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 .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/28/2026 has been entered.
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 1-2, 6-9, 12-16 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Francis (US 2024/0303594 A1) in view of Fu et al. (US 2021/0139256 A1).
Regarding Claim 1, modified Francis teaches:
A method comprising:
at a computer system comprising at least one processor and non-transitory memory (See Francis ¶ [0073-0074] – an order filling system (OFS) comprising a central computing system (CCS) and one or more mobile scanning devices (MSDs), [0217-0219] – processing module of the MSDs sending data and [0267] – CCS and MSDs using data stored in memory):
obtaining historical pick data for a plurality of items located in a warehouse (See Francis ¶ [0073-0074] – an order filling system (OFS) for items located in a retail store, warehouse, etc. and [0356] – using historical scanning/ picking data for items to determine an efficient picking route), the historical pick data comprising product data for each of the items picked and pick times between each of the items picked (See Francis ¶ [0356] – using historical scanning/ picking data for items comprising a time each item was scanned/ picked, [0390] – scan data comprises item IDs [product data] and [0724] – historical scan data is used to guide user movement for picking items);
determining a taxonomy of the plurality of items offered by the warehouse, the taxonomy identifying a plurality of product categories structured in a hierarchy, each level of the hierarchy corresponding to a particular level of granularity of product data (The specification of the instant application gives no special definition of the limitation taxonomy, for the purpose of examination, said taxonomy is interpreted to mean a hierarchy relating to product categories. Therefore, see Francis ¶ [0360] – grouping items based on item types/ categories showing item location data associated with aisle numbers [a particular level of granularity of product data], [0491] – location data comprising department names [another particular level of granularity of product data and [0677] – using a hierarchy of operations to calculate picking routes), wherein the hierarchy includes an aisle level and a sub-aisle level (See Francis ¶ [0112] – similar types of items may be grouped together along an aisle in a typical store. For example, the items located along aisle 122-1 [aisle level] (i.e., the items accessible in aisle 122-1 from rack 116-1 and rack 116-2 [sub-aisle level]) may be items of a similar type), wherein each product category in the sub-aisle level is a sub-category of and corresponds to a different subsection of an aisle associated with a respective product category in the aisle level (See Francis ¶ [0112] – the items along aisle 122-1 may be cereal items (e.g., bags or boxes of cereal [sub-categories of cereal by example]), [0433] - The captured image depicts a plurality of items 5804 (e.g., boxes) that are included on a rack 5802. For example, the image depicts, inter alia, a box of Froot Loops 5806, a location indicator 5808 (e.g., a barcode) attached to a shelf 5810, and an aisle sign 5812 including text (e.g., an aisle name and/or number), [0469] - the CCS and/or MSDs can identify text on aisle signs that indicate an aisle number and/or item category/type, such as frozen food, frozen pizzas, frozen dinners, etc. [sub-category of and corresponds to a different subsection of an aisle by example] and [0543] - location descriptors may include aisle names and/or numbers. Additional example location descriptors may also include aisle descriptors, such as first/second aisle ends, aisle middle, and/or shelf location (e.g., bottom shelf, top shelf) [subsection of an aisle by example]), and wherein the sub-aisle level has a higher level of granularity of product data than the aisle level (See Francis ¶ [0365] – the MSDs and/or CCS may add granularity to, or enhance resolution of, relative locations of the items included in the zone and [0401] - if a location indicator covers an entire aisle of items, the item adjacency map may define more granular arrangements of items on the display);
applying the historical pick data to a trained machine learning model (See Francis ¶ [0441] – MSD using a machine learning model for image classification while in “picking” mode, thereby training said MSD from pick data) to generate pairwise relations between product categories in the plurality of product categories, wherein the machine learning model generates a pairwise relation for each pair of product categories at each level of the taxonomy (See Francis ¶ [0392-0393] – scanning multiple items and recognizing paired relationships between items signifying adjacency based on time between each scan of each item and [0441-0442] – MSD using a machine learning image classification models based on product categories at each level of the taxonomy by example by segmenting said model based on the particular areas/ sections [departments or aisles as noted above regarding ¶ [0360] & [0491]] of the store), the machine learning model trained by:
applying, to the machine learning model configured with a first set of weights, training data including historical pick data and target pairwise relations (See Francis ¶[0392-0393] - the CCS may determine that the three items in two pairs of adjacent items are adjacent if the sum of the scan times between the first pair of items and the second pair of items is less than a threshold value… The CCS may make the determination in block 5212 using various scan times associated with the pairs, such as the most recent scan times, average scan times, etc. In block 5214, the CCS updates the item adjacency map to indicate the items in the pairs of items are adjacent if the criteria in block 5212 are satisfied and [0629] – the CCS/MSDs may implement a scoring model in which different possible routes for an MSD are scored, and the highest scoring route is assigned (e.g., highest scoring indicates a best route)… a scoring module at the CCS/MSDs may apply different weights and scores to different aspects of a route (e.g., route length, item number, distance from MSD, etc.) … the CCS/MSDs may implement a machine learned model for generating routes/scores, where the machine learned model receives different routing factors as inputs. The machine learned model may be trained on historic picking/movement data);
determining, by the machine learning model configured with the first set of weights (See Francis ¶[0392-0393] and [0629] as noted above), a first predicted pairwise relation for each pair of product categories at each level of the taxonomy based on the historical pick data for items in the respective pair of product categories (See Francis ¶[0377] - the MSD determines that the first and second items are adjacent to one another… reinforcing a determination of adjacency may include updating data associated with the adjacent items that indicates the scan times between items have satisfied item adjacency criterion multiple times… the MSD and/or CCS may update the item adjacency map to include, for each pair of adjacent items, a reinforcement indicator value that indicates the number of times the two items have been considered adjacent); and
updating the machine learning model with a second set of weights, wherein the updated machine learning model is configured to produce a second predicted pairwise relation for each pair of product categories (See Francis ¶ [0435] - The image processing module may feed the set of features to one or more machine-learned image classification models, which respectively output a classification of the blob based on the set of features, [0441] - an MSD may include an image processing module that classifies images … the MSD may implement a set of machine-learned image classification models stored thereon that the MSD uses to perform image classification… the image classification model(s) of an MSD may be updated when new items or objects are identified… the image classification models used by the MSD when the MSD is in a “picking” mode may be trained to identify a reduced subset of the items in the store… and [0629] - the CCS/MSDs may implement a scoring model in which different possible routes for an MSD are scored… a scoring module at the CCS/MSDs may apply different weights and scores to different aspects of a route … the CCS/MSDs may implement a machine learned model for generating routes/scores, where the machine learned model receives different routing factors as inputs… the machine learned model may be trained on historic picking/movement data), wherein a value between the second predicted pairwise relation and a respective target pairwise relation is smaller than the value between the first predicted pairwise relation and the respective target pairwise relation (See Francis ¶ [0373] - an indication of whether two of the items are adjacent to one another may be determined and/or reinforced based on relatively temporally close scan times associated with the items, indicating that the items are adjacent to one another. Alternatively, the indication of whether the two of the items are adjacent to one another may be removed based on relatively temporally distant scan times associated with the items, indicating that the items are not adjacent to one another and [0492] - the MSD and/or the CCS may generate/update an item association table that associates object-based IDs with items. Although the scanned items may be associated with previously determined object-based IDs, in some cases, the MSD and/or the CCS may be configured to associate a scanned item with a later determined object-based ID, such as when the later determined object-based ID [second predicted pairwise relation] is determined closer in time [smaller than the value between the first predicted pairwise relation] to the scanned item than a previously determined object-based ID [target pairwise relation]. The association between object-based IDs and items may be updated over time.);
