of the package includes a plurality of images of the package, each of a different one of a plurality of package faces of the package”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, received images in the context of this claim encompasses the user mentally receiving a plurality of images by looking at them.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 20 the limitation wherein the package contains a plurality of beverage containers and wherein the at least one model is trained on a plurality of images of packages of beverage containers., as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, training a model in the context of this claim encompasses the user mentally training a mental model using the images.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 21 the limitation” wherein operation b) includes inferring at least one of a plurality of brands independently for each of the plurality of package faces”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, inferring in the context of this claim encompasses the user mentally making an inference.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 22 the limitation “wherein the plurality of package faces includes a first package face and a second package face, wherein step b) includes inferring a first plurality of brands based upon the first package face and inferring a second plurality of brands based upon the second package face, wherein the inference based upon the first package face is performed independently of the inference of the second package face.”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, inferring in the context of this claim encompasses the user mentally making the inferences.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 23 the limitation wherein step b) includes inferring at least one of a plurality of package types independently for each of the plurality of package faces, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, inferring in the context of this claim encompasses the user mentally making the inferences.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 24 the limitation of the limitation wherein step b) includes inferring a first plurality of package types based upon the first package face and inferring a second plurality of package types based upon the second package face, wherein the inference of the first plurality of package types is performed independently of the inference of the second plurality of package types, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, inferring in the context of this claim encompasses the user mentally making the inferences.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 25 the limitation wherein step b) includes inferring the at least one classification for each of the plurality of images of the packages independently of one another, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, inferring in the context of this claim encompasses the user mentally making the inferences.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 26 the limitation determining a best classification independently for each of the plurality of images based upon the inference of the at least one classification and the optical character recognition of the image, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, determining a classification in the context of this claim encompasses the user mentally determining the best classification.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 27 the limitation wherein step d) is performed based upon the best classifications of the plurality of images, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, performing operation d in the context of this claim encompasses the user mentally associating a SKU with the package.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 28 the limitation wherein step b) includes inferring at least one of a plurality of package types independently for each of the plurality of package faces, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, inferring package types in the context of this claim encompasses the user mentally making inferences.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 29 the limitation wherein the plurality of package faces includes a first package face and a second package face, wherein step b) includes inferring a first plurality of package types based upon the first package face and inferring a second plurality of package types based upon the second package face, wherein the inference based upon the first package face is performed independently of the inference of the second package face, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, inferring in the context of this claim encompasses the user mentally making inferences.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 30 the limitation wherein the at least one classification is a plurality of classifications and wherein step b) includes inferring each of the plurality of classifications at a confidence level, and wherein the method further include augmenting at least one of the confidence levels associated with the plurality of classifications based upon step c) and wherein step d) is performed based upon the at least one augmented confidence level, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, inferring classification, augmenting classification and associating an SKU in the context of this claim encompasses the user mentally inferring a classification mentally changing a classification and mentally determining an SKU.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 31 e) receiving an expected classification; and f) comparing the classification of the package determined in step d) with the expected classification, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, receiving a expected classification and comparing it in the context of this claim encompasses the user mentally learning of an expected classification and comparing it to the inferred one.
Re claim 32 the limitation including: g) comparing results of step c) with the inferred at least one classification; and h) comparing the results of step c) with the expected classification, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, comparing in the context of this claim encompasses the user mentally making comparisons.
The analysis for this claim with respect to integration into an abstract idea and significantly more are not meaningfully different from the claim from which this claim depends.
Re claim 33
The limitation at least one model that has been trained with a plurality of images of packages, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a trained model in the context of this claim encompasses the user having a mental model trained on packages.
The limitation receiving at least one image of the package, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, receiving and image in the context of this claim encompasses the user mentally looking at an image to receive it in the brain.
The limitation using the at least one model, inferring at least one classification based upon the at least one image, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, using a model to infer in the context of this claim encompasses the user mentally inferring based on a mental model.
The limitation performing character recognition on the at least one image, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, performing character recognition in the context of this claim encompasses the user mentally performing character recognition on the object.
