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 .
DETAILED ACTION
1. This is a first non-final Office Action on the merits for application 19210251. Claims 1-20 are pending examination.
Claim Rejections - 35 USC § 101
2. 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim(s) 1 is/are drawn to method (i.e., a process), claim(s) 11 is/are drawn to a system (i.e., a machine/manufacture). As such, claims 1, and 11 is/are drawn to one of the statutory categories of invention.
Claims 1-20 are directed to receiving location and movements with removing an item from a shelf for a customer and adding the item to a virtual shopping cart and charging the customer for the item. Specifically, claim(s) 1, and 11 recite(s) identifying, by a computer program, a customer that is present in an area; monitoring, by the computer program, a location of the customer in the area; receiving, by the computer program and from a near the location of the customer, a customer movement associated with removing an item from a shelf; identifying, by the computer program, the item; predicting, by the computer program, that the customer has removed the item from the shelf; adding, by the computer program, the item to a virtual shopping cart for the customer; decreasing, by the computer program, a stored inventory of the item; and charging, by the computer program, the customer for the item, which is grouped within the Methods Of Organizing Human Activity and is similar to the concept of (commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors business relations) grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 54 (January 7, 2019)). Accordingly, the claims recite an abstract idea (See pages 7, 10, Alice Corporation Pty. Ltd. v. CLS Bank International, et al., US Supreme Court, No. 13-298, June 19, 2014; 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 53-54 (January 7, 2019)).
The Claim limitations are listed under Methods Of Organizing Human Activity and grouped as following:
identifying, by a computer program, a customer that is present in an area; monitoring, by the computer program, a location of the customer in the area; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
receiving, by the computer program and from a near the location of the customer, a customer movement associated with removing an item from a shelf; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
identifying, by the computer program, the item; predicting, by the computer program, that the customer has removed the item from the shelf; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
adding, by the computer program, the item to a virtual shopping cart for the customer; decreasing, by the computer program, a stored inventory of the item; and which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
charging, by the computer program, the customer for the item; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 54-55 (January 7, 2019)), the additional element(s) of the claim(s) such as sensor, system merely use(s) a computer as a tool to perform an abstract idea and/or generally link(s) the use of a judicial exception to a particular technological environment. Specifically, the sensor, system perform(s) the steps or functions of identifying, by a computer program, a customer that is present in an area; monitoring, by the computer program, a location of the customer in the area; receiving, by the computer program and from a near the location of the customer, a customer movement associated with removing an item from a shelf; identifying, by the computer program, the item; predicting, by the computer program, that the customer has removed the item from the shelf; adding, by the computer program, the item to a virtual shopping cart for the customer; decreasing, by the computer program, a stored inventory of the item; and charging, by the computer program, the customer for the item. The use of a processor/computer as a tool to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional element(s) of using a sensor, system to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of receiving location and movements with removing an item from a shelf for a customer and adding the item to a virtual shopping cart and charging the customer for the item. As discussed above, taking the claim elements separately, the sensor, system perform(s) the steps or functions of identifying, by a computer program, a customer that is present in an area; monitoring, by the computer program, a location of the customer in the area; receiving, by the computer program and from a near the location of the customer, a customer movement associated with removing an item from a shelf; identifying, by the computer program, the item; predicting, by the computer program, that the customer has removed the item from the shelf; adding, by the computer program, the item to a virtual shopping cart for the customer; decreasing, by the computer program, a stored inventory of the item; and charging, by the computer program, the customer for the item. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of receiving location and movements with removing an item from a shelf for a customer and adding the item to a virtual shopping cart and charging the customer for the item. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
As for dependent claims 2-10, and 12-20 further describe the abstract idea of receiving location and movements with removing an item from a shelf for a customer and adding the item to a virtual shopping cart and charging the customer for the item. Claim(s) 2-10, and 12-20 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional element(s) of using a electronic device, system, machine learning engine, ultrawide band (UWB) sensor, sensor, wearable UWB- enabled device, camera to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of receiving location and movements with removing an item from a shelf for a customer and adding the item to a virtual shopping cart and charging the customer for the item. As discussed above, taking the claim elements separately, the electronic device, system, machine learning engine, ultrawide band (UWB) sensor, sensor, wearable UWB- enabled device, camera perform(s) the steps or functions of predicting, by the computer program, a complementary item to the item; and suggesting, by the computer program, the complementary item associated with the customer; wherein the computer program predicts the complementary item using a trained on historical purchase data; wherein the computer program predicts the complementary item based on a recipe including the item; wherein the computer program predicts the complementary item based on an inventory of the complementary item; applying, by the computer program, a discount in response predicting that the customer has removed the complementary item from a second shelf; wherein the customer is identified based on a presence of a customer; wherein the customer is identified using facial recognition. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of receiving location and movements with removing an item from a shelf for a customer and adding the item to a virtual shopping cart and charging the customer for the item. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 103
3. 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.
