DETAILED ACTION
In the response filed October 21, 2025, the Applicant amended claims 1-4, 6-13, and 15-20; and canceled claims 5 and 14. Claims 1-4, 6-13, and 15-20 are pending in the current application.
Notice of 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 .
Response to Arguments
Claims 1 and 11-18 were objected to for informalities. Examiner thanks the Applicant for revising and amending the claim language and hereby withdraws the objection from the previous Office action.
Applicant’s arguments for claims 1-4, 6-13, and 15-20 with respect to the 35 U.S.C. 101 rejection have been considered but are unpersuasive. Applicant argues that the claims are not directed to a judicial exception. Examiner respectfully disagrees. Here, the limitation “for each retail location in the zone over a past period of time: determining a batch volume for the retail location; determining an average batch volume for the zone; and generating a waiting time by inputting the batch volume for the retail location and the average batch volume for the zone into a model, wherein the model is trained on historical batch volumes for the retail location and historical batch volumes for the zone and historical waiting times for receiving batches at the retail location and for the zone;” under broadest reasonable interpretation, describe or set-forth determining a waiting time based on locations of retail locations to pick up batches to fulfill, which amounts to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). These limitations therefore fall within the “mathematical concepts” subject matter grouping of abstract ideas.
In addition, the steps and limitations as claimed “providing, by the computer system, a map of at least a portion of the zone for display within a user interface of the picker client device, wherein the map includes at least a subset of the plurality of retail locations;” “modifying the map displayed in the user interface at the picker client device to emphasize a first set of retail locations with a waiting time above a threshold value by performing a first visual adjustment to each of the retail locations in the first set within the map, wherein the modifying causes the picker client device to display the modified map,” (claims 1, 10, and 19), is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. See § MPEP 2106.05(f).
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. Applicant’s arguments remain unpersuasive. The 35 U.S.C. 101 rejection is hereby maintained.
Applicant’s arguments for claims 1-4, 6-13, and 15-20 with respect to the 35 U.S.C. 103 rejection have been considered but are moot because the arguments do not apply to the combination of references being used in the current rejection.
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 1-4, 6-13, and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Claims 1-4, 6-9 are drawn to a process; claims 10-13 and 15-18 are drawn to a product of manufacture; and claims 19 and 20 are drawn to a machine, each of which is within the four statutory categories (e.g., a process, a machine). (Step 1: YES).
Step 2A – Prong One: In prong one of step 2A, the claims are analyzed to evaluate whether they recite a judicial exception.
Claim 1 (representative of claims 10 and 19) recites/describes the following steps:
“obtaining, …, a location of the picker client device;”
“identifying, …, a plurality of retail locations within a threshold distance of the location of the picker client device, wherein the threshold distance from the location of the picker client device defines a zone;”
“for each retail location in the zone over a past period of time: determining a batch volume for the retail location; determining an average batch volume for the zone; and generating a waiting time by inputting the batch volume for the retail location and the average batch volume for the zone into a model, wherein the model is trained on historical batch volumes for the retail location and historical batch volumes for the zone and historical waiting times for receiving batches at the retail location and for the zone;”
These steps, under broadest reasonable interpretation, describe or set-forth determining a waiting time based on locations of retail locations to pick up batches to fulfill, which amounts to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). These limitations therefore fall within the “certain methods of organizing human activity” subject matter grouping of abstract ideas.
As such, the Examiner concludes that claim 1 recites an abstract idea (Step 2A – Prong One: YES).
Each of the depending claims 2-4, 6-9, 11-13, 15-18, and 20, likewise recite/describe these steps (by incorporation - and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis. Any elements recited in a dependent claim that are not specifically identified/addressed by the Examiner under step 2A (prong two) or step 2B of this analysis shall be understood to be an additional part of the abstract idea recited by that particular claim.
