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 .
Drawings
The drawings filed on 01/09/2025 are accepted by the examiner.
Claim Rejections - 35 USC § 102
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 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-13 and 17-22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Application Publication No. 2020/0096349 to Black et al.
With regard to claims 1 and 17, Black discloses a system, comprising:
a non-transitory computer readable storage media that stores program code (fig. 9, paragraph 90); and
a processor in communication with the non-transitory computer readable storage medium (fig. 9, paragraph 90), the processor configured to execute the program code to:
receive, for each mobile device of one or more mobile device, scan-location data that includes product scan data and associated location data for the mobile device, wherein the product scan data is generated in response to scanning of a product by the mobile device and the location data is indicative of a location of the mobile device at a time of the scanning of the product (paragraph 40, a data collection device 150 (e.g., a mobile computing device such as a handheld computer. Scanning an item, for example, may include capturing an image of the item and/or an item identifier (e.g., a barcode) using an image capture device (e.g., a camera) of the data collection device 150. After receiving the identifier of the selected or scanned item and the location in the space at which the selection or scan occurred, for example, the item location data can be maintained by the item location data source 130, along with a corresponding timestamp.); and
process the scan-location data of the one or more mobile device to produce a realogram of products scanned by the one or more mobile device (paragraph 87, In general, updating map data stored in the map information data source 110 (e.g., modifying CAD files to reflect a current layout of a mapped space)).
With regard to claims 2 and 18, Black discloses the processing the scan-location data includes: filtering the scan-location data of the one or more mobile device to produce filtered scan-location data sets (paragraph 49, In some implementations, determining a representative location of a cluster may include eliminating one or more outliers of points within the cluster (e.g., using a filter)), and for each product in the filtered scan-location data sets, performing a clustering algorithm to produce a product cluster data set that includes a cluster of product placement locations descriptive of potential product locations associated with the product (paragraph 49, In general, a representative location for a cluster may be based on analyzing the locations of points within the cluster, and may represent a most probable location of an item within a mapped space with respect to the cluster. In some implementations, determining a representative location of a cluster may include determining a median point of the points within the cluster.), wherein the realogram of the products scanned by the one or more mobile device is produced based at least in part on the product cluster data sets (fig. 3b, paragraphs 45 and 49, For example, each set of item location coordinates 132 can be represented as a data point (e.g., data points 330a, 330b, 340a, 340b, 350a, 350b, etc.) in a two dimensional space, and can be positioned within the two dimensional space based on its location coordinate values (e.g., XY coordinate values). In the present example, each of the data points 330a, 330b, 340a, 340b, 350a, 350b, etc., corresponds to a location within the mapped space 300 at which a selection or scan of a particular item (e.g., the Albert Einstein t-shirt) occurred, and optionally, a location at which a selection or scan of a related item (e.g., a men's printed t-shirt) occurred. Referring again to FIG. 3B, for example, representative point 332 is determined for cluster 330, representative point 342 is determined for cluster 340, and representative point 352 is determined for cluster 350.).
With regard to claim 3, Black discloses filtering the scan-location data includes filtering out data based on one or more of: time of the scanning of the product, accuracy of the location data, a vicinity of the location indicated by the location data to a store containing the product, consistency of accuracy of product scans by mobile devices, or presence of batch product scans (paragraph 49 and 51, For example, some individuals (e.g., shoppers) may tend to scan or select items (e.g., products) after collecting the items (e.g., at the end of a shopping trip, before checking out). In the present example, area 320 can be designated as an area that excludes item locations, and the cluster 340 can be eliminated.).
With regard to claim 4, Black discloses the clustering algorithm produces a number of clusters that is unknown prior to execution of the clustering algorithm (paragraph 48, As shown in FIG. 1A, for example, during stage (C), the computing servers 102 can determine a plurality of clusters in a two dimensional space for data points corresponding to the received item location coordinates 132. In some implementations, a technique for determining the clusters may include an unsupervised machine learning algorithm. For example, the machine learning algorithm can accept as a parameter a number of clusters (e.g., three clusters, four clusters, five clusters, or another suitable value) in which to group the data points.).
