DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This is in reply to communication filed on 07/29/2025.
Claims 1 and 7 have been amended.
Claims 5-6 and 11-12 have been canceled.
Claims 1-4 and 7-10 are currently pending and have been examined.
Response to Arguments
In response to Applicant Arguments /Remarks made in an amendment filled on 07/29/2025:
Regarding 35 USC § 101 rejection:
Applicant argument submitted under the title “Claim Rejections - 35 USC § 101” in pages 8-12:
Applicant's arguments have been fully considered but they are not persuasive.
In response, the examiner respectfully disagrees and emphasizes none of the receiving, generating, converting, determining, comparing, obtaining, generating, outputting steps, whether taken individually or collectively, have not been shown to affect any form of technical change or improvement whatsoever, and are abstract idea. Applicant's claims have not been shown to modify, reconfigure, manipulate, or transform the computer, computer software, or any technical elements in any discernible manner, much less yield an improvement thereto. There is simply no showing of implementing any of the claim steps, individually or in combination, amounts to a technological improvement, nor the alleged “technical solution implemented through the following specific” suggested by Applicant. Although Applicant asserts that “The system does not merely computerize a manual checkout process; it uses novel ML inference to generate product identifiers from visual cues and execute automated billing a solution impossible without specific computing components” the Examiner first notes that managing self-billing interactions is not reasonably understood as a technology, but instead involves organizing of human activity.
The data collection, recognition, and storage concept described in the claim is similar to the data collection and management concepts that were held to be abstract ideas in Content Extraction, TLI Communications, and Electric Power Group. Although the claim enumerates the type of information (i.e., the images, and location data) that is acquired, stored and analyzed, the Federal Circuit has explained in Electric Power Group and Digitech that the mere selection and manipulation of particular information by itself does not make an abstract concept any less abstract. Further, the claim is not made any less abstract by the invocation of a programmed computer. Unlike Enfish, where the claims were focused on a specific improvement in how the computer functioned, the claim here merely uses the computer as a tool to perform the abstract concepts.
Furthermore, the recited “processors”, “the one or more electronic devices associated with the user”, “convolutional neural network model”, “queryable data structure in a storage database”, “a user interface of the one or more electronic devices associated with the user”, “convolutional neural network model”, “database”, this recitation to the generic computer technology that is being used as a tool to execute the steps that define the abstract idea do not provide for integration at the 2nd prong and do not provide for significantly more at step 2B.
Even assuming, for the sake of argument, that the claims amount to an improvement over prior art techniques for managing self-billing interactions, such an improvement would be considered, at most, an improvement confined within the abstract idea itself, which is not enough to confer eligibility on the claim. For the reasons above, Applicant’s argument is not persuasive.
Regarding 35 USC § 103 rejection:
Applicant argument submitted under the title “Claim Rejections - 35 USC § 103” in pages 13-16:
Applicant's arguments have been fully considered but they are not persuasive.
Uchiyama teaches system and method of training an appearance signature extractor using a training data set, the training data set including input images associated with a plurality of domains utilizing CNN (Convolutional Neural Network). Uchiyama further teaches data processed by ANN (Artificial Neural Network) could be a multi-dimensional array. In mathematical terms, the multi-dimensional array is referred to as a “tensor”, as Convolution is a commonly known filter operation. See [0010-0011], and the domain label is a one-hot vector, The domain label is converted into a one-hot vector, see [0095], which reads on the claimed subject matter as explained further in the Claim Rejections - 35 USC § 103 below.
Accordingly, it would have been obvious to one of ordinary skill in the image processing art before the effective filing date to modify Crooks to include the known technique of raining an appearance signature extractor using a training data set utilizing CNN (Convolutional Neural Network), as taught by Uchiyama, where this would be performed in order to substantially overcome, or at least ameliorate, one or more disadvantages of existing arrangements. See Uchiyama [0024].
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.
Claims 1-4 and 7-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1:
Claims 1-4 recite a method, which is directed to a process.
Claims 7-10 recite a system, which is directed to a machine.
Therefore, each claim falls within one of the four statutory categories.
