DETAILED ACTION
Amendment received on June 15, 2026 has been acknowledged. Claims 2 and 7 have been previously cancelled and amendments to 1 and 6 have been entered. Therefore, claims 1, 3-6 and 8-10 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on June 15, 2026 has been entered.
Examiner Interview
In the spirit of compact prosecution, the examiner has made several attempts to reach attorney James Scott Nolan at the phone number on record. However, the answering service does not have the attorney listed and does not provide an alternate method to contact the attorney of record. Filing an Internet Authorization Form will allow for e-mail correspondence between the examiner and the attorney if this form of communication is preferred.
Response to Arguments
Applicant’s arguments with respect to claim(s) claims 1 and 6 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 and 6 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claims 1 and 6 recite: “a static pixel region”. The specification provides support for static content, which does not disclose a static pixel region, moreover provide a distinction between pixels found in dynamic content versus a static pixel region. Examiner recommends utilizing the technical language within the specification to support the claimed limitations.
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 pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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.
Claim(s) 1, 3-6 and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Moshkovitz et al., WIPO WO 222/144888 in view of Doke et al., U.S. Patent #12,394,194 in view of Ron et al., #11,620,822 further in view of Liu et al., U.S. Patent Application Publication 2021/0334533.
Asper Claim 1, Moshkovitz et al. discloses a low-power, self-checkout shopping receptacle (Figure 1, Shopping Cart 1050) comprising:
one or more image sensors arranged on the shopping receptacle (Figure 4A, cameras 403i and 404j); and
computer hardware connected with the one or more image sensors (pg.11, ¶ [0044] discusses the digital camera can comprise an image capturing unit or module, a capture controlling module, a processing unit (which can be the same or separate from the central processing module, pg.20, ¶ [0062] discusses the term module refers to software, hardware, for example, a processor, or a combination thereof that is programmed with instructions for carrying an algorithm or method), and configured to perform operations comprising:
demarcate an area of interest associated with the shopping receptacle within a field of view (FOV) of the one or more image sensors (pg.1, ¶ [0023] discusses the system defines a Virtual Recognition Grid (VRG) by creating a virtual area in 3D space, and by monitoring and recognizing objects traversing e.g., “breach…pg.2, ¶ [0027] discusses Camera's FOV is another aspect for forming a VRG);
detect one or more features of one or more products that are imaged by the one or more image sensors in the area of interest associated with the shopping receptacle (pg.4, ¶ [0035] discusses Images derived by the processors from objects' motion through and or within the VRG frame, would contain information about the objects intersecting the VRG, rather than the entire 3D volume as captured by the VRG sensors and/or cameras); and
associate the detected features of the one or more products with a product database to determine a product identification for each of the one or more products that are imaged by the one or more image sensors (pg.19, ¶ [0061] discusses detection of product 500 trajectory through e.g., layer 7033, the stable bottom, will for example trigger the system to communicate with the product database and initiate product classification of an incoming product 500…detection of product 500 trajectory through e.g., layer 7031, the insertion VRG layer, can for example, trigger the classification of the product, update the list of items and so on);
identify dynamic content within the area of interest associated with the shopping receptacle (pg.1, ¶ [0024] discusses the VRG sensors are cameras, such as RGB cameras, utilized for capturing an image of the object traversing through the VRG construct),
the dynamic content related to image content from the one or more image sensors that changes over time (pg.4, ¶ [0024] discusses Capturing of moving object with sufficient image quality needed to visualize such small details, i.e., capturing sharp images of objects during their motion, may require low exposure times); and
process only the dynamic content to determine one or more features of product that is imaged by the one or more sensors (pg.22, ¶ [0065] discusses detecting motion of the object through and/or within the 3D virtual construct, and detect the object/s type).
Moshkovitz teaches a system and method capable of identifying items placed within a shopping cart.
However, Moshkovitz fails to explicitly state detected features of the one or more products with a product database to determine a product identification for each of the one or more products that are imaged by the one or more image sensors.