The limitation a determining the classification of the package based upon operations b) and c), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, associated in the context of this claim encompasses the user mentally making the association
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements –
at least one processor; and at least one non-transitory computer-readable media storing at least one processor; and at least one non-transitory computer-readable media storing: at least one machine learning model at least one processor; and at least one non-transitory computer-readable media storing: at least one machine learning model; and instructions that, when executed by the at least one processor, cause the computing system to perform the method; and Optical Character recognition.
The processor and non-transitory computer-readable media are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function and a generic computer readable medium storing instructions for performing generic processing functions)) such that it amounts no more than mere instructions to apply the exception using generic computer components. The machine learning model is merely a generic learning model that only limits the judicial exception to the field of machine learning. Optical character recognition is a well-known function of using a computer to recognize text in images simply used as a tool to perform the abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor and non-transitory computer readable media to perform steps amounts to no more than mere instructions to apply the exception using a generic computer component. The machine learning model is merely a generic learning model that only limits the judicial exception to the field of machine learning. Optical character recognition is a well-known function of using a computer to recognize text in images simply used as a tool to perform the abstract idea. Mere instructions to apply an exception using generic computer components combined with the generic concepts of OCR and machine learning cannot provide an inventive concept. The claim is not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1,2,18,19 and 33 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Voegele US 2022/0332504.
Re claim 1 Voegele discloses
A computing system for identifying a SKU associated with a package comprising:
at least one processor (see paragraph 215 note that the device is implemented by a memory and a processor); and at least one non-transitory computer-readable media storing (see paragraph 215 note that the device is implemented by a memory and a processor): at least one machine learning model (see paragraph 215 note that memory stores the computer instructions see paragraph 209 note that different types of label are detected from a machine learning model) that has been trained with a plurality of images of packages (see paragraph 145 note that machine learning is trained using images of pallets see paragraph 74 note that the pallets contain cases corresponding to the packages); and instructions that, when executed by the at least one processor, cause the computing system to perform the following operations (see paragraph 215 note that memory stores the computer instructions):
a) receiving at least one image of the package (see paragraph 209 and abstract note that images of the pallet are captured which include cases [i.e. packages]);
b) using the at least one machine learning model, inferring at least one classification based upon the at least one image (see paragraph 178 and 209 note that details such as labels on the cases may be determined using machine learning);
c) performing optical character recognition on the at least one image (see paragraph 209 text comprising OCR may by used to identify information on the packaging);
and d) associating one of a plurality of SKUs with the package based upon operations b) and c) (see paragraph 209 “The second image processing operation can include, for example, identifying details on the contents and cases included on the pallet, such as text, markings, codes, and/or other labeling on the cases. The second image processing operations can aim to identify, specifically, what is included in the pallet, such as the specific sku’s that are contained in the pallet” note that the second processing operations including label detection and text detection are used to determine an SKU of the package).
Re claim 2 Voegele discloses wherein the at least one image of the package includes a plurality of images of the package, each of a different one of a plurality of package faces of the package. (see claim 26 “Images can also be captured of every side of the pallet, thereby providing a more complete view of the pallet for profiling via image analysis techniques.” See paragraph 26 note that a complete view of the pallet will include images of multiple sides of some packages see for example figure 1 b note that in this scenario a picture of each side of the pallet will include multiple faces of the packages depicted).
Re claim 18 Vogel discloses
A computer method for determining a classification of a plurality of classifications of a package including:
a) receiving in at least one computer at least one image of the package; (see paragraph 209 and abstract note that images of the pallet are captured which include cases [i.e. packages]);
b) the at least one computer using at least one machine learning model to infer at least one inferred classification based upon each of the at least one image; (see paragraph 178 and 209 note that details such as labels on the cases may be determined using machine learning);
c) the at least one computer performing optical character recognition on the at least one image; (see paragraph 209 text comprising OCR maybe used to identify information on the packaging);
and d) the at least one computer determining the classification of the package based upon steps b) and c). (See paragraph 209 “The second image processing operation can include, for example, identifying details on the contents and cases included on the pallet, such as text, markings, codes, and/or other labeling on the cases. The second image processing operations can aim to identify, specifically, what is included in the pallet, such as the specific sku’s that are contained in the pallet” note that the second processing operations including label detection and text detection are used to determine an SKU of the package which could be considered a class).