generating a plurality of sequences of product categories based on the generated pairwise relations, wherein each sequence in the plurality of sequences is associated with a level of the taxonomy, the plurality of sequences including a first sequence associated with the sub-aisle level of the taxonomy and a second sequence associated with the aisle level of the taxonomy, (See Francis ¶ [0360] – grouping items based on item types/ categories showing item location data associated with aisle numbers [aisle level of the taxonomy], [0371] – generating adjacency maps [sequence of products] shown as a list of adjacent items [products], [0392-0393] – scanning multiple items and recognizing paired relationships between items signifying adjacency based on time between each scan of each item and [0440] - the map generation module may generate the image-based map to indicate locations of items and/or store objects with respect to one another… if a first brand of cereal and a second brand of cereal are classified in the same image (e.g., they are both on the same rack [sub-aisle level of taxonomy]), the map generation module may indicate an adjacency between the first and second brand of cereal in the image-based map), by:
selecting, for each level of the taxonomy, a product category in the level most associated with an initial pick in the historical pick data (See Francis ¶ [0357] – one or more other items in a customer order are included for a particular user based on adjacency of said items to said seed item, wherein said adjacent items are determined to be closest to said user and designated as the next item to be picked, [0654] – the initially selected item acting as a seeding item and [0718-0720] – ranking items by individual item or item type [category] and using said item ranking and user movement data to generate a pick sequence); and
traversing, at a level of the selected product category in the taxonomy, the generated pairwise relations in order of strongest pairwise relation to a currently selected product category in the taxonomy (As the specification of the instant application describes the strongest pairwise relation as the shortest distance from an initial “seed” item to the next item in a sequence of items, see Francis ¶ [0385-0387] – displaying items [product category] to a user MSD device in order of closest item to said device at the top of a list with other items following based on relative proximity to said user device when an initial item is scanned [pairwise relations in order of strongest pairwise relation to a currently selected product category in the taxonomy by example] and Fig. 48 – showing a sequence of items to pick relative to a location of a user MSD [at a level of the selected product category in the taxonomy by example]);
receiving an order for items offered by the warehouse (See Francis ¶ [0073-0075] – receiving customer orders to be fulfilled by a warehouse), wherein the order includes at least a first item associated with a first product category and a second item associated with a second product category and not associated with the first product category (See Francis ¶ [0360-0361] – one or more of the MSDs and/or the CCS may be configured to use metadata indicating a type/category (e.g., produce, dairy, meat products) [at least first and second product category by example] associated with an item included in a customer order to group (e.g., cluster) the item with one or more other items that are also included in the customer order… one or more of the MSDs and/or the CCS may be configured to identify a first item included in a first group and identify a second, different item, that is adjacent to the first item but not included in the first group [second product category and not associated with the first product category by example]); and
comparing the order to two or more sequences to generate a pick sequence for the order (See Francis ¶ [0365-0367] – the MSDs and/or CCS may be configured to identify a first item (e.g., an anchor item) included in a particular zone associated with a location indicator, and then determine relative locations of one or more different items also included in the zone [sequence by example] with respect to the first item using an adjacency map… one or more of the MSDs and/or CCS may be configured to make use of items (e.g., an anchor item) included in zones and MSDs transitioning from one zone to another zone [two or more sequences in the plurality of sequences by example]. For example, the MSDs and/or the CCS may determine that an item included in a first zone is proximate to an item included in a second zone upon an MSD scanning the two items within a predetermined threshold amount of time when the MSD transitions between the two zones, thereby defining edges of the zones… if an aisle has one or more zones in a line, the arrangement of items for picking may be organized in a linear fashion down the aisle from zone to zone based on the edge items and sequential items between the edge items… an MSD may be configured to display a subset of the items included in a customer order (e.g., items that are located proximate to the MSD at that time) [comparing the order by example]) by:
identifying, from each item in the order, one or more product categories associated with the respective item and the compared sequence (See Francis ¶ [0360-0361] – one or more of the MSDs and/or the CCS may be configured to use metadata indicating a type/category (e.g., produce, dairy, meat products) [at least first and second product category by example] associated with an item included in a customer order to group (e.g., cluster) the item with one or more other items that are also included in the customer order… one or more of the MSDs and/or the CCS may be configured to identify a first item included in a first group and identify a second, different item, that is adjacent to the first item but not included in the first group);
…each item in the order based on position of its identified one or more product categories in the first sequence, wherein …, in response to determining that none of the identified product categories of a respective item are contained in the first sequence (See Francis ¶ [0361] - one or more of the MSDs and/or the CCS may be configured to identify a first item included in a first group and identify a second, different item, that is adjacent to the first item but not included in the first group), estimating … for the respective item based on a position in the second sequence of a product category at the aisle level associated with the respective item… (See Francis ¶ [0360-0361] - one or more of the MSDs and/or the CCS may be configured to use metadata indicating a type/category (e.g., produce, dairy, meat products) associated with an item included in a customer order to group (e.g., cluster) the item with one or more other items that are also included in the customer order, adjacent to the item, and associated with the same type… the item adjacency map may group dairy products together based on type and/or aisle location (e.g., aisle number). The item adjacency map may then further define the location of the items within the group (e.g., item type and/or aisle) using item adjacency or other location indicator mapping… one or more of the MSDs and/or the CCS may be configured to identify a first item included in a first group and identify a second, different item, that is adjacent to the first item but not included in the first group [second sequence of a product category at the aisle level associated with the respective item by example] and [0365-0367] as noted above, as well as [0383] - the MSD may display the initial item at the top of a list and display the other items lower on the list based on the relative distances and/or times associated with the items and the initial item, [0387] - with respect to FIG. 48 , an MSD near item 4804-5 may display items 4804-3, 4804-4, 4804-5, and 4804-6 higher on the display to prompt the user to pick those items due to their adjacency. Due to the item adjacency map, this may even be the case if items 4804-3 and/or 4804-4 are in different zones (e.g., associated with different location values) than item 4804-5, wherein said different zones are different aisles [second sequence] as shown in Fig. 48); and
outputting the pick sequence, … (See Francis ¶ [0366-0367] – identifying a sequence of items in a customer order and generating a displayed list of said sequence, wherein said sequence may not be based on adjacency of said items in said order).
