Status of Claims
This action is in reply to the communications filed on 09/15/2025.
Claims 1-3 and 5-11 have been amended.
Claims 1-20 are currently pending and have been examined.
Response to Applicant’s Remarks
Applicant’s arguments and remarks filed on 09/15/2025 have been fully considered and each argument will be respectfully addressed in the following final office action.
Response to 35 U.S.C. § 101 Remarks
Applicant’s remarks filed on pages 9-15 of the Response concerning the 35 U.S.C. § 101 rejection of claims 1-20 have been fully considered and are found to be persuasive. The Applicant’s amendments have overcome the previous 35 U.S.C. § 101 rejections of claims 1-20. Thus, the 35 U.S.C. § 101 rejections of claims 1-20 have been withdrawn accordingly.
The Examiner notes that the reasons for which claims 1-20 are considered patent eligible under 35 U.S.C. § 101 are because the additional elements of independent claims 1 and 11 are considered to integrate the recited abstract ideas into a practical application and the specific combination of elements applies the judicial exception in a way that is beyond general linking to a particular field of use, insignificant extra solution activity, or applying the judicial exception to generic computer components. Claims 1 and 11 recite specific, technical limitations that involve a processor configured to generate an initial physical transfer as a function of a first request and transfer party by: (1) receiving an initial training set correlating a plurality of alimentary combination requests and a plurality transfer parties to a plurality of initial physical transfer paths, (2) training an initial machine-learning model using the initial training set, wherein training the initial machine-learning model includes retraining the initial machine-learning model based on previous iterations of the initial machine-learning model, and (3) generating the initial physical transfer path as a function of the first alimentary combination and the first transfer party by using the trained initial machine-learning model. Furthermore, as disclosed by the Applicant at ¶ [0047] of the specification, “any process of training, retraining, deployment, and/or instantiation of any machine-learning model and/or algorithm may be performed and/or repeated after an initial deployment and/or instantiation to correct, refine, and/or improve the machine-learning model and/or algorithm”. Thus, these limitations that recite features directed to improving a technological field, in combination with the other claim limitations, integrate the abstract idea into a practical application.
Response to 35 U.S.C. § 102 Remarks
Applicant’s remarks filed on pages 15-17 of the Response concerning the 35 U.S.C. § 102 rejection of claims 1-3 and 11-13 have been fully considered but are moot in view of the amended prior art rejection.
On pages 15-17 of the Response, the Applicant argues that the prior art of record, namely Laury, does not teach or suggest the features of the amended independent claims. The Examiner agrees that Laury does not teach or suggest these amended claim elements. However, in view of the amendments, an amended §103 rejection of the independent claims with newly cited prior art has been set forth herein starting on page 4.
Response to 35 U.S.C. § 103 Remarks
Applicant’s remarks filed on pages 17-19 of the Response concerning the 35 U.S.C. § 103 rejection of the claims have been fully considered but are moot in view of the amended prior art rejection of the independent claims.
On pages 17-19 of the Response, the Applicant argues that the prior art of record - namely Walch, Raut, Bounasser, and Sujan -do not teach or suggested the features of the amended independent claims. In view of the amendments to the claims, an amended §103 rejection of the independent claims with newly cited prior art has been set forth herein starting on page 4.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3 and 11-13 are rejected under 35 U.S.C. § 103 as being unpatentable over Laury et al. U.S. Publication No. 2019/0228375, hereafter known as Laury, in view of Rusnak et al. U.S. Publication No. 2021/0142283, hereafter known as Rusnak.
Claim 1: Laury teaches the following:
A system for modifying a physical transfer path, the system comprising: at least a processor and a memory communicatively connected to the at least processor, wherein the memory contains instructions configuring the at least processor to: (¶ [0014]: delivery management system comprising a processor, non-transitory memory, and computer-executable instructions).