Re claim 19 Voegele discloses wherein the at least one image of the package includes a plurality of images of the package, each of a different one of a plurality of package faces of the package. (See claim 26 “Images can also be captured of every side of the pallet, thereby providing a more complete view of the pallet for profiling via image analysis techniques.” See paragraph 26 note that a complete view of the pallet will include images of multiple sides of some packages see for example figure 1 b note that in this scenario a picture of each side of the pallet will include multiple faces of the packages depicted).
Re claim 33 Voegele discloses
A computing system for identifying a classification associated with a package comprising, wherein the classification is one of a plurality of classifications:
at least one processor (see paragraph 215 note that the device is implemented by a memory and a processor); and at least one non-transitory computer-readable media storing (see paragraph 215 note that the device is implemented by a memory and a processor): at least one machine learning model (see paragraph 215 note that memory stores the computer instructions see paragraph 209 note that different types of label are detected from a machine learning model) that has been trained with a plurality of images of packages (see paragraph 145 note that machine learning is trained using images of pallets see paragraph 74 note that the pallets contain cases corresponding to the packages); and instructions that, when executed by the at least one processor, cause the computing system to perform the following operations (see paragraph 215 note that memory stores the computer instructions):
a) receiving at least one image of the package (see paragraph 209 and abstract note that images of the pallet are captured which include cases[i.e packages]);
b) using the at least one machine learning model, inferring at least one inferred classification based upon the at least one image (see paragraph 178 and 209 note that details such as labels on the cases may be determined using machine learning );
c) performing optical character recognition on the at least one image (see paragraph 209 text comprising OCR may be used to identify information on the packaging);
and d) determining the classification of the package based upon operations b) and c) (see paragraph 209 “The second image processing operation can include, for example, identifying details on the contents and cases included on the pallet, such as text, markings, codes, and/or other labeling on the cases. The second image processing operations can aim to identify, specifically, what is included in the pallet, such as the specific sku’s that are contained in the pallet” note that the second processing operations including label detection and text detection are used to determine an SKU of the package, note that a SKU could be considered a classification of the package).
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.
Claim(s) 3, 7, 8, 11, 15, 17, 20, 21, 25, 28 and 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Voegele US 2022/0332504 in view of Martin Jr US 2020/0273131.
Re claim 3 Voegele discloses all of the features of claim 2 Voegele does not expressly disclose wherein operation b) includes inferring at least one of a plurality of brands independently for each of the plurality of package faces. Martin Jr discloses wherein operation b) includes inferring at least one of a plurality of brands independently for each of the plurality of package faces (see paragraph 60-63 “The package type of each item 20 is identified by the computer, such as reusable beverage crate, corrugated tray with translucent plastic wrap, or fully enclosed cardboard or paperboard box. The branding of each item 20 is also identified by the computer (e.g. a specific flavor from a specific manufacturer), such as by reading the images/text on the packaging” “After individual items 20 are identified on each of the four sides of the loaded pallet 22, based upon the known dimensions of the items 20 and pallet 22, duplicates are removed, i.e. it is determined which items are visible from more than one side and appear in more than one image. If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used” note that brands are determined for each pack in each image and each side, subsequently if the item is identified on multiple sides the highest confidence side is used. The examiner notes that these operations are performed separately i.e. independently as they are generated with different confidences). The motivation to combine is “The individual items 20 are then identified on each of the four sides of the loaded pallet 22.” See paragraph 60 and “If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used.” (see paragraph 62). One of ordinary skill in the art could have easily used the method of Martin Jr to modify the teachings of Voegele to determine brands of multiple faces as part of the recognition process. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing dates of the claimed invention to combine Voegele and Martin Jr. to reach the aforementioned advantage.