Step 2A, Prong 1 (Is a judicial exception recited?):
The independent claims 1 and 7 recite an abstract idea. This idea is described by the steps of:
receiving at least one of one or more images and one or more videos from a user, wherein the one or more images correspond to a format of tensor of order three;
generating one or more visual signatures based on the at least one of: received one or more images and one or more videos using a visual signature, wherein generating the one or more visual signatures based on the received one or more images and one or more videos comprises:
converting the tensor of order three format of the one or more images into a tensor of order one format using the visual signature based, wherein the one or more visual signatures corresponds to the form of tensor of order one format,
wherein the generated one or more visual signatures are stored in the form of a queryable data;
determining one or more products corresponding to the generated one or more visual signatures, wherein determining one or more products corresponding to the generated one or more visual signatures further comprises:
comparing the one or more visual signatures with one or more prestored visual signatures in the prestored visual signature, wherein the comparison is performed using at least one of: Euclidain distance and Cosine distance;
obtaining one or more product parameters from the determined one or more products, wherein the one or more product parameters comprises name of the one or more products, price details of the one or more products, offers presented for the one or more products, manufactured date and expiry date of the one or more products, and coupons applicable for the one or more products;
generating one or more electronic records based on the received one or more image and the one or more product parameters, wherein the one or more electronic records comprise: a list of the one or more products purchased by the user and a total sum of amount to be paid for the one or more products; and
outputting the generated one or more electronic records
These claims recite a certain method of organizing human activity. The claims recite to a certain method of organizing human activity as the above abstract idea limitations are directed to commercial interaction. The examiner finds the claims to simply recite steps of a sales activities or behaviors of an activity that involves person and a computer (i.e., a method of processing payment). The Examiner additionally finds the claims to be similar to an example the courts have identified as being a certain method of organizing human activity:
i) buySAFE, Inc. v. Google, Inc., 765 F.3d. 1350, 112 USPQ2d 1093 (Fed. Cir. 2014). The agreement at issue in buySAFE was a transaction performance guaranty, which is a contractual relationship. 765 F.3d at 1355, 112 USPQ2d at 1096. The patentee claimed a method in which a computer operated by the provider of a safe transaction service receives a request for a performance guarantee for an online commercial transaction, the computer processes the request by underwriting the requesting party in order to provide the transaction guarantee service, and the computer offers, via a computer network, a transaction guaranty that binds to the transaction upon the closing of the transaction. 765 F.3d at 1351-52, 112 USPQ2d at 1094.
ii) structuring a sales force or marketing company, which pertains to marketing or sales activities or behaviors, In re Ferguson, 558 F.3d 1359, 1364, 90 USPQ2d 1035, 1038 (Fed. Cir. 2009).
iii) Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 123 USPQ2d 1100 (Fed. Cir. 2017). The business relation at issue in Credit Acceptance is the relationship between a customer and dealer when processing a credit application to purchase a vehicle. The patentee claimed a "system for maintaining a database of information about the items in a dealer’s inventory, obtaining financial information about a customer from a user, combining these two sources of information to create a financing package for each of the inventoried items, and presenting the financing packages to the user." 859 F.3d at 1054, 123 USPQ2d at 1108.
These claims recite a certain method of organizing human activity. The claims recite to a certain method of organizing human activity as the above abstract idea limitations are directed to managing personal behavior or relationships or interactions between people. The examiner finds the claims to simply recites steps of following rules or instructions to process a payment. The Examiner additionally finds the claims to be similar to an example the courts have identified as being a certain method of organizing human activity:
i) filtering content, BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016) (finding that filtering content was an abstract idea under step 2A, but reversing an invalidity judgment of ineligibility due to an inadequate step 2B analysis).
ii) considering historical usage information while inputting data, BSG Tech. LLC v. Buyseasons, Inc., 899 F.3d 1281, 1286, 127 USPQ2d 1688, 1691 (Fed. Cir. 2018).
Step 2A, Prong 2 (Is the exception integrated into a practical application?):
This judicial exception is not integrated into a practical application because the claims satisfy the following criteria, which indicate that the claims do not integrate the abstract idea into practical application:
The claimed additional limitations are:
Claim 1: one or more hardware processors, the one or more electronic devices associated with the user, convolutional neural network model, queryable data structure in a storage database, a user interface of the one or more electronic devices associated with the user, convolutional neural network model, database,
Claim 7: computing system capable of self-billing the one or more products, the computing system comprising: one or more hardware processors; and a memory coupled to the one or more hardware processors, wherein the memory comprises plurality of modules in the form of programmable instructions executable by the one or more hardware processors, image receiver module, the one or more electronic devices associated with the user, visual signature generation module, convolutional neural network model, queryable data structure in the storage database, product determination module, product parameter extraction module, record generation module, output module, convolutional neural network model, database,
The additional limitations are directed to using a generic computer to process information and perform the abstract idea. Therefore, the limitations merely amount to adding the words “apply it” (or an equivalent) to the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
Step 2B (Does the claim recite additional elements that amount to significantly more that the judicial exception?):
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As for Step 2B analysis, knowing the consideration is overlapping with Step 2A, Prong 2. The Step 2B considerations have already been substantially addressed under Step 2A Prong 2, see Step 2A Prong 2 analysis above. As discussed above, the additional imitations amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
In addition, the dependent claims recite:
Step 2A, Prong 1 (Is a judicial exception recited?):
Dependent claims 2-4 and 8-10 recitations further narrowing the abstract idea recited in the independent claims 1 and 7 and therefore directed towards the same abstract idea.
