DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Based on the provisional application, for the purpose of examination herein, the effective filing date regarding prior art consideration is December 16th, 2021.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 and 15-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Whether a Claim is to a Statutory Category
In the instant case, claims 1-13 and 15-21 recite methods/ processes that are performing a series of functions. Therefore, these claims fall within the four statutory categories of invention of a process and step 1 is satisfied.
Step2A – Prong 1: Does the Claim Recite a Judicial Exception
Exemplary claim 1 (and similarly claims 17 and 19) recites the following abstract concepts that are found to include an enumerated “abstract idea”:
A method comprising:
during a scan cycle, at a fixed camera installed in a store, via an optical sensor integrated in the fixed camera, capturing images of inventory structures arranged in the store; and
by a computer system:
accessing a first image of an inventory structure captured by the optical sensor at a first time during the scan cycle;
retrieving a geometry of a field of view of the fixed camera at the first time;
based on a projection of the geometry of the field of view onto a planogram of the store, identifying a first cluster of regions, in the first image, depicting a first set of slots assigned to product types supplied by the first supplier;
based on the projection of the geometry of the field of view onto the planogram, identifying a second cluster of regions, in the first image, depicting a second set of slots assigned to product types supplied by a second set of suppliers excluding the first supplier;
obfuscating the second cluster of regions in the first image to generate a masked image;
detecting a first set of features in the first cluster of regions in the first image;
interpreting a first set of stock conditions of the first set of slots at the first time based on the first set of features;
via a supplier portal, receiving a query for stock conditions of products from a first supplier, in a set of suppliers, affiliated with the store; and
in response to receiving the query:
serving the masked image to the first supplier; and
serving the first set of stock conditions of the first set of slots to the first supplier.
[Emphasis added to show the abstract idea being executed by additional elements that do not meaningfully limit the abstract idea]
This system claim is grouped within the "certain methods of organizing human activity” grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test because the claims involve a series of steps for business relations for maintaining inventory data by receiving a query for stock conditions of products from a first supplier and serving the first set of stock conditions of the first set of slots to the first supplier which is a process that is encompassed by the abstract idea of commercial or legal interactions. The examiner has reviewed each abstract idea from each step individually and in combination with each other limitation, and still finds that the claim 1 recites abstract idea. See e.g., MPEP 2106.04(a)(2)(II)(B). Accordingly, the claims recite an abstract idea.
Step2A – Prong 2: Does the Claim Recite Additional Elements that Integrate the Judicial Exception into a Practical Application
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test, the additional elements of the claims such as a camera, optical sensor, computer system and supplier portal merely use a computer as a tool to perform an abstract idea and/or generally link the use of a judicial exception to a particular technological environment. Specifically, the camera, optical sensor, computer system and supplier portal performs the steps or functions of business relations for maintaining inventory data by receiving a query for stock conditions of products from a first supplier and serving the first set of stock conditions of the first set of slots to the first supplier. 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 (or technical elements disclosed at a high level of generality such as camera, optical sensor, computer system and supplier portal) performing functions of capturing, accessing, retrieving, identifying, obfuscating, detecting, interpreting, receiving and serving that correspond to acts required to carry out the abstract idea (MPEP 2106.05(f) and (h)). Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
Step2B: Does the Claim Amount to Significantly More
The claims 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, the additional elements of camera, optical sensor, computer system and supplier portal being used to perform the steps of capturing, accessing, retrieving, identifying, obfuscating, detecting, interpreting, receiving and serving amounts to no more than using a computer or processor to automate and/or implement the abstract idea of business relations for maintaining inventory data by receiving a query for stock conditions of products from a first supplier and serving the first set of stock conditions of the first set of slots to the first supplier. As discussed above, taking the claim elements separately, optical sensor performs the steps or functions of commercial or legal interactions of business relations for maintaining inventory data by receiving a query for stock conditions of products from a first supplier and serving the first set of stock conditions of the first set of slots to the first supplier. 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 commercial or legal interactions of business relations for maintaining inventory data by receiving a query for stock conditions of products from a first supplier and serving the first set of stock conditions of the first set of slots to the first supplier because said combination of elements remains disclosed at a high level of generality. 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(l)(A)(f) & (h)). Therefore, the claim limitations are not patent eligible.
Independent claims 17 and 19 describe the abstract idea of commercial or legal interactions of business relations for maintaining inventory data by accessing a query from a first supplier of a product and serving the first set of stock conditions of the first set of slots to the first supplier. Independent claims 17 and 19 do not include additional elements to perform the respective functions of accessing, identifying, obfuscating, detecting, interpreting and serving beyond technical elements disclosed at a high level of generality, such as mobile robotic system, optical sensor and computer system that integrate the abstract idea into a practical application or that provide significantly more than the abstract idea for the same reasons as noted above regarding claim 1. Therefore, independent claims 17 and 19 are also not patent eligible.
Dependent claims 2-13 and 15 further describe the abstract idea of commercial or legal interactions of business relations for maintaining inventory data by receiving a query for stock conditions of products from a first supplier and serving the first set of stock conditions of the first set of slots to the first supplier. Said dependent claims do not include additional elements to perform the respective functions of accessing, identifying, retrieving, analyzing, detecting, receiving, extracting, interpreting, viewing, generating, deriving, serving, selecting, calculating, annotating, obfuscating, overlaying, deploying, navigating and compiling beyond the technical elements disclosed at a high level of generality, such as a camera, optical sensor and robotic system and as in independent claim 1 that integrate the abstract idea into a practical application or that provide significantly more than the abstract idea. Therefore, said dependent claims are also not patent eligible. Further, the dependency of these claims on ineligible independent claim 1 also renders said dependent claims as not patent eligible.
Dependent claims 18 and 20-21 further describe the abstract idea of commercial or legal interactions of business relations for maintaining inventory data by accessing a query from a first supplier of a product and serving the first set of stock conditions of the first set of slots to the first supplier. Said dependent claims do not include additional elements to perform the respective functions of detecting, calculating, annotating, identifying, defining, accessing, receiving, viewing, obfuscating and generating beyond the technical elements disclosed at a high level of generality, such as a computer system, mobile robotic system, optical sensor and as in independent claims 17 and 19 that integrate the abstract idea into a practical application or that provide significantly more than the abstract idea. Therefore, said dependent claims are also not patent eligible. Further, the dependency of these claims on ineligible independent claims 17 and 19 also renders said dependent claims as not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-13 and 15-21 are rejected under 35 U.S.C. 103 as being unpatentable over Adato et al. (US 2019/0236531 A1) in view of Bogolea (US 2020/0074371 A1).