Re claim 7 Voegele discloses all of the features of claim 2 Voegele does not expressly disclose wherein operation b) includes inferring the at least one classification for each of the plurality of images of the packages independently of one another. Martin Jr discloses wherein operation b) includes inferring the at least one classification for each of the plurality of images of the packages independently of one another (see paragraph 60-63 “The package type of each item 20 is identified by the computer, such as reusable beverage crate, corrugated tray with translucent plastic wrap, or fully enclosed cardboard or paperboard box. The branding of each item 20 is also identified by the computer (e.g. a specific flavor from a specific manufacturer), such as by reading the images/text on the packaging” “After individual items 20 are identified on each of the four sides of the loaded pallet 22, based upon the known dimensions of the items 20 and pallet 22, duplicates are removed, i.e. it is determined which items are visible from more than one side and appear in more than one image. If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used” note that brands are determined for each pack in each image and each side, subsequently if the item is identified on multiple sides the highest confidence side is used. The examiner notes that these operations are performed separately i.e. independently as they are generated with different confidences). The motivation to combine is “The individual items 20 are then identified on each of the four sides of the loaded pallet 22.” See paragraph 60 and “If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used.” (see paragraph 62). One of ordinary skill in the art could have easily used the method of Martin Jr to modify the teachings of Voegele to determine brands of multiple faces as part of the recognition process. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing dates of the claimed invention to combine Voegele and Martin Jr. to reach the aforementioned advantage.
Re claim 8 Voegele discloses and wherein the at least one machine learning model is trained on a plurality of images of packages. (see paragraph 145 note that machine learning models are trained using images of pallets see paragraph 74 note that the pallets contain cases corresponding to the packages). Voegele does not expressly disclose wherein the package contains a plurality of beverage containers. Martin Jr discloses wherein the package contains a plurality of beverage containers (See paragraph 2 “for example, the products may be cases of beverage containers (e.g. cartons of cans and beverage crates containing bottles or cans, etc). There are many different permutations of flavors, sizes, and types of beverage containers delivered to each store.” The motivation to combine is “The improved delivery system facilitates order accuracy from the warehouse to the store by combining machine learning and computer vision software” of the beverage containers of paragraph 2. One of ordinary skill in the art could have easily applied the method of Voegele to the beverage containers of Martin Jr to recognize cases of beverages. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Voegele and Martin Jr. to reach the aforementioned advantage.
Re claim 11 Voegele discloses all of the features of claim 2 Voegele does not expressly disclose wherein operation b) includes inferring at least one of a plurality of package types independently for each of the plurality of package faces. Martin Jr discloses w wherein operation b) includes inferring at least one of a plurality of package types independently for each of the plurality of package faces. (see paragraph 60-63 “The package type of each item 20 is identified by the computer, such as reusable beverage crate, corrugated tray with translucent plastic wrap, or fully enclosed cardboard or paperboard box. The branding of each item 20 is also identified by the computer (e.g. a specific flavor from a specific manufacturer), such as by reading the images/text on the packaging” “After individual items 20 are identified on each of the four sides of the loaded pallet 22, based upon the known dimensions of the items 20 and pallet 22, duplicates are removed, i.e. it is determined which items are visible from more than one side and appear in more than one image. If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used” note that brands are determined for each pack in each image and each side, subsequently if the item is identified on multiple sides the highest confidence side is used. The examiner notes that these operations are performed separately i.e. independently as they are generated with different confidences). The motivation to combine is “The individual items 20 are then identified on each of the four sides of the loaded pallet 22.” See paragraph 60 and “If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used.” (see paragraph 62). One of ordinary skill in the art could have easily used the method of Martin Jr to modify the teachings of Voegele to determine brands of multiple faces as part of the recognition process. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing dates of the claimed invention to combine Voegele and Martin Jr. to reach the aforementioned advantage.