Doke et al. teaches detected features of the one or more products with a product database to determine a product identification for each of the one or more products that are imaged by the one or more image sensors (Col.36, lines 50-54 discusses the item-identification component 1338 may determine an item identifier 1370 for the item in the image data 1356 ( or multiple item identifiers 1370) that corresponds to an item in the item data 1358 to which the item corresponds).
Therefore it would have been obvious to one of ordinary skill in the art of item-identifying carts before the effective filing date of the claimed invention to modify the system of Moshkovitz et al., to include the ability to identify multiple products entering a shopping cart as taught by Doke et al., to provide an item-identifying, mobile cart that may be utilized by a user in a materials handling facility to automatically identify items that the user places into a basket of the cart. Abstract
Moshkovitz et al., including incorporated references and Doke et al. form the Moshkovitz-Doke combination and teach the limitations of the claimed invention.
Doke et al. Col.9, lines 43-49 teaches by turning off or decreasing the rate at which the item-identification component operates during time windows of no activity, battery power of the cart may be conserved without sacrificing the accuracy of the item-identification component.
Col.13, lines 66-67 discusses the cart may begin analyzing the image data 126 to attempt to identify an item placed into or removed from the basket or other storage location of the cart 104, or may increase a frame rate associate with this analysis….lines 33-38 discusses the activity-detection component 118 may generate feature data for individual frames of the image data 126 may input this feature data into the model 120, which may be trained to output respective labels 128 indicating whether each individual frame represents the predefined activity.
The cited portions of Doke teaches a shopping cart having the capability of entering a low power state, detect activity (i.e. adding or removing an item) and extracting feature data from images.
However, the Moshkovitz-Doke combination fails to explicitly state process only the dynamic content to determine one or more features of an additional product that is imaged by the one or more sensors.
Ron et al. teaches process only the dynamic content to determine one or more features of an additional product that is imaged by the one or more sensors (Col.9, lines 44-51 discusses the cart may attempt to determine a quantity of the items involved, such as whether one, two, or any other number of instances was placed into the cart or removed from the cart… in some instances, the outcome of a particular event may involve multiple identified items and quantities. For instance, after identifying a first item and a second item, the cart may determine that two instances of the first item were added to the cart).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have the ability to identify a second item placed within a shopping cart as in the improvement discussed in Ron et al., in the system executing the method of the Moshkovitz-Doke combination. As in Ron et al., it is within the capabilities of one of ordinary skill in the art to determine whether a second item has been added to a shopping cart to the Moshkovitz-Doke combination with the predicted result of identifying an additional item as needed in the Moshkovitz-Doke combination
The Moshkovitz-Doke-Ron combination teaches the claimed invention. However, the Moshkovitz-Doke-Ron combination fails to disclose the dynamic content comprising only those pixel regions, within the area of interest, that exhibit a pixel-level change between successive image frames captured by the one or more image sensors, while static pixel regions within the area of interest are excluded from the dynamic content;
wherein the static pixel regions within the area of interest are not processed by the computer hardware to determine the one or more features, such that a computational load and power consumption of the computer hardware are reduced relative to processing an entirety of each image frame.
Liu et al. teaches the dynamic content comprising only those pixel regions, within the area of interest, that exhibit a pixel-level change between successive image frames captured by the one or more image sensors (pg.6, ¶ [0121] discusses the object identification method shown in FIG.1 is mainly used to acquire motion ( each individual motion, for example, placing the item into the shopping cart each time) pixels by comparing the captured images (comprising depth images and color images) fran1e by frame to enable identification of an object that is newly entered into the camera's field of view, such as items newly added to the smart shopping cart),
while static pixel regions within the area of interest are excluded from the dynamic content (pg.4, ¶ [0079] discusses a set of pixels having a pixel difference value greater than a predetermined sixth threshold are obtained by pixel-by-pixel comparison of the motion end frame and the motion start frame in the color images. Such a set of pixels can be considered as a set of pixels in which motion occurs1);
Liu et al. further teaches wherein the static pixel regions within the area of interest are not processed by the computer hardware to determine the one or more features, such that a computational load and power consumption of the computer hardware are reduced relative to processing an entirety of each image frame2 (pg.4, ¶ [0082] discusses after obtaining the initial connected component by analyzing the set of motion pixels, the component in the initial connected component that is obviously abnormal may be removed….This effectively reduces the number of possible connected components, that is, reduces the number of components for subsequent steps of dividing, identification, etc., and reduces processing complexity).