Regarding Claim 1, Adato teaches:
A method comprising:
during a scan cycle, at a fixed camera installed in a store, via an optical sensor integrated in the fixed camera, capturing images of inventory structures arranged in the store (See Adato ¶ [0116] – image sensors processing optical data, [0154] – image sensor mounted on store shelves to capture images of opposing retail shelving and [0223] – triggered acquisition of color images); and
by a computer system:
accessing a first image of an inventory structure captured by the optical sensor at a first time during the scan cycle (See Adato ¶ [0154] – image sensor on store shelves to capture images of opposing retail shelving and [0226] – using system to generate various metrics or information based on automated analysis of actual, timely images acquired from the retail stores, e.g.: market research entity may track how quickly or at what rate new products are introduced to retail store shelves);
retrieving a geometry of a field of view of the fixed camera at the first time (See Adato ¶ [0228-0229] – server may use data derived from images captured in a plurality of retail stores to recommend a planogram and Fig. 11B – showing an example of a planogram output for a supplier);
based on a projection of the geometry of the field of view onto a planogram of the store (See Adato ¶ [0228-0229] – server may use data derived from images captured in a plurality of retail stores to recommend a planogram and Fig. 11B – showing an example of a planogram output for a supplier), identifying a first cluster of regions, in the first image, depicting a first set of slots assigned to product types supplied by the first supplier (See Adato ¶ [0120] – using image analysis of products in shelf spaces [slots] on retail shelves to send product information to multiple suppliers of said products and [0127] – presenting suppliers with a level of planogram compliance for products assigned to supplier, thereby identifying a first cluster of regions, in the first image, depicting a first set of slots assigned to product types supplied by the first supplier by example);
based on the projection of the geometry of the field of view onto the planogram (See Adato ¶ [0228-0229] – server may use data derived from images captured in a plurality of retail stores to recommend a planogram and Fig. 11B – showing an example of a planogram output for a supplier), identifying a second cluster of regions, in the first image, depicting a second set of slots assigned to product types supplied by a second set of suppliers excluding the first supplier (See Adato ¶ [0120] and [0127] as noted above – wherein presenting multiple suppliers with information about multiple products in multiple shelf spaces is identifying a second cluster of regions, in the first image, depicting a second set of slots assigned to product types supplied by a second set of suppliers by example excluding the first supplier and [0226-0228] – providing supplier’s with product sales information for their current products and performance information relating to products of their competitors based on products recognized from image analysis of products on retail store shelves);
…;
detecting a first set of features in the first cluster of regions in the first image (See Adato ¶ [0125] – detecting product features from analysis of image data and [0120] & [0127] as noted above – regarding clusters of regions in the first image);
interpreting a first set of stock conditions of the first set of slots at the first time based on the first set of features (See Adato ¶ [0125] – detecting product features from analysis of image data and [0231] – real-time alerts when products are out of shelf [stock condition] or near out of shelf or other issues with planogram compliance);
via a supplier portal, receiving a query for stock conditions of products from a first supplier, in a set of suppliers, affiliated with the store (See Adato ¶ [0120] – system working with multiple suppliers [115A-C], [0226] –allow market research entity and suppliers to continuously monitor product-related activities at retail stores, [0228] – GUI for output device used by supplier [supplier portal by example] …server may use data derived from images captured in a plurality of retail stores to recommend a planogram, which often determines sales success of different products and [0231] – real-time alerts when products are out of shelf [stock condition] or near out of shelf or other issues with planogram compliance); and
in response to receiving the query:
serving the … image to the first supplier; and
serving the first set of stock conditions of the first set of slots to the first supplier (See Adato ¶ [0215-0216] – transmitting planogram compliance information to a supplier, wherein product facings [stock condition example] are referenced).
While Adato teaches a system of managing inventory of products on retail shelves through analysis of captured images showing stock conditions of said shelves and providing said images to suppliers of said products (Adato ¶ [0120], [0127] and [0226-0228]), Adato does not explicitly teach obfuscating the second cluster of regions in the first image to generate a masked image and serving the masked image to the first supplier. This is taught by Bogolea (See Bogolea ¶ [0131-0133] – redacting [masking by example] photographic images of slots [cluster of regions] to depict only slots contracted to a corresponding distributor [supplier] and then serving this redacted image to said distributor [supplier]). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include in the product image data based inventory management system that provides said product image data to suppliers of the products depicted in said image data of Adato the use of redacted image data to provide suppliers with only the image data for products they are contracted to manage as taught by Bogolea to improve efficiency of remote slot management by the distributor and in order decrease aggregate out-of-stock and understock durations of high-value products stocked throughout the distributor's geographic region (Bogolea ¶ [0016]).
Regarding Claim 2, modified Adato teaches:
The method of Claim 1:
wherein accessing the first image comprises accessing the first image comprising a photographic image captured by the fixed camera (As the specification of the instant application uses the limitation photographic image to mean a color image with no other special definition, see Adato ¶ [0154] – image sensor mounted on store shelves to capture images of opposing retail shelving, [0223] – triggered acquisition of color images and [0226] – using system to generate various metrics or information based on automated analysis of actual, timely images acquired from the retail stores, e.g.: market research entity may track how quickly or at what rate new products are introduced to retail store shelves);
wherein detecting the first set of features in the first cluster of regions of the first image comprises extracting a first constellation of features from a first region of the photographic image corresponding to a first slot in the first set of slots (As the specification of the instant application gives no special definition for the limitation constellation of features, said constellation of features is interpreted as a grouping of features for the purpose of examination herein. Therefore, see Adato ¶ [0125] – identifying products based on stored product features such as shape, size, colors, texture, name, price, logo, text, shelf association, adjacent products in a planogram or location within a retail store, thereby showing a constellation of features by example and [0120] & [0127] as noted above – regarding clusters of regions of the first image); and
wherein interpreting the first set of stock conditions of the first set of slots at the first time comprises:
retrieving a first product model representing a first set of visual characteristics of a first product type assigned to the first slot by the planogram (See Adato ¶ [0125] – identifying products in shelf spaces [slots] based on product features stored in one or more product models comprising visual and contextual properties of said products and [0127] – determining planogram compliance for said identified products); and
detecting presence of a first product unit of the first product type occupying the first slot in the inventory structure at the first time in response to the first constellation of features approximating the first set of visual characteristics represented in the first product model (See Adato ¶ [0125] – identifying products in shelf spaces [slots] based on product features stored in one or more product models comprising visual and contextual properties of said products and [0127] – determining planogram compliance for said identified products, wherein said planogram compliance includes user notification of through visual depiction [approximation] of a misplace product).
Regarding Claim 3, modified Adato teaches:
The method of Claim 1, wherein receiving the query from the first supplier comprises receiving the query to remotely view the first set of stock conditions, of product types manufactured by the first supplier, in the store (See Adato ¶ [0216] – remote servers transmitting planogram compliance data and [0226-0227] –allow market research entity and suppliers to continuously monitor product-related activities at retail stores through server provided information regarding products on retail shelves).
Regarding Claim 4, modified Adato teaches:
The method of Claim 1:
wherein accessing the first image comprises accessing the first image comprising a photographic image captured by the fixed camera (As the specification of the instant application uses the limitation photographic image to mean a color image with no other special definition, see Adato ¶ [0154] – image sensor mounted on store shelves to capture images of opposing retail shelving, [0223] – triggered acquisition of color images and [0226] – using system to generate various metrics or information based on automated analysis of actual, timely images acquired from the retail stores, e.g.: market research entity may track how quickly or at what rate new products are introduced to retail store shelves);
While Adato teaches a system of managing inventory of products on retail shelves through analysis of captured images showing stock conditions of said shelves and providing said images to suppliers of said products (Adato ¶ [0120], [0127] and [0226-0228]), Adato does not explicitly teach identifying the first cluster of regions in the first image and identifying the second cluster of regions in the first image comprises accessing a predefined image mask:
associated with the first supplier and the fixed camera;
transparent to the first cluster of regions; and
opaque to the second cluster of regions; and
wherein obfuscating the second cluster of regions in the first image to generate the masked image comprises applying the predefined image mask to the photographic image to generate the masked image.