With regard to claim 5, Black discloses the clustering algorithm is an algorithm based on one or more of the following algorithms: ordering points to identify clustering structure (OPTICS) algorithm, density-based spatial clustering of applications with noise (DBSCAN) algorithm, affinity propagation, and mean-shift (paragraph 78, The item location coordinates 132 for an item, for example, can be associated with metadata that indicates when a selection/scan of an item occurred, a context of the selection/scan that indicates a type of user who performed the selection scan and for what reason (e.g., a customer selection/scan using a mobile shopping application, a customer checking the item off a digital shopping list, an employee scanning an item when assembling an order for a customer, an employee scanning an item when performing an inventory audit, etc.).).
With regard to claim 6, Black discloses the clustering algorithm produces the product cluster data based on time data and location data. (fig. 3b, paragraphs 40, 42, and 61, the item location data can be maintained by the item location data source 130, along with a corresponding timestamp. Item location coordinates may be retrieved only for item selections or scans that have occurred within a particular time window. the computing servers 102 can add the pairing, corresponding item location coordinates, and optional timestamp data to the cache 140. )
With regard to claims 7 and 19, Black discloses the processing the scan-location data further includes: post-processing the product cluster data sets to produce post-processed product cluster data sets (paragraphs 51 and 53, In the present example, area 320 can be designated as an area that excludes item locations, and the cluster 340 can be eliminated.), and estimating, for each product in the post-processed product cluster data sets, a placement location confidence indicative of a confidence of locating the product in each potential product location, wherein the realogram of the products scanned by the one or more mobile device is produced based at least in part on the post-processed product cluster data sets and the estimated placement location confidence (paragraphs 69 and 73, FIG. 5 shows an example process 500 for determining a location of an item based on a confidence evaluation. At box 508, a location of the item is selected from the selection or scan-based location and the planogram location of the item. In general, an item location having a greater confidence value may be selected as an estimated location of the item. In the present example, the representative point 332 of cluster 330 is selected as being a likely location of the Albert Einstein men's t-shirt instead of the center point 334 of the menswear department 310, based on the selection or scanned-based location having a higher confidence value than the planogram location).
With regard to claim 8, Black discloses the estimated placement location confidence includes an estimated recency confidence indicative of a confidence, for each potential product location, that the product is still located at the potential product location (paragraphs 72 and 84, At box 506, confidence values of the selection or scan-based location and the planogram location of the item are evaluated. In general, confidence values of selection or scan-based locations may be based on various factors, such as a number of selections or scans of an item used to determine the locations (e.g., a greater number of selections or scans being correlated with greater confidence values) a recency of item selections or scans (e.g., with more recent selections or scans being correlated with greater confidence values). In some implementations, a work task for indicating an item's location may be generated when a confidence value of the item's location is below a threshold confidence value).
With regard to claim 9, Black discloses the estimated placement location confidence for each product is a function of the post-processed product cluster data sets (paragraph 72, confidence values of the selection or scan-based location and the planogram location of the item are evaluated. In general, confidence values of selection or scan-based locations may be based on various factors, such as a number of selections or scans of an item used to determine the locations (e.g., a greater number of selections or scans being correlated with greater confidence, and/or selection or scanning techniques having been used by individuals who selected or scanned the item (e.g., with more distributed selections or scans by a particular individual being correlated with greater confidence values than concentrated selections or scans by the individual). ).