Generate an initial physical transfer path by: (¶ [0021]: a delivery management system configured to generate delivery routes for delivery vehicles); receiving a first request for a first alimentary combination; (¶ [0016]: delivery recipient can order items/products from merchants such as restaurants; [¶ 0017]: delivery management system receives requests for delivery).
determining a first alimentary combination source location as a function of the first request; (¶ [0019]: system generates delivery mission based on the user order/request, where the delivery mission includes tasks for picking up ordered items from one or more pickup locations; ¶ [0026]: pickup location corresponds to merchant geographical location).
determining a first transfer party as a function of the first alimentary combination source location; (¶ [0031]: system selects delivery vehicles to carry out delivery mission based on vehicle current location relative to a pickup location corresponding to requested payload).
and generating the initial physical transfer path as a function of the first request and the first transfer party […]; (¶[0032]: system calculates delivery route for delivery mission based on delivery locations and vehicle selection. Route includes pickup location and delivery location).
receive a second request for a second alimentary combination; (¶ [0020]: system can combine multiple delivery missions, e.g., multiple orders for multiple recipients, and generate one delivery route that covers the combined set of delivery missions).
and generate a first modified physical transfer path by: determining a second alimentary combination source location as a function of the second request; (¶[0042]: system can update/adjust delivery route to accommodate further delivery missions in real-time, e.g., while the delivery vehicle is in transit on the existing/previous delivery route).
determining a first delivery time threshold as a function of the first request and a first spoilage threshold associated with the first alimentary combination; (¶ [0097]: system selects vehicle for delivery mission based on restrictions on delivery time associated with an order); (¶ [0103]: system considers the ordered item or type thereof, time restriction or condition of the items such as hot/cold delivery, and impact of potential routes on the delivery time and condition of the items); (¶ [0021]: optimization mechanism of system considers payload concerns, e.g., cold/hot prioritization).
determining a second delivery time threshold as a function of the second request […]; (¶ [0097]: system selects vehicle for delivery mission based on restrictions on delivery time associated with an order); (¶ [0103]: system considers the ordered item or type thereof, time restriction or condition of items such as hot/cold delivery, and impact of potential routes on the delivery time and condition of the items); (¶ [0021]: optimization mechanism of system considers payload concerns, e.g., cold/hot prioritization).
and generating the first modified physical transfer path as a function of the first delivery time threshold, the second delivery time threshold, and the second alimentary combination source location. (¶ [0020]: system can combine multiple delivery missions, e.g., multiple orders for multiple recipients, and generate one delivery route that covers the combined set of delivery missions); (¶[0032]: Route includes pickup location and delivery location for ordered items); (¶[0042]: system can update/adjust delivery route to accommodate further delivery missions in real-time, e.g., while the delivery vehicle is in transit on the existing/previous delivery route); (¶ [0103]: system considers the ordered item or type thereof, time restriction or condition of items such as hot/cold delivery, and impact of potential routes on the delivery time and condition of the items).
Laury does not explicitly teach (1) receiving an initial training set correlating a plurality of alimentary combination requests and a plurality transfer parties to a plurality of initial physical transfer paths, (2) training an initial machine-learning model using the initial training set, wherein training the initial machine-learning model includes retraining the initial machine-learning model based on previous iterations of the initial machine-learning model, and (3) generating the initial physical transfer path as a function of the first alimentary combination and the first transfer party by using the trained initial machine-learning model.
However, Rusnak teaches the following:
Generating the initial physical transfer path as a function of the first request and te first request and the first transfer party by: receiving an initial training set correlating a plurality of alimentary combination requests and a plurality transfer parties to a plurality of initial physical transfer paths; (Abstract: a system for managing a user request for a transport of a good. The system receives a request for transport of the good from a pickup location to a destination); (¶ [0093]: Transporter submodule may identify eligible transporters for transporting the goods from available transporters. The goods may require special handling, such as refrigerated produce, and thus require specialized transport); (¶ [0094]: Transporter submodule may determine if eligible transporters are solicitable, such as if they are able to transport the goods to the destination by a specified time); (¶ [0074]: Transportation submodule includes a transport time/cost model 608 that is a machine-learning model trained on previously utilized transport data (e.g., used routes, traffic for particular routes, average amount of travel time) for a particular or average transporter utilizing a particular mode of transportation); (¶ [0096]: Transporter submodule can utilize a trained transporter model that includes the transportation submodule’s transportation time model 610 and time/cost model 608. The transportation time model 610, determines an estimated amount of time for transporters to transport the specified goods, and time/cost model 608 may determine an estimated amount of time for transporters to tender the goods).
Training an initial machine-learning model using the initial training set, wherein training the initial machine-learning model includes retraining the initial machine-learning model based on previous iterations of the initial machine-learning model; (¶ [0074]: Transport submodule includes a transport time/cost model 608 that is a machine-learning model trained on previously utilized transport data (e.g., used routes, traffic for particular routes, average amount of travel time) for a particular or average transporter utilizing a particular mode of transportation).