A. Claim(s) 1-3, 5, 7-13, 15, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bronicki et al., (U.S. Patent Application Publication No. 20220114868) in view of Blair et al., (U.S. Patent Application Publication No. 20180338006).
As to Claim 1, Bronicki teaches a method, comprising: identifying, by a computer program, a customer that is present in an area; (0241: analyzing the image data to identify at least one shopper at one or more locations of the retail store, identifying a person in the image data, recognizing the person and associating an identifier (e.g., name, customer ID, account number, telephone number, etc.) with the recognized person), (Examiner notes: customer is being identified and present in the area),monitoring, by the computer program, a location of the customer in the area; (0241: analyzing the image data to identify at least one shopper at one or more locations of the retail store), (Examiner notes: analyzing can be monitoring one or more location of the user using images taken by a sensor or camera),receiving, by the computer program and from a sensor near the location of the customer, a customer movement associated with removing an item from a shelf; (0236: sensors used to track movement of the shopper and/or products within the retail environment… 0286: The tracking of products may occur through the implementation of sensors used to track movement of the shopper and/or products within the retail environment… 0494: Server 3601 may perform the detection based on a movement of the shopper in the moving images captured by image sensor 3614… 0223: determining a type of event associated with the change. For example, a type of event may include a product removal, a product placement, movement of a product, or the like.), identifying, by the computer program, the item; (0108: identify a product… 0118: analyzing images to detect and identify different products. As used herein, the term “detecting a product” may broadly refer to determining an existence of the product. For example, the system may determine the existence of a plurality of distinct products displayed on a store shelf. By detecting the plurality of products, the system may acquire different details relative to the plurality of products (e.g., how many products on a store shelf are associated with a same product type), but it does not necessarily gain knowledge of the type of product. In contrast, the term “identifying a product” may refer to determining a unique identifier associated with a specific type of product that allows inventory managers to uniquely refer to each product type in a product catalogue. Additionally or alternatively, the term “identifying a product” may refer to determining a unique identifier associated with a specific brand of products that allows inventory managers to uniquely refer to products, e.g., based on a specific brand in a product catalogue… 0545: In step 4230, process 4200 includes analyzing the image data to detect a product selection event involving a shopper. For example, this may include detecting product selection event 4000, as shown in FIG. 40A. In some embodiments, the product selection event may be an ambiguous product selection event. In other words, at least one aspect of the selected product may not be clear or fully identified based on analysis of the image data alone. In some embodiments, the shopper may also be the customer of the retail store of step 4210), and (Examiner notes: 0108, 0118, and Fig. 40A-B: product is being identified by a camera),predicting, by the computer program, that the customer has removed the item from the shelf; (claim 219: predicting… at least one product is removed from a retail shelf),adding, by the computer program, the item to a virtual shopping cart for the customer; (0507: automatically update the virtual shopping cart by adding a product to an invoice associated with the virtual shopping cart. The processor may also add a product type, a product name, and a quantity of the product placed in the shopping receptacle).
Bronicki does not teach decreasing, by the computer program, a stored inventory of the item; and charging, by the computer program, the customer for the item.
However Blair teaches decreasing, by the computer program, a stored inventory of the item; and charging, by the computer program, the customer for the item; (0056: The removed item can then be associated with the customer, and the customer automatically charged for the item/purchase without the need to stop at a point of sale. The inventory for the store may be automatically updated to reflect the purchase).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bronicki to include decreasing, by the computer program, a stored inventory of the item; and charging, by the computer program, the customer for the item of Blair. Motivation to do so comes from the knowledge well known in the art that decreasing, by the computer program, a stored inventory of the item; and charging, by the computer program, the customer for the item would help the notify the merchant and help the merchant detect if an item is out and that would therefore make the method/system more effective.
As to Claim 2, Bronicki and Blair teach the method of claim 1.