Step 2A – Prong Two:
The claims recite the additional elements/limitations of:
“a computer system comprising a processor and a non-transitory computer-readable storage medium,”
“providing, by the computer system and to a picker client device, a picker client application through which a picker can identify batches to fulfill for a plurality of different retail locations,” (Claim 1);
“a non-transitory computer-readable medium storing instructions,” “a processor,”
“providing, by a computer system and to a picker client device, a picker client application through which a picker can identify batches to fulfill for a plurality of different retail locations,” (Claim 10);
“a system comprising: a processor; and a non-transitory computer readable storage medium,”
“providing, by a computer system and to a picker client device, a picker client application through which a picker can identify batches to fulfill for a plurality of different retail locations,” (Claim 19);
“a batch availability user interface element” (Claims 3 and 12).
The claims also recite the additional elements/limitations of:
“providing, by the computer system, a map of at least a portion of the zone for display within a user interface of the picker client device, wherein the map includes at least a subset of the plurality of retail locations;” “modifying the map displayed in the user interface at the picker client device to emphasize a first set of retail locations with a waiting time above a threshold value by performing a first visual adjustment to each of the retail locations in the first set within the map, wherein the modifying causes the picker client device to display the modified map,” (Claims 1, 10, and 19).
The requirement to execute the claimed steps/functions using “a computer system comprising a processor and a non-transitory computer-readable storage medium,” “providing, by the computer system and to a picker client device, a picker client application through which a picker can identify batches to fulfill for a plurality of different retail locations,” (claim 1); “a non-transitory computer-readable medium storing instructions,” “a processor,” “providing, by a computer system and to a picker client device, a picker client application through which a picker can identify batches to fulfill for a plurality of different retail locations,” (claim 10); “a system comprising: a processor; and a non-transitory computer readable storage medium,” “providing, by a computer system and to a picker client device, a picker client application through which a picker can identify batches to fulfill for a plurality of different retail locations,” (claim 19); and “a batch availability user interface element” (claims 3 and 12); “providing, by the computer system, a map of at least a portion of the zone for display within a user interface of the picker client device, wherein the map includes at least a subset of the plurality of retail locations;” “modifying the map displayed in the user interface at the picker client device to emphasize a first set of retail locations with a waiting time above a threshold value by performing a first visual adjustment to each of the retail locations in the first set within the map, wherein the modifying causes the picker client device to display the modified map,” (claims 1, 10, and 19), is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. See § MPEP 2106.05(f).
Remaining dependent claims 2, 4, 6-9, 11, 13, 15-18, and 20, either recite the same additional elements as noted above or fail to recite any additional elements (in which case, note prong one analysis as set forth above – those claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B:
As discussed above in “Step 2A – Prong 2,” the requirement to execute the claimed steps/functions using “a computer system comprising a processor and a non-transitory computer-readable storage medium,” “providing, by the computer system and to a picker client device, a picker client application through which a picker can identify batches to fulfill for a plurality of different retail locations,” (claim 1); “a non-transitory computer-readable medium storing instructions,” “a processor,” “providing, by a computer system and to a picker client device, a picker client application through which a picker can identify batches to fulfill for a plurality of different retail locations,” (claim 10); “a system comprising: a processor; and a non-transitory computer readable storage medium,” “providing, by a computer system and to a picker client device, a picker client application through which a picker can identify batches to fulfill for a plurality of different retail locations,” (claim 19); and “a batch availability user interface element” (claims 3 and 12); “providing, by the computer system, a map of at least a portion of the zone for display within a user interface of the picker client device, wherein the map includes at least a subset of the plurality of retail locations;” “modifying the map displayed in the user interface at the picker client device to emphasize a first set of retail locations with a waiting time above a threshold value by performing a first visual adjustment to each of the retail locations in the first set within the map, wherein the modifying causes the picker client device to display the modified map,” (claims 1, 10, and 19), is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as “significantly more.” See MPEP § 2106.05(f).
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer.
Remaining dependent claims 2, 4, 6-9, 11, 13, 15-18, and 20, either recite the same additional elements as noted above or fail to recite any additional elements (in which case, note prong one analysis as set forth above – those claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim).
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claims amount to significantly more than the abstract idea identified above (Step 2B: NO).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 6, 8-12, 15, and 17-20, are rejected under 35 U.S.C. 103 as being unpatentable over Reiss et al. (US 10,133,995 B1), hereinafter Reiss, in view of Zhang et al. (US 2024/0185175 A1), hereinafter Zhang; and Han et al. (US 2019/0130354 A1), hereinafter Han.