Re claim 15 Voegele discloses all the features of claim 1 Voegele does not expressly disclose wherein the operations further include: e) receiving an expected SKU; and f) comparing the associated one of the plurality of SKUs with the expected SKU. Martin Jr. discloses the operations further include: e) receiving an expected SKU; and f) comparing the associated one of the plurality of SKUs with the expected SKU (see paragraph 50 “The SKUs of the products 20 on the pallet 22 are compared to the pick sheet 64 by the DC computer 26 in step 160, to ensure that all the SKUs associated with the pallet id of the pallet 22 on the pick sheet 64 are present on the correct pallet 22”). The motivation to combine is to ensure that all the SKUs associated with the pallet id of the pallet 22 on the pick sheet 64 are present on the correct pallet 22”. One of ordinary skill in the art could have easily modified Vogele to compare the detected SKU to an expected listed as described in Martin Jr. Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Vogele and Martin Jr to reach the aforementioned advantage.
Re claim 17 Voegele discloses and wherein the at least one machine learning model is trained on a plurality of images of packages. (see paragraph 145 note that machine learning models are trained using images of pallets see paragraph 74 note that the pallets contain cases corresponding to the packages). Voegele does not expressly disclose wherein the package contains a plurality of beverage containers. Martin Jr discloses wherein the package contains a plurality of beverage containers (See paragraph 2 “for example, the products may be cases of beverage containers (e.g. cartons of cans and beverage crates containing bottles or cans, etc). There are many different permutations of flavors, sizes, and types of beverage containers delivered to each store.” The motivation to combine is “The improved delivery system facilitates order accuracy from the warehouse to the store by combining machine learning and computer vision software” of the beverage containers of paragraph 2. One of ordinary skill in the art could have easily applied the method of Voegele to the beverage containers of Martin Jr to recognize cases of beverages. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Voegele and Martin Jr. to reach the aforementioned advantage.
Re claim 20 Voegele discloses and wherein the at least one machine learning model is trained on a plurality of images of packages. (see paragraph 145 note that machine learning models are trained using images of pallets see paragraph 74 note that the pallets contain cases corresponding to the packages). Voegele does not expressly disclose wherein the package contains a plurality of beverage containers. Martin Jr discloses wherein the package contains a plurality of beverage containers (See paragraph 2 “for example, the products may be cases of beverage containers (e.g. cartons of cans and beverage crates containing bottles or cans, etc). There are many different permutations of flavors, sizes, and types of beverage containers delivered to each store.” The motivation to combine is “The improved delivery system facilitates order accuracy from the warehouse to the store by combining machine learning and computer vision software” of the beverage containers of paragraph 2. One of ordinary skill in the art could have easily applied the method of Voegele to the beverage containers of Martin Jr to recognize cases of beverages. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Voegele and Martin Jr. to reach the aforementioned advantage.
Re claim 21 Voegele discloses all of the features of claim 20, Voegele does not expressly disclose wherein operation b) includes inferring at least one of a plurality of brands independently for each of the plurality of package faces. Martin Jr discloses wherein operation b) includes inferring at least one of a plurality of brands independently for each of the plurality of package faces (see paragraph 60-63 “The package type of each item 20 is identified by the computer, such as reusable beverage crate, corrugated tray with translucent plastic wrap, or fully enclosed cardboard or paperboard box. The branding of each item 20 is also identified by the computer (e.g. a specific flavor from a specific manufacturer), such as by reading the images/text on the packaging” “After individual items 20 are identified on each of the four sides of the loaded pallet 22, based upon the known dimensions of the items 20 and pallet 22, duplicates are removed, i.e. it is determined which items are visible from more than one side and appear in more than one image. If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used” note that brands are determined for each pack in each image and each side, subsequently if the item is identified on multiple sides the highest confidence side is used. The examiner notes that these operations are performed separately i.e. independently as they are generated with different confidences). The motivation to combine is “The individual items 20 are then identified on each of the four sides of the loaded pallet 22.” See paragraph 60 and “If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used.” (see paragraph 62). One of ordinary skill in the art could have easily used the method of Martin Jr to modify the teachings of Voegele to determine brands of multiple faces as part of the recognition process. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing dates of the claimed invention to combine Voegele and Martin Jr. to reach the aforementioned advantage.