Bronicki further teaches predicting, by the computer program, a complementary item to the item; and suggesting, by the computer program, the complementary item to an electronic device associated with the customer; (0556: predicting… whether a replacement item should be selected, and provide product best suit's the customer's needs or preferences… 0568: replacement item 4442 may correspond to product 4330 shown in FIG. 43. In some embodiments, substitute item 4442 may be selected to have at least one attribute in common with item 3904. For example, product 4330 may be a regular bottle of ketchup of the same brand and size as product 4310. In some embodiments, substitute item 4442 may be selected to maximize the number of attributes in common with item 3904. For example, product 4330 may be selected over product 4340, which may be of a different product type (e.g., non-organic) and may be of a different brand than product 4310. In some embodiments, one or more attributes may be ranked or weighted higher than others. For example, it may be more important that substitute item 4442 have the same size and same product type (e.g., organic) than the same brand, or vice versa. In some embodiments, the rankings or relative weights of attributes may be customer-specific. For example, based on a shopping history for a customer, customer preference inputs, or the like, server 135 may identify certain attributes as being more important to a particular customer than others. Alternatively or additionally, the ranking or relative weights of attributes may vary based on a product category, a geographic location of a retail store, demographic information for a shopper (e.g., age, gender, etc.), or any other relevant factors); (Examiner notes: complementary items can be similar items that are similar to the original items the customer or consumer wants to purchase).
As to Claim 3, Bronicki and Blair teach the method of claim 2.
Bronicki further teaches wherein the computer program predicts the complementary item using a machine learning engine that is trained on historical purchase data; (0137: a model obtained by training machine learning algorithm using training examples, an artificial neural network, and more) that may be used to identify products in received images; contract-related data 242 (e.g., planograms, promotions data, etc.) that may be used to determine if the placement of products on the store shelves and/or the promotion execution are consistent with obligations of retail store 105… 0570: selected based on product affinity information associated with the customer. For example, server 135 may access product affinity data 4140, as described above. For example, the product affinity information may be a ranking of historical purchases by a customer, which may indicate an affinity for a particular product. The product affinity information may also indicate an affinity for a particular product type. For example, if the customer consistently buys organic products, bulk products, low fat or fat-free varieties, or the like, it may indicate an affinity between the customer and this product type. As another example, the product affinity information may be based on a preference or other input from the customer. In some embodiments, the product affinity information may indicate a pairwise affinity between products or product types. For example, customers who commonly buy Brand X of deodorant may be more likely to buy Brand X (or even Brand Y) of shaving cream. As another example, customers who frequently buy organic products may commonly purchase sulfate-free products).
As to Claim 5, Bronicki and Blair teach the method of claim 2.
Bronicki further teaches wherein the computer program predicts the complementary item based on an inventory of the complementary item; (0108: identify a product… 0118: identifying a product” may refer to determining a unique identifier associated with a specific type of product that allows inventory managers to uniquely refer to each product type in a product catalogue. Additionally or alternatively, the term “identifying a product” may refer to determining a unique identifier associated with a specific brand of products that allows inventory managers to uniquely refer to products, e.g., based on a specific brand in a product catalogue… 0545: In step 4230, process 4200 includes analyzing the image data to detect a product selection event involving a shopper. For example, this may include detecting product selection event 4000, as shown in FIG. 40A. In some embodiments, the product selection event may be an ambiguous product selection event. In other words, at least one aspect of the selected product may not be clear or fully identified based on analysis of the image data alone. In some embodiments, the shopper may also be the customer of the retail store of step 4210).
As to Claim 7, Bronicki and Blair teach the method of claim 1.
Bronicki further teaches wherein the customer is identified based on a presence of a customer electronic device; (0141: capturing device 125 may be connected to at least one image sensor 310 associated with at least one lens 312 for capturing image data in an associated field of view. In some configurations, capturing device 125 may include a plurality of image sensors associated with a plurality of lenses 312. In other configurations, image sensor 310 may be part of a camera included in capturing device 125. According to some embodiments, peripherals interface 308 may also be connected to other sensors (not shown), such as a motion sensor, a light sensor, infrared sensor, sound sensor, a proximity sensor, a temperature sensor, a biometric sensor, or other sensing devices to facilitate related functionalities. In addition, a positioning sensor may also be integrated with, or connected to, capturing device 125. For example, such positioning sensor may be implemented using one of the following technologies: Global Positioning System (GPS), GLObal NAvigation Satellite System (GLONASS), Galileo global navigation system, BeiDou navigation system, other Global Navigation Satellite Systems (GNSS), Indian Regional Navigation Satellite System (IRNSS), Local Positioning Systems (LPS), Real-Time Location Systems (RTLS), Indoor Positioning System (IPS), Wi-Fi based positioning systems, cellular triangulation, and so forth. For example, the positioning sensor may be built into mobile capturing device 125, such as smartphone devices. In another example, position software may allow mobile capturing devices to use internal or external positioning sensors (e.g., connecting via a serial port or Bluetooth).
As to Claim 8, Bronicki and Blair teach the method of claim 1.
Bronicki further teaches wherein the customer is identified using facial recognition; (0437: the shopping history database may store facial signatures of shoppers that previously visited retail store 105. The facial signatures may be used in identifying the shopper via the analysis of the image data and in retrieving the shopping history for the shopper from the shopping history database. In additional embodiments, the shopping history database may store a history records of returns made to the retail store by different shoppers. Thereafter, shopping history determination module 2826 may use the history of returns in determining the likelihood that a shopper will be involved in shoplifting).