The cited portion of Liu et al. teaches a system and method of analyzing pixels in motion and removing pixels during the analysis, which aids in reducing processing.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have the ability to remove pixels that are not involved in the motion detected as in the improvement discussed in Liu et al. in the system executing the method of the Moshkovitz-Doke-Ron combination. As in Liu et al., it is within the capabilities of one of ordinary skill in the art to analyze a set of motion pixels within a field of view to Moshkovitz-Doke-Ron combination with the predicted result of removing pixels within a set of pixel to reduce processing complexity as needed in the Moshkovitz-Doke-Ron combination.
Asper Claim 3, Moshkovitz et al. discloses the low-power, self-checkout shopping receptacle in accordance with claim 1, wherein the operations further comprise:
recognize, using computational logic and the product database, the product identification for each of the one or more products in the shopping receptacle (pg.9, ¶ [0040] discusses capturing textual information located on a retail-product wrap from two or more sides, by OCR algorithms, leads to improved matching of the textual sequences on that product to a database of textual words from all products in a given store); and
display the product identification for each of the one or more products in an electronic display (pg.18, ¶ [0055] discusses the user interface module is capable of displaying any data that it reads from the imaging/sensing module).
As per Claim 4, Moshkovitz et al. discloses the low-power, self-checkout shopping receptacle in accordance with claim 1, wherein the operations further comprise, wherein the computer hardware comprises:
a programmable processor; and a non-transitory machine-readable medium storing instructions that, when executed by the processor, cause the at least one programmable processor to perform at least some of the operations (pg.25, ¶ [0071] discusses a non-transitory memory storage device storing thereon a computer readable medium (CRM) for recognizing an object motion through a three-dimensional (3D) virtual construct, the CRM comprising a set of executable instructions configured to, when executed by at least one processor, cause the at least one processor to perform the steps of: using a panel's sensor in communication with the article of manufacture, detecting motion of the object through and/or within the 3D virtual construct).
Asper Claim 5, Moshkovitz et al., discloses the low-power, self-checkout shopping receptacle in accordance with claim 1. However, Moshkovitz et al. fails to disclose wherein the one or more features includes a barcode.
Doke et al. teaches wherein the one or more features includes a barcode (Col.9, lines 3-6 discusses the item-identification component may analyze each frame of image data using an item- or barcode localization component, that may identify a region of an individual frame of data that includes an item or a barcode).