Regarding claim 1, Reiss discloses method, performed at a computer system comprising a processor and a non-transitory computer-readable storage medium, the method comprising:
providing, by the computer system and to a picker client device, a picker client application (Col. 5: Lines 63-67, The courier application 138 may be configured to receive the order information 122 from the service computing device 102 to provide a particular courier 120 with information for picking up a particular order from a merchant's pickup location 124 and for delivering the order to a buyer's delivery location 126) through which a picker can identify batches to fulfill for a plurality of different retail locations (Col. 18: Lines 1-8, the computing device may send, to the couriers, the recommended locations to which the couriers are to move to be in position for picking up items from respective merchants predicted to receive orders. For example, the computing device may send the recommended location to the courier application, which may present the recommended location to the courier in a GUI on the display of the courier device);
obtaining, by the computer system and from the picker client application, a location of the picker client device (Col. 6: Lines 5-7, the courier application 138 may provide the service computing device 102 with an indication of a current location of a particular courier 120);
identifying, by the computer system, a plurality of retail locations within a threshold distance of the location of the picker client device (Col. 7: Lines 53-57, Various other factors may also be taken into consideration when assigning delivery jobs, such as distance of each courier from a pickup location, whether a particular courier has been posted at the pickup location, is waiting at a nearby location, or the like. As one example, delivery jobs may be assigned using a round robin technique among a subset of couriers who are within a threshold distance of the pickup location of a particular job that is to be assigned), wherein the threshold distance from the location of the picker client device defines a zone (Col. 8: Lines 13-17, For instance, the GUI may present a map of a geographic region within which the courier 120 is currently located. For instance, the GUI may present the current detected location of the courier 120, a recommended location, locations of other couriers, or the like);
providing, by the computer system, a map of at least a portion of the zone for display within a user interface of the picker client device, wherein the map includes at least a subset of the plurality of retail locations (Col 8: Lines 13-17, For instance, the GUI may present a map of a geographic region within which the courier 120 is currently located. For instance, the GUI may present the current detected location of the courier 120, a recommended location, locations of other couriers, or the like); and
for each retail location in the zone over a past period of time: determining a batch volume for the retail location (Col. 3; Lines 4-8);
modifying the map displayed in the user interface at the picker client device to emphasize a first set of retail locations, wherein the modifying causes the picker client device to display the modified map, by performing a first visual adjustment to each of the retail locations in the first set within the map (Col. 10: Lines 33-36; Col. 10: Lines 54-61, After the courier management module 150 has determined, for an upcoming period of time, predicted order recipients 244, a predicted number of orders per recipient 246, predicted items 248, and/or predicted preparation times 250 per predicted order, the courier management module 150 may apply courier management logic 252 to these predictions, such as to generate one or more courier location recommendations; Col. 8: Lines 13-17, For instance, the GUI may present a map of a geographic region within which the courier 120 is currently located. For instance, the GUI may present the current detected location of the courier 120, a recommended location, locations of other couriers, or the like).
Reiss does not explicitly disclose determining an average batch volume for the zone; and generating a waiting time by inputting the batch volume for the retail location and the average batch volume for the zone into a model, wherein the model is trained on historical batch volumes for the retail location and historical batch volumes for the zone and historical waiting times for receiving batches at the retail location and for the zone; and modifying the map displayed in the user interface at the picker client device to emphasize a first set of retail locations with a waiting time above a threshold value.