This is taught by Bogolea (See Bogolea ¶ [0131-0133] – redacting [masking by example] photographic images of slots [cluster of regions] to depict only slots contracted to a corresponding distributor [supplier], wherein the distributor based image redacting is a predefined image mask by example, and then serving this redacted image to said distributor [supplier]). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include in the product image data based inventory management system that provides said product image data to suppliers of the products depicted in said image data of Adato the use of redacted image data to provide suppliers with only the image data for products they are contracted to manage as taught by Bogolea to improve efficiency of remote slot management by the distributor and in order decrease aggregate out-of-stock and understock durations of high-value products stocked throughout the distributor's geographic region (Bogolea ¶ [0016]).
Regarding Claim 5, modified Adato teaches:
The method of Claim 1, further comprising:
accessing a second image of the inventory structure captured by the optical sensor at a second time (See Adato ¶ [0154] – image sensor mounted on store shelves to capture images of opposing retail shelving and [0160] – capturing images periodically at various time intervals);
detecting a second set of features in regions in the first image depicting the first set of slots (See Adato ¶ [0125] – identifying products in shelf spaces [slots] based on product features stored in one or more product models [set of features] comprising visual and contextual properties of said products);
interpreting a second set of stock conditions of the first set of slots at the second time based on the second set of features (See Adato ¶ [0160] – identifying products in shelf spaces [slots] based on product features stored in one or more product models [set of features] comprising visual and contextual properties of said products and [0764-0766] – determining planogram compliance with captured images of shelves in three distinct retail stores, wherein said compliance measure includes an amount time product stock conditions on said shelves are in compliance or out of compliance);
deriving a stock flow of a first set of product types, supplied by the first supplier, between the first time and the second time based on the first set of stock conditions and the second set of stock conditions (See Adato ¶ [0226] – suppliers monitoring product activity in stores to launch [supplied by the first supplier] a new product, [0719] – product flow determined from a series of images captured over a period of time and [0722-0723] – determining a flow of products indicative of product turnover and recommending increases or decrease to product facings based on said flow); and
based on the query, serving the stock flow of the first set of product types to the first supplier (See Adato ¶ [0226-0228] – providing supplier’s with product sales information for their current products and performance information relating to products of their competitors based on products recognized from image analysis of products on retail store shelves and [0722-0723] – determining a flow of products indicative of product turnover and recommending increases or decrease to product facings based on said flow).
Regarding Claim 6, modified Adato teaches:
The method of Claim 5:
wherein receiving the query comprises receiving the query for product flow between restocking periods within the store (See Adato ¶ [0701-0703] – determining product flow by monitoring image data of products on store shelves during product removal by a customer and restocking events);
wherein accessing the first image comprises, based on the query, selecting the first image captured at the first time succeeding a first scheduled restocking period in the store (See Adato ¶ [0732] – product supply information including a schedule of arrivals of additional products [scheduled restocking] derived from image data and [0778] – determining restocking rate compliance based on analysis of a stream of images enabling a determination of a number of products added to a shelf location over a particular time period );
wherein accessing the second image comprises, based on the query, selecting the second image captured at the second time preceding a second scheduled restocking period in the store (See Adato ¶ [0732] – product supply information including a schedule of arrivals of additional products [scheduled restocking] based on past image data capturing conditions of products on store shelves, wherein said past image data is functioning as the second time preceding a second scheduled restocking period in the store by example and [0778] – determining restocking rate compliance based on analysis of a stream of images enabling a determination of a number of products added to a shelf location over a particular time period); and
wherein deriving the stock flow of the first set of product types comprises deriving the stock flow of the first set of product types between the first scheduled restocking period and the second scheduled restocking period (See Adato ¶ [0701-0703] – monitoring restocking events between restocking periods by analyzing image data to determine product removal from a shelf at a first time and later restocking of said products, [0732] – product supply information including a schedule of arrivals of additional products [scheduled restocking] and [0778] – determining restocking rate compliance based on analysis of a stream of images enabling a determination of a number of products added to a shelf location over a particular time period).
Regarding Claim 7, modified Adato teaches:
The method of Claim 5:
wherein receiving the query comprises receiving the query for product flow between restocking periods within the store (See Adato ¶ [0701-0703] – determining product flow by monitoring image data of products on store shelves during product removal by a customer and restocking events); and
further comprising:
…
based on the query, serving the time duration between restocking of the inventory structure to the first supplier (See Adato ¶ [0229-0230] – typical in-store execution may involve dealing with ongoing service events, such as restocking events, which are provided to suppliers to develop planograms).
While Adato teaches a system of managing inventory of products on retail shelves through analysis of captured images showing stock conditions of said shelves and providing said images to suppliers of said products (Adato ¶ [0120], [0127] and [0226-0228]), Adato does not explicitly teach in response to the first set of stock conditions indicating a low frequency of understock conditions in the first set of slots, selecting the first image as depicting a post-restocking state of the inventory structure. This is taught by Bogolea (See Bogolea ¶ [0129-130] – deriving and providing to a distributor metrics for restocking frequency a maximum or average fully-stocked time of a slot, thereby showing a post-restocking state by example).
Bogolea further teaches in response to the second set of stock conditions indicating a high frequency of understock conditions in the first set of slots, selecting the second image as depicting a pre-restocking state of the inventory structure (See Bogolea ¶ [0129-130] – deriving and providing to a distributor metrics for restocking frequency a maximum or average understock time of the slot, thereby showing a pre-restocking state by example);
Bogolea further teaches deriving a time duration between restocking of the inventory structure based on a time difference between the first image and the second image (See Bogolea ¶ [0129-130] – deriving and providing to a distributor metrics for restocking frequency based on time between consecutive scan cycles);
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include in the product image data based inventory management system that provides said product image data to suppliers of the products depicted in said image data of Adato the recognition of post-restocking states and pre-restocking states of products on shelves as determined from periodic image scanning of said products on shelves as taught by Bogolea to improve efficiency of remote slot management by the distributor and in order decrease aggregate out-of-stock and understock durations of high-value products stocked throughout the distributor's geographic region (Bogolea ¶ [0016]).
Regarding Claim 8, modified Adato teaches:
The method of Claim 1, further comprising:
detecting a set of shelf tags on the inventory structure in the first image (See Adato ¶ [0398] – For example, system 100 may detect product 2332 by a determination that it is on shelf 2302 or that it is associated with, for example, price label E2 [shelf tag] and shown in Fig. 23);
calculating slot boundaries of the first set of slots based on locations of corresponding shelf tags in the set of shelf tags (See Adato ¶ [0439] – boxes [boundaries] around products in shelf spaces [slots] denoted by price labels (A1-A3, B1-B3, C1-C3, etc.) [shelf tags] and shown in Fig. 23); and
annotating the first image with slot boundaries of the first set of slots (See Adato Fig. 23 – boxes 2310, 2320 and 2330 shown on an image of products on shelves with their associated shelf labels).