As to Claim 9, Bronicki and Blair teach the method of claim 1.
Bronicki further teaches wherein the sensor comprises a ultrawide band (UWB) sensor, and the customer is associated with a wearable UWB- enabled device; (0469: FIG. 33B illustrates an example visual indicator showing the frictionless checkout eligibility status of a shopping receptacle. Specifically, the frictionless eligibility indicator may identify which of the shopping receptacles associated with a particular shopper is ineligible for frictionless checkout. Consistent with the present disclosure, a frictionless eligibility indicator may be delivered to a communication device. In one embodiment, the communication device may be a wearable device associated with a shopper, for example, a mobile device (e.g., smartphone, smartwatch, or a store pager that the shopper collects when entering retail store 105). In another embodiment, the communication device may be a wearable device may be associated with a store associate. FIG. 33B depicts the view from smart glasses 3312 of store associate 3310. As shown, a visual indicator 3350 may overlay the image of shopping bag 3304C and may indicate that shopping bag 3304C is ineligible for frictionless checkout. The absence of visual indicator 3350 overlaying the image of shopping bags 3304A, 3304B, and 3304D, and cart 3302 may indicate these shopping receptacles are eligible for frictionless checkout. Based on the delivered indicator, store associate 3310 may asked shopper 3300A to take out all the items only from shopping bag 3304C (i.e., not from shopping bags 3304A, 3304B, and 3304D, and cart 3302) in order to scan them manually.).
As to Claim 10, Bronicki and Blair teach the method of claim 1.
Bronicki further teaches wherein the sensor comprises an infrared camera; (0116: Consistent with the present disclosure, the system may process images and image data acquired by a capturing device to determine information associated with products displayed in the retail store. The term “capturing device” refers to any device configured to acquire image data representative of products displayed in the retail store. Examples of capturing devices may include a digital camera, a time-of-flight camera, a stereo camera, an active stereo camera, a depth camera, a Lidar system, a laser scanner, CCD based devices, or any other sensor based system capable of converting received light into electric signals. The term “image data” refers to any form of data generated based on optical signals in the near-infrared, infrared, visible, and ultraviolet spectrums (or any other suitable radiation frequency range). Consistent with the present disclosure, the image data may include pixel data streams, digital images, digital video streams, data derived from captured images, and data that may be used to construct a 3D image. The image data acquired by a capturing device may be transmitted by wired or wireless transmission to a remote server.).
As to Claim 11, Bronicki teaches a system, comprising: a plurality of shelves in an area, each shelf with a plurality of items; (0118: a plurality of distinct products displayed on a store shelf),a plurality of sensors in the area; and a computer program executed by an electronic device in communication with the plurality of sensors; (0141: capturing device 125 may include a plurality of image sensors),wherein: the computer program identifies a customer that is present in the area; (0241: analyzing the image data to identify at least one shopper at one or more locations of the retail store, identifying a person in the image data, recognizing the person and associating an identifier (e.g., name, customer ID, account number, telephone number, etc.) with the recognized person), (Examiner notes: customer is being identified and present in the area),the computer program monitors a location of the customer in the area; (0241: analyzing the image data to identify at least one shopper at one or more locations of the retail store), (Examiner notes: analyzing can be monitoring one or more location of the user using images taken by a sensor or camera),one of the plurality of sensors detects a customer movement near the location; the computer program receives the customer movement from the sensor; (0242: the method may include detecting, based on the analysis of the image data, at least one product interaction event associated with an action of the at least one shopper at the one or more locations of the retail store. For example, as a shopper passes through the retail store, a shopper may interact with one or more products located in the store by performing one or more actions. For example, as illustrated in FIG. 12A, shopper 1202 may be standing near shelf 850 that may be carrying products 1210, 1212, 1214, etc. Shopper 1202 may have shopping cart 1220. In some embodiments, the action of the at least one shopper may include removing a product from a shelf associated with the retail store. For example, as illustrated in FIG. 12A, shopper 1202 may interact with the one or more products 1210, 1212, 1214, etc., by picking up product 1210 and removing product 1210 from shelf 850. In some embodiments, the action of the at least one shopper may include returning a product to a shelf associated with the retail store. For example, shopper may pick up a product (e.g., 1210, 1212, 1214, etc.) by removing product 1210 from shelf 850 associated with retail store (e.g., 105A, 105B, 105C, etc.), inspect product 1210, position product 1210 in various orientations, return product 1210 back to shelf 850, place product 1210 in shopping cart 1220, remove product 1210 from shopping cart 1220, and/or move product 1210 from one location to another (e.g., move product 1210 from shelf 850 to a different position on the same shelf, or to another shelf, etc.). Each of these actions by shopper 1202 may constitute a product interaction event. Other examples of product interaction events may include, for example, shopper 1202 picking up a product (e.g., 1210, 1212, 1214, etc.) and checking its price using a price scanner, shopper 1202 picking up a plurality of products (e.g., one or more of 1210, 1212, 1214, etc.), shopper 1202 returning some