Zhang teaches for each retail location in the zone over a past period of time (Par. [0048], training a machine learning model based on historical input data for pickups by users that occurred during a historical time period): determining a batch volume for the retail location (Par. [0049], the historical input data and the output data can include data for each of the pickup orders that occurred during the historical time period); and generating a waiting time by inputting the batch volume for the retail location, wherein the model is trained on historical batch volumes for the retail location and historical waiting times for receiving batches at the retail location (Par. [0055], generating an estimated wait time using the machine learning model, as trained, and based on input data comprising the estimated arrival time, order information for each assembled checked-in order in the queue of assembled checked-in orders, historical information about the user, historical information about the physical store, and dynamic wait time data for the physical store); and determining a waiting time above a threshold value (Par. [0063], determining that the estimated wait time exceeds a predetermined threshold). Han teaches determining an average batch volume for the zone (Par. [0096], the average orders per hour in the particular region. This may include all orders made for all merchants in defined region at a given time).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the courier management system of Reiss to include waiting time determination abilities of Zhang and Han to teach “determining an average batch volume for the zone; and generating a waiting time by inputting the batch volume for the retail location and the average batch volume for the zone into a model, wherein the model is trained on historical batch volumes for the retail location and historical batch volumes for the zone and historical waiting times for receiving batches at the retail location and for the zone; and modifying the map displayed in the user interface at the picker client device to emphasize a first set of retail locations with a waiting time above a threshold value,” as a need exists for an improved prediction system for pick up orders (Zhang, Par. [0003]). Incorporating batch information with courier delivery systems would improve forecasting courier movement and enable efficient delivery of items to consumers.
Regarding claim 2, Reiss, Zhang, and Han teach the method of claim 1. Reiss discloses wherein modifying the map comprises removing retail locations with a score below the threshold value from the map displayed at the user interface (Col. 10: Lines 33-36; Col. 10: Lines 54-61, After the courier management module 150 has determined, for an upcoming period of time, predicted order recipients 244, a predicted number of orders per recipient 246, predicted items 248, and/or predicted preparation times 250 per predicted order, the courier management module 150 may apply courier management logic 252 to these predictions, such as to generate one or more courier location recommendations; Col. 8: Lines 13-17, For instance, the GUI may present a map of a geographic region within which the courier 120 is currently located. For instance, the GUI may present the current detected location of the courier 120, a recommended location, locations of other couriers, or the like). Reiss does not explicitly disclose a waiting time below the threshold value. Zhang teaches determining whether a waiting time is above or below a threshold value (Par. [0063], determining that the estimated wait time exceeds a predetermined threshold).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the courier management system of Reiss to include waiting time determination abilities of Zhang and Han as a need exists for an improved prediction system for pick up orders (Zhang, Par. [0003]). Incorporating batch information with courier delivery systems would improve forecasting courier movement and enable efficient delivery of items to consumers.
Regarding claim 3, Reiss, Zhang, and Han teach the method of claim 2. Reiss discloses, further comprising: providing, by the computer system, a batch availability user interface element for display within the user interface (Col. 15: Lines 50-53) that, when selected by a picker, causes retail locations with a score below the threshold value to be hidden from view on the map (Col. 10: Lines 33-36; Col. 10: Lines 54-61, After the courier management module 150 has determined, for an upcoming period of time, predicted order recipients 244, a predicted number of orders per recipient 246, predicted items 248, and/or predicted preparation times 250 per predicted order, the courier management module 150 may apply courier management logic 252 to these predictions, such as to generate one or more courier location recommendations; Col. 8: Lines 13-17, For instance, the GUI may present a map of a geographic region within which the courier 120 is currently located. For instance, the GUI may present the current detected location of the courier 120, a recommended location, locations of other couriers, or the like). Reiss does not explicitly disclose a waiting time below the threshold value. Zhang teaches determining whether a waiting time is above or below a threshold value (Par. [0063], determining that the estimated wait time exceeds a predetermined threshold).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the courier management system of Reiss to include waiting time determination abilities of Zhang and Han as a need exists for an improved prediction system for pick up orders (Zhang, Par. [0003]). Incorporating batch information with courier delivery systems would improve forecasting courier movement and enable efficient delivery of items to consumers.
Regarding claim 6, Reiss, Zhang, and Han teach the method of claim 1. Reiss discloses generating a numerical value in minutes predicting a waiting time until the picker receives a batch at the retail location (Col. 9: Lines 43-45, the courier historic information 206 may include wait times 240, which may indicate how long each courier had to wait after delivering an order before picking up another order).