Regarding Claim 9, modified Adato teaches:
The method of Claim 8, further comprising, for a first slot in the first set of slots:
detecting a first product unit, of a first product type assigned to the first slot, in a first region of the image depicting the first slot and contained within a first slot boundary of the first slot (See Adato ¶ [0439] – detecting products through visual and physical characteristics in boxes [boundaries] around said products in shelf spaces [slots] denoted by price labels (A1-A3, B1-B3, C1-C3, etc.) [shelf tags] and shown in Fig. 23);
deriving a first organization metric of the first slot based on a position of the first product unit relative to the first slot boundary (See Adato ¶ [0193] – determining product orientation of stocked products on shelves or empty spaces [organization metric] from at least image data of products on shelves and [0439] – detecting products through visual and physical characteristics in boxes [boundaries] around said products in shelf spaces [slots] denoted by price labels (A1-A3, B1-B3, C1-C3, etc.) [shelf tags] as shown in Fig. 23); and
serving the first organization metric, …, to the first supplier (See Adato ¶ [0450-0451] – the visual representation of boxes around products on shelves is provided to multiple users, including suppliers).
While Adato teaches a system of managing inventory of products on retail shelves through analysis of captured images showing stock conditions of said shelves and providing said images to suppliers of said products (Adato ¶ [0120], [0127] and [0226-0228]), Adato does not explicitly teach serving metrics with the masking image to the first supplier. This is taught by Bogolea (See Bogolea ¶ [0131-0133] – redacting [masking by example] photographic images of slots [cluster of regions] to depict only slots contracted to a corresponding distributor [supplier] and then serving this redacted image to said distributor [supplier]). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include in the product image data based inventory management system that provides said product image data to suppliers of the products depicted in said image data of Adato the use of redacted image data to provide suppliers with only the image data for products they are contracted to manage as taught by Bogolea to improve efficiency of remote slot management by the distributor and in order decrease aggregate out-of-stock and understock durations of high-value products stocked throughout the distributor's geographic region (Bogolea ¶ [0016]).
Regarding Claim 10, modified Adato teaches:
The method of Claim 1:
further comprising:
retrieving a first product category associated with the first supplier (See Adato ¶ [0406-0407] – categories of products are associated with different companies distributing [supplying] said products);
identifying a third set of product types:
in the first product category; and
supplied by a third set of manufacturers distinct from the first supplier (See Adato ¶ [0406-0408] – companies A, B or X [third by example] all distributing [supplying] drink products [category by example]); and
identifying a third cluster of regions, in the first image, depicting a third set of slots assigned to product types in the third set of product types (See Adato Fig. 23 – boxes 2310, 2320 and 2330 [third by example] shown on an image of products on shelves with their associated shelf labels); and
wherein serving the … image to the first supplier comprises serving the … image, depicting the first cluster of regions and the third cluster of regions, to the first supplier (See Adato ¶ [0450-0452] – the visual representation of boxes around products on shelves is provided to multiple users, including suppliers).
While Adato teaches a system of managing inventory of products on retail shelves through analysis of captured images showing stock conditions of said shelves and providing said images to suppliers of said products (Adato ¶ [0120], [0127] and [0226-0228]), Adato does not explicitly teach serving the masked image to the first supplier. This is taught by Bogolea (See Bogolea ¶ [0131-0133] – redacting [masking by example] photographic images of slots [cluster of regions] to depict only slots contracted to a corresponding distributor [supplier] and then serving this redacted image to said distributor [supplier]). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include in the product image data based inventory management system that provides said product image data to suppliers of the products depicted in said image data of Adato the use of redacted image data to provide suppliers with only the image data for products they are contracted to manage as taught by Bogolea to improve efficiency of remote slot management by the distributor and in order decrease aggregate out-of-stock and understock durations of high-value products stocked throughout the distributor's geographic region (Bogolea ¶ [0016]).
Regarding Claim 11, modified Adato teaches:
The method of Claim 10, further comprising:
detecting a set of shelf tags, corresponding to the third set of slots, in the image (See Adato ¶ [0439] – boxes [boundaries] around products in shelf spaces [slots] denoted by price labels (A1-A3, B1-B3, C1-C3, etc.) [shelf tags] and shown in Fig. 23 – three boxes 2310, 2320 and 2330); and
....
While Adato teaches a system of managing inventory of products on retail shelves through analysis of captured images showing stock conditions of said shelves and providing said images to suppliers of said products (Adato ¶ [0120], [0127] and [0226-0228]), Adato does not explicitly teach obfuscating the set of shelf tags in the masked image. This is taught by Bogolea (See Bogolea ¶ [0064] – each slot on a shelving segment/structure is manually labelled [tagged] and [0131-0133] – redacting [masking by example] photographic images of slots [cluster of regions] to depict only slots contracted to a corresponding distributor [supplier] and then serving this redacted image to said distributor [supplier], wherein said slots comprise product labels as noted above in ¶ [0064]). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include in the product image data based inventory management system that provides said product image data to suppliers of the products depicted in said image data of Adato the use of redacted image data to provide suppliers with only the image data for products they are contracted to manage as taught by Bogolea to improve efficiency of remote slot management by the distributor and in order decrease aggregate out-of-stock and understock durations of high-value products stocked throughout the distributor's geographic region (Bogolea ¶ [0016]).
Regarding Claim 12, modified Adato teaches:
The method of Claim 1:
further comprising:
retrieving a first product category associated with the first supplier (See Adato ¶ [0406-0407] – categories of products are associated with different companies distributing [supplying] said products);
identifying a third set of product types:
in the first product category; and
supplied by a third set of manufacturers distinct from the first supplier (See Adato ¶ [0406-0408] – companies A, B or X [third by example] all distributing [supplying] drink products [category by example]);
identifying a third cluster of regions, in the first image, depicting a third set of slots assigned to the third set of product types (See Adato Fig. 23 – boxes 2310, 2320 and 2330 [third by example] shown on an image of products on shelves with their associated shelf labels);
retrieving a set of stock product images of the third set of product types (See Adato ¶ [0406-0408] – companies A, B or X [third by example] all distributing [supplying] drink products [category by example] and as the specification of the instant application gives no special definition for the limitation stock product images, for the purpose of examination herein, said limitation is interpreted as any image representing a product, therefore, see ¶ [0753] – inventory indicators include images and other types of data representing a product); and
overlaying the set of stock product images over regions, in the third cluster of regions in the first image, depicting corresponding slots in the third set of slots (See Adato ¶ [0753] – The inventory indicator may include, for example, text, icons, graphics, or a combination thereof. In one embodiment, the inventory indicator may be overlaid on the acquired image data or in proximity to an identifier of the product (e.g., text, an image, an icon, etc.). In some embodiments, the inventory indicators in first display area may be partially transparent to avoid obscuring areas of interest in image data and Fig. 23 – boxes 2310, 2320 and 2330 [third by example] shown on an image of products on shelves with their associated shelf labels); and
wherein serving the … image to the first supplier comprises serving the … image to the first supplier, the … image:
depicting the first cluster of regions; and
depicting the set of stock images overlayed on the third cluster of regions (See Adato ¶ [0450-0452] – the visual representation of boxes around products on shelves is provided to multiple users, including suppliers, [0753] – The inventory indicator may include, for example, text, icons, graphics, or a combination thereof. In one embodiment, the inventory indicator may be overlaid on the acquired image data or in proximity to an identifier of the product (e.g., text, an image, an icon, etc.). In some embodiments, the inventory indicators in first display area may be partially transparent to avoid obscuring areas of interest in image data and Fig. 23 – boxes 2310, 2320 and 2330 [third by example] shown on an image of products on shelves with their associated shelf labels).