of the plurality of products (e.g., one or more of 1210, 1212, 1214, etc.) previously removed by shopper 1202 from shelf 850, etc. It is also contemplated that a product interaction event may include a combination of one or more of the actions or events described above.),the computer program identifies the customer movement as being associated with removing one of the items from a shelf; (0236: sensors used to track movement of the shopper and/or products within the retail environment… 0286: The tracking of products may occur through the implementation of sensors used to track movement of the shopper and/or products within the retail environment… 0494: Server 3601 may perform the detection based on a movement of the shopper in the moving images captured by image sensor 3614… 0223: determining a type of event associated with the change. For example, a type of event may include a product removal, a product placement, movement of a product, or the like.),the computer program identifies the item; (0108: identify a product… 0118: analyzing images to detect and identify different products. As used herein, the term “detecting a product” may broadly refer to determining an existence of the product. For example, the system may determine the existence of a plurality of distinct products displayed on a store shelf. By detecting the plurality of products, the system may acquire different details relative to the plurality of products (e.g., how many products on a store shelf are associated with a same product type), but it does not necessarily gain knowledge of the type of product. In contrast, the term “identifying a product” may refer to determining a unique identifier associated with a specific type of product that allows inventory managers to uniquely refer to each product type in a product catalogue. Additionally or alternatively, the term “identifying a product” may refer to determining a unique identifier associated with a specific brand of products that allows inventory managers to uniquely refer to products, e.g., based on a specific brand in a product catalogue… 0545: In step 4230, process 4200 includes analyzing the image data to detect a product selection event involving a shopper. For example, this may include detecting product selection event 4000, as shown in FIG. 40A. In some embodiments, the product selection event may be an ambiguous product selection event. In other words, at least one aspect of the selected product may not be clear or fully identified based on analysis of the image data alone. In some embodiments, the shopper may also be the customer of the retail store of step 4210), and (Examiner notes: 0108, 0118, and Fig. 40A-B: product is being identified by a camera),the computer program predicts that the customer has removed the item from the shelf; (claim 219: predicting… at least one product is removed from a retail shelf),the computer program adds the item to a virtual shopping cart for the customer; (0507: automatically update the virtual shopping cart by adding a product to an invoice associated with the virtual shopping cart. The processor may also add a product type, a product name, and a quantity of the product placed in the shopping receptacle).
Bronicki does not teach the computer program decreases a stored inventory of the item; and the computer program charges the customer for the item.
However Blair teaches the computer program decreases a stored inventory of the item; and the computer program charges the customer for the item; (0056: The removed item can then be associated with the customer, and the customer automatically charged for the item/purchase without the need to stop at a point of sale. The inventory for the store may be automatically updated to reflect the purchase).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bronicki to include the computer program decreases a stored inventory of the item; and the computer program charges the customer for the item of Blair. Motivation to do so comes from the knowledge well known in the art that the computer program decreases a stored inventory of the item; and the computer program charges the customer for the item would help the notify the merchant and help the merchant detect if an item is out and that would therefore make the method/system more effective.
As to Claim 12, Bronicki and Blair teach the system of claim 11.
Bronicki further teaches wherein the computer program predicts a complementary item to the item and suggests the complementary item to an electronic device associated with the customer; (0556: predicting… whether a replacement item should be selected, and provide product best suit's the customer's needs or preferences… 0568: replacement item 4442 may correspond to product 4330 shown in FIG. 43. In some embodiments, substitute item 4442 may be selected to have at least one attribute in common with item 3904. For example, product 4330 may be a regular bottle of ketchup of the same brand and size as product 4310. In some embodiments, substitute item 4442 may be selected to maximize the number of attributes in common with item 3904. For example, product 4330 may be selected over product 4340, which may be of a different product type (e.g., non-organic) and may be of a different brand than product 4310. In some embodiments, one or more attributes may be ranked or weighted higher than others. For example, it may be more important that substitute item 4442 have the same size and same product type (e.g., organic) than the same brand, or vice versa. In some embodiments, the rankings or relative weights of attributes may be customer-specific. For example, based on a shopping history for a customer, customer preference inputs, or the like, server 135 may identify certain attributes as being more important to a particular customer than others. Alternatively or additionally, the ranking or relative weights of attributes may vary based on a product category, a geographic location of a retail store, demographic information for a shopper (e.g., age, gender, etc.), or any other relevant factors); (Examiner notes: complementary items can be similar items that are similar to the original items the customer or consumer wants to purchase).