With regard to claims 10 and 20, Black discloses the product in each product cluster data belongs to one or more category, the method further comprising: receiving input data derived from the product cluster data sets (paragraph 48, data points 340a, 340b, etc. have been grouped into cluster 340, and data points 350a, 350b, etc. have been grouped into cluster 350.); filtering the input data to produce filtered data (paragraph 49, determining a representative location of a cluster may include eliminating one or more outliers of points within the cluster (e.g., using a filter)); for each category in the filtered data, performing a clustering algorithm to produce a category cluster data set that includes a cluster of product placement locations descriptive of potential product locations of different products belonging to a common category (paragraph 49, determining an average or central point of the remaining points within the cluster. Referring again to FIG. 3B, for example, representative point 332 is determined for cluster 330, representative point 342 is determined for cluster 340, and representative point 352 is determined for cluster 350.); post-processing the category cluster data sets to produce post-processed category cluster data sets (paragraph 50, one of the clusters 330, 340, and 350 may be selected, based on a determined representative location of the cluster with respect to the mapped space 300 and/or based on one or more attributes of the cluster.); and estimating, for each category in the post-processed category cluster data sets, a second placement location confidence indicative of a confidence of locating a product that belongs to the category in each potential product location, wherein the realogram of the products scanned by the one or more mobile device is produced further based in part on the post-processed category cluster data sets and the estimated second placement location confidence (paragraphs 52-54, selecting one of the determined clusters may include eliminating one or more clusters that include fewer data points relative to other clusters. In some implementations, selecting one of the determined clusters may include selecting one or more clusters that have a representative point that is located within an area that corresponds to an assigned location for an item, based on its planogram information. For example, the item planogram information can indicate a general location, an intermediate locations within a general location, or a discrete locations within an intermediate location or general location for the item. In the present example, the Albert Einstein t-shirt's item planogram information indicates that the item is assigned to a general location of the menswear department 310. Since the representative point 332 of the cluster 330 is located within an area that corresponds to the menswear department 310, for example, cluster 330 can be selected. In the present example, the representative point 332 represents an estimated location of the item (e.g., the Albert Einstein t-shirt) within the area 310 (e.g., the menswear department) of the mapped space 300 (e.g., Building A), based on crowdsourced data that includes item selections or scans previously performed by various different individuals in the mapped space 300. ).
With regard to claims 11 and 21, Black discloses the input data derived from the product cluster data sets is the product cluster data (paragraph 48, Referring again to FIG. 3B, for example, data points 330a, 330b, etc. have been grouped into cluster 330, data points 340a, 340b, etc. have been grouped into cluster 340, and data points 350a, 350b, etc. have been grouped into cluster 350.).
With regard to claims 12 and 22, Black discloses the input data derived from the product cluster data sets includes the post-processed product cluster data sets and the estimated placement location confidence (paragraph 72, confidence values of selection or scan-based locations may be based on various factors, such as a number of selections or scans of an item used to determine the locations (e.g., a greater number of selections or scans being correlated with greater confidence values)).
With regard to claim 13, Black discloses the method is optimized based on ground truth data that is based on at least one of: i) on actual in-store placement of products, or ii) high-confidence estimates of placement locations of products in the produced realogram (paragraph 72).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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.
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2020/0096349 to Black et al.., in view of U.S. Patent Application Publication No. 2021/0398198 to Adato et al.
With regard to claim 23, Black substantially discloses the claimed invention, however, Black does not disclose an indoor positioning system (IPS) in communication with each mobile of device of the one or more mobile device, the IPS configured to determine, for each mobile device, a location of the mobile device based at least in part on sensor data received from one or more sensor of the mobile device.
However, Adato teaches an indoor positioning system (IPS) in communication with each mobile of device of the one or more mobile device, the IPS configured to determine, for each mobile device, a location of the mobile device based at least in part on sensor data received from one or more sensor of the mobile device (capturing device 125 may include a plurality of image sensors associated with a plurality of lenses 312. 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: Indoor Positioning System (IPS), paragraph 138).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Black to include, an indoor positioning system (IPS) in communication with each mobile of device of the one or more mobile device, the IPS configured to determine, for each mobile device, a location of the mobile device based at least in part on sensor data received from one or more sensor of the mobile device, as taught in Adato, in order to determine locations of products from images and/or videos (Adato, paragraph 359).
Conclusion
Please refer to form 892 for cited references.
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/ARIEL J YU/Primary Examiner, Art Unit 3627
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