DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Application status
This office action is in response to application filed on 04/01/2024. Claims 1-17 and 22 are pending. Claims 1-17 and 22 are rejected.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in the parent Application PCT/JP2021/038222 filed on 10/15/2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/01/2024 and 11/14/2024. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement are being considered by the examiner.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The disclosure is objected to because of the following informalities:
legal phraseology “means” is used in line 1, 3, and 6 of the abstract.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-5, 13-17, and 22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Healey (US 20220027852 A1).
Regarding claim 1, Healey teaches (Currently amended) A delivery management apparatus (Healey, at least one para. 0002; “Embodiments pertain to autonomous or semi-autonomous vehicles. Some embodiments relate to transferring of goods between autonomous road vehicles.”) comprising:
at least one memory storing instructions (Healey, at least one para. 0048; “The above methods, systems, and machine readable mediums may be performed on computing devices integral to, or communicatively coupled with an autonomous vehicle.”) and (Healey, at least one para. 0051; “Machine (e.g., computer system) 10000 may include a hardware processor 10002 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 10004 and a static memory 10006, some or all of which may communicate with each other via an interlink (e.g., bus) 10008.”); and
at least one processor configured to execute the instructions to (Healey, at least one para. 0048; “The above methods, systems, and machine readable mediums may be performed on computing devices integral to, or communicatively coupled with an autonomous vehicle.”) and (Healey, at least one para. 0051; “Machine (e.g., computer system) 10000 may include a hardware processor 10002 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 10004 and a static memory 10006, some or all of which may communicate with each other via an interlink (e.g., bus) 10008.”):
acquire position information of a user vehicle in which a user who purchases a product or is provided with a service rides, and position information of a delivery vehicle configured to deliver the product or the service to the user (Healey, at least one para. 0042; “Route planner 9050 may receive a request for a goods transfer and the location of a rust vehicle 9020 of the transfer. Route planner 9050 may receive the location and/or route of the second vehicle 9030 in the transfer request or else via updates of route and position sent by the second vehicle to the network-accessible autonomous goods transfer service 9010 and stored in the directory 9040. Route planner 9050 may plan a route that brings both vehicles 9020, 9030 within a predetermined proximity of each other at a predetermined time.”); and
generate speed adjustment information for bringing the user vehicle close to the delivery vehicle based on the position information of the user vehicle and the position information of the delivery vehicle, and transmit the generated speed adjustment information to the user vehicle and the delivery vehicle (Healey, at least one para. 0039; “At operation 6020, the machine learning algorithm may output either one or more next states (e.g., change lanes, slow down, speed up), which are then used to determine vehicle control signals to achieve those states, or may output control signals directly. At operation 6030 these signals are then fed to the vehicle control systems to achieve the desired state.”).
Regarding claim 2, Healey teaches (Original) The delivery management apparatus according to claim 1, wherein the speed adjustment information includes first speed adjustment information transmitted to the delivery vehicle and second speed adjustment information transmitted to the user vehicle (Healey, at least one para. 0039; “At operation 6020, the machine learning algorithm may output either one or more next states (e.g., change lanes, slow down, speed up), which are then used to determine vehicle control signals to achieve those states, or may output control signals directly. At operation 6030 these signals are then fed to the vehicle control systems to achieve the desired state.”).
Regarding claim 3, Healey teaches (Currently amended) The delivery management apparatus according to claim 2, wherein the at least one processor is configured to execute the instructions to generate the first speed adjustment information and the second speed adjustment information (Healey, at least one para. 0039; “At operation 6020, the machine learning algorithm may output either one or more next states (e.g., change lanes, slow down, speed up), which are then used to determine vehicle control signals to achieve those states, or may output control signals directly. At operation 6030 these signals are then fed to the vehicle control systems to achieve the desired state.”) in accordance with a positional relationship between the user vehicle and the delivery vehicle (Healey, at least one para. 0028; “At operation 3035, the first autonomous vehicle may determine that the second autonomous vehicle is within a predetermined proximity of it. The predetermined proximity may be hard-coded as a distance at which the autonomous vehicles may reliably be expected to sense and/or communicate with each other using short range wireless technologies such that they may maneuver into position to transfer the item.”).