While Francis teaches arranging items in an order into a picking sequence based on item groupings associated with item categories (Francis ¶ [0360-0361] and [0365-0367]), Francis does not explicitly teach ranking that comprises estimating the rank of said items in compared picking sequences or that the pick sequence is based on the ranking. This is taught by Fu (See Fu ¶ [0051-0052] – As each available case [item] is identified in progression (ranked and resolved into a sequence solution as further described) the matrix 999A, 999B is updated and reduced… where the flexible sequencer sequences case for picking … each available sequence solution of available cases of the generated sequence solution set (e.g., solution A, solution B, . . . , solution N) is ranked). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the item pick sequence generation system of Francis the use of ranked item pick sequences from multiple pick sequence solution options as taught by Fu to formulate an optimal, yet flexible sequence solution (Fu ¶ [0027]), thereby increasing the accuracy and efficiency of the item pick sequence generation system of Francis.
Regarding Claim 2, modified Francis teaches:
The method of claim 1, further comprising:
While Francis teaches arranging items in an order into a picking sequence based on item groupings associated with item categories (Francis ¶ [0360-0361] and [0365-0367]), Francis does not explicitly teach that the pick sequence is generated based on the ranking. This is taught by Fu (See Fu ¶ [0051-0052] – As each available case [item] is identified in progression (ranked and resolved into a sequence solution as further described) the matrix 999A, 999B is updated and reduced… where the flexible sequencer sequences case for picking … each available sequence solution of available cases of the generated sequence solution set (e.g., solution A, solution B, . . . , solution N) is ranked). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the item pick sequence generation system of Francis the use of ranked item pick sequences from multiple pick sequence solution options as taught by Fu to formulate an optimal, yet flexible sequence solution (Fu ¶ [0027]), thereby increasing the accuracy and efficiency of the item pick sequence generation system of Francis.
Regarding Claim 6, modified Francis teaches:
The method of claim 1, wherein the plurality of sequences includes a third sequence associated with a department level of the taxonomy (See Francis ¶ [0360] – grouping items based on item types/ categories showing item location data associated with aisle numbers and [0491] – location data comprising department names).
Regarding Claim 7, modified Francis teaches:
The method of claim 1, wherein each of the pairwise relations comprises a distance value (See Francis ¶ [0387] – displaying items based on relative distances between said items and [0391] – items are paired together based on satisfying adjacency criterion).
Regarding Claim 8, modified Francis teaches:
The method of claim 7, wherein the distance value is calculated based on or more of: a median pick time or a weighted average pick time (See Francis ¶ [0356] – describing scanning and picking to be the same activity and [0371] – estimating relative distance between adjacent items based on average/ median scan times).
Regarding Claim 9, modified Francis teaches:
The method of claim 7, wherein selecting, for each level of the taxonomy, a product category in the level most associated with an initial pick in the historical pick data (See claim 1 above) comprises:
a) selecting a particular level in the hierarchy (See Francis ¶ [0357] – user selecting one of a plurality of items from a display to start picking a customer order); and
b) selecting a seed from the particular level as a currently selected product category, the seed establishing a first product category in a generated sequence (See Francis ¶ [0357] – said item is a “seed” or “re-seed” item that is a next item to be picked by the user, thereby starting a picking sequence).
Regarding Claim 12, modified Francis teaches:
The method of claim 1, wherein the pairwise relations are stored in a symmetrical matrix (See Francis ¶ [0377] – adjacent items are paired together in an adjacency map, [0382] – adjacency map includes a table and Fig. 49 – said table comprises item IDs and adjacent item IDs for each respective item ID, thereby showing a symmetrical matrix by example).
Regarding Claim 13, modified Francis teaches:
The method of claim 1, wherein the pick sequence is rendered as a shopping list at a client device (See Francis ¶ [0501] – maps and tables used for picking routes may include a customer’s shopping list, wherein said list is sent to a customer in a GUI [rendered]).
Regarding Claim 14, modified Francis teaches:
The method of claim 13, wherein the order for the items offered at the warehouse is generated by a first client device operated by a first user, and wherein the shopping list is rendered at a second client device operated by a second user (See Francis ¶ [0095-0097] – customers placing orders on mobile devices and third party pickers picking items for said orders based websites or application running on devices used by said third parties and [0501] – maps and tables used for picking routes may include a customer’s shopping list, wherein said list is sent to a customer in a GUI [rendered]).