While Adato teaches a system of managing inventory of products on retail shelves through analysis of captured images showing stock conditions of said shelves and providing said images to suppliers of said products (Adato ¶ [0120], [0127] and [0226-0228]), Adato does not explicitly teach serving the masked image to the first supplier. This is taught by Bogolea (See Bogolea ¶ [0131-0133] – redacting [masking by example] photographic images of slots [cluster of regions] to depict only slots contracted to a corresponding distributor [supplier] and then serving this redacted image to said distributor [supplier]). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include in the product image data based inventory management system that provides said product image data to suppliers of the products depicted in said image data of Adato the use of redacted image data to provide suppliers with only the image data for products they are contracted to manage as taught by Bogolea to improve efficiency of remote slot management by the distributor and in order decrease aggregate out-of-stock and understock durations of high-value products stocked throughout the distributor's geographic region (Bogolea ¶ [0016]).
Regarding Claim 13, modified Adato teaches:
The method of Claim 1:
wherein identifying the second cluster of regions, in the first image, depicting the second set of slots comprises detecting a particular product unit in a particular region in the first cluster of regions based on the first set of features (See Adato Fig. 23 – boxes 2310, 2320 and 2330 [at least second cluster of regions by example] shown on an image of products on shelves with their associated shelf labels), the particular product unit supplied by a second supplier distinct from the first supplier (See Adato ¶ [0406-0408] – companies A, B or X [at least second supplier by example] all distributing [supplying] drink products [category by example]);
…; and
wherein serving the … image to the first supplier comprises serving the … image, depicting the first cluster of regions …, to the first supplier (See Adato ¶ [0450-0452] – the visual representation of boxes around products on shelves is provided to multiple users, including suppliers).
While Adato teaches a system of managing inventory of products on retail shelves through analysis of captured images showing stock conditions of said shelves and providing said images to suppliers of said products (Adato ¶ [0120], [0127] and [0226-0228]), Adato does not explicitly teach obfuscating the second cluster of regions in the first image to generate the masked image comprises obfuscating the particular region of the first image to hide the particular product unit in the masked image and serving the masked image to the first supplier, wherein said masked image comprises a cluster of regions that are obfuscated over the particular product unit. This is taught by Bogolea (See Bogolea ¶ [0131-0133] – redacting [masking by example] photographic images of slots [cluster of regions] to depict only slots contracted to a corresponding distributor [supplier] and then serving this redacted image to said distributor [supplier]). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include in the product image data based inventory management system that provides said product image data to suppliers of the products depicted in said image data of Adato the use of redacted image data to provide suppliers with only the image data for products they are contracted to manage as taught by Bogolea to improve efficiency of remote slot management by the distributor and in order decrease aggregate out-of-stock and understock durations of high-value products stocked throughout the distributor's geographic region (Bogolea ¶ [0016]).
Regarding Claim 15, modified Adato teaches:
The method of Claim 1:
further comprising:
accessing a second image captured by the robotic system during the scan cycle while traversing a second aisle facing a second inventory structure in the store (See Adato ¶ [0149] – robotic devices with cameras traversing aisles of retail stores to capture images of products on shelves [facing the inventory structure by example], wherein said captured images form a sequence of photographic images by example as said robotic device traverses said aisle of shelves);
identifying a third cluster of regions, in the second image, depicting a third set of slots assigned to product types supplied by the first supplier (See Adato ¶ [0406-0408] – companies A, B or X [third by example] all distributing [supplying] drink products [category by example] and Fig. 23 – boxes 2310, 2320 and 2330 [third by example] shown on an image of products on shelves with their associated shelf labels);
identifying a fourth cluster of regions, in the second image, depicting a fourth set of slots assigned to product types supplied by a third set of suppliers excluding the first supplier (See Adato ¶ [0406-0408] and Fig. 23 as noted directly above and ¶ [0442] – differentiating product 2322 from product 2324 based on labels P1 and C2, thereby showing a fourth cluster of regions by example);
… the fourth cluster of regions in the second image to generate a second … image (See Adato ¶ [0452] – generating visual representation based on products identified from ¶ [0406-0408] , [0442] and Fig. 23 as noted directly above);
detecting a second set of features in the third cluster of regions in the second image (See Adato ¶ [0406-0408] and Fig. 23 as noted directly above and ¶ [0442] – label pricing as a second set of features);
interpreting a second set of stock conditions of the third set of slots during the scan cycle based on the second set of features (See Adato ¶ [0149] – robotic devices with cameras traversing aisles of retail stores to capture images of products on shelves periodically [scan cycle by example], [0406-0408] and Fig. 23 as noted directly above and ¶ [0442] – label pricing as a second set of features and product proximity [stock conditions] to said label);
compiling the first set of stock conditions and the second set of stock conditions into a table identifying locations and stock conditions of slots in the store, assigned to product types supplied by the first supplier, during the scan cycle (See Adato ¶ [0149] – robotic devices with cameras traversing aisles of retail stores to capture images of products on shelves periodically [scan cycle by example], [0349] – product data is stored in a table or other forms that is updated as needed, [0406-0408] and Fig. 23 as noted directly above and ¶ [0442] – label pricing as a second set of features and product proximity [stock conditions] to said label); and
serving the table to the first supplier (See Adato ¶ [0450-0452] – the visual representation of boxes around products on shelves is provided to multiple users, including suppliers); and
wherein receiving the query comprises receiving selection of a particular slot, in the first set of slots, from the table (See Adato ¶ [0228-0229] – generating reports [tables] from data derived from product images to help a supplier determine which products should be in particular locations on store shelves [slots] and [0349] – product data is stored in a table or other forms that is updated as needed).
While Adato teaches a system of managing inventory of products on retail shelves through analysis of captured images showing stock conditions of said shelves and providing said images to suppliers of said products (Adato ¶ [0120], [0127] and [0226-0228]), Adato does not explicitly teach obfuscating a cluster of regions in the second image to generate a second masked image. This is taught by Bogolea (See Bogolea ¶ [0131-0133] – redacting [masking by example] photographic images of slots [cluster of regions] to depict only slots contracted to a corresponding distributor [supplier] and then serving this redacted image to said distributor [supplier]). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include in the product image data based inventory management system that provides said product image data to suppliers of the products depicted in said image data of Adato the use of redacted image data to provide suppliers with only the image data for products they are contracted to manage as taught by Bogolea to improve efficiency of remote slot management by the distributor and in order decrease aggregate out-of-stock and understock durations of high-value products stocked throughout the distributor's geographic region (Bogolea ¶ [0016]).