As to Claim 13, Bronicki and Blair teach the system of claim 12.
Bronicki further teaches wherein the computer program predicts the complementary item using a machine learning engine that is trained on historical purchase data; (0137: a model obtained by training machine learning algorithm using training examples, an artificial neural network, and more) that may be used to identify products in received images; contract-related data 242 (e.g., planograms, promotions data, etc.) that may be used to determine if the placement of products on the store shelves and/or the promotion execution are consistent with obligations of retail store 105… 0570: selected based on product affinity information associated with the customer. For example, server 135 may access product affinity data 4140, as described above. For example, the product affinity information may be a ranking of historical purchases by a customer, which may indicate an affinity for a particular product. The product affinity information may also indicate an affinity for a particular product type. For example, if the customer consistently buys organic products, bulk products, low fat or fat-free varieties, or the like, it may indicate an affinity between the customer and this product type. As another example, the product affinity information may be based on a preference or other input from the customer. In some embodiments, the product affinity information may indicate a pairwise affinity between products or product types. For example, customers who commonly buy Brand X of deodorant may be more likely to buy Brand X (or even Brand Y) of shaving cream. As another example, customers who frequently buy organic products may commonly purchase sulfate-free products).
As to Claim 15, Bronicki and Blair teach the system of claim 12.
Bronicki further teaches wherein the computer program predicts the complementary item based on an inventory of the complementary item; (0108: identify a product… 0118: identifying a product” may refer to determining a unique identifier associated with a specific type of product that allows inventory managers to uniquely refer to each product type in a product catalogue. Additionally or alternatively, the term “identifying a product” may refer to determining a unique identifier associated with a specific brand of products that allows inventory managers to uniquely refer to products, e.g., based on a specific brand in a product catalogue… 0545: In step 4230, process 4200 includes analyzing the image data to detect a product selection event involving a shopper. For example, this may include detecting product selection event 4000, as shown in FIG. 40A. In some embodiments, the product selection event may be an ambiguous product selection event. In other words, at least one aspect of the selected product may not be clear or fully identified based on analysis of the image data alone. In some embodiments, the shopper may also be the customer of the retail store of step 4210).
As to Claim 17, Bronicki and Blair teach the system of claim 11.
Bronicki further teaches wherein the customer is identified based on a presence of a customer electronic device; (0141: capturing device 125 may be connected to at least one image sensor 310 associated with at least one lens 312 for capturing image data in an associated field of view. In some configurations, capturing device 125 may include a plurality of image sensors associated with a plurality of lenses 312. In other configurations, image sensor 310 may be part of a camera included in capturing device 125. According to some embodiments, peripherals interface 308 may also be connected to other sensors (not shown), such as a motion sensor, a light sensor, infrared sensor, sound sensor, a proximity sensor, a temperature sensor, a biometric sensor, or other sensing devices to facilitate related functionalities. In addition, a positioning sensor may also be integrated with, or connected to, capturing device 125. For example, such positioning sensor may be implemented using one of the following technologies: Global Positioning System (GPS), GLObal NAvigation Satellite System (GLONASS), Galileo global navigation system, BeiDou navigation system, other Global Navigation Satellite Systems (GNSS), Indian Regional Navigation Satellite System (IRNSS), Local Positioning Systems (LPS), Real-Time Location Systems (RTLS), Indoor Positioning System (IPS), Wi-Fi based positioning systems, cellular triangulation, and so forth. For example, the positioning sensor may be built into mobile capturing device 125, such as smartphone devices. In another example, position software may allow mobile capturing devices to use internal or external positioning sensors (e.g., connecting via a serial port or Bluetooth).
As to Claim 18, Bronicki and Blair teach the system of claim 11.
Bronicki further teaches wherein the customer is identified using facial recognition; (0437: the shopping history database may store facial signatures of shoppers that previously visited retail store 105. The facial signatures may be used in identifying the shopper via the analysis of the image data and in retrieving the shopping history for the shopper from the shopping history database. In additional embodiments, the shopping history database may store a history records of returns made to the retail store by different shoppers. Thereafter, shopping history determination module 2826 may use the history of returns in determining the likelihood that a shopper will be involved in shoplifting).
As to Claim 19, Bronicki and Blair teach the system of claim 11.