Step 2A, Prong 2 and Step 2B:
The dependent claims 2-4 and 8-10 further narrow the abstract idea recited in the independent claims 1 and 7 and are therefore directed towards the same abstract idea.
The dependent claims recite the following additional limitations:
Claim 3: database,
Claim 4: user interface of the one or more electronic devices,
Claim 8: computing system, image receiver module,
Claim 9: computing system visual signature generation module, database,
Claim 10: computing system product determination module, user interface of the one or more electronic devices,
However, the examiner finds each of these additional elements to be directed to merely “apply it” or applying a generic technology to perform the recited abstract idea of receiving input information from a user and presenting received information, the recitation to the generic computer technology that is being used as a tool to execute the steps that define the abstract idea do not provide for integration at the 2nd prong and do not provide for significantly more at step 2B.
Therefore, the limitations on the invention of claims 1-4 and 7-10, when viewed individually and in ordered combination are directed to in-eligible subject matter.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4 and 7-10 are rejected under 35 U.S.C 103 as being unpatentable over Crooks (US 2022/0051213 A1, hereinafter “Crooks”) in view of Uchiyama (US 20200272860 A1, hereinafter “Uchiyama”) further in view of Mathias et al. (US 2022/0129972 A1, hereinafter “Mathias”) furthermore in view Jaber et al. (US 2015/0278224, hereinafter “Jaber”) of further in view of Roy et al. (US 2013/0080289, hereinafter “Roy”).
Regarding claims 1 and 7. Crooks discloses a method for self-billing one or more products, the method comprising:
receiving, by one or more hardware processors, at least one of one or more images and from a user via the one or more electronic devices associated with the user, wherein the one or more images correspond to a format of tensor of order three; (Crooks, [0023]; “mobile device 120 or handheld scanner with produce identifier device 110 (hereinafter just “specialized shopping device 110”) are used to capture a color, texture, or shape image of the produce being purchased at step 2”)
generating, by one or more hardware processors, one or more visual signatures based on the at least one of: received one or more images and using a visual signature based convolutional neural network model, wherein the generated one or more visual signatures are stored in the form of a queryable data structure in a storage database; (Crooks, [0024]; “The sample image from either the camera 121 of the mobile device 120 or produce meter 112 is then processed by mobile app 122 or transaction manager 113 to generate a color, texture, and/or shape signature for the produce item. This image-based signature for the produce item is provided over a network connection 170 to produce identifier 131. Produce identifier 131 searches produce signature data store 132 for a match or a close match on the produce signature”)
wherein generating the one or more visual signatures based on the received one or more images and one or more videos comprising:
determining, by one or more hardware processors, one or more products corresponding to the generated one or more visual signatures (Crooks, [0024]; “This image-based signature for the produce item is provided over a network connection 170 to produce identifier 131. Produce identifier 131 searches produce signature data store 132 for a match or a close match on the produce signature and returns a single produce identifier or a list of substantially close produce identifiers”), wherein determining one or more products corresponding to the generated one or more visual signatures further comprises: comparing the one or more visual signatures with one or more prestored visual signatures in the prestored visual signature (Crooks, Fig. 3, [0030]; “Identification can be achieved through generation of the image signature of the produce item and comparing such image signature with the image signature recorded and linked in the shopping cart”), wherein
obtaining, by one or more hardware processors, one or more product parameters from the determined one or more products, wherein the one or more product parameters comprises offers presented for the one or more products, and coupons applicable for the one or more products; (Crooks, [0026]; “At step 2, the image signature may be used to identify … alerts the shopper to either a discounted price on the user-scanned produce item based on image signature”)
generating, by one or more hardware processors, one or more electronic records based on the received one or more image and the one or more product parameters (Crooks, [0030]; “Identification can be achieved through generation of the image signature of the produce item and comparing such image signature with the image signature recorded and linked in the shopping cart”), wherein the one or more electronic records comprise: a list of the one or more products purchased by the user and a total sum of amount to be paid for the one or more products; and (Crooks, [0031]; “The proper weight and price for the produce item is recorded through interaction between transaction manager 161 and transaction manager 141 over a network connection 170”, [0053-0055]; “At 330, the weigh terminal item verifier associated a recorded weight for the item provided by the weigh scale with the particular item identifier in the list when 320 matches the image signature to the item image signature … at 332, the weigh terminal item verifier assigns a price to the particular item identifier in the list based on the recorded weight”)