Regarding Claim 16, modified Adato teaches:
The method of Claim 1:
further comprising receiving a second query from a second supplier to the store, the second supplier distinct from the first supplier (See Adato ¶ [0120] – system working with multiple suppliers [115A-C], [0226] –allow market research entity and suppliers to continuously monitor product-related activities at retail stores);
wherein identifying the second cluster of regions in the first image comprises identifying the second cluster of regions, in the first image, depicting the second set of slots assigned to product types supplied by the second supplier (See Adato ¶ [0120] – using image analysis of products in shelf spaces [slots] on retail shelves to send product information to multiple suppliers of said products, [0127] – presenting suppliers with a level of planogram compliance for products assigned to supplier, thereby identifying a second cluster of regions, in the first image, depicting the second set of slots assigned to product types supplied by the second supplier by example and [0226-0228] – providing supplier’s with product sales information for their current products and performance information relating to products of their competitors based on products recognized from image analysis of products on retail store shelves); and
further comprising:
…;
detecting a second set of features in the second cluster of regions in the first image (See Adato ¶ [0125] – detecting product features from analysis of image data and [0120] & [0127] as noted above – regarding clusters of regions in the first image);
interpreting a second set of stock conditions of the second set of slots at the first time based on the second set of features (See Adato ¶ [0125] – detecting product features from analysis of image data and [0231] – real-time alerts when products are out of shelf [stock condition] or near out of shelf or other issues with planogram compliance); and
in response to receiving the second query:
serving the second … image to the second supplier; and
serving the second set of stock conditions of the second set of slots to the second supplier.
While Adato teaches a system of managing inventory of products on retail shelves through analysis of captured images showing stock conditions of said shelves and providing said images to suppliers of said products (Adato ¶ [0120], [0127] and [0226-0228]), Adato does not explicitly teach obfuscating the first cluster of regions in the first image to generate a second masked image and serving the masked image to a supplier. This is taught by Bogolea (See Bogolea ¶ [0111] – a second scan cycle is run to generate a second image of stock conditions of products on shelves and [0131-0133] – redacting [masking by example] photographic images of slots [cluster of regions] to depict only slots contracted to a corresponding distributor [supplier] and then serving this redacted image to said distributor [supplier]). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include in the product image data based inventory management system that provides said product image data to suppliers of the products depicted in said image data of Adato the use of redacted image data to provide suppliers with only the image data for products they are contracted to manage as taught by Bogolea to improve efficiency of remote slot management by the distributor and in order decrease aggregate out-of-stock and understock durations of high-value products stocked throughout the distributor's geographic region (Bogolea ¶ [0016]).
Regarding Claim 17, Adato teaches:
A method comprising:
during a scan cycle, by a mobile robotic system:
autonomously navigating throughout a store during the scan cycle (See Adato ¶ [0149] – robotic devices with cameras [optical sensor] traversing aisles of retail stores to capture images of products on shelves, wherein said robots are operated remotely or autonomously); and
via an optical sensor integrated in the mobile robotic system, capturing images of inventory structures arranged in the store (See Adato ¶ [0149] – robotic devices with cameras traversing aisles of retail stores to capture images of products on shelves [facing the inventory structure by example], wherein said captured images form a sequence of photographic images by example as said robotic device traverses said aisle of shelves); and
by a computer system:
accessing a query from a first supplier of product to a store (See Adato ¶ [0226] –allow market research entity and suppliers to continuously monitor product-related activities at retail stores);
accessing a first image of an inventory structure captured by the optical sensor at a first time (See Adato ¶ [0154] – image sensor on store shelves to capture images of opposing retail shelving and [0226] – using system to generate various metrics or information based on automated analysis of actual, timely images acquired from the retail stores, e.g.: market research entity may track how quickly or at what rate new products are introduced to retail store shelves);
retrieving a geometry of a field of view of the optical sensor integrated in the mobile robotic system at the first time (See Adato ¶ [0149] as noted above and [0228-0229] – server may use data derived from images captured in a plurality of retail stores to recommend a planogram and Fig. 11B – showing an example of a planogram output for a supplier);
based on a projection of the geometry of the field of view onto a planogram of the store (See Adato ¶ [0228-0229] – server may use data derived from images captured in a plurality of retail stores to recommend a planogram and Fig. 11B – showing an example of a planogram output for a supplier), identifying a first cluster of regions, in the first image, depicting a first set of slots assigned to product types supplied by the first supplier (See Adato ¶ [0120] – using image analysis of products in shelf spaces [slots] on retail shelves to send product information to multiple suppliers of said products and [0127] – presenting suppliers with a level of planogram compliance for products assigned to supplier, thereby identifying a first cluster of regions, in the first image, depicting a first set of slots assigned to product types supplied by the first supplier by example);
based on a projection of the geometry of the field of view onto a planogram of the store (See Adato ¶ [0228-0229] – server may use data derived from images captured in a plurality of retail stores to recommend a planogram and Fig. 11B – showing an example of a planogram output for a supplier), identifying a second cluster of regions, in the first image, depicting a second set of slots assigned to product types supplied by a second set of suppliers excluding the first supplier (See Adato ¶ [0120] and [0127] as noted above – wherein presenting multiple suppliers with information about multiple products in multiple shelf spaces is identifying a second cluster of regions, in the first image, depicting a second set of slots assigned to product types supplied by a second set of suppliers by example excluding the first supplier and [0226-0228] – providing supplier’s with product sales information for their current products and performance information relating to products of their competitors based on products recognized from image analysis of products on retail store shelves);
…; and
based on the query, serving the … image to the first supplier (See Adato ¶ [0215-0216] – transmitting planogram compliance information to a supplier, wherein product facings [stock condition example] are referenced).
While Adato teaches a system of managing inventory of products on retail shelves through analysis of captured images showing stock conditions of said shelves and providing said images to suppliers of said products (Adato ¶ [0120], [0127] and [0226-0228]), Adato does not explicitly teach obfuscating the second cluster of regions in the first image to generate a masked image and serving the masked image to the first supplier. This is taught by Bogolea (See Bogolea ¶ [0131-0133] – redacting [masking by example] photographic images of slots [cluster of regions] to depict only slots contracted to a corresponding distributor [supplier] and then serving this redacted image to said distributor [supplier]). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include in the product image data based inventory management system that provides said product image data to suppliers of the products depicted in said image data of Adato the use of redacted image data to provide suppliers with only the image data for products they are contracted to manage as taught by Bogolea to improve efficiency of remote slot management by the distributor and in order decrease aggregate out-of-stock and understock durations of high-value products stocked throughout the distributor's geographic region (Bogolea ¶ [0016]).