Regarding Claim 15, modified Francis teaches:
A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to (See Francis ¶ [0073-0074] – an order filling system (OFS) comprising a central computing system (CCS) and one or more mobile scanning devices (MSDs), [0217-0219] – processing module of the MSDs sending data and [0267] – CCS and MSDs using data stored in memory):
obtain historical pick data for a plurality of items located in a warehouse (See Francis ¶ [0073-0074] – an order filling system (OFS) for items located in a retail store, warehouse, etc. and [0356] – using historical scanning/ picking data for items to determine an efficient picking route), the historical pick data comprising product data for each of the items picked and pick times between each of the items picked (See Francis ¶ [0356] – using historical scanning/ picking data for items comprising a time each item was scanned/ picked, [0390] – scan data comprises item IDs [product data] and [0724] – historical scan data is used to guide user movement for picking items);
determine a taxonomy of the plurality of items offered by the warehouse, the taxonomy identifying a plurality of product categories structured in a hierarchy, each level of the hierarchy corresponding to a particular level of granularity of product data (The specification of the instant application gives no special definition of the limitation taxonomy, for the purpose of examination, said taxonomy is interpreted to mean a hierarchy relating to product categories. Therefore, see Francis ¶ [0360] – grouping items based on item types/ categories showing item location data associated with aisle numbers [a particular level of granularity of product data], [0491] – location data comprising department names [another particular level of granularity of product data and [0677] – using a hierarchy of operations to calculate picking routes), wherein the hierarchy includes an aisle level and a sub-aisle level (See Francis ¶ [0112] – similar types of items may be grouped together along an aisle in a typical store. For example, the items located along aisle 122-1 [aisle level] (i.e., the items accessible in aisle 122-1 from rack 116-1 and rack 116-2 [sub-aisle level]) may be items of a similar type), wherein each product category in the sub-aisle level is a sub-category of and corresponds to a different subsection of an aisle associated with a respective product category in the aisle level (See Francis ¶ [0112] – the items along aisle 122-1 may be cereal items (e.g., bags or boxes of cereal [sub-categories of cereal by example]), [0433] - The captured image depicts a plurality of items 5804 (e.g., boxes) that are included on a rack 5802. For example, the image depicts, inter alia, a box of Froot Loops 5806, a location indicator 5808 (e.g., a barcode) attached to a shelf 5810, and an aisle sign 5812 including text (e.g., an aisle name and/or number), [0469] - the CCS and/or MSDs can identify text on aisle signs that indicate an aisle number and/or item category/type, such as frozen food, frozen pizzas, frozen dinners, etc. [sub-category of and corresponds to a different subsection of an aisle by example] and [0543] - location descriptors may include aisle names and/or numbers. Additional example location descriptors may also include aisle descriptors, such as first/second aisle ends, aisle middle, and/or shelf location (e.g., bottom shelf, top shelf) [subsection of an aisle by example]), and wherein the sub-aisle level has a higher level of granularity of product data than the aisle level (See Francis ¶ [0365] – the MSDs and/or CCS may add granularity to, or enhance resolution of, relative locations of the items included in the zone and [0401] - if a location indicator covers an entire aisle of items, the item adjacency map may define more granular arrangements of items on the display);
apply the historical pick data to a trained machine learning model (See Francis ¶ [0441] – MSD using a machine learning model for image classification while in “picking” mode, thereby training said MSD from pick data) to generate pairwise relations between product categories in the plurality of product categories, wherein the machine learning model generates a pairwise relation for each pair of product categories at each level of the taxonomy (See Francis ¶ [0392-0393] – scanning multiple items an recognizing paired relationships between items signifying adjacency based on time between each scan of each item and [0441-0442] – MSD using a machine learning image classification models based on product categories at each level of the taxonomy by example by segmenting said model based on the particular areas/ sections [departments or aisles as noted above regarding ¶ [0360] & [0491]] of the store) , the machine learning model trained by:
applying, to the machine learning model configured with a first set of weights, training data including historical pick data and target pairwise relations (See Francis ¶[0392-0393] - the CCS may determine that the three items in two pairs of adjacent items are adjacent if the sum of the scan times between the first pair of items and the second pair of items is less than a threshold value… The CCS may make the determination in block 5212 using various scan times associated with the pairs, such as the most recent scan times, average scan times, etc. In block 5214, the CCS updates the item adjacency map to indicate the items in the pairs of items are adjacent if the criteria in block 5212 are satisfied and [0629] – the CCS/MSDs may implement a scoring model in which different possible routes for an MSD are scored, and the highest scoring route is assigned (e.g., highest scoring indicates a best route)… a scoring module at the CCS/MSDs may apply different weights and scores to different aspects of a route (e.g., route length, item number, distance from MSD, etc.) … the CCS/MSDs may implement a machine learned model for generating routes/scores, where the machine learned model receives different routing factors as inputs. The machine learned model may be trained on historic picking/movement data);
determining, by the machine learning model configured with the first set of weights (See Francis ¶[0392-0393] and [0629] as noted above), a first predicted pairwise relation for each pair of product categories at each level of the taxonomy based on the historical pick data for items in the respective pair of product categories (See Francis ¶[0377] - the MSD determines that the first and second items are adjacent to one another… reinforcing a determination of adjacency may include updating data associated with the adjacent items that indicates the scan times between items have satisfied item adjacency criterion multiple times… the MSD and/or CCS may update the item adjacency map to include, for each pair of adjacent items, a reinforcement indicator value that indicates the number of times the two items have been considered adjacent); and
updating the machine learning model with a second set of weights, wherein the updated machine learning model is configured to produce a second predicted pairwise relation for each pair of product categories (See Francis ¶ [0435] - The image processing module may feed the set of features to one or more machine-learned image classification models, which respectively output a classification of the blob based on the set of features, [0441] - an MSD may include an image processing module that classifies images … the MSD may implement a set of machine-learned image classification models stored thereon that the MSD uses to perform image classification… the image classification model(s) of an MSD may be updated when new items or objects are identified… the image classification models used by the MSD when the MSD is in a “picking” mode may be trained to identify a reduced subset of the items in the store… and [0629] - the CCS/MSDs may implement a scoring model in which different possible routes for an MSD are scored… a scoring module at the CCS/MSDs may apply different weights and scores to different aspects of a route … the CCS/MSDs may implement a machine learned model for generating routes/scores, where the machine learned model receives different routing factors as inputs… the machine learned model may be trained on historic picking/movement data), wherein a value between the second predicted pairwise relation and a respective target pairwise relation is smaller than the value between the first predicted pairwise relation and the respective target pairwise relation (See Francis ¶ [0373] - an indication of whether two of the items are adjacent to one another may be determined and/or reinforced based on relatively temporally close scan times associated with the items, indicating that the items are adjacent to one another. Alternatively, the indication of whether the two of the items are adjacent to one another may be removed based on relatively temporally distant scan times associated with the items, indicating that the items are not adjacent to one another and [0492] - the MSD and/or the CCS may generate/update an item association table that associates object-based IDs with items. Although the scanned items may be associated with previously determined object-based IDs, in some cases, the MSD and/or the CCS may be configured to associate a scanned item with a later determined object-based ID, such as when the later determined object-based ID [second predicted pairwise relation] is determined closer in time [smaller than the value between the first predicted pairwise relation] to the scanned item than a previously determined object-based ID [target pairwise relation]. The association between object-based IDs and items may be updated over time.);