Therefore it would have been obvious to one of ordinary skill in the art of item-identifying carts before the effective filing date of the claimed invention to modify the system of Moshkovitz et al., to include the ability to identify multiple products entering a shopping cart by reading bar codes as taught by Doke et al., to provide an item-identifying, mobile cart that may be utilized by a user in a materials handling facility to automatically identify items that the user places into a basket of the cart. Abstract
As per Claim 6, Moshkovitz et al. discloses a system comprising:
a shopping receptacle configured to be moved within a shopping environment (Figure 1, Shopping Cart 1050);
one or more image sensors arranged on the shopping receptacle (Figure 4A, cameras 403i and 404j); and
computer hardware connected with the one or more image sensors (pg.11, ¶ [0044] discusses the digital camera can comprise an image capturing unit or module, a capture controlling module, a processing unit (which can be the same or separate from the central processing module, pg.20, ¶ [0062] discusses the term module refers to software, hardware, for example, a processor, or a combination thereof that is programmed with instructions for carrying an algorithm or method), and
configured to perform operations comprising:
demarcate an area of interest associated with the shopping receptacle within a field of view (FOV) of the one or more image sensors and within the shopping environment (pg.1, ¶ [0023] discusses the system defines a Virtual Recognition Grid (VRG) by creating a virtual area in 3D space, and by monitoring and recognizing objects traversing e.g., “breach…pg.2, ¶ [0027] discusses Camera's FOV is another aspect for forming a VRG...Incorporated reference 17/26,839 pg.9, ¶ [0087] discusses a plurality of imaging modules coupled to the cart, adapted to, at least one of image an item inserted into the cart, and image an area of interest outside the cart);
detect features of one or more products that are imaged by the one or more image sensors in the area of interest associated with the shopping receptacle (pg.4, ¶ [0035] discusses Images derived by the processors from objects' motion through and or within the VRG frame, would contain information about the objects intersecting the VRG, rather than the entire 3D volume as captured by the VRG sensors and/or cameras); and
associate the detected features of the one or more products with a product database to determine a product identification for each of the one or more products that are imaged by the one or more image sensors (pg.19, ¶ [0061] discusses detection of product 500 trajectory through e.g., layer 7033, the stable bottom, will for example trigger the system to communicate with the product database and initiate product classification of an incoming product 500…detection of product 500 trajectory through e.g., layer 7031, the insertion VRG layer, can for example, trigger the classification of the product, update the list of items and so on) ;
identify dynamic content within the area of interest associated with the shopping receptacle (pg.1, ¶ [0024] discusses the VRG sensors are cameras, such as RGB cameras, utilized for capturing an image of the object traversing through the VRG construct),
the dynamic content related to image content from the one or more image sensors that changes over time (pg.4, ¶ [0024] discusses Capturing of moving object with sufficient image quality needed to visualize such small details, i.e., capturing sharp images of objects during their motion, may require low exposure times); and
process only the dynamic content to determine one or more features of product that is imaged by the one or more sensors (pg.22, ¶ [0065] discusses detecting motion of the object through and/or within the 3D virtual construct, and detect the object/s type).
Moshkovitz teaches a system and method capable of identifying items placed within a shopping cart.
However, Moshkovitz fails to explicitly state detected features of the one or more products with a product database to determine a product identification for each of the one or more products that are imaged by the one or more image sensors.
Doke et al. teaches detected features of the one or more products with a product database to determine a product identification for each of the one or more products that are imaged by the one or more image sensors (Col.36, lines 50-54 discusses the item-identification component 1338 may determine an item identifier 1370 for the item in the image data 1356 ( or multiple item identifiers 1370) that corresponds to an item in the item data 1358 to which the item corresponds).
Therefore it would have been obvious to one of ordinary skill in the art of item-identifying carts before the effective filing date of the claimed invention to modify the system of Moshkovitz et al., to include the ability to identify multiple products entering a shopping cart as taught by Doke et al., to provide an item-identifying, mobile cart that may be utilized by a user in a materials handling facility to automatically identify items that the user places into a basket of the cart. Abstract
Moshkovitz et al., including incorporated references and Doke et al. form the Moshkovitz-Doke combination and teach the limitations of the claimed invention.
Doke et al. Col.9, lines 43-49 teaches by turning off or decreasing the rate at which the item-identification component operates during time windows of no activity, battery power of the cart may be conserved without sacrificing the accuracy of the item-identification component.
Col.13, lines 66-67 discusses the cart may begin analyzing the image data 126 to attempt to identify an item placed into or removed from the basket or other storage location of the cart 104, or may increase a frame rate associate with this analysis….lines 33-38 discusses the activity-detection component 118 may generate feature data for individual frames of the image data 126 may input this feature data into the model 120, which may be trained to output respective labels 128 indicating whether each individual frame represents the predefined activity.
The cited portions of Doke teaches a shopping cart having the capability of entering a low power state, detect activity (i.e. adding or removing an item) and extracting feature data from images.