Re claim 25 Voegele discloses all of the features of claim 20, Voegele does not expressly disclose wherein step b) includes inferring the at least one classification for each of the plurality of images of the packages independently of one another. Martin Jr discloses wherein step b) includes inferring the at least one classification for each of the plurality of images of the packages independently of one another (see paragraph 60-63 “The package type of each item 20 is identified by the computer, such as reusable beverage crate, corrugated tray with translucent plastic wrap, or fully enclosed cardboard or paperboard box. The branding of each item 20 is also identified by the computer (e.g. a specific flavor from a specific manufacturer), such as by reading the images/text on the packaging” “After individual items 20 are identified on each of the four sides of the loaded pallet 22, based upon the known dimensions of the items 20 and pallet 22, duplicates are removed, i.e. it is determined which items are visible from more than one side and appear in more than one image. If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used” note that brands are determined for each pack in each image and each side, subsequently if the item is identified on multiple sides the highest confidence side is used. The examiner notes that these operations are performed separately i.e. independently as they are generated with different confidences). The motivation to combine is “The individual items 20 are then identified on each of the four sides of the loaded pallet 22.” See paragraph 60 and “If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used.” (see paragraph 62). One of ordinary skill in the art could have easily used the method of Martin Jr to modify the teachings of Voegele to determine brands of multiple faces as part of the recognition process. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing dates of the claimed invention to combine Voegele and Martin Jr. to reach the aforementioned advantage.
Re claim 28 Voegele discloses all of the features of claim 20, Voegele does not expressly disclose wherein operation b) includes inferring at least one of a plurality of package types independently for each of the plurality of package faces. Martin Jr discloses w wherein operation b) includes inferring at least one of a plurality of package types independently for each of the plurality of package faces. (See paragraph 60-63 “The package type of each item 20 is identified by the computer, such as reusable beverage crate, corrugated tray with translucent plastic wrap, or fully enclosed cardboard or paperboard box. The branding of each item 20 is also identified by the computer (e.g. a specific flavor from a specific manufacturer), such as by reading the images/text on the packaging” “After individual items 20 are identified on each of the four sides of the loaded pallet 22, based upon the known dimensions of the items 20 and pallet 22, duplicates are removed, i.e. it is determined which items are visible from more than one side and appear in more than one image. If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used” note that brands are determined for each pack in each image and each side, subsequently if the item is identified on multiple sides the highest confidence side is used. The examiner notes that these operations are performed separately i.e. independently as they are generated with different confidences). The motivation to combine is “The individual items 20 are then identified on each of the four sides of the loaded pallet 22.” See paragraph 60 and “If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used.” (see paragraph 62). One of ordinary skill in the art could have easily used the method of Martin Jr to modify the teachings of Voegele to determine brands of multiple faces as part of the recognition process. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing dates of the claimed invention to combine Voegele and Martin Jr. to reach the aforementioned advantage.
Re claim 31 Voegele discloses all the features of claim 18 Voegele does not expressly disclose e) receiving an expected classification; and f) comparing the classification of the package determined in step d) with the expected classification. Martin Jr. d e) receiving an expected classification; and f) comparing the classification of the package determined in step d) with the expected classification (see paragraph 50 “The SKUs of the products 20 on the pallet 22 are compared to the pick sheet 64 by the DC computer 26 in step 160, to ensure that all the SKUs associated with the pallet id of the pallet 22 on the pick sheet 64 are present on the correct pallet 22”). The motivation to combine is to ensure that all the SKUs associated with the pallet id of the pallet 22 on the pick sheet 64 are present on the correct pallet 22”. One of ordinary skill in the art could have easily modified Vogele to compare the detected SKU to an expected listed as described in Martin Jr. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Vogele and Martin Jr to reach the aforementioned advantage.