generate a plurality of sequences of product categories based on the generated pairwise relations, wherein each sequence in the plurality of sequences is associated with a level of the taxonomy, the plurality of sequences including a first sequence associated with the sub-aisle level of the taxonomy and a second sequence associated with the aisle level of the taxonomy, (See Francis ¶ [0360] – grouping items based on item types/ categories showing item location data associated with aisle numbers [aisle level of the taxonomy], [0371] – generating adjacency maps [sequence of products] shown as a list of adjacent items [products], [0392-0393] – scanning multiple items and recognizing paired relationships between items signifying adjacency based on time between each scan of each item and [0440] - the map generation module may generate the image-based map to indicate locations of items and/or store objects with respect to one another… if a first brand of cereal and a second brand of cereal are classified in the same image (e.g., they are both on the same rack [sub-aisle level of taxonomy]), the map generation module may indicate an adjacency between the first and second brand of cereal in the image-based map), by:
selecting, for each level of the taxonomy, a product category in the level most associated with an initial pick in the historical pick data (See Francis ¶ [0357] – one or more other items in a customer order are included for a particular user based on adjacency of said items to said seed item, wherein said adjacent items are determined to be closest to said user and designated as the next item to be picked, [0654] – the initially selected item acting as a seeding item and [0718-0720] – ranking items by individual item or item type [category] and using said item ranking and user movement data to generate a pick sequence); and
traversing, at a level of the selected product category in the taxonomy, the generated pairwise relations in order of strongest pairwise relation to a currently selected product category in the taxonomy (As the specification of the instant application describes the strongest pairwise relation as the shortest distance from an initial “seed” item to the next item in a sequence of items, see Francis ¶ [0385-0387] – displaying items [product category] to a user MSD device in order of closest item to said device at the top of a list with other items following based on relative proximity to said user device when an initial item is scanned [pairwise relations in order of strongest pairwise relation to a currently selected product category in the taxonomy by example] and Fig. 48 – showing a sequence of items to pick relative to a location of a user MSD [at a level of the selected product category in the taxonomy by example]);
receive an order for items offered by the warehouse (See Francis ¶ [0073-0075] – receiving customer orders to be fulfilled by a warehouse), wherein the order includes at least a first item associated with a first product category and a second item associated with a second product category and not associated with the first product category (See Francis ¶ [0360-0361] – one or more of the MSDs and/or the CCS may be configured to use metadata indicating a type/category (e.g., produce, dairy, meat products) [at least first and second product category by example] associated with an item included in a customer order to group (e.g., cluster) the item with one or more other items that are also included in the customer order… one or more of the MSDs and/or the CCS may be configured to identify a first item included in a first group and identify a second, different item, that is adjacent to the first item but not included in the first group [second product category and not associated with the first product category by example]); and
compare the order to two or more sequences to generate a pick sequence for the order (See Francis ¶ [0365-0367] – the MSDs and/or CCS may be configured to identify a first item (e.g., an anchor item) included in a particular zone associated with a location indicator, and then determine relative locations of one or more different items also included in the zone [sequence by example] with respect to the first item using an adjacency map… one or more of the MSDs and/or CCS may be configured to make use of items (e.g., an anchor item) included in zones and MSDs transitioning from one zone to another zone [two or more sequences in the plurality of sequences by example]. For example, the MSDs and/or the CCS may determine that an item included in a first zone is proximate to an item included in a second zone upon an MSD scanning the two items within a predetermined threshold amount of time when the MSD transitions between the two zones, thereby defining edges of the zones… if an aisle has one or more zones in a line, the arrangement of items for picking may be organized in a linear fashion down the aisle from zone to zone based on the edge items and sequential items between the edge items… an MSD may be configured to display a subset of the items included in a customer order (e.g., items that are located proximate to the MSD at that time) [comparing the order by example]) by:
identifying, from each item in the order, one or more product categories associated with the respective item and the compared sequence (See Francis ¶ [0360-0361] – one or more of the MSDs and/or the CCS may be configured to use metadata indicating a type/category (e.g., produce, dairy, meat products) [at least first and second product category by example] associated with an item included in a customer order to group (e.g., cluster) the item with one or more other items that are also included in the customer order… one or more of the MSDs and/or the CCS may be configured to identify a first item included in a first group and identify a second, different item, that is adjacent to the first item but not included in the first group);
…each item in the order based on position of its identified one or more product categories the first sequence, wherein…, in response to determining that none of the identified product categories of a respective item are contained in the first sequence (See Francis ¶ [0361] - one or more of the MSDs and/or the CCS may be configured to identify a first item included in a first group and identify a second, different item, that is adjacent to the first item but not included in the first group), estimating … for the respective item based on a position in the second sequence of a product category at the aisle level associated with the respective item… (See Francis ¶ [0360-0361] - one or more of the MSDs and/or the CCS may be configured to use metadata indicating a type/category (e.g., produce, dairy, meat products) associated with an item included in a customer order to group (e.g., cluster) the item with one or more other items that are also included in the customer order, adjacent to the item, and associated with the same type… the item adjacency map may group dairy products together based on type and/or aisle location (e.g., aisle number). The item adjacency map may then further define the location of the items within the group (e.g., item type and/or aisle) using item adjacency or other location indicator mapping… one or more of the MSDs and/or the CCS may be configured to identify a first item included in a first group and identify a second, different item, that is adjacent to the first item but not included in the first group [second sequence of a product category at the aisle level associated with the respective item by example] and [0365-0367] as noted above, as well as [0383] - the MSD may display the initial item at the top of a list and display the other items lower on the list based on the relative distances and/or times associated with the items and the initial item, [0387] - with respect to FIG. 48 , an MSD near item 4804-5 may display items 4804-3, 4804-4, 4804-5, and 4804-6 higher on the display to prompt the user to pick those items due to their adjacency. Due to the item adjacency map, this may even be the case if items 4804-3 and/or 4804-4 are in different zones (e.g., associated with different location values) than item 4804-5, wherein said different zones are different aisles [second sequence] as shown in Fig. 48); and
outputting the pick sequence, … (See Francis ¶ [0366-0367] – identifying a sequence of items in a customer order and generating a displayed list of said sequence, wherein said sequence may not be based on adjacency of said items in said order).
