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 .
Claims 1-20 remain pending in the application under prosecution and have been re-examined.
In response to this Office action, the Examiner respectfully requests that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line numbers in the specification and/or drawing figure(s). This will assist the Examiner in prosecuting this application.
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Response to Arguments
Applicant's arguments filed 05/01/2026 have been fully considered but they are not persuasive. Explanation is provided in the body of the rejection.
Claim 11-18 are allowable.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-10 and 19-20 are rejected under 35 U.S.C. 103 as being obvious over US 20200042491 (LICHT et al) in view of US 20240005750 A1 (SCHOCH et al).
The applied reference has a common assignee with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2).
This rejection under 35 U.S.C. 103 might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C.102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B); or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement. See generally MPEP § 717.02.
With respect to claim 1, LICHT teaches a method, comprising: receiving transaction information for a transaction, an item image for an item of the transaction, and a candidate item identifier for the item (item/consumer tracker interfaced to cameras/scanners for receiving item images for a transaction and a transaction manager for receiving item details/identifier by scanning the item from images taking of the transaction at the transaction terminal) [Par. 0015]; selecting a head machine learning model (MLM) from a plurality of head MLMs based at least on the transaction information; obtaining an item classification data from a root MLM based on the item image (machine-learning item detector grouping/classifying the item images as batches collected for a number of the checkout transactions) [Par. 0031]; obtaining a predicted item identifier for the item from the head MLM based on the candidate item identifier, the item classification data, and localized metadata for the head MLM (the item identifier training a machine-learning algorithm on the item image to recognize the item from subsequent images taken of the item based on the item identifier, the expected output being the item identifier identifying features, factors, weights taking the item image as input and produces the item identifier as output) [Par. 0031]; and receiving an actual item identifier for the item and updating the actual item identifier and the item classification data in the localized metadata of the head MLM (the images for each item and its actual identification (item description) provided by the item/consumer tracker to the machine-learning item detector at the end of each transaction or during each transaction with the machine-learning item detector maintaining the item images and item descriptions) [Par. 0022-0025].
Independent claims 1 has been amended to recite MLM, wherein the localized metadata comprises at least one of a retailer identifier, a store identifier, a terminal identifier, and item purchase statistics. However, one having at least ordinary skill in the art having LICHT’s disclosure before the effective filing date of the instant application, would have the motivation to come with the claimed feature because LICTH teaches:
method for reinforcement machine learning for item detection by obtaining multiple images of the item with an item identifier that are captured for the item from when the item is picked by a consumer from a store shelf and carried through the store to a checkout station where the checkout is processed;
the method employs a variety of image processing needed to properly identify the item in the images and generates metadata information including:
a plurality of transaction terminals, each having one or more hardware processors that execute executable instruction or machine-learning item detection using machine learning approaches to identify transaction manager terminal; the historical availability of each item; the store picked items; the identified image of the item for payment, the item code and/or item description; newly introduced items within the store for which the machine-learning item detector may have never previously received any training on (item purchase statistics) [Par. 0018-0021; Par. 0037-042]; wherein
small portions of those images or metadata associated with those images are provided by an image/consumer tracker to machine-learning item detector, and the machine-learning item detector returns to the item/consumer tracker an item identification, an item identification with a confidence factor [Par. 0030-0031; Par. 0037-0042].
SCHOCH teaches computer-readable media for detecting candidate items in response to a learning model event at a self-checkout (SCO) machine, determining that the event is an item-identifying event, triggering capture of image data of the item identified by the item-identifying event responsive to detecting the event and generating enhanced item identification data that associates the captured image data with identifying information of the item, wherein the identifying information include: item identifying information; store record information; customer interface terminal; retail establishment; financial transaction information [Par. 0037-0042; Par. 0027-0032; Par. 0048-0052], the self-checkout machine to obtain digital information and analyze the item using the trained information to return transaction with confidence [Par. 0027-0029; Par. 0048-0052].
Therefore, it would have been obvious to one having at ordinary skill in the art, before the effective filing date of the instant application, to combine the maintained local metadata of LICHT with that of SCHOCH in order to produce an enhanced item identification data to obtain a more refined model capable of identifying items with greater accuracy and/or capable of identifying a larger set of items with at least the minimum acceptable confidence level, as taught by SCHOCH [Par. 0028].
With respect to claim 19, the system “comprising: at least one server comprising at least one processor and a non- transitory computer-readable storage medium; the non-transitory computer-readable storage medium comprising executable instructions; and the executable instructions when executed by at least one processor cause the at least one processor to perform operations” [plurality of transaction terminals, each having one or more hardware processors that execute executable instructions from a non-transitory computer-readable storage medium representing a transaction manager (LICHT’s Par. 0014; Par. 0046)] corresponds to method of claim 1, is therefore rejected in view of similar reasoning.
With respect to claim 2, LICHT and SCHOCH, combined teach a method comprising: maintaining the localized metadata with statistics relevant to sales of a store, department of a store, and customers of the store (images taken where the item/consumer tracker flags each image with a unique identifier, such that a single item can be flagged and associated with multiple images, metadata associated with those images are provided by the image/consumer tracker to the machine-learning item detector) [LICHT’s Par. 0031].
With respect to claim 3, LICHT and SCHOCH, combined, teach the method further comprising: maintaining and updating the localized metadata without any manual intervention (self-Service terminal for self-checkout where the transaction terminal) [LICHT’s Par. 0023-0024].