Claim(s) 16 and 32 is/are rejected under 35 U.S.C. 103 as being unpatentable over Voegele US 2022/0332504 in view of Martin Jr US 2020/0273131 Jindal US 20220261579 A1 and Hogg US 2022/0318773.
Re claim 16 Voegele further discloses inferring a classification using a machine learning model (see paragraph 178 and 209 note that details such as labels on the cases may be determined using machine learning);
Voegele and Martin Jr does not disclose wherein the operations further include: g) comparing results of operation c) with the inferred at least one classification; and h) comparing the results of operation c) with the expected SKU.
Jindal discloses comparing results of operation c) with the at least one classification (see paragraph 93 “the extracted text from the identified objects in the captured scene is compared to the inputted name of the target object to determine if any of the identified objects are a match. In some embodiments, the extracted text is used from each and every identified object within the captured scene. In some other embodiments, the extracted text is used from only those identified objects having sufficiently matching label vectors where one or more classification labels match to one or more classification labels associated with the target object”) One of oinrdary skill in the art could have easily modified the combination of Voegele and Martin Jr with teaching of Jindal and the results text of a classified object is compared with OCR text and the results(i.e. text associated with a classification is compared with the text of the OCR), would have been predictable. These elements perform the same function in combination as they do separately as the combination on requires an additional comparison of data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jindal Voegele and Martin Jr.
Jindal Voegele and Martin Jr further do not disclose comparing the results of operation c) with the expected SKU.
Hogg discloses and h) comparing the results of operation c) with the expected SKU (see paragraph 48 note that OCR is compared to known structures of SKU). One of ordinary skill in the art could have easily compared OCR data to a expected SKU and the results (i.e. the two pieces of information are compared) would have been predictable. The combined features function the same in combination as the do separately as comparison operation is to simply an extra step to compare information against known information and the features of Hogg Vogel and Jindal are not meaningly changed. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hogg with Voegele, Martin Jr and Jindal.
Re claim 32 Voegele further discloses inferring a classification using a machine learning model (see paragraph 178 and 209 note that details such as labels on the cases may be determined using machine learning);
Voegele and Martin Jr does not disclose g) comparing results of step c) with the inferred at least one classification; and h) comparing the results of step c) with the expected classification.
Jindal discloses comparing results of step c) with the inferred at least one classification (see paragraph 93 ”the extracted text from the identified objects in the captured scene is compared to the inputted name of the target object to determine if any of the identified objects are a match. In some embodiments, the extracted text is used from each and every identified object within the captured scene. In some other embodiments, the extracted text is used from only those identified objects having sufficiently matching label vectors where one or more classification labels match to one or more classification labels associated with the target object”) One of ordinary skill in the art could have easily modified the combination of Voegele and Martin Jr with teaching of Jindal and the results text of a classified object is compared with OCR text and the results(i.e. text associated with a classification is compared with the text of the OCR), would have been predictable. These elements perform the same function in combination as they do separately as the combination on requires an additional comparison of data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jindal Voegele and Martin Jr.
Jindal Voegele and Martin Jr further do not disclose comparing the results of step c) with the expected classification.
Hogg US 2022/0318773 discloses and h) comparing the results of step c) with the expected classification (see paragraph 48 note that OCR is compared to known structures of SKU). One of ordinary skill in the art could have easily compared OCR data to a expected SKU and the results (i.e. the two pieces of information are compared) would have been predictable. The combined features function the same in combination as the do separately as comparison operation is to simply an extra step to compare information against known information and the features of Hogg Vogel and Jindal are not meaningly changed. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hogg with Voegele, Martin Jr and Jindal.
Cited Art
The following is a listing of cited art considered relevant to the application but no used in a rejection above:
Nechiporenko US 20120106787 A1 discloses An apparatus for constructing a data model of a goods package from a series of images, one of the series of images comprising an image of the goods package, comprises a processor and a memory for storing one or more routines. When the one or more routines are executed under control of the processor the apparatus extracts element data from goods package elements in the series of images and constructs the data model by associating element data from a number of visible sides of the goods package with the goods package. The apparatus may also analyse a candidate character string read in an OCR process from one of the series of images of the goods package. The apparatus may also analyse a barcode read from an image of a goods package. (see abstract).