Bronicki further teaches wherein the sensor comprises a ultrawide band (UWB) sensor, and the customer is associated with a wearable UWB- enabled device; (0469: FIG. 33B illustrates an example visual indicator showing the frictionless checkout eligibility status of a shopping receptacle. Specifically, the frictionless eligibility indicator may identify which of the shopping receptacles associated with a particular shopper is ineligible for frictionless checkout. Consistent with the present disclosure, a frictionless eligibility indicator may be delivered to a communication device. In one embodiment, the communication device may be a wearable device associated with a shopper, for example, a mobile device (e.g., smartphone, smartwatch, or a store pager that the shopper collects when entering retail store 105). In another embodiment, the communication device may be a wearable device may be associated with a store associate. FIG. 33B depicts the view from smart glasses 3312 of store associate 3310. As shown, a visual indicator 3350 may overlay the image of shopping bag 3304C and may indicate that shopping bag 3304C is ineligible for frictionless checkout. The absence of visual indicator 3350 overlaying the image of shopping bags 3304A, 3304B, and 3304D, and cart 3302 may indicate these shopping receptacles are eligible for frictionless checkout. Based on the delivered indicator, store associate 3310 may asked shopper 3300A to take out all the items only from shopping bag 3304C (i.e., not from shopping bags 3304A, 3304B, and 3304D, and cart 3302) in order to scan them manually.).
As to Claim 20, Bronicki and Blair teach the system of claim 12.
Bronicki further teaches wherein the sensor comprises an infrared camera; (0116: Consistent with the present disclosure, the system may process images and image data acquired by a capturing device to determine information associated with products displayed in the retail store. The term “capturing device” refers to any device configured to acquire image data representative of products displayed in the retail store. Examples of capturing devices may include a digital camera, a time-of-flight camera, a stereo camera, an active stereo camera, a depth camera, a Lidar system, a laser scanner, CCD based devices, or any other sensor based system capable of converting received light into electric signals. The term “image data” refers to any form of data generated based on optical signals in the near-infrared, infrared, visible, and ultraviolet spectrums (or any other suitable radiation frequency range). Consistent with the present disclosure, the image data may include pixel data streams, digital images, digital video streams, data derived from captured images, and data that may be used to construct a 3D image. The image data acquired by a capturing device may be transmitted by wired or wireless transmission to a remote server.).
B. Claim(s) 4, 6, 14, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bronicki et al., (U.S. Patent Application Publication No. 20220114868) in view of Blair et al., (U.S. Patent Application Publication No. 20180338006) in view of Jouhikainen et al., (U.S. Patent Application Publication No. 20170372403).
As to Claim 4, Bronicki and Blair teach the method of claim 2.
Bronicki and Blair do not teach wherein the computer program predicts the complementary item based on a recipe including the item.
However Jouhikainen teaches wherein the computer program predicts the complementary item based on a recipe including the item; (0049: Box 520 represents manual input of collections of complementary product sets. In some embodiments, these sets may be manually entered (e.g., via I/O device 220 of service provider terminal 110 or merchant terminal 140) by system administrators or a merchant based on common knowledge. For example, knowing that an outfit may comprise pants or a skirt, a shirt or blouse, and accessories such as belts, shoes, or jewelry, an administrator may include various sets based on these combinations. As another example, an administrator may input grocery product sets based on recipes from one or more cookbooks. In some embodiments, product manufacturers or retailer merchants may provide promotional complementary product sets, such as a video game console manufacturer providing a complementary product set that includes an additional controller or a new video game.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bronicki and Blair to include wherein the computer program predicts the complementary item based on a recipe including the item of Jouhikainen. Motivation to do so comes from the knowledge well known in the art that wherein the computer program predicts the complementary item based on a recipe including the item would help provide a product or item that is similar to the items or products that the consumer is purchasing and that would encourage the consumer to purchase the item or product and that would increase the sales therefore make the method/system more profitable.
As to Claim 6, Bronicki and Blair teach the method of claim 2.
Bronicki and Blair do not teach applying, by the computer program, a discount in response predicting that the customer has removed the complementary item from a second shelf.
However Jouhikainen teaches applying, by the computer program, a discount in response predicting that the customer has removed the complementary item from a second shelf; (0049: Box 520 represents manual input of collections of complementary product sets. In some embodiments, these sets may be manually entered (e.g., via I/O device 220 of service provider terminal 110 or merchant terminal 140) by system administrators or a merchant based on common knowledge. For example, knowing that an outfit may comprise pants or a skirt, a shirt or blouse, and accessories such as belts, shoes, or jewelry, an administrator may include various sets based on these combinations. As another example, an administrator may input grocery product sets based on recipes from one or more cookbooks. In some embodiments, product manufacturers or retailer merchants may provide promotional complementary product sets, such as a video game console manufacturer providing a complementary product set that includes an additional controller or a new video game.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bronicki and Blair to include applying, by the computer program, a discount in response predicting that the customer has removed the complementary item from a second shelf of Jouhikainen. Motivation to do so comes from the knowledge well known in the art that applying, by the computer program, a discount in response predicting that the customer has removed the complementary item from a second shelf would help provide a product or item incentive that the consumer can use and that would encourage the consumer to purchase the item or product which would increase the sales therefore make the method/system more profitable.