However, the Moshkovitz-Doke combination fails to explicitly state process only the dynamic content to determine one or more features of an additional product that is imaged by the one or more sensors.
Ron et al. teaches process only the dynamic content to determine one or more features of an additional product that is imaged by the one or more sensors (Col.9, lines 44-51 discusses the cart may attempt to determine a quantity of the items involved, such as whether one, two, or any other number of instances was placed into the cart or removed from the cart… in some instances, the outcome of a particular event may involve multiple identified items and quantities. For instance, after identifying a first item and a second item, the cart may determine that two instances of the first item were added to the cart).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have the ability to identify a second item placed within a shopping cart as in the improvement discussed in Ron et al., in the system executing the method of the Moshkovitz-Doke combination. As in Ron et al., it is within the capabilities of one of ordinary skill in the art to determine whether a second item has been added to a shopping cart to the Moshkovitz-Doke combination with the predicted result of identifying an additional item as needed in the Moshkovitz-Doke combination.
The Moshkovitz-Doke-Ron combination teaches the claimed invention. However, the Moshkovitz-Doke-Ron combination fails to disclose the dynamic content comprising only those pixel regions, within the area of interest, that exhibit a pixel-level change between successive image frames captured by the one or more image sensors, while static pixel regions within the area of interest are excluded from the dynamic content;
wherein the static pixel regions within the area of interest are not processed by the computer hardware to determine the one or more features, such that a computational load and power consumption of the computer hardware are reduced relative to processing an entirety of each image frame.
Liu et al. teaches the dynamic content comprising only those pixel regions, within the area of interest, that exhibit a pixel-level change between successive image frames captured by the one or more image sensors (pg.6, ¶ [0121] discusses the object identification method shown in FIG.1 is mainly used to acquire motion ( each individual motion, for example, placing the item into the shopping cart each time) pixels by comparing the captured images (comprising depth images and color images) fran1e by frame to enable identification of an object that is newly entered into the camera's field of view, such as items newly added to the smart shopping cart),
while static pixel regions within the area of interest are excluded from the dynamic content (pg.4, ¶ [0079] discusses a set of pixels having a pixel difference value greater than a predetermined sixth threshold are obtained by pixel-by-pixel comparison of the motion end frame and the motion start frame in the color images. Such a set of pixels can be considered as a set of pixels in which motion occurs3);
Liu et al. further teaches wherein the static pixel regions within the area of interest are not processed by the computer hardware to determine the one or more features, such that a computational load and power consumption of the computer hardware are reduced relative to processing an entirety of each image frame4 (pg.4, ¶ [0082] discusses after obtaining the initial connected component by analyzing the set of motion pixels, the component in the initial connected component that is obviously abnormal may be removed….This effectively reduces the number of possible connected components, that is, reduces the number of components for subsequent steps of dividing, identification, etc., and reduces processing complexity).
The cited portion of Liu et al. teaches a system and method of analyzing pixels in motion and removing pixels during the analysis, which aids in reducing processing.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have the ability to remove pixels that are not involved in the motion detected as in the improvement discussed in Liu et al. in the system executing the method of the Moshkovitz-Doke-Ron combination. As in Liu et al., it is within the capabilities of one of ordinary skill in the art to analyze a set of motion pixels within a field of view to Moshkovitz-Doke-Ron combination with the predicted result of removing pixels within a set of pixel to reduce processing complexity as needed in the Moshkovitz-Doke-Ron combination.
Asper Claim 8, Moshkovitz et al. discloses the system in accordance with claim 6, wherein the operations further comprise:
recognize, using computational logic and the product database (pg.9, ¶ [0040] discusses capturing textual information located on a retail-product wrap from two or more sides, by OCR algorithms, leads to improved matching of the textual sequences on that product to a database of textual words from all products in a given store); and
display the product identification for each of the one or more products in an electronic display (pg.18, ¶ [0055] discusses the user interface module is capable of displaying any data that it reads from the imaging/sensing module).