Eckman US 20210133666 discloses In one implementation, a system for automatically profiling pallets includes a frame defining an opening that is sized and shaped for a pallet to pass through, and cameras mounted to the frame, the cameras being configured to capture images of a pallet as it passes through the frame. The system further includes a profiling computer system that is configured to receive the images captured by the cameras and to automatically profile the pallet based, at least in part, on analysis of the images. Automatically profiling the pallet includes generating a point cloud representing the pallet based on the images, determining a size of the pallet based on the point cloud generated from images of the pallet, identifying contents of the pallet based on the images, and providing pallet information identifying, at least, the size and contents of the pallet to a warehouse management system in association with the pallet. (See abstract).
Kushner US 20210350432 A1 discloses In some embodiments, the interactive graphical element comprises a hyperlink to a sales webpage for the product. In some embodiments, the interactive graphical element is an embedded hotspot. In some embodiments, (b) comprises processing a plurality of identification codes associated with the one or more items that appear in the electronic version of the catalog. In some embodiments, the plurality of identification codes comprises stock-keeping units. In some embodiments, (b) comprises processing a plurality of images of the one or more items that appear in the electronic version of the catalog. In some embodiments, (b) comprises processing a plurality of descriptions of the one or more items that appear in the electronic version of the catalog. In some embodiments, the algorithm is a machine learning algorithm. In some embodiments, the machine learning algorithm comprises an optical character recognition algorithm and an image processing algorithm. In some embodiments, the machine learning algorithm has been trained on a labeled set of images of products. In some embodiments, the method further comprises generating an interactive visualization of the digital map, wherein the interactive visualization is configured to be edited by a user. In some embodiments, the data in the digital map comprises a stock keeping unit, universal product code, description, price, catalog geometric coordinates, and hyperlinks associated with the item. In some embodiments, the method further comprises using the digital map to generate an electronic commerce website comprising shopping cart functionality and payment integration. (see paragraph 6)
White US 20220116737 A1 discloses Determining the one or more triggering features 720 may comprise one or more of determining a plurality of triggering features 720, determining a pattern of triggering features 720, determining a plurality of feature vectors, identifying an object, inputting the imaging data into a machine learning model, performing optical character recognition, detecting a graphic, detecting a symbol, or determining that one or more characters indicate an identifier. One or more machine learning models may be trained to recognize assets in the environment (e.g., products or other assts at a store). An image of one or more of the assets may be captured. The resulting imaging data may be input into the one or more machine learning models (e.g., which may be accessed by the user device, the content platform 102, or the association service 701, and/or the like). The one or more machine learning models may output an indication (e.g., identifier, SKU, UPC) of the one or more assets and/or one or more services. In some scenarios, the one or more services may be determined based on the assets. (see paragraph 135)
Patchen US 20210158274 discloses Referring to FIG. 3B, if the card is determined to be a small run style card (e.g., under 300 card IDs) at 308, the card is next checked whether it is an operation card having a custom printed bar code at step 314. At step 316 machine learning OCR or object detection is used to read a SKU from the bar code, which is then updated in a database at step 318. Otherwise, at step 320 machine learning classification blocks construct a unique SKU number based on characteristics of the card, such as alternate artwork, collector's numbers, and/or special stamps, to name a few. In some embodiments, the system includes one or more array tables to guide the system's determine of what features to check for on the cards. At step 322 the SKU is recorded in the database. In some embodiments, the SKU format can comprise: (Promo[P], Token[T], or Foil[F] optional)+(3 letter edition code)+“−”+(3 digit collector's number)+(variant optional). Example SKUs following this format can include: (see paragraph 44)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEAN T MOTSINGER whose telephone number is (571)270-1237. The examiner can normally be reached 9AM-5PM.
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/SEAN T MOTSINGER/ Primary Examiner, Art Unit 2673
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