Thus, Rusnak teaches a machine-learning model that is trained on previously utilized data. Accordingly, one of ordinary skill in the art would recognize that the transport time/cost model is trained/retrained with respect to any previously utilized data.
Generating the initial physical transfer path as a function of the first alimentary combination and the first transfer party by using the trained initial machine-learning model. (¶ [0074]: transportation submodule includes a transportation time/cost model 608 that is a trained machine-learning model); (¶ [0070]: transporter module includes the transportation submodule. Transportation submodule 604 can provide a transportation edge corresponding to an actual transport time or actual transport cost for each possible route from each of pickup location, destination location, and each possible intermediate location); (¶ [0069] - ¶ [0070]: The route generator module is in communication with the transporter module, wherein the transporter module includes the transportation submodule); ¶ [0078]: the route generation module may determine an optimal route); (¶ [0079]: the route generator module may generate a preferred route from pickup location to destination location based on least amount of time and/or on a user preference).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Laury with the teachings of Rusnak by incorporating the features for generating the initial physical transfer path as a function of the first request and the first request and the first transfer party by (1) receiving an initial training set correlating a plurality of alimentary combination requests and a plurality transfer parties to a plurality of initial physical transfer paths, (2) training an initial machine-learning model using the initial training set, wherein training the initial machine-learning model includes retraining the initial machine-learning model based on previous iterations of the initial machine-learning model, and (3) generating the initial physical transfer path as a function of the first alimentary combination and the first transfer party by using the trained initial machine-learning model, as taught by Rusnak. One of ordinary skill in the art would have been motivated to make this modification with the purpose to “accurately consider different possible legs and routes that have different times and costs” (¶ [0082]) and “determine an optimal route” (¶ [0078]) for delivering goods based on machine-learning techniques, as suggested by Rusnak. One of ordinary skill in the art would have recognized that the teachings of Rusnak are compatible with the system of Laury as they share capabilities and characteristics; namely, they are both systems configured to receive delivery orders from consumers and generate corresponding delivery routes.
Claim 2: Laury/Rusnak teaches the limitations of claim 1. Furthermore, Laury teaches the following:
Wherein: the first request comprises a first destination; (¶ [0028]: delivery recipient orders/requests payload, and recipient address is provided as part of the order/request);
the second request comprises a second destination; (¶ [0028]).
the memory contains instructions configuring the at least a processor to generate the first modified physical transfer path as a function of the first destination and the second destination (¶ [0028: system generates delivery mission based on the recipient order/request; ¶ [0020]: system can combine multiple delivery missions, e.g., multiple orders for multiple recipients, and generate one delivery route that covers the combined set of delivery missions; ¶[0042]: system can update/adjust delivery route to accommodate further delivery missions in real-time, e.g., while the delivery vehicle is in transit on the existing/previous delivery route).
Claim 3: Laury/Rusnak teaches the limitations of claim 1. Furthermore, Laury teaches the following:
wherein the memory contains instructions configuring the at least a processor to generate a second modified physical transfer path by: identifying a first alternate transfer party as a function of the first delivery time threshold and the second delivery time threshold; (¶ [0040]: system can generate multiple potential groupings of delivery missions with consideration to delivery time of one or more vehicles, time requirements of the delivery missions, travel distance of one or more vehicles, and travel range of one or more vehicles; (¶ [0042]: delivery routes are generated for the groupings of delivery missions).
generating the second modified physical transfer path as a function of the second delivery time threshold and the first alternate transfer party using a transfer machine-learning model. (¶ [0076]: system optimization mechanism calculates routes corresponding to groupings of delivery missions and calculates scores for each of the groupings. Optimization mechanism considers locations and statuses of available delivery vehicles; (¶ [0040]: system generates multiple potential groupings of delivery missions with consideration to time requirements of the delivery missions, travel distance of one or more vehicles, and travel range of one or more vehicles; ¶ [0021]: optimization mechanism implements machine-learning algorithms).
Claim 11: Claim 11 recites limitations that are substantially similar to the limitations of claim 1. Accordingly, claim 11 is rejected for the same reasons and rationale as discussed above with regard to claim 1.