Moshkovitz teaches identifying objects/products entering and exiting a shopping cart throughout. However, Moshkovitz fails to explicitly state the product identification for each of the one or more products in the shopping receptacle.
Doke et al. teaches the product identification for each of the one or more products in the shopping receptacle (Col.37, lines 26-37 discusses the cart management system 1330 may also include a virtual-cart management component 1342 configured to manage virtual shopping cart data 1368 for the cart 1300. For instance, the virtual-cart management component 1342 may utilize the item data 1358, event-description data 1360, and confidence level data 1362 to add item identifier(s) 1370 to the virtual shopping cart data 1368 for items 806 that were added to the cart 1300, remove item identifier(s) 1370 from the virtual shopping cart data 569 for items 806 that were removed from the cart 1300, and track item quantity data 1372 indicating quantities of particular items 806 in the cart).
Therefore it would have been obvious to one of ordinary skill in the art of item-identifying carts before the effective filing date of the claimed invention to modify the system of Moshkovitz et al., to include the ability to identify products within a shopping cart as taught by Doke et al., to provide an item-identifying, mobile cart that may be utilized by a user in a materials handling facility to automatically identify items that the user places into a basket of the cart. Abstract
Asper Claim 9, Moshkovitz discloses the system in accordance with claim 6, wherein the one or more image sensors includes a barcode reader (Incorporated Reference 17/267,843 pg.3, ¶ [0032] discusses a sensor array 107 includes a barcode reader).
Asper Claim 10, Moshkovitz discloses the system in accordance with claim 6. However, Moshkovitz fails to disclose wherein the one or more image sensors includes a QR code reader.
Doke et al. teaches wherein the one or more image sensors includes a QR code reader (Col.23, lines 57-60 discusses the carts 804 may include a first imaging device 834(1) (e.g., an image sensor such as a camera, photodetector, or other sensing apparatus designed to read a one or two-dimensional barcode).
Therefore it would have been obvious to one of ordinary skill in the art of item-identifying carts before the effective filing date of the claimed invention to modify the system of Moshkovitz et al., to include the ability to utilize a two dimensional barcode reader to identify multiple products entering a shopping cart as taught by Doke et al., to provide an item-identifying, mobile cart that may be utilized by a user in a materials handling facility to automatically identify items that the user places into a basket of the cart. Abstract
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chaubard et al., U.S. Patent Application Publication 2018/0218351 discusses a mobile shopping unit of an automated checkout system includes one or more sensors within a shopping cart that detect when a change to the contents of the shopping cart has occurred. If the mobile shopping unit detects a change, the mobile shopping unit captures image data of the contents of the shopping cart using one or more cameras mounted to the shopping cart. The mobile shopping unit uses the image data to identify the item added to or removed from the cart. The mobile shopping unit applies a machine-learned item identification model to the image data received from the cameras to determine an item identifier for the added or removed item. When the mobile shopping unit determines the identifier for the added or removed item, the mobile shopping unit updates a contents list associated with the customer that stores the items currently collected by the customer. Abstract
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHFORD S HAYLES whose telephone number is (571)270-5106. The examiner can normally be reached M-F 6AM-4PM with Flex.
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/ASHFORD S HAYLES/Primary Examiner, Art Unit 3627
1 The Examiner is construing that only motion pixel are selected and thereby ignoring any static pixels in the field of view.
2 The Examiner notes, the italicized portions of the above claim are used to denote intended use and are given little patentable weight. It has been held that a recitation with respect to the manner in which a claimed apparatus is intended to be employed does not differentiate the claimed apparatus from a prior art apparatus satisfying the claimed structural limitations.
3 The Examiner is construing that only motion pixel are selected and thereby ignoring any static pixels in the field of view.
4 The Examiner notes, the italicized portions of the above claim are used to denote intended use and are given little patentable weight. It has been held that a recitation with respect to the manner in which a claimed apparatus is intended to be employed does not differentiate the claimed apparatus from a prior art apparatus satisfying the claimed structural limitations.
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