Regarding claim 8, Reiss, Zhang, and Han teach the method of claim 1. Reiss does not explicitly disclose wherein the model is further trained on a number of batches at a same time of day over a past week for the retail location and a number of batches at the same time of day over the past week for the zone. Han teaches wherein the model is further trained on a number of batches at a same time of day over a past week for the retail location and a number of batches at the same time of day over the past week for the zone (Par. [0096], the average orders per hour in the particular region. This may include all orders made for all merchants in defined region at a given time). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the courier management system of Reiss to include waiting time determination abilities of Zhang and Han as a need exists for an improved prediction system for pick up orders (Zhang, Par. [0003]). Incorporating batch information with courier delivery systems would improve forecasting courier movement and enable efficient delivery of items to consumers.
Regarding claim 9, Reiss, Zhang, and Han teach the method of claim 1. Reiss discloses wherein the model is further trained on historical wait times for pickers to receive a batch at the retail location and historical wait times for pickers to receive a batch in the zone (Col. 9: Lines 43-45, the courier historic information 206 may include wait times 240, which may indicate how long each courier had to wait after delivering an order before picking up another order).
Regarding claim 10, Reiss discloses a non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform steps comprising:
providing, by a computer system and to a picker client device, a picker client application (Col. 5: Lines 63-67, The courier application 138 may be configured to receive the order information 122 from the service computing device 102 to provide a particular courier 120 with information for picking up a particular order from a merchant's pickup location 124 and for delivering the order to a buyer's delivery location 126) through which a picker can identify batches to fulfill for a plurality of different retail locations (Col. 18: Lines 1-8, the computing device may send, to the couriers, the recommended locations to which the couriers are to move to be in position for picking up items from respective merchants predicted to receive orders. For example, the computing device may send the recommended location to the courier application, which may present the recommended location to the courier in a GUI on the display of the courier device);
obtaining, by the computer system and from the picker client application, a location of the picker client device (Col. 6: Lines 5-7, the courier application 138 may provide the service computing device 102 with an indication of a current location of a particular courier 120);
identifying, by the computer system, a plurality of retail locations within a threshold distance of the location of the picker client device (Col. 7: Lines 53-57, Various other factors may also be taken into consideration when assigning delivery jobs, such as distance of each courier from a pickup location, whether a particular courier has been posted at the pickup location, is waiting at a nearby location, or the like. As one example, delivery jobs may be assigned using a round robin technique among a subset of couriers who are within a threshold distance of the pickup location of a particular job that is to be assigned), wherein the threshold distance from the location of the picker client device defines a zone (Col. 8: Lines 13-17, For instance, the GUI may present a map of a geographic region within which the courier 120 is currently located. For instance, the GUI may present the current detected location of the courier 120, a recommended location, locations of other couriers, or the like);
providing, by the computer system, a map of at least a portion of the zone for display within a user interface of the picker client device, wherein the map includes at least a subset of the plurality of retail locations (Col 8: Lines 13-17, For instance, the GUI may present a map of a geographic region within which the courier 120 is currently located. For instance, the GUI may present the current detected location of the courier 120, a recommended location, locations of other couriers, or the like); and
for each retail location in the zone over a past period of time: determining a batch volume for the retail location (Col. 3; Lines 4-8);
modifying the map displayed in the user interface at the picker client device to emphasize a first set of retail locations, wherein the modifying causes the picker client device to display the modified map, by performing a first visual adjustment to each of the retail locations in the first set within the map (Col. 10: Lines 33-36; Col. 10: Lines 54-61, After the courier management module 150 has determined, for an upcoming period of time, predicted order recipients 244, a predicted number of orders per recipient 246, predicted items 248, and/or predicted preparation times 250 per predicted order, the courier management module 150 may apply courier management logic 252 to these predictions, such as to generate one or more courier location recommendations; Col. 8: Lines 13-17, For instance, the GUI may present a map of a geographic region within which the courier 120 is currently located. For instance, the GUI may present the current detected location of the courier 120, a recommended location, locations of other couriers, or the like).
Reiss does not explicitly disclose determining an average batch volume for the zone; and generating a waiting time by inputting the batch volume for the retail location and the average batch volume for the zone into a model, wherein the model is trained on historical batch volumes for the retail location and historical batch volumes for the zone and historical waiting times for receiving batches at the retail location and for the zone; and modifying the map displayed in the user interface at the picker client device to emphasize a first set of retail locations with a waiting time above a threshold value.