Claim 12: Laury/Rusnak teaches the limitations of claim 11. Furthermore, claim 12 recites limitations that are substantially similar to the limitations of claim 2. Accordingly, claim 12 is rejected for the same reasons and rationale as discussed above with regard to claim 2.
Claim 13: Laury/Rusnak teaches the limitations of claim 11. Furthermore, claim 13 recites limitations that are substantially similar to the limitations of claim 3. Accordingly, claim 13 is rejected for the same reasons and rationale as discussed above with regard to claim 3.
Claims 4 and 14 are rejected under 35 U.S.C. § 103 as being unpatentable over Laury et al. U.S. Publication No. 2019/0228375, hereafter known as Laury, in view of Rusnak et al. U.S. Publication No. 2021/0142283, hereafter known as Rusnak, in further view of Walch et al. U.S. Publication No. 2002/0152128, hereafter known as Walch.
Claim 4: Laury/Rusnak teaches the limitations of claim 1. Furthermore, Laury does not explicitly teach, however Walch does teach, the following:
wherein the first request is an instance of a regularly recurring set of requests. (¶ [0048]: consumer may order items via a handheld Internet access device); (¶ [0025]: consumers may approve a subscription whereby routinely exhaustible items, such as milk or cereal, are automatically put on the consumers current purchase order and delivered on a preestablished periodic basis).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Laury with the teachings of Walch by simple substitution. In particular, it would have been obvious to one of ordinary skill in the art to have simply substituted the features for electronically receiving a recurring delivery order from a consumer, as taught by Bounasser, for the features of electronically receiving a delivery order from a delivery recipient, as taught by Laury. One of ordinary skill in the art would have recognized that the teachings of Walch are compatible with the system of Laury as they share capabilities and characteristics; namely, they are both systems configured to receive delivery orders from consumers.
Claim 14: Laury/Rusnak teaches the limitations of claim 11. Furthermore, claim 14 recites limitations that are substantially similar to the limitations of claim 4. Accordingly, claim 14 is rejected for the same reasons and rationale as discussed above with regard to claim 4.
Claims 5-6 and 15-16 are rejected under 35 U.S.C. § 103 as being unpatentable over Laury et al. U.S. Publication No. 2019/0228375, hereafter known as Laury, in view of Rusnak et al. U.S. Publication No. 2021/0142283, hereafter known as Rusnak, in further view of Raut et al. U.S. Publication No. 2018/0341918, hereafter known as Raut.
Claim 5: Laury/Rusnak teaches the limitations of claim 1. Furthermore, Laury teaches the following:
wherein the memory contains instructions configuring the at least a processor to: determine a trouble state as a function of the initial physical transfer path; and (¶ [0034]: system generates multiple potential routes (i.e., including an initial physical transfer path) from starting point to destination, and calculates scores for routes based on real-time conditions including reported accidents on same road, weather conditions, black-out areas, etc.).
[…] generating the third modified physical transfer path as a function of the first delivery time threshold and the […] transfer party using a transfer machine-learning model. (¶[0042]: system can update/adjust delivery routes while a delivery vehicle is in transit on an existing/previous delivery route; ¶ [0040]: system generates delivery missions with consideration to time requirements of the delivery missions, travel distance of one or more vehicles, and travel range of one or more vehicles; ¶ [0021]: system optimization mechanism implements machine-learning algorithms).
Laury does not explicitly teach generating a third modified physical transfer path by: identifying a second alternate transfer party as a function of the trouble state, and generating the third modified physical transfer path as a function of the first delivery time threshold and the second alternate transfer party.
However, Raut teaches the following:
generate a third modified physical transfer path by: identifying a second alternate transfer party as a function of the trouble state; and generating the third modified physical transfer path as a function of the first delivery time threshold and the second alternate transfer party using a transfer machine-learning model; (¶ [0081]: system can re-assign a delivery order to an alternate vehicle when a breakdown scenario is determined for the initially assigned vehicle, and the system dynamically routes the two vehicles to intersect at a point to transfer the delivery cargo; (¶ [0047]: the parameters considered for route planning include a latest time to deliver an order accounting for perishable products; ¶ [0083]: route planning process uses optimization algorithms).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Laury with the teachings of Raut by incorporating the features for re-assigning an alternate vehicle to complete a delivery in response to determining a trouble state associated with an initially assigned vehicle and dynamically generating a modified route for both vehicles with consideration to a delivery time threshold for the delivery order, as taught by Raut, into the system of Laury that is configured to modify/alter delivery routes for vehicles using machine-learning algorithms and with consideration to delivery time requirements, travel delays (e.g., reported accidents along delivery route), and vehicle conditions. One of ordinary skill in the art would have been motivated to make this modification when one considers that such features for taking corrective action to reassign a delivery order to an alternate delivery vehicle in response to an identified trouble state of an initially assigned delivery vehicle “helps with the order delivery” (see Abstract), as suggested by Raut.