Crooks substantially discloses the claimed invention; however, Crooks fails to explicitly disclose the “the comparison is performed using at least one of: Euclidain distance and Cosine distance, converting the tensor of order three format of the one or more images into a tensor of order one format using the visual signature based convolutional neural network model, wherein the one or more visual signatures corresponds to the form of tensor of order one format”. However, Uchiyama teaches:
the comparison is performed using at least one of: Euclidain distance and Cosine distance; (Uchiyama, [0108]; “The method 400 continues to a determining step 430 upon completion of steps 420 and 425. Step 430 determines a distance between the first and second appearance signatures determined at steps 420 and 425. The distance can be determined using Euclidian distance between vectors of the first and second appearance signatures or other known techniques such as cosine distance and L1 distance”)
converting the tensor (Uchiyama, [0010]; “Data processed by ANN (Artificial Neural Network) could be a multi-dimensional array. In mathematical terms, the multi-dimensional array is referred to as a ‘tensor’”) of order three format of the one or more images into a tensor of order one format using the visual signature based convolutional neural network model (Uchiyama, [0011-0019]; “Convolution is a commonly known filter operation … In machine learning, a “one-hot vector” is commonly used to represent a “class”, a “category” or an “identity” of object”, [0095]; “The method 500 continues from step 570 to a determining step 540 … The domain label is converted into a one-hot vector, where a vector element that corresponds to the correct domain is one and other remaining vector elements are zero”), wherein the one or more visual signatures corresponds to the form of tensor of order one format, (Uchiyama, [0087]; “The input 301 is a tensor of W×H×3 (width×height×3). The convolution layer 310 outputs a tensor of W×H×C1. The convolution layer 320 outputs a tensor of W×H×C2. The max pooling layer 327 outputs a tensor of W/2×H/2×C2. The convolution 330 layer outputs a tensor of W/2×H/2×C3 … The fully connected layer 360 outputs a vector of C5 being an appearance signature 399”)
Therefore, it would have been obvious to one of ordinary skill in the image processing art before the effective filing date to modify Crooks to include the comparison is performed using at least one of: Euclidain distance and Cosine distance, converting the tensor of order three format of the one or more images into a tensor of order one format using the visual signature based convolutional neural network model, wherein the one or more visual signatures corresponds to the form of tensor of order one format, as taught by Uchiyama, where this would be performed in order to substantially overcome, or at least ameliorate, one or more disadvantages of existing arrangements. See Uchiyama [0024].
The combination of Crooks in view of Uchiyama substantially discloses the claimed invention; however, The combination of Crooks in view of Uchiyama fails to explicitly disclose the “name of the one or more products, price details of the one or more products, outputting, by one or more hardware processors, the generated one or more electronic records on a user interface of the one or more electronic devices associated with the user”. However, Mathias teaches
name of the one or more products, price details of the one or more products, (Mathias, [0045]; “The image processor is configured to determine a product identifier associated with the image (e.g., a product identifier associated with the barcode). The product identifier can be an identifier for the product, including the name, a photo of the product, or other information that identifies the product associated with the image”, [0026]; “The database 120 can include … price of a product, etc.”)
outputting, by one or more hardware processors, the generated one or more electronic records on a user interface of the one or more electronic devices associated with the user (Mathias, [0046]; “At step 206, the method 200 includes updating the ecommerce website that is displayed on the web browser to include the product associated with the first image (and the product identifier) in an online shopping cart that is on the ecommerce website. The online shopping cart can contain the product name, price, and other relevant data associated with the image taken by the camera 160”).
Therefore, it would have been obvious to one of ordinary skill in the retail management art before the effective filing date to modify Crooks to include name of the one or more products, price details of the one or more products, outputting, by one or more hardware processors, the generated one or more electronic records on a user interface of the one or more electronic devices associated with the user, as taught by Mathias, where this would be performed in order to provide simplified means for shopping and paying in retail environments that simplifies the whole shopping and payment process. See Mathias [0003].