Regarding Claim 18, modified Adato teaches:
The method of Claim 17:
further comprising, by the computer system:
detecting a set of shelf tags on the inventory structure in the first image (See Adato ¶ [0398] – For example, system 100 may detect product 2332 by a determination that it is on shelf 2302 or that it is associated with, for example, price label E2 [shelf tag] and shown in Fig. 23);
calculating a first set of slot boundaries of the first set of slots based on locations of corresponding shelf tags in the set of shelf tags (See Adato ¶ [0439] – boxes [boundaries] around products in shelf spaces [slots] denoted by price labels (A1-A3, B1-B3, C1-C3, etc.) [shelf tags] and shown in Fig. 23); and
annotating the first image with the first set of slot boundaries (See Adato Fig. 23 – boxes 2310, 2320 and 2330 shown on an image of products on shelves with their associated shelf labels); and
wherein identifying the first cluster of regions, in the first image, depicting a first set of slots comprises defining the first cluster of regions bounded by the first set of slot boundaries (See Adato ¶ [0439] – boxes [boundaries] around products in shelf spaces [slots] denoted by price labels (A1-A3, B1-B3, C1-C3, etc.) [shelf tags] and shown in Fig. 23).
Regarding Claim 19, Adato teaches:
A method comprising:
during a scan cycle, by a mobile robotic system:
autonomously navigating throughout a store during the scan cycle (See Adato ¶ [0149] – robotic devices with cameras [optical sensor] traversing aisles of retail stores to capture images of products on shelves, wherein said robots are operated remotely or autonomously); and
via an optical sensor integrated in the mobile robotic system, capturing images of inventory structures arranged in the store (See Adato ¶ [0149] – robotic devices with cameras traversing aisles of retail stores to capture images of products on shelves [facing the inventory structure by example], wherein said captured images form a sequence of photographic images by example as said robotic device traverses said aisle of shelves);
accessing a first image of an inventory structure captured by the optical sensor at a first time (See Adato ¶ [0154] – image sensor on store shelves to capture images of opposing retail shelving and [0226] – using system to generate various metrics or information based on automated analysis of actual, timely images acquired from the retail stores, e.g.: market research entity may track how quickly or at what rate new products are introduced to retail store shelves);
detecting a group of slots, in the inventory structure, depicted in the first image (See Adato ¶ [0120] – using image analysis of products in shelf spaces [slots] on retail shelves to send product information to multiple suppliers of said products);
identifying a first set of product types assigned to the group of slots (See Adato ¶ [0120] – using image analysis of products in shelf spaces [slots] on retail shelves to send product information to multiple suppliers of said products and [0127] – presenting suppliers with a level of planogram compliance for products assigned to supplier);
detecting a set of features in regions of the first image corresponding to the group of slots (See Adato ¶ [0125] – detecting product features from analysis of image data and [0120] & [0127] as noted above – regarding regions in the first image);
detecting a first set of stock conditions of the first set of product types occupying the group of slots at the first time based on the set of features (See Adato ¶ [0125] – detecting product features from analysis of image data and [0231] – real-time alerts when products are out of shelf [stock condition] or near out of shelf or other issues with planogram compliance);
accessing a query from a first supplier of product to the store (See Adato ¶ [0226] –allow market research entity and suppliers to continuously monitor product-related activities at retail stores);
identifying a first cluster of regions, in the first image, depicting a first set of slots assigned to product types supplied by the first supplier (See Adato ¶ [0120] – using image analysis of products in shelf spaces [slots] on retail shelves to send product information to multiple suppliers of said products and [0127] – presenting suppliers with a level of planogram compliance for products assigned to supplier, thereby identifying a first cluster of regions, in the first image, depicting a first set of slots assigned to product types supplied by the first supplier by example);
identifying a second cluster of regions, in the first image, depicting a second set of slots assigned to product types supplied by a second set of suppliers excluding the first supplier (See Adato ¶ [0120] and [0127] as noted above – wherein presenting multiple suppliers with information about multiple products in multiple shelf spaces is identifying a second cluster of regions, in the first image, depicting a second set of slots assigned to product types supplied by a second set of suppliers by example excluding the first supplier and [0226-0228] – providing supplier’s with product sales information for their current products and performance information relating to products of their competitors based on products recognized from image analysis of products on retail store shelves);
…; and
based on the query:
serving the … image to the supplier; and
serving the first set of stock conditions of the first set of slots to the supplier (See Adato ¶ [0215-0216] – transmitting planogram compliance information to a supplier, wherein product facings [stock condition example] are referenced).
While Adato teaches a system of managing inventory of products on retail shelves through analysis of captured images showing stock conditions of said shelves and providing said images to suppliers of said products (Adato ¶ [0120], [0127] and [0226-0228]), Adato does not explicitly teach obfuscating the second cluster of regions in the first image to generate a masked image and serving the masked image to the supplier. This is taught by Bogolea (See Bogolea ¶ [0131-0133] – redacting [masking by example] photographic images of slots [cluster of regions] to depict only slots contracted to a corresponding distributor [supplier] and then serving this redacted image to said distributor [supplier]). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include in the product image data based inventory management system that provides said product image data to suppliers of the products depicted in said image data of Adato the use of redacted image data to provide suppliers with only the image data for products they are contracted to manage as taught by Bogolea to improve efficiency of remote slot management by the distributor and in order decrease aggregate out-of-stock and understock durations of high-value products stocked throughout the distributor's geographic region (Bogolea ¶ [0016]).
Regarding Claim 20, modified Adato teaches:
The method of Claim 19:
wherein accessing the query from the first supplier comprises receiving the query to remotely view the first set of stock conditions, of product types manufactured by the first supplier, in the store (See Adato ¶ [0216] – remote servers transmitting planogram compliance data and [0226-0227] –allow market research entity and suppliers to continuously monitor product-related activities at retail stores through server provided information regarding products on retail shelves); and
wherein … the second cluster of regions in the first image comprises blurring the second cluster of regions in the first image to generate the … image (See Adato ¶ [0232] – blurring or erasing depictions of customers from a near real time display that is generated from image data).
While Adato teaches a system of managing inventory of products on retail shelves through analysis of captured images showing stock conditions of said shelves and providing said images to suppliers of said products (Adato ¶ [0120], [0127] and [0226-0228]), Adato does not explicitly teach obfuscating a cluster of regions in the first image to generate a masked image. This is taught by Bogolea (See Bogolea ¶ [0131-0133] – redacting [masking by example] photographic images of slots [cluster of regions] to depict only slots contracted to a corresponding distributor [supplier] and then serving this redacted image to said distributor [supplier]). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include in the product image data based inventory management system that provides said product image data to suppliers of the products depicted in said image data of Adato the use of redacted image data to provide suppliers with only the image data for products they are contracted to manage as taught by Bogolea to improve efficiency of remote slot management by the distributor and in order decrease aggregate out-of-stock and understock durations of high-value products stocked throughout the distributor's geographic region (Bogolea ¶ [0016]).