Claim 6: Laury/Rusnak/Raut teaches the limitations of claim 5. Furthermore, Laury teaches the following:
wherein the memory contains instructions configuring the at least a processor to determine a delayed delivery profile datum as a function of the trouble state. [ “delayed delivery profile datum” has been interpreted as a datum indicating that a delivery associated with a particular user profile has been delayed. See Specification ¶ 0121]; (¶ [0065]: receiver-access profile associates a user/recipient with an delivery payload; ¶[0080]: system receives travel status (e.g., traffic conditions) from the delivery vehicle; ¶ [0074]: system updates vehicle route and trip details based on a delay to a delivery mission according to the vehicle location; ¶ [0087]: system updates vehicle stop location, according to recipient’s request, based on traffic delays).
Claim 15: Laury/Rusnak teaches the limitations of claim 11. Furthermore, claim 15 recites limitations that are substantially similar to the limitations of claim 5. Accordingly, claim 15 is rejected for the same reasons and rationale as discussed above with regard to claim 5.
Claim 16: Laury/Rusnak teaches the limitations of claim 15. Furthermore, claim 16 recites limitations that are substantially similar to the limitations of claim 6. Accordingly, claim 16 is rejected for the same reasons and rationale as discussed above with regard to claim 6.
Claims 7-9 and 17-19 are rejected under 35 U.S.C. § 103 as being unpatentable over Laury et al. U.S. Publication No. 2019/0228375, hereafter known as Laury, in view of Rusnak et al. U.S. Publication No. 2021/0142283, hereafter known as Rusnak, in further view of Bounasser et al. U.S. Publication No. 2018/0349844, hereafter known as Bounasser.
Claim 7: Laury/Rusnak teaches the limitations of claim 1. Furthermore, Laury teaches the following:
receive the first request from a first user device; (¶ [0017]: user device/smartphone corresponding to delivery recipient; ¶ [0016]: delivery recipient orders items/products online; ¶ [0028]: delivery recipient orders/requests payload via system).
and configure the first user device to communicate […] a status of the first transfer party to a user. (¶ [0039]: system sends message/notification to user device when the delivery vehicle crosses a distance threshold and indicates an estimated time of arrival); (¶ [0070: vehicle may send real-time status updates to recipient user device).
Laury does not explicitly teach that the user device communicates a status of the first transfer party using a chatbot.
However, Bounasser teaches the following:
configure the first user device to communicate, using a chatbot, a status of the first transfer party to a user. (¶ [0115]: system may message a user/client device to inform the user that delivery is delayed because of traffic); (¶ [0018]: system may use a chatbot to intelligently communicate with user/user device).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Laury with the teachings of Bounasser by simple substitution. In particular, it would have been obvious to one of ordinary skill in the art to have simply substituted the features for communicating a delivery status notification to a client device using a chatbot, as taught by Bounasser, for the features of communicating a delivery status notification to a client device, as taught by Laury. One of ordinary skill in the art would have recognized that the teachings of Bounasser are compatible with the system of Laury as they share capabilities and characteristics; namely, they are both systems configured to communicate delivery status notifications to delivery recipients via a client device.
Claim 8: Laury/Rusnak/Bounasser teaches the limitations of claim 7. Furthermore, Laury does not explicitly teach, however Bounasser does teach, the following:
wherein the memory contains instructions configuring the at least a processor to: configure the first user device to communicate, using a chatbot, a delivery confirmation request to the user; (¶ [0115]: system may message a user/client device to inform the user that delivery is delayed because of traffic, and ask the user whether they would like to reschedule the delivery); (¶ [0018]: system may use a chatbot to intelligently communicate with user/user device).
and receive, using the first user device, a response. (¶ [0117]: user/client device may provide a message to the system indicating that the user does not want to reschedule the delivery- thus confirming the current/scheduled delivery).