While Francis teaches arranging items in an order into a picking sequence based on item groupings associated with item categories (Francis ¶ [0360-0361] and [0365-0367]), Francis does not explicitly teach ranking that comprises estimating the rank of said items in compared picking sequences or that the pick sequence is based on the ranking. This is taught by Fu (See Fu ¶ [0051-0052] – As each available case [item] is identified in progression (ranked and resolved into a sequence solution as further described) the matrix 999A, 999B is updated and reduced… where the flexible sequencer sequences case for picking … each available sequence solution of available cases of the generated sequence solution set (e.g., solution A, solution B, . . . , solution N) is ranked). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the item pick sequence generation system of Francis the use of ranked item pick sequences from multiple pick sequence solution options as taught by Fu to formulate an optimal, yet flexible sequence solution (Fu ¶ [0027]), thereby increasing the accuracy and efficiency of the item pick sequence generation system of Francis.
Regarding Claim 16, modified Francis teaches:
The computer program product of claim 15, wherein the instructions further cause the processor to:
While Francis teaches arranging items in an order into a picking sequence based on item groupings associated with item categories (Francis ¶ [0360-0361] and [0365-0367]), Francis does not explicitly teach that the pick sequence is generated based on the ranking. This is taught by Fu (See Fu ¶ [0051-0052] – As each available case [item] is identified in progression (ranked and resolved into a sequence solution as further described) the matrix 999A, 999B is updated and reduced… where the flexible sequencer sequences case for picking … each available sequence solution of available cases of the generated sequence solution set (e.g., solution A, solution B, . . . , solution N) is ranked). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the item pick sequence generation system of Francis the use of ranked item pick sequences from multiple pick sequence solution options as taught by Fu to formulate an optimal, yet flexible sequence solution (Fu ¶ [0027]), thereby increasing the accuracy and efficiency of the item pick sequence generation system of Francis.
Regarding Claim 18, modified Francis teaches:
The computer program product of claim 15, wherein generating the plurality of sequences comprises:
a) selecting a particular level in the hierarchy (See Francis ¶ [0357] – user selecting one of a plurality of items from a display to start picking a customer order); and
b) selecting a seed from the particular level as a currently selected product category, the seed establishing a first product category in a generated sequence (See Francis ¶ [0357] – said item is a “seed” or “re-seed” item that is a next item to be picked by the user, thereby starting a picking sequence).
Regarding Claim 19, modified Francis teaches:
A system comprising:
a processor; and
a memory comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to (See Francis ¶ [0073-0074] – an order filling system (OFS) comprising a central computing system (CCS) and one or more mobile scanning devices (MSDs), [0217-0219] – processing module of the MSDs sending data and [0267] – CCS and MSDs using data stored in memory):
receive, from a first client device, an order for items offered by a warehouse (See Francis ¶ [0073-0075] – receiving customer orders to be fulfilled by a warehouse), wherein the order includes at least a first item associated with a first product category and a second item associated with a second product category and not associated with the first product category (See Francis ¶ [0360-0361] – one or more of the MSDs and/or the CCS may be configured to use metadata indicating a type/category (e.g., produce, dairy, meat products) [at least first and second product category by example] associated with an item included in a customer order to group (e.g., cluster) the item with one or more other items that are also included in the customer order… one or more of the MSDs and/or the CCS may be configured to identify a first item included in a first group and identify a second, different item, that is adjacent to the first item but not included in the first group [second product category and not associated with the first product category by example]);
for each item in the order, identify one or more product categories associated with at least one sequence in a plurality of sequences of product categories (See Francis ¶ [0360] – the system using item type/ category information to associated items with customer orders, [0366-0367] – identifying a sequence of items in a customer order), wherein each sequence in the plurality of sequences is associated with a level of a taxonomy (The specification of the instant application gives no special definition of the limitation taxonomy, for the purpose of examination, said taxonomy is interpreted to mean a hierarchy relating to product categories. Therefore, see Francis ¶ [0360] – grouping items based on item types/ categories showing item location data associated with aisle numbers [a particular level of granularity of product data], [0491] – location data comprising department names [another particular level of granularity of product data and [0677] – using a hierarchy of operations to calculate picking routes), the plurality of sequences including a first sequence associated with a sub-aisle level of the taxonomy and a second sequence associated with an aisle level of the taxonomy (See Francis ¶ [0112] – similar types of items may be grouped together along an aisle in a typical store. For example, the items located along aisle 122-1 [aisle level] (i.e., the items accessible in aisle 122-1 from rack 116-1 and rack 116-2 [sub-aisle level]) may be items of a similar type), wherein each product category in the sub-aisle level is a sub-category of and corresponds to a different subsection of an aisle associated with a respective product category in the aisle level (See Francis ¶ [0112] – the items along aisle 122-1 may be cereal items (e.g., bags or boxes of cereal [sub-categories of cereal by example]), [0433] - The captured image depicts a plurality of items 5804 (e.g., boxes) that are included on a rack 5802. For example, the image depicts, inter alia, a box of Froot Loops 5806, a location indicator 5808 (e.g., a barcode) attached to a shelf 5810, and an aisle sign 5812 including text (e.g., an aisle name and/or number), [0469] - the CCS and/or MSDs can identify text on aisle signs that indicate an aisle number and/or item category/type, such as frozen food, frozen pizzas, frozen dinners, etc. [sub-category of and corresponds to a different subsection of an aisle by example] and [0543] - location descriptors may include aisle names and/or numbers. Additional example location descriptors may also include aisle descriptors, such as first/second aisle ends, aisle middle, and/or shelf location (e.g., bottom shelf, top shelf) [subsection of an aisle by example]), and wherein the sub-aisle level has a higher level of granularity of product data than the aisle level (See Francis ¶ [0365] – the MSDs and/or CCS may add granularity to, or enhance resolution of, relative locations of the items included in the zone and [0401] - if a location indicator covers an entire aisle of items, the item adjacency map may define more granular arrangements of items on the display), wherein the sub-aisle level has a higher level of granularity of product data than the aisle level (See Francis ¶ [0365] – the MSDs and/or CCS may add granularity to, or enhance resolution of, relative locations of the items included in the zone and [0401] - if a location indicator covers an entire aisle of items, the item adjacency map may define more granular arrangements of items on the display), the plurality of sequences generated by:
selecting, for each level of the taxonomy, a product category in the level most associated with an initial pick in the historical pick data (See Francis ¶ [0357] – one or more other items in a customer order are included for a particular user based on adjacency of said items to said seed item, wherein said adjacent items are determined to be closest to said user and designated as the next item to be picked, [0654] – the initially selected item acting as a seeding item and [0718-0720] – ranking items by individual item or item type [category] and using said item ranking and user movement data to generate a pick sequence); and
traversing, at a level of the selected product category in the taxonomy, pairwise relations between product categories in order of strongest pairwise relation to a currently selected product category in the taxonomy (As the specification of the instant application describes the strongest pairwise relation as the shortest distance from an initial “seed” item to the next item in a sequence of items, see Francis ¶ [0385-0387] – displaying items [product category] to a user MSD device in order of closest item to said device at the top of a list with other items following based on relative proximity to said user device when an initial item is scanned [pairwise relations in order of strongest pairwise relation to a currently selected product category in the taxonomy by example] and Fig. 48 – showing a sequence of items to pick relative to a location of a user MSD [at a level of the selected product category in the taxonomy by example]), wherein the pairwise relations are generated by applying the historical pick data to a trained machine learning model, wherein the trained machine learning model generates a pairwise relation for each pair of product categories at each level of the taxonomy (See Francis ¶ [0392-0393] – scanning multiple items and recognizing paired relationships between items signifying adjacency based on time between each scan of each item and [0441-0442] – MSD using a machine learning image classification models based on product categories at each level of the taxonomy by example by segmenting said model based on the particular areas/ sections [departments or aisles as noted above regarding ¶ [0360] & [0491]] of the store), the machine learning model trained by:
applying, to the machine learning model configured with a first set of weights, training data including historical pick data and target pairwise relations (See Francis ¶[0392-0393] - the CCS may determine that the three items in two pairs of adjacent items are adjacent if the sum of the scan times between the first pair of items and the second pair of items is less than a threshold value… The CCS may make the determination in block 5212 using various scan times associated with the pairs, such as the most recent scan times, average scan times, etc. In block 5214, the CCS updates the item adjacency map to indicate the items in the pairs of items are adjacent if the criteria in block 5212 are satisfied and [0629] – the CCS/MSDs may implement a scoring model in which different possible routes for an MSD are scored, and the highest scoring route is assigned (e.g., highest scoring indicates a best route)… a scoring module at the CCS/MSDs may apply different weights and scores to different aspects of a route (e.g., route length, item number, distance from MSD, etc.) … the CCS/MSDs may implement a machine learned model for generating routes/scores, where the machine learned model receives different routing factors as inputs. The machine learned model may be trained on historic picking/movement data);
determining, by the machine learning model configured with the first set of weights (See Francis ¶[0392-0393] and [0629] as noted above), a first predicted pairwise relation for each pair of product categories at each level of the taxonomy based on the historical pick data for items in the respective pair of product categories (See Francis ¶[0377] - the MSD determines that the first and second items are adjacent to one another… reinforcing a determination of adjacency may include updating data associated with the adjacent items that indicates the scan times between items have satisfied item adjacency criterion multiple times… the MSD and/or CCS may update the item adjacency map to include, for each pair of adjacent items, a reinforcement indicator value that indicates the number of times the two items have been considered adjacent); and
updating the machine learning model with a second set of weights, wherein the updated machine learning model is configured to produce a second predicted pairwise relation for each pair of product categories (See Francis ¶ [0435] - The image processing module may feed the set of features to one or more machine-learned image classification models, which respectively output a classification of the blob based on the set of features, [0441] - an MSD may include an image processing module that classifies images … the MSD may implement a set of machine-learned image classification models stored thereon that the MSD uses to perform image classification… the image classification model(s) of an MSD may be updated when new items or objects are identified… the image classification models used by the MSD when the MSD is in a “picking” mode may be trained to identify a reduced subset of the items in the store… and [0629] - the CCS/MSDs may implement a scoring model in which different possible routes for an MSD are scored… a scoring module at the CCS/MSDs may apply different weights and scores to different aspects of a route … the CCS/MSDs may implement a machine learned model for generating routes/scores, where the machine learned model receives different routing factors as inputs… the machine learned model may be trained on historic picking/movement data), wherein a value between the second predicted pairwise relation and a respective target pairwise relation is smaller than the value between the first predicted pairwise relation and the respective target pairwise relation (See Francis ¶ [0373] - an indication of whether two of the items are adjacent to one another may be determined and/or reinforced based on relatively temporally close scan times associated with the items, indicating that the items are adjacent to one another. Alternatively, the indication of whether the two of the items are adjacent to one another may be removed based on relatively temporally distant scan times associated with the items, indicating that the items are not adjacent to one another and [0492] - the MSD and/or the CCS may generate/update an item association table that associates object-based IDs with items. Although the scanned items may be associated with previously determined object-based IDs, in some cases, the MSD and/or the CCS may be configured to associate a scanned item with a later determined object-based ID, such as when the later determined object-based ID [second predicted pairwise relation] is determined closer in time [smaller than the value between the first predicted pairwise relation] to the scanned item than a previously determined object-based ID [target pairwise relation]. The association between object-based IDs and items may be updated over time.);
