DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 09/24/2025 has been entered.
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
The instant application does not claim priority to any other application. Therefore, for the purpose of examination herein with regard to prior art consideration, the effective filing date is recognized as July 1st, 2022.
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 1-9 and 12-20 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, independent claim 1 recites a method/process and independent claim 15 recites a computer program product comprising a non-transitory computer readable storage medium/machine and independent claim 19 recites a system/machine that are performing a series of functions. Therefore, these claims fall within the four statutory categories of invention of a process and a machine.
Step2A – Prong 1: Does the Claim Recite a Judicial Exception
Exemplary claim 1 recites the following abstract concepts that are found to include an enumerated “abstract idea”:
A method comprising:
at a computer system comprising at least one processor and non-transitory memory:
obtaining historical pick data for a plurality of items located in a warehouse, the historical pick data comprising product data for each of the items picked and pick times between each of the items picked;
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;
applying the historical pick data to a machine learning model 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;
generating a plurality of sequences of product categories 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; and
traversing, at a level of the selected product category in the taxonomy, pairwise relations in order of strongest pairwise relation to a currently selected product category in the taxonomy;
receiving an order for items offered by the 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;
comparing the order to two or more sequences in the plurality of sequences to generate a pick sequence for the order by:
identifying, from each item in the order, one or more product categories associated with the respective item and the compared sequence;
ranking each item in the order based on position of its identified one or more product categories in each of the compared sequences; and
outputting the pick sequence, wherein the pick sequence is based on the ranking.
[Emphasis added to show the abstract idea being executed by additional elements that do not meaningfully limit the abstract idea]
This method 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 following rules or instructions to generate a pick sequence for the order which is a process that is encompassed by the abstract idea of managing personal behavior. See e.g., MPEP 2106.04(a)(2)(II)(C); 2106.05(h); and July 2024 Subject Matter Eligibility Example 47 claim 2. 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 computer system, processor, non-transitory memory and machine learning model 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 computer system, processor, non-transitory memory and machine learning model perform the steps or functions of following rules or instructions to generate a pick sequence for the order. 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 computer system, processor, non-transitory memory and machine learning model) performing functions of obtaining, determining, applying, generating, selecting, traversing, receiving, comparing, identifying, ranking and outputting 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 computer system, processor, non-transitory memory and machine learning model being used to perform the steps of obtaining, determining, applying, generating, selecting, traversing, receiving, comparing, identifying, ranking and outputting amounts to no more than using a computer or processor to automate and/or implement the abstract idea of following rules or instructions to generate a pick sequence for the order. As discussed above, taking the claim elements separately, computer system, processor, non-transitory memory and machine learning model perform the steps or functions of managing personal behavior by following rules or instructions to generate a pick sequence for the order. 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 managing personal behavior by following rules or instructions to generate a pick sequence for the order 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 claims are not patent eligible.
Independent claim 15 describes the abstract idea of managing personal behavior by following rules or instructions to generate a pick sequence for the order. Independent claim 15 does not include additional elements to perform the respective functions of obtaining, determining, applying, generating, selecting, traversing, receiving, comparing, identifying, ranking and outputting beyond technical elements disclosed at a high level of generality, such as a computer program product, non-transitory computer readable medium, processor and machine learning model 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 claim 15 is also not patent eligible.
Independent claim 19 describes the abstract idea of managing personal behavior by following rules or instructions to generate a pick sequence for the order. Independent claim 19 does not include additional elements to perform the respective functions of receiving, identifying, selecting, traversing, ranking, generating, sending and performing beyond technical elements disclosed at a high level of generality, such as a processor, memory, non-transitory computer readable medium, first client device and second client device 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 claim 19 is also not patent eligible.
Dependent claims 2-9, 12, 16-18 and 20 further describe the abstract idea of managing personal behavior by following rules or instructions to generate a pick sequence for the order. Said dependent claims do not include additional elements to perform the respective functions of ranking, generating, determining, estimating, calculating and selecting beyond the technical elements disclosed at a high level of generality 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 13-14 further describes the abstract idea of managing personal behavior by following rules or instructions to generate a pick sequence for the order. Said dependent claims do not include additional elements to perform the respective functions of rendering and generating beyond the technical elements disclosed at a high level of generality such as a client device, first client device, second client device and as disclosed 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.
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 1-9 and 12-20 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);
applying the historical pick data to a 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);
generating a plurality of sequences of product categories by (See Francis ¶ [0360] – grouping items based on item types/ categories showing item location data associated with aisle numbers, [0371] – generating adjacency maps [sequence of products] shown as a list of adjacent items [products] and [0392-0393] – scanning multiple items and recognizing paired relationships between items signifying adjacency based on time between each scan of each item):
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 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]);
comparing the order to two or more sequences in the plurality of 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 … (See Francis ¶ [0360-0361] and [0365-0367] as noted above); 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 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 3, modified Francis teaches:
The method of claim 1, wherein the two or more sequences comprise a most granular sequence in the plurality of sequences (As the specification of the instant application describes the most granular sequence as a sequence relating to an aisle sequence, see Francis ¶ [0360] – grouping items based on item types/ categories showing item location data associated with aisle numbers and [0365-0367] as noted above in claim 1 regarding the two or more sequences).
Regarding Claim 4, modified Francis teaches:
The method of claim 3, wherein the two or more sequences further comprise a sequence generated one level above the most granular sequence in the hierarchy (As described by the specification of the instant application, one level above the most granular sequence relates to a department in a store, see Francis ¶ [0491] – location data comprising department names and [0365-0367] as noted above in claim 1 regarding the two or more sequences).