The combination of Crooks in view of Uchiyama further in view of Mathias substantially discloses the claimed invention; however, the combination of Crooks in view of Uchiyama further in view of Mathias fails to explicitly disclose the “one or more videos”. However, Jaber teaches: one or more videos (Jaber, [0050]; “The query image 103 can comprise still image data or video image data. Examples of still image formats include RAW, JPEG, GIF, PNG, BMP, etc. Examples of types of video image formats include MPEG, AVI, WMV, MP4, etc.”)
Therefore, it would have been obvious to one of ordinary skill in the retail management art before the effective filing date to modify Crooks to include known technology of utilizing one or more videos and/or images, as taught by Jaber, where this would be performed in order to successfully recognize a subject in an image and positively identify any subject in the environment of the customer. See Jaber [0005].
The combination of Crooks in view of Uchiyama further in view of Mathias furthermore in view of Jaber substantially discloses the claimed invention; however, the combination fails to explicitly disclose the “manufactured date and expiry date of the one or more products”. However, Roy teaches
manufactured date and expiry date of the one or more products, (Roy, [0156-0160]; “encodes information relating to a product including product identifying information … Date of Manufacture … Date of Expiry”)
Therefore, it would have been obvious to one of ordinary skill in the retail management art before the effective filing date to modify Crooks to include manufactured date and expiry date of the one or more products, as taught by Roy, where this would be performed in order to make a customer's shopping experience as efficient and effortless within the strict cost constraints. See Roy [0002].
Regarding claims 2 and 8. The combination of Crooks in view of Uchiyama further in view of Mathias furthermore in view of Jaber further in view of Roy disclose the method as claimed in claim 1, wherein receiving the one of or more images or one or more videos from the user further comprises: the one or more images of the one or more products comprising groceries, medicines, vegetables and dairy products. (Crooks, [0002-0003]; “grocery items … items sold by weight, such as produce (i.e., the claimed groceries and vegetables) … Produce is either sold by quantity (i.e., the claimed medicines and dairy products or by weight”).
Regarding claims 3 and 9. The combination of Crooks in view of Uchiyama further in view of Mathias furthermore in view of Jaber further in view of Roy disclose the method as claimed in claim 1, wherein
The combination of Crooks in view of Uchiyama substantially discloses the claimed invention; however, The combination of Crooks in view of Uchiyama explicitly disclose the “generating the one or more visual signatures based on the received one or more images or one or more videos further comprises: updating a prestored visual signature database by adding and deleting inventory items based on addition or deletion of the one or more products on an image registry comprising a data associated with an inventory of a store”. However, Mathias teaches
generating the one or more visual signatures based on the received one or more images or one or more videos further comprises: updating a prestored visual signature database by adding and deleting inventory items based on addition or deletion of the one or more products on an image registry comprising a data associated with an inventory of a store. (Mathias, [0020]; “the computer platform can send purchase information (e.g., identity and quantity of items purchased) to the database so for purchase history storage and inventory update”, [0059]; “the method 200 can include sending an inventory update message to the database 120 … include at least the products purchased, the number of each product purchased, and the store location where the items were purchased”)
Therefore, it would have been obvious to one of ordinary skill in the retail management art before the effective filing date to modify Crooks to include generating the one or more visual signatures based on the received one or more images or one or more videos further comprises: updating a prestored visual signature database by adding and deleting inventory items based on addition or deletion of the one or more products on an image registry comprising a data associated with an inventory of a store, as taught by Mathias, where this would be performed in order to allowing the retailer to keep up to date inventory on the business's website. See Mathias [0059].
Regarding claims 4 and 10. The combination of Crooks in view of Uchiyama further in view of Mathias furthermore in view of Jaber further in view of Roy disclose the method as claimed in claim 1, wherein
determining, the one or more products corresponding to the generated one or more visual signatures further comprises: obtaining more than one of one or more visual signatures corresponding to the one or more products; (Crooks, Fig. 3, [0049]; “In an embodiment of 321 and at 322, the weigh terminal item verifier obtains registered item image signatures for the list based on the item identifiers present in the list of identifiers”) and
prompting a notification for clarifying the one or more visual signatures to be paired with the one or more products on the user interface of the one or more electronic devices. (Crooks, Fig. 3, [0052]; “In an embodiment of 324 and at 325, the weigh terminal item verifier presents a correction on a display of the weigh terminal for selection of a substitute item identifier to replace the particular item identifier when the image signature matches one of the other registered item image signatures”)
Conclusion
1. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
2. Any inquiry concerning this communication or earlier communications from the examiner should be directed to AVIA SALMAN whose telephone number is (313)446-4901. The examiner can normally be reached Monday thru Friday; 9:00 AM to 5:00 PM EST.
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/AVIA SALMAN/Primary Patent Examiner, Art Unit 3627
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