Zhang teaches for each retail location in the zone over a past period of time (Par. [0048], training a machine learning model based on historical input data for pickups by users that occurred during a historical time period): determining a batch volume for the retail location (Par. [0049], the historical input data and the output data can include data for each of the pickup orders that occurred during the historical time period); and generating a waiting time by inputting the batch volume for the retail location, wherein the model is trained on historical batch volumes for the retail location and historical waiting times for receiving batches at the retail location (Par. [0055], generating an estimated wait time using the machine learning model, as trained, and based on input data comprising the estimated arrival time, order information for each assembled checked-in order in the queue of assembled checked-in orders, historical information about the user, historical information about the physical store, and dynamic wait time data for the physical store); and determining a waiting time above a threshold value (Par. [0063], determining that the estimated wait time exceeds a predetermined threshold). Han teaches determining an average batch volume for the zone (Par. [0096], the average orders per hour in the particular region. This may include all orders made for all merchants in defined region at a given time).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the courier management system of Reiss to include waiting time determination abilities of Zhang and Han to teach “determining an average batch volume for the zone; and generating a waiting time by inputting the batch volume for the retail location and the average batch volume for the zone into a model, wherein the model is trained on historical batch volumes for the retail location and historical batch volumes for the zone and historical waiting times for receiving batches at the retail location and for the zone; and modifying the map displayed in the user interface at the picker client device to emphasize a first set of retail locations with a waiting time above a threshold value,” as a need exists for an improved prediction system for pick up orders (Zhang, Par. [0003]). Incorporating batch information with courier delivery systems would improve forecasting courier movement and enable efficient delivery of items to consumers.
Regarding claim 11, Reiss, Zhang, and Han teach the medium of claim 10. Reiss discloses wherein modifying the map comprises removing retail locations with a score below the threshold value from the map displayed at the user interface (Col. 10: Lines 33-36; Col. 10: Lines 54-61, After the courier management module 150 has determined, for an upcoming period of time, predicted order recipients 244, a predicted number of orders per recipient 246, predicted items 248, and/or predicted preparation times 250 per predicted order, the courier management module 150 may apply courier management logic 252 to these predictions, such as to generate one or more courier location recommendations; Col. 8: Lines 13-17, For instance, the GUI may present a map of a geographic region within which the courier 120 is currently located. For instance, the GUI may present the current detected location of the courier 120, a recommended location, locations of other couriers, or the like). Reiss does not explicitly disclose a waiting time below the threshold value. Zhang teaches determining whether a waiting time is above or below a threshold value (Par. [0063], determining that the estimated wait time exceeds a predetermined threshold).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the courier management system of Reiss to include waiting time determination abilities of Zhang and Han as a need exists for an improved prediction system for pick up orders (Zhang, Par. [0003]). Incorporating batch information with courier delivery systems would improve forecasting courier movement and enable efficient delivery of items to consumers.
Regarding claim 12, Reiss, Zhang, and Han teach the medium of claim 11. Reiss discloses, further comprising: providing, by the computer system, a batch availability user interface element for display within the user interface (Col. 15: Lines 50-53) that, when selected by a picker, causes retail locations with a score below the threshold value to be hidden from view on the map (Col. 10: Lines 33-36; Col. 10: Lines 54-61, After the courier management module 150 has determined, for an upcoming period of time, predicted order recipients 244, a predicted number of orders per recipient 246, predicted items 248, and/or predicted preparation times 250 per predicted order, the courier management module 150 may apply courier management logic 252 to these predictions, such as to generate one or more courier location recommendations; Col. 8: Lines 13-17, For instance, the GUI may present a map of a geographic region within which the courier 120 is currently located. For instance, the GUI may present the current detected location of the courier 120, a recommended location, locations of other couriers, or the like). Reiss does not explicitly disclose a waiting time below the threshold value. Zhang teaches determining whether a waiting time is above or below a threshold value (Par. [0063], determining that the estimated wait time exceeds a predetermined threshold).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the courier management system of Reiss to include waiting time determination abilities of Zhang and Han as a need exists for an improved prediction system for pick up orders (Zhang, Par. [0003]). Incorporating batch information with courier delivery systems would improve forecasting courier movement and enable efficient delivery of items to consumers.