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 system of Laury the ability to configure a user device to communicate a delivery confirmation request to a user using a chatbot and receive a response using the user device, as taught by Bounasser, since the claimed invention is merely a combination of old elements. In combination, each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Furthermore, one of ordinary skill in the art would have been motivated to make this modification with the purpose to further “improve the “last mile” and/or “last hour” of a delivery by providing real-time intelligent and dynamic delivery scheduling” (¶ [0117]), as suggested by Bounasser.
Claim 9: Laury/Rusnak/Bounasser teaches the limitations of claim 7. Furthermore, Laury does not explicitly teach, however Bounasser does teach, the following:
Wherein the memory contains instructions configuring the at least a processor to configure a second user device to communicate, using a chatbot, to the first transfer party a step of the first modified physical transfer path. (¶ [0076]: delivery system sends a message to a client device associated with a driver of a delivery vehicle, the message identifying a modification to a delivery that includes a set of instructions to use a different route to the location at which an item is to be delivered); (¶ [0018]: system may use a chatbot to intelligently communicate with user/user device).
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 system of Laury the ability to configure a user device associated with a delivery driver to communicate, using a chatbot, to the driver a step of a first modified physical transfer path, as taught by Bounasser, since the claimed invention is merely a combination of old elements. In combination, each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Furthermore, one of ordinary skill in the art would have been motivated to make this modification with the purpose to further “improve the “last mile” and/or “last hour” of a delivery by providing real-time intelligent and dynamic delivery scheduling” (¶ [0117]), as suggested by Bounasser. One of ordinary skill in the art would have recognized that the teachings of Bounasser are compatible with the system of Laury as they share capabilities and characteristics; namely, they are both systems configured to update/modify routes associated with a delivery and assign the updated routes to the vehicles (see Laury, ¶ [0074]).
Claim 17: Laury/Rusnak teaches the limitations of claim 11. Furthermore, claim 17 recites limitations that are substantially similar to the limitations of claim 7. Accordingly, claim 17 is rejected for the same reasons and rationale as discussed above with regard to claim 7.
Claim 18: Laury/Rusnak/Bounasser teaches the limitations of claim 17. Furthermore, claim 18 recites limitations that are substantially similar to the limitations of claim 8. Accordingly, claim 18 is rejected for the same reasons and rationale as discussed above with regard to claim 8.
Claim 19: Laury/Rusnak teaches the limitations of claim 11. Furthermore, claim 19 recites limitations that are substantially similar to the limitations of claim 9. Accordingly, claim 19 is rejected for the same reasons and rationale as discussed above with regard to claim 9.
Claims 10 and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Laury et al. U.S. Publication No. 2019/0228375, hereafter known as Laury, in view of Rusnak et al. U.S. Publication No. 2021/0142283, hereafter known as Rusnak, in further view of Sujan et al. U.S. Publication No. 2016/0075333, hereafter known as Sujan.
Claim 10: Laury/Rusnak teaches the limitations of claim 1. Furthermore, Laury does not explicitly teach, however Sujan does teach, the following:
wherein the memory contains instructions configuring the at least a processor to determine the first modified physical transfer path as a function of a vehicle emissions estimate. (¶ [0023]: system controller generates one or more route reference recommendations, e.g., a route plan, based on various factors; ¶ [0052]: the route reference recommendations are based on an emission condition; ¶ [0053]: the emission condition includes an emission output and emission performance level; ¶ [0023]: system controller can alter the route reference recommendations in real-time).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Laury with the teachings of Sujan by incorporating the features for generating and altering route recommendations in real-time based on emission outputs, as taught by Sujan, into the system of Laury that is configured to generate and alter/update routes while delivery vehicles are in transit. One of ordinary skill in the art would have been motivated to make this modification with the purpose of further “minimizing an emission output cost” (¶ [0053]), as suggested by Sujan.
Claim 20: Laury/Rusnak teaches the limitations of claim 11. Furthermore, claim 20 recites limitations that are substantially similar to the limitations of claim 10. Accordingly, claim 20 is rejected for the same reasons and rationale as discussed above with regard to claim 10.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORGE G DEL TORO-ORTEGA whose telephone number is (571)272-5319. The examiner can normally be reached Monday-Friday 9:00AM-6:00PM.
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/JORGE G DEL TORO-ORTEGA/ Examiner, Art Unit 3628
/JEFF ZIMMERMAN/ Supervisory Patent Examiner, Art Unit 3628