… each item based on the order of the identified one or more product categories of the item in the first sequence, wherein…, in response to determining that none of the identified product categories of a respective item are contained in the first sequence, estimating … for the respective item based on a position in the second sequence of a product category at the aisle level associated with the respective item… (See Francis ¶ [0360-0361] - one or more of the MSDs and/or the CCS may be configured to use metadata indicating a type/category (e.g., produce, dairy, meat products) associated with an item included in a customer order to group (e.g., cluster) the item with one or more other items that are also included in the customer order, adjacent to the item, and associated with the same type… the item adjacency map may group dairy products together based on type and/or aisle location (e.g., aisle number). The item adjacency map may then further define the location of the items within the group (e.g., item type and/or aisle) using item adjacency or other location indicator mapping… one or more of the MSDs and/or the CCS may be configured to identify a first item included in a first group and identify a second, different item, that is adjacent to the first item but not included in the first group [second sequence of a product category at the aisle level associated with the respective item by example] and [0365-0367] as noted above, as well as [0383] - the MSD may display the initial item at the top of a list and display the other items lower on the list based on the relative distances and/or times associated with the items and the initial item, [0387] - with respect to FIG. 48 , an MSD near item 4804-5 may display items 4804-3, 4804-4, 4804-5, and 4804-6 higher on the display to prompt the user to pick those items due to their adjacency. Due to the item adjacency map, this may even be the case if items 4804-3 and/or 4804-4 are in different zones (e.g., associated with different location values) than item 4804-5, wherein said different zones are different aisles [second sequence] as shown in Fig. 48);
generate a pick sequence for the order based … (See Francis ¶ [0718-0720] – ranking items by individual item or item type [category] and using said item ranking and user movement data to generate a pick sequence); and
send the pick sequence to a second client device, wherein the second client device performs one or more actions based on the pick sequence (See Francis ¶ [0095-0097] – customers placing orders on mobile devices and third party pickers picking items for said orders based websites or application running on devices used by said third parties and [0501] – maps and tables used for picking routes may include a customer’s shopping list, wherein said list is sent to a customer in a GUI [rendered]).
While Francis teaches arranging items in an order into a picking sequence based on item groupings associated with item categories (Francis ¶ [0360-0361] and [0365-0367]), Francis does not explicitly teach ranking that comprises estimating the rank of said items in the at least one sequence or that the pick sequence is based on the ranking. This is taught by Fu (See Fu ¶ [0051-0052] – As each available case [item] is identified in progression (ranked and resolved into a sequence solution as further described) the matrix 999A, 999B is updated and reduced… where the flexible sequencer sequences case for picking … each available sequence solution of available cases of the generated sequence solution set (e.g., solution A, solution B, . . . , solution N) is ranked). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the item pick sequence generation system of Francis the use of ranked item pick sequences from multiple pick sequence solution options as taught by Fu to formulate an optimal, yet flexible sequence solution (Fu ¶ [0027]), thereby increasing the accuracy and efficiency of the item pick sequence generation system of Francis.
Response to Arguments
Applicant's arguments filed 08/28/2026 have been fully considered but they are not persuasive.
Rejection under 35 U.S.C. § 112:
The amendments to independent claims 1 and 15 resolve the previous new matter issue and the rejection of claims 1-2, 6-9, 12-16, 18 under 35 U.S.C. § 112 (a) is withdrawn.
Rejection under 35 U.S.C. § 101:
In light of the amended claims, the previous rejection of claim 19 under 35 U.S.C. § 101 is withdrawn.
Independent claim 19 comprises the limitations “updating the machine learning model with a second set of weights, wherein the updated machine learning model is configured to produce a second predicted pairwise relation for each pair of product categories, wherein a value between the second predicted pairwise relation and a respective target pairwise relation is smaller than the value between the first predicted pairwise relation and the respective target pairwise relation”. These limitations, along the other limitations of claim 19, show the claims as a whole as reciting specific elements that reflect an improvement to how the machine learning model itself operates, such as by reducing storage requirements or system complexity. Therefore, claim 19 is patent eligible.
Rejection under 35 U.S.C. § 103:
Considering the applicant’s arguments and the amendments to independent claims 1, 15 and 19, the claims as they are currently limited do not overcome the prior art combination of Francis and Fu and the previous rejection under 35 U.S.C. § 103 is maintained.
While the applicant asserts that the previously cited sections (¶ [0112], [0433] and [0543]) of Francis do not teach that each product category in the sub-aisle level be "a sub-category of ... a respective product category in the aisle level," so that the finer level of the taxonomy subdivides the aisle-level product category into narrower product categories rather than subdividing the aisle into physical locations, as is described in amended claim 1 (and similarly claims 15 and 19), these requirements are taught by Francis by example in newly cited ¶ [0469] as described above in the current rejection under 35 U.S.C. § 103.
While the applicant asserts that the previously cited sections (¶ [0383] and [0387]) of Francis do not teach that the “ranking comprises ... estimating the rank” for an item based on a position in the second sequence of a product category at the aisle level associated with the item “in response to determining that none of the identified product categories of a respective item are contained in the first sequence”, as is described in amended claim 1 (and similarly claims 15 and 19), these requirements are taught by the combination of Francis and Fu by example as previously cited. Namely, Francis ¶ [0360-0361] teaches estimating a position of an item in a second sequence when said item is not in a first sequence. While Fu, for their part, teaches ranking item pick sequences as described above in the current rejection under 35 U.S.C. § 103.
In response to applicant's arguments against the references individually, 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)).
Conclusion
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/MATTHEW S WERONSKI/Examiner, Art Unit 3627
/MICHAEL JARED WALKER/Primary Examiner, Art Unit 3627
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