Regarding claim 15, Reiss, Zhang, and Han teach the medium of claim 10. Reiss discloses generating a numerical value in minutes predicting a waiting time until the picker receives a batch at the retail location (Col. 9: Lines 43-45, the courier historic information 206 may include wait times 240, which may indicate how long each courier had to wait after delivering an order before picking up another order).
Regarding claim 17, Reiss, Zhang, and Han teach the medium of claim 10. Reiss does not explicitly disclose wherein the model is further trained on a number of batches at a same time of day over a past week for the retail location and a number of batches at the same time of day over the past week for the zone. Han teaches wherein the model is further trained on a number of batches at a same time of day over a past week for the retail location and a number of batches at the same time of day over the past week for the zone (Par. [0096], the average orders per hour in the particular region. This may include all orders made for all merchants in defined region at a given time). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the courier management system of Reiss to include waiting time determination abilities of Zhang and Han as a need exists for an improved prediction system for pick up orders (Zhang, Par. [0003]). Incorporating batch information with courier delivery systems would improve forecasting courier movement and enable efficient delivery of items to consumers.
Regarding claim 18, Reiss, Zhang, and Han teach the medium of claim 10. Reiss discloses wherein the model is further trained on historical wait times for pickers to receive a batch at the retail location and historical wait times for pickers to receive a batch in the zone (Col. 9: Lines 43-45, the courier historic information 206 may include wait times 240, which may indicate how long each courier had to wait after delivering an order before picking up another order).
Regarding claim 19, Reiss discloses a system comprising: a processor; and a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by the processor, cause the processor to perform steps comprising:
providing, by a computer system and to a picker client device, a picker client application (Col. 5: Lines 63-67, The courier application 138 may be configured to receive the order information 122 from the service computing device 102 to provide a particular courier 120 with information for picking up a particular order from a merchant's pickup location 124 and for delivering the order to a buyer's delivery location 126) through which a picker can identify batches to fulfill for a plurality of different retail locations (Col. 18: Lines 1-8, the computing device may send, to the couriers, the recommended locations to which the couriers are to move to be in position for picking up items from respective merchants predicted to receive orders. For example, the computing device may send the recommended location to the courier application, which may present the recommended location to the courier in a GUI on the display of the courier device);
obtaining, by the computer system and from the picker client application, a location of the picker client device (Col. 6: Lines 5-7, the courier application 138 may provide the service computing device 102 with an indication of a current location of a particular courier 120);
identifying, by the computer system, a plurality of retail locations within a threshold distance of the location of the picker client device (Col. 7: Lines 53-57, Various other factors may also be taken into consideration when assigning delivery jobs, such as distance of each courier from a pickup location, whether a particular courier has been posted at the pickup location, is waiting at a nearby location, or the like. As one example, delivery jobs may be assigned using a round robin technique among a subset of couriers who are within a threshold distance of the pickup location of a particular job that is to be assigned), wherein the threshold distance from the location of the picker client device defines a zone (Col. 8: Lines 13-17, For instance, the GUI may present a map of a geographic region within which the courier 120 is currently located. For instance, the GUI may present the current detected location of the courier 120, a recommended location, locations of other couriers, or the like);
providing, by the computer system, a map of at least a portion of the zone for display within a user interface of the picker client device, wherein the map includes at least a subset of the plurality of retail locations (Col 8: Lines 13-17, For instance, the GUI may present a map of a geographic region within which the courier 120 is currently located. For instance, the GUI may present the current detected location of the courier 120, a recommended location, locations of other couriers, or the like); and
for each retail location in the zone over a past period of time: determining a batch volume for the retail location (Col. 3; Lines 4-8);
modifying the map displayed in the user interface at the picker client device to emphasize a first set of retail locations, wherein the modifying causes the picker client device to display the modified map, by performing a first visual adjustment to each of the retail locations in the first set within the map (Col. 10: Lines 33-36; Col. 10: Lines 54-61, After the courier management module 150 has determined, for an upcoming period of time, predicted order recipients 244, a predicted number of orders per recipient 246, predicted items 248, and/or predicted preparation times 250 per predicted order, the courier management module 150 may apply courier management logic 252 to these predictions, such as to generate one or more courier location recommendations; Col. 8: Lines 13-17, For instance, the GUI may present a map of a geographic region within which the courier 120 is currently located. For instance, the GUI may present the current detected location of the courier 120, a recommended location, locations of other couriers, or the like).