Regarding Claim 21, modified Adato teaches:
The method of Claim 19:
wherein accessing the first image of the inventory structure captured by the optical sensor at the first time comprises accessing the first image captured by the optical sensor integrated within the mobile robotic system (See Adato ¶ [0149] – robotic devices with cameras [optical sensor] traversing aisles of retail stores to capture images of products on shelves, wherein said robots are operated remotely or autonomously), the mobile robotic system traversing a first aisle facing the inventory structure at the first time (See Adato ¶ [0149] – robotic devices with cameras traversing aisles of retail stores to capture images of products on shelves [facing the inventory structure by example], wherein said captured images form a sequence of photographic images by example as said robotic device traverses said aisle of shelves);
further comprising:
accessing a second image captured by the mobile robotic system while traversing a second aisle facing a second inventory structure in the store (See Adato ¶ [0149] – robotic devices with cameras traversing aisles of retail stores to capture images of products on shelves [facing the inventory structure by example], wherein said captured images form a sequence of photographic images by example as said robotic device traverses said aisle of shelves);
identifying a third cluster of regions, in the second image, depicting a third set of slots assigned to product types supplied by the first supplier (See Adato ¶ [0406-0408] – companies A, B or X [third by example] all distributing [supplying] drink products [category by example] and Fig. 23 – boxes 2310, 2320 and 2330 [third by example] shown on an image of products on shelves with their associated shelf labels);
identifying a fourth cluster of regions, in the second image, depicting a fourth set of slots assigned to product types supplied by a third set of suppliers excluding the first supplier (See Adato ¶ [0406-0408] and Fig. 23 as noted directly above and ¶ [0442] – differentiating product 2322 from product 2324 based on labels P1 and C2, thereby showing a fourth cluster of regions by example);
… the fourth cluster of regions in the second image to generate a second … image (See Adato ¶ [0452] – generating visual representation based on products identified from ¶ [0406-0408] , [0442] and Fig. 23 as noted directly above);
detecting a second set of features in the third cluster of regions in the second image (See Adato ¶ [0406-0408] and Fig. 23 as noted directly above and ¶ [0442] – label pricing as a second set of features);
interpreting a second set of stock conditions of the third set of slots based on the second set of features (See Adato ¶ [0149] – robotic devices with cameras traversing aisles of retail stores to capture images of products on shelves periodically [scan cycle by example], [0406-0408] and Fig. 23 as noted directly above and ¶ [0442] – label pricing as a second set of features and product proximity [stock conditions] to said label);
compiling the first set of stock conditions and the second set of stock conditions into a table identifying locations and stock conditions of slots in the store assigned to product types supplied by the first supplier (See Adato ¶ [0149] – robotic devices with cameras traversing aisles of retail stores to capture images of products on shelves periodically [scan cycle by example], [0349] – product data is stored in a table or other forms that is updated as needed, [0406-0408] and Fig. 23 as noted directly above and ¶ [0442] – label pricing as a second set of features and product proximity [stock conditions] to said label); and
serving the table to the first supplier (See Adato ¶ [0450-0452] – the visual representation of boxes around products on shelves is provided to multiple users, including suppliers); and
wherein accessing the query comprises receiving selection of a particular slot, in the first set of slots, from the table(See Adato ¶ [0228-0229] – generating reports [tables] from data derived from product images to help a supplier determine which products should be in particular locations on store shelves [slots] and [0349] – product data is stored in a table or other forms that is updated as needed).
While Adato teaches a system of managing inventory of products on retail shelves through analysis of captured images showing stock conditions of said shelves and providing said images to suppliers of said products (Adato ¶ [0120], [0127] and [0226-0228]), Adato does not explicitly teach obfuscating a cluster of regions in the second image to generate a second masked image. This is taught by Bogolea (See Bogolea ¶ [0131-0133] – redacting [masking by example] photographic images of slots [cluster of regions] to depict only slots contracted to a corresponding distributor [supplier] and then serving this redacted image to said distributor [supplier]). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include in the product image data based inventory management system that provides said product image data to suppliers of the products depicted in said image data of Adato the use of redacted image data to provide suppliers with only the image data for products they are contracted to manage as taught by Bogolea to improve efficiency of remote slot management by the distributor and in order decrease aggregate out-of-stock and understock durations of high-value products stocked throughout the distributor's geographic region (Bogolea ¶ [0016]).
Response to Arguments
Applicant's arguments filed 09/09/2025 have been fully considered but they are not persuasive.
Rejection under 35 U.S.C. § 101:
The amendments to independent claims 1, 17 and 19 do not improve patent eligibility of the claimed invention of the instant application and the previous rejection under 35 U.S.C. § 101 in maintained.
While the examiner agrees with the applicant insofar as the independent claims have been amended in manner that cannot be performed in the human mind and therefore no longer recite a mental process, said independent claims do recite the abstract idea of commercial or legal interactions as described above in the current rejection under 35 U.S.C. § 101.
Contrary to the applicant’s assertion that amended independent claims 1, 17 and 19 improve automated inventory management systems, said amendments leave the methods of said claims as executed by technical elements disclosed at a high level of generality such that said methods are not more than merely applying a computer to perform the functions required by said methods, which does not show integration into a practical application nor does it show significantly more than the abstract ideas discussed above in the current rejection under 35 U.S.C. § 101. The amended claims as they are currently amended, as a whole, merely show the intended use of a camera capturing image data with an optical sensor without any additional action beyond a computer processing data, which is required to show a practical application or significantly more than the abstract idea. Any improvement of a claimed invention must be clearly reflected by said claims.
Dependent claims 2-13, 15, 18 and 20-21 also remain rejected as described above in the current rejection under 35 U.S.C. § 101.
The specification of an instant application is not read into the claims during examination.
Rejection under 35 U.S.C. § 103:
The amendments to independent claims 1, 17 and 19 as they are currently limited do not overcome the prior art references of record and the previous rejection under 35 U.S.C. § 103 is maintained.
Contrary to the applicant’s assertion that Adato does not teach the amended claim limitations of independent claims 1, 17 and 19 requiring: “retrieving a geometry of a field of view of the fixed camera at the first time; based on a projection of the geometry of the field of view onto a planogram of the store, identifying a first cluster of regions, in the first image, depicting a first set of slots assigned to product types supplied by the first supplier; based on the projection of the geometry of the field of view onto the planogram, identifying a second cluster of regions, in the first image, depicting a second set of slots assigned to product types supplied by a second set of suppliers excluding the first supplier”, these features remain to be taught by Adato. While the applicant’s argument is based solely on ¶ [0171], these amended features are taught by ¶ [0228-0229] and Fig. 11B as noted above in the current rejection under 35 U.S.C. § 103.
Contrary to the applicant’s assertion that the combination of Adato and Bogolea does not teach, suggest, or motivate identifying and/or obfuscating image regions specifically based on which supplier supplies the products detected in these regions of images, said combination does in fact teach these features. As described above in the current rejection under 35 U.S.C. § 103, Adato teaches identifying regions of images and sending the resulting image data to respective suppliers while Bogolea teaches obfuscating parts of said images. One cannot show non-obviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
The applicant is generally reminded that prior art must be considered in its entirety (MPEP 2141.02 (VI)) and that the specification of an instant application is not read into the claims during examination.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Bogolea et al. (US 2020/0286032 A1) teaches including a 2D overlay of a shelving structure within a store, aligning said overlay with a panoramic image of corresponding shelving structures and then serve the composite image to a corporate representative.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MATTHEW S WERONSKI/ Examiner, Art Unit 3627
/MICHAEL JARED WALKER/Primary Examiner, Art Unit 3627