As to Claim 14, Bronicki and Blair teach the system of claim 12.
Bronicki and Blair do not teach wherein the computer program predicts the complementary item based on a recipe including the item.
However Jouhikainen teaches wherein the computer program predicts the complementary item based on a recipe including the item; (0049: Box 520 represents manual input of collections of complementary product sets. In some embodiments, these sets may be manually entered (e.g., via I/O device 220 of service provider terminal 110 or merchant terminal 140) by system administrators or a merchant based on common knowledge. For example, knowing that an outfit may comprise pants or a skirt, a shirt or blouse, and accessories such as belts, shoes, or jewelry, an administrator may include various sets based on these combinations. As another example, an administrator may input grocery product sets based on recipes from one or more cookbooks. In some embodiments, product manufacturers or retailer merchants may provide promotional complementary product sets, such as a video game console manufacturer providing a complementary product set that includes an additional controller or a new video game.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bronicki and Blair to include wherein the computer program predicts the complementary item based on a recipe including the item of Jouhikainen. Motivation to do so comes from the knowledge well known in the art that wherein the computer program predicts the complementary item based on a recipe including the item would help provide a product or item that is similar to the items or products that the consumer is purchasing and that would encourage the consumer to purchase the item or product and that would increase the sales therefore make the method/system more profitable.
As to Claim 16, Bronicki and Blair teach the system of claim 12.
Bronicki and Blair do not teach wherein the computer program applies a discount in response predicting that the customer has removed the complementary item from a second shelf.
However Jouhikainen teaches wherein the computer program applies a discount in response predicting that the customer has removed the complementary item from a second shelf; (0049: Box 520 represents manual input of collections of complementary product sets. In some embodiments, these sets may be manually entered (e.g., via I/O device 220 of service provider terminal 110 or merchant terminal 140) by system administrators or a merchant based on common knowledge. For example, knowing that an outfit may comprise pants or a skirt, a shirt or blouse, and accessories such as belts, shoes, or jewelry, an administrator may include various sets based on these combinations. As another example, an administrator may input grocery product sets based on recipes from one or more cookbooks. In some embodiments, product manufacturers or retailer merchants may provide promotional complementary product sets, such as a video game console manufacturer providing a complementary product set that includes an additional controller or a new video game.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bronicki and Blair to include wherein the computer program applies a discount in response predicting that the customer has removed the complementary item from a second shelf of Jouhikainen. Motivation to do so comes from the knowledge well known in the art that wherein the computer program applies a discount in response predicting that the customer has removed the complementary item from a second shelf would help provide a product or item incentive that the consumer can use and that would encourage the consumer to purchase the item or product which would increase the sales therefore make the method/system more profitable.
NPL Reference
4. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The NPL “Effective Ways To Detect People Using Common Sensors” describes “Being able to determine if a person is (or is not) physically present can be useful for a range of IoT and Home Automation applications, such as automatic lighting and surveillance systems. There are many creative ways to build people detectors using common electrical components, however, in this guide, we're going to focus on a handful of purpose-built sensors that are cheap, easy to source, and most importantly easy to use! The sensors we will be looking at all have one thing in common: they detect people by detecting changes to a specific attribute of their physical environment, such as changes to light or sound. The methods these sensors use to detect these changes fall into two broad categories: Area-based detection and Distance-based detection. Area-based detection works by the sensor measuring radiation - such as Infrared or Microwave - across a wide viewing area, and then comparing those measurements to look for fluctuations beyond what is 'normal', such as a person walking across the viewing angle.”.
Pertinent Art
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Reference#20250139687 teaches similar invention which describes the shopping cart detects that the user may have changed which items are present in the shopping cart's storage area to determine when to detect the pose of a user. To do so, the shopping cart 100 may detect a possible change in the shopping cart 100 through the use of sensors coupled to the shopping cart. For example, the shopping cart may detect a change in weight in the storage area 115 of the shopping cart based on data from load sensors. Similarly, the shopping cart may use proximity sensors to detect when an object has moved into or out of the shopping cart 100.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAREK ELCHANTI whose telephone number is (571) 272-9638. The examiner can normally be reached on Flex Mon - Thur 7-7:00 and Fri 7-4:00.
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/TAREK ELCHANTI/Primary Examiner, Art Unit 3621B
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