Reiss does not explicitly disclose determining an average batch volume for the zone; and generating a waiting time by inputting the batch volume for the retail location and the average batch volume for the zone into a model, wherein the model is trained on historical batch volumes for the retail location and historical batch volumes for the zone and historical waiting times for receiving batches at the retail location and for the zone; and modifying the map displayed in the user interface at the picker client device to emphasize a first set of retail locations with a waiting time above a threshold value.
Zhang teaches for each retail location in the zone over a past period of time (Par. [0048], training a machine learning model based on historical input data for pickups by users that occurred during a historical time period): determining a batch volume for the retail location (Par. [0049], the historical input data and the output data can include data for each of the pickup orders that occurred during the historical time period); and generating a waiting time by inputting the batch volume for the retail location, wherein the model is trained on historical batch volumes for the retail location and historical waiting times for receiving batches at the retail location (Par. [0055], generating an estimated wait time using the machine learning model, as trained, and based on input data comprising the estimated arrival time, order information for each assembled checked-in order in the queue of assembled checked-in orders, historical information about the user, historical information about the physical store, and dynamic wait time data for the physical store); and determining a waiting time above a threshold value (Par. [0063], determining that the estimated wait time exceeds a predetermined threshold). Han teaches determining an average batch volume for the zone (Par. [0096], the average orders per hour in the particular region. This may include all orders made for all merchants in defined region at a given time).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the courier management system of Reiss to include waiting time determination abilities of Zhang and Han to teach “determining an average batch volume for the zone; and generating a waiting time by inputting the batch volume for the retail location and the average batch volume for the zone into a model, wherein the model is trained on historical batch volumes for the retail location and historical batch volumes for the zone and historical waiting times for receiving batches at the retail location and for the zone; and modifying the map displayed in the user interface at the picker client device to emphasize a first set of retail locations with a waiting time above a threshold value,” as a need exists for an improved prediction system for pick up orders (Zhang, Par. [0003]). Incorporating batch information with courier delivery systems would improve forecasting courier movement and enable efficient delivery of items to consumers.
Regarding claim 20, Reiss, Zhang, and Han teach the system of claim 19. Reiss discloses wherein modifying the map comprises removing retail locations with a score below the threshold value from the map displayed at the user interface (Col. 10: Lines 33-36; Col. 10: Lines 54-61, After the courier management module 150 has determined, for an upcoming period of time, predicted order recipients 244, a predicted number of orders per recipient 246, predicted items 248, and/or predicted preparation times 250 per predicted order, the courier management module 150 may apply courier management logic 252 to these predictions, such as to generate one or more courier location recommendations; Col. 8: Lines 13-17, For instance, the GUI may present a map of a geographic region within which the courier 120 is currently located. For instance, the GUI may present the current detected location of the courier 120, a recommended location, locations of other couriers, or the like). Reiss does not explicitly disclose a waiting time below the threshold value. Zhang teaches determining whether a waiting time is above or below a threshold value (Par. [0063], determining that the estimated wait time exceeds a predetermined threshold).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the courier management system of Reiss to include waiting time determination abilities of Zhang and Han as a need exists for an improved prediction system for pick up orders (Zhang, Par. [0003]). Incorporating batch information with courier delivery systems would improve forecasting courier movement and enable efficient delivery of items to consumers.
Allowable Subject Matter
Claims 4, 7, 13, and 16, are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all the limitations of the base claim and any intervening claim and revised and amended to overcome the rejection under 35 U.S.C. 101 as set forth in this Office action.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Patrick Kim/Examiner, Art Unit 3628
/RESHA DESAI/Supervisory Patent Examiner, Art Unit 3648