With respect to claim 4, LICHT and SCHOCH, combined teach a method, wherein receiving the transaction information further includes receiving at least the transaction information and the candidate item identifier from a transaction terminal that is performing the transaction (item/consumer tracker uniquely keeping track of images representing a particular unique store and on images of items possessed by the consumer within the store) [LICHT’s Par. 0019].
With respect to claim 5, LICHT and SCHOCH, combined teach a method, wherein selecting further includes identifying a retailer, a store of the retailer, and a geographic region for the store based on a terminal identifier provided in the transaction information (machine-learning item detector using machine learning approaches to identify factors and features from the training set of images that classify the items such that the machine-learning item detector can predict and identify the item classification from provided images) [LICHT’s Par. 0015-0017; Par. 0037-0042].
With respect to claim 6, LICHT and SCHOCH, combined teach a method, wherein selecting further includes traversing a hierarchy linked to the plurality of head MLMs using the transaction information, the retailer, the store, and the geographic region to identify the head MLM (the item to be trained on multiple store locations for multiple stores, with each store including its own local item/consumer tracker interfacing the machine-learning item detector) [LICHT’s Par. 0027; Par. 0030].
With respect to claim 7, LICHT and SCHOCH, combined teach a method, wherein selecting further includes selecting the head MLM based on a customer identifier for a customer provided in the transaction information (the item identifier obtaining an image of an item with an item identifier, the item identifier receiving images for the item that the item identifier having been uniquely identified) [LICHT’s Par. 0037-0038].
With respect to claim 8, LICHT and SCHOCH, combined teach a method, wherein selecting further includes selecting the head MLM based on a store identifier for a store provided in the transaction information (item identifier obtaining images of the item with the item identifier that were captured for the item from when the item was picked by a consumer from a store shelf and carried through the store to a checkout station where the checkout is processed) [LICHT’s Par. 0038-0039].
With respect to claim 9, LICHT and SCHOCH, combined teach a method, wherein obtaining the predicted item identifier further includes providing the item classification data as a seed item classification vector produced by the root MLM based on the item image (images of items captured as consumers located within the store with the item/consumer tracker uniquely keeps track of images representing a particular class) [LICHT’s Par. 0019-0020].
With respect to claim 10, LICHT and SCHOCH, combined teach method, wherein obtaining the predicted item identifier further includes providing the predicted item identifier as a price lookup (PLU) code to a transaction terminal that is performing the transaction, wherein the item is a produce item (item/consumer tracker keeping track of images representing a particular, the transaction terminal for item checkout, the item/consumer tracker identifying the transaction when the consumer scans the identified image of the item for payment, the item code and/or item description resolved by the transaction manager) [LICHT’s Par. 0020-0021].
With respect to claim 20, LICHT and SCHOCH, combined teach the system, wherein the transaction terminal is a self- service terminal operated by a customer during the transaction or the transaction terminal is a point-of-sale terminal operated by a cashier on behalf of the customer during the transaction [consumers processing with a Self-Service Terminal (SST), a store attendant, their mobile devices to process sate transaction (Par. 0014-0018); enhanced item identification data from learning input received at a self-checkout (SCO) machine to perform object detection/using machine learning model trained using training) SCHOCH’ Par. 0022-0026].
Allowable Subject Matter
Claims 11-18 are allowed.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
A. H. Kopap and E. -E. Elfakharany, "Association and Classification Analysis in Retail Case Study," 2013 23rd International Conference on Computer Theory and Applications (ICCTA), Alexandria, Egypt, 2013, pp. 141-149.
US 20260037948 A1 (DEERIN) teaching computer-implemented method that generates a conversion matrix for mapping an output of a second machine learning model to an expected output of a first machine learning model, a set of example data being iteratively fed to the first machine learning model to create a set of first outputs and to the second machine learning model to create a set of second outputs.
US 11488400 B2 (ZUCKER et al) teaching systems, methods, software, and data structures for context-aided machine vision item differentiation including: receiving an image of the customer holding an item, and performing item identification processing on the image to identify the item the customer is holding, the item identification processing includes generating a multidimensional feature vector with a data representation of each feature extracted from the image forming each dimension of the multidimensional feature vector; the item identification processing further including matching the multidimensional feature vector to a stored multidimensional feature vector of a plurality of stored multidimensional feature vectors that are each associated with a respective product and passing a representation of the identified item for use by another process.
US 20230252760 A1 (BJELCEVIC et al) teaching single item image captured of an item situated within a given zone of a transaction wherein: each different zone for each given item is associated a plurality of single item images captured by different cameras at different angles and perspectives of the transaction area; the single item images are passed to an existing segmentation Machine-Learning Model (MLM) and accurate masks for the items produced by the existing MLM are retained for background image of an empty transaction area is obtained, each retained single item image is cropped.
US 20230252343 A1 (McDanie et al) teaching multiple images of multiple items captured of a transaction area during a checkout, wherein: the Red-Green-Blue (RGB) data associated with each item image patch is collected across the images and provided as input by a Machine-Learning Model (MLM), which returns an item code for the item; the patches associated with the images are presented to an operator of a checkout and the operator scans an item barcode for that item; the patches are labeled within the images with the item code and additional images of the item are captured and labeled with the item code, the labeled images are used in a subsequent training session with the MLM to improve its item recognition accuracy for the item.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PIERRE MICHEL BATAILLE whose telephone number is (571)272-4178. The examiner can normally be reached Monday - Thursday 7-6 ET.
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PIERRE MICHEL BATAILLE
Primary Examiner
Art Unit 2138
Prosecution Insights