Regarding Claim 5, modified Francis teaches:
The method of claim 4, wherein ranking each item comprises:
ranking each item based on a position of an identified product category of the item in the most granular sequence (As the specification of the instant application describes the most granular sequence as a sequence relating to an aisle sequence, see Francis ¶ [0360] – grouping items based on item types/ categories showing item location data associated with aisle numbers and [0721] – ranking locations); and
based on determining that none of the identified product categories of a respective item are contained in the most granular sequence, estimating a ranking for the respective item based on the sequence generated one level above the most granular sequence (As the specification of the instant application describes the most granular sequence as a sequence relating to an aisle sequence and one level above the most granular sequence as relating to a department in a store, see Francis ¶ [0721-0726] – ranking locations based on department or aisle locations of items and constraints relating to said items).
Regarding Claim 6, modified Francis teaches:
The method of claim 4, wherein the most granular sequence comprises an aisle sequence , (See Francis ¶ [0360] – grouping items based on item types/ categories showing item location data associated with aisle numbers), and wherein the sequence generated one level above in the hierarchy comprises a department sequence (See Francis ¶ [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);
apply the historical pick data to a 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);
generate a plurality of sequences of product categories by: (See Francis ¶ [0360] – grouping items based on item types/ categories showing item location data associated with aisle numbers, [0371] – generating adjacency maps [sequence of products] shown as a list of adjacent items [products] and [0392-0393] – scanning multiple items and recognizing paired relationships between items signifying adjacency based on time between each scan of each item);
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 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]);
compare the order to two or more sequences in the plurality of 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 … (See Francis ¶ [0360-0361] and [0365-0367] as noted above); 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 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 17, modified Francis teaches:
The computer program product of claim 16, wherein ranking each item comprises:
ranking each item based on the order of a product category of the item in a most granular sequence (As the specification of the instant application describes the most granular sequence as a sequence relating to an aisle sequence, see Francis ¶ [0360] – grouping items based on item types/ categories showing item location data associated with aisle numbers and [0721] – ranking locations); and
based on determining that none of the identified product categories of a respective item are contained in the most granular sequence, estimating a ranking for the respective item based on the sequence generated one level above the most granular sequence in the hierarchy (As the specification of the instant application describes the most granular sequence as a sequence relating to an aisle sequence and one level above the most granular sequence as relating to a department in a store, see Francis ¶ [0721-0726] – ranking locations based on department or aisle locations of items and constraints relating to said items).
Regarding Claim 18, modified Francis teaches:
The computer program product of claim 17, 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), 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]);
… each item based on the order of the identified one or more product categories of the item … (See Francis ¶ [0366-0367] – identifying a sequence of items in a customer order and [0718] – ranking items by individual item or item type [category]);
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 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.
Regarding Claim 20, modified Francis teaches:
The system of claim 19, wherein ranking each item comprises:
ranking each item based on the order of an identified product category of the item in a most granular sequence (As the specification of the instant application describes the most granular sequence as a sequence relating to an aisle sequence, see Francis ¶ [0360] – grouping items based on item types/ categories showing item location data associated with aisle numbers and [0721] – ranking locations); and
based on determining that none of the identified product categories of a respective item are contained in the most granular sequence, estimating a ranking for the respective item based on the sequence generated one level above the most granular sequence in a hierarchy (As the specification of the instant application describes the most granular sequence as a sequence relating to an aisle sequence and one level above the most granular sequence as relating to a department in a store, see Francis ¶ [0721-0726] – ranking locations based on department or aisle locations of items and constraints relating to said items).
Response to Arguments
Applicant's arguments filed 09/24/2025 have been fully considered but they are not persuasive.
Rejection under 35 U.S.C. § 101:
In light of the amended claims, said claim amendments are not significant enough to overcome the previous issues regarding 35 U.S.C. § 101. Therefore, the previous rejection of claims 1-9 and 12-20 is maintained.
Contrary to the applicant’s assertion that claimed invention of claim 1 (and similarly claims 15 and 19) shows a specific structural strategy that improves the functioning of the computer-implemented model itself through implementing a specific hierarchical decomposition that improves model training, reduces noise, and enhances computational efficiency, any improvement shown in the claim as currently disclosed is an improvement to the abstract idea of managing personal behavior, rather than the underlying technology. This is because claim 1 does not recite any model training, noise reduction or a clear enhancement to computational efficiency, but rather, claim 1 remains limited to only showing use of a machine learning model to process certain sets of data. Therefore, claim 1 remains limited as being executed by technical elements disclosed at a high level of generality in a manner that is not more than mere computer implementation of the abstract idea. Mere computer implementation cannot be significantly more than said abstract idea as noted above in the current rejection under 35 U.S.C. § 101. The claims themselves must clearly show the improvement and the specification of an instant application is not read into the claims during examination.
Independent claims 15 and 19 are of similar scope as claim 1 and remain as not patent eligible for the same reasons as claim 1.
Rejection under 35 U.S.C. § 102:
Considering the applicant’s arguments and the amendments to independent claims 1, 15 and 19, the claims as they are currently limited overcome the Francis prior art reference and the previous rejection under 35 U.S.C. § 102 is withdrawn. Any arguments solely against Francis are herein rendered moot. However, the invention of the instant application remains unpatentable because it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate features from Fu in the invention of Francis as described above in the current rejection under 35 U.S.C. § 103.
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
/FAHD A OBEID/Supervisory Patent Examiner, Art Unit 3627