Regarding claim 4, Healey teaches (Currently amended) The delivery management apparatus according to claim 3, wherein when the delivery vehicle is positioned in front of the user vehicle, the at least one processor is configured to execute the instructions to generate the first speed adjustment information for reducing a traveling speed of the delivery vehicle and generate the second speed adjustment information for making a traveling speed of the user vehicle higher than a traveling speed of the delivery vehicle (Healey, at least one para. 0039; “At operation 6020, the machine learning algorithm may output either one or more next states (e.g., change lanes, slow down, speed up), which are then used to determine vehicle control signals to achieve those states, or may output control signals directly. At operation 6030 these signals are then fed to the vehicle control systems to achieve the desired state.”, In other words, to complete the package transfer, the speed of the truck 1010 and passenger vehicle 1020 are adjusted so that both vehicles can be positioned next to each other. As a result, it is inherent when the truck 1010 is in front of the passenger vehicle 1020, the speed of the truck 1010 is reduced, and the speed of the passenger vehicle 1020 is increased so that the both vehicles can be efficiently and safely positioned next to each other).
Regarding claim 5, Healey teaches (Currently amended) The delivery management apparatus according to claim 3, wherein when the user vehicle is positioned in front of the delivery vehicle, the at least one processor is configured to execute the instructions to generate the second speed adjustment information for reducing a traveling speed of the user vehicle and generate the first speed adjustment information for making a traveling speed of the delivery vehicle higher than a traveling speed of the user vehicle (Healey, at least one para. 0039; “At operation 6020, the machine learning algorithm may output either one or more next states (e.g., change lanes, slow down, speed up), which are then used to determine vehicle control signals to achieve those states, or may output control signals directly. At operation 6030 these signals are then fed to the vehicle control systems to achieve the desired state.”, In other words, to complete the package transfer, the speed of the truck 1010 and passenger vehicle 1020 are adjusted so that both vehicles can be positioned next to each other. As a result, it is inherent when the passenger vehicle 1020 is in front of the truck 1010, the speed of passenger vehicle 1020 is reduced, and the speed of the truck 1010 is increased so that the both vehicles can be efficiently and safely positioned next to each other).
Regarding claim 13, Healey teaches (Currently amended) A delivery management system (Healey, at least one para. 0002; “Embodiments pertain to autonomous or semi-autonomous vehicles. Some embodiments relate to transferring of goods between autonomous road vehicles.”) comprising:
a user vehicle in which a user who purchases a product or is provided with a service rides (Healey, at least one para. 0022; “passenger vehicle 1020”;
a delivery vehicle configured to deliver the product or the service to the user (Healey, at least one para. 0022; “Truck 1010 is transferring a package 1030 to passenger vehicle 1020”); and
a delivery management apparatus connected to the user vehicle and the delivery vehicle, wherein the delivery management apparatus comprises (Healey, at least one para. 0048; “The above methods, systems, and machine readable mediums may be performed on computing devices integral to, or communicatively coupled with an autonomous vehicle.”):
at least one first memory storing first instructions (Healey, at least one para. 0051; “Machine (e.g., computer system) 10000 may include a hardware processor 10002 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 10004 and a static memory 10006, some or all of which may communicate with each other via an interlink (e.g., bus) 10008.”); and
at least one first processor configured to execute the instructions to (Healey, at least one para. 0051; “Machine (e.g., computer system) 10000 may include a hardware processor 10002 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 10004 and a static memory 10006, some or all of which may communicate with each other via an interlink (e.g., bus) 10008.”):
acquire position information of the user vehicle and position information of the delivery vehicle (Healey, at least one para. 0042; “Route planner 9050 may receive a request for a goods transfer and the location of a rust vehicle 9020 of the transfer. Route planner 9050 may receive the location and/or route of the second vehicle 9030 in the transfer request or else via updates of route and position sent by the second vehicle to the network-accessible autonomous goods transfer service 9010 and stored in the directory 9040. Route planner 9050 may plan a route that brings both vehicles 9020, 9030 within a predetermined proximity of each other at a predetermined time.”); and
generate speed adjustment information for bringing the user vehicle close to the delivery vehicle based on the position information of the user vehicle and the position information of the delivery vehicle, and transmit the generated speed adjustment information to the user vehicle and the delivery vehicle (Healey, at least one para. 0039; “At operation 6020, the machine learning algorithm may output either one or more next states (e.g., change lanes, slow down, speed up), which are then used to determine vehicle control signals to achieve those states, or may output control signals directly. At operation 6030 these signals are then fed to the vehicle control systems to achieve the desired state.”),
the user vehicle comprises:
at least one second memory storing second instructions (Healey, at least one para. 0048; “The above methods, systems, and machine readable mediums may be performed on computing devices integral to, or communicatively coupled with an autonomous vehicle.”); and
at least one second processor configured to execute the instructions to (Healey, at least one para. 0048; “The above methods, systems, and machine readable mediums may be performed on computing devices integral to, or communicatively coupled with an autonomous vehicle.”):
transmit position information of the user vehicle to the delivery management apparatus (Healey, at least one para. 0042; “Route planner 9050 may receive a request for a goods transfer and the location of a rust vehicle 9020 of the transfer. Route planner 9050 may receive the location and/or route of the second vehicle 9030 in the transfer request or else via updates of route and position sent by the second vehicle to the network-accessible autonomous goods transfer service 9010 and stored in the directory 9040. Route planner 9050 may plan a route that brings both vehicles 9020, 9030 within a predetermined proximity of each other at a predetermined time.”); and
receive the speed adjustment information and instruct the user vehicle to adjust a traveling speed of the user vehicle based on the received speed adjustment information (Healey, at least one para. 0039; “At operation 6020, the machine learning algorithm may output either one or more next states (e.g., change lanes, slow down, speed up), which are then used to determine vehicle control signals to achieve those states, or may output control signals directly. At operation 6030 these signals are then fed to the vehicle control systems to achieve the desired state.”), and
the delivery vehicle comprises:
at least one third memory storing third instructions (Healey, at least one para. 0048; “The above methods, systems, and machine readable mediums may be performed on computing devices integral to, or communicatively coupled with an autonomous vehicle.”); and
at least one third processor configured to execute the instructions to (Healey, at least one para. 0048; “The above methods, systems, and machine readable mediums may be performed on computing devices integral to, or communicatively coupled with an autonomous vehicle.”):
transmit position information of the delivery vehicle to the delivery management apparatus (Healey, at least one para. 0042; “Route planner 9050 may receive a request for a goods transfer and the location of a rust vehicle 9020 of the transfer. Route planner 9050 may receive the location and/or route of the second vehicle 9030 in the transfer request or else via updates of route and position sent by the second vehicle to the network-accessible autonomous goods transfer service 9010 and stored in the directory 9040. Route planner 9050 may plan a route that brings both vehicles 9020, 9030 within a predetermined proximity of each other at a predetermined time.”); and
receive the speed adjustment information and instruct the delivery vehicle to adjust a traveling speed of the delivery vehicle based on the received speed adjustment information (Healey, at least one para. 0039; “At operation 6020, the machine learning algorithm may output either one or more next states (e.g., change lanes, slow down, speed up), which are then used to determine vehicle control signals to achieve those states, or may output control signals directly. At operation 6030 these signals are then fed to the vehicle control systems to achieve the desired state.”).
Regarding claim 14, Healey teaches (Original) The delivery management system according to claim 13, wherein the speed adjustment information includes first speed adjustment information transmitted to the delivery vehicle and second speed adjustment information transmitted to the user vehicle (Healey, at least one para. 0039; “At operation 6020, the machine learning algorithm may output either one or more next states (e.g., change lanes, slow down, speed up), which are then used to determine vehicle control signals to achieve those states, or may output control signals directly. At operation 6030 these signals are then fed to the vehicle control systems to achieve the desired state.”).
Regarding claim 15, Healey teaches (Currently amended) The delivery management system according to claim 14, wherein the at least one first processor is configured to execute the first instructions to generate the first speed adjustment information and the second speed adjustment information (Healey, at least one para. 0039; “At operation 6020, the machine learning algorithm may output either one or more next states (e.g., change lanes, slow down, speed up), which are then used to determine vehicle control signals to achieve those states, or may output control signals directly. At operation 6030 these signals are then fed to the vehicle control systems to achieve the desired state.”) in accordance with a positional relationship between the user vehicle and the delivery vehicle (Healey, at least one para. 0028; “At operation 3035, the first autonomous vehicle may determine that the second autonomous vehicle is within a predetermined proximity of it. The predetermined proximity may be hard-coded as a distance at which the autonomous vehicles may reliably be expected to sense and/or communicate with each other using short range wireless technologies such that they may maneuver into position to transfer the item.”).
Regarding claim 16, Healey teaches (Currently amended) The delivery management system according to claim 13, wherein the at least one third processor is further configured to execute the third instructions to control the delivery vehicle to travel autonomously (Healey, at least one para. 0022; “Turning now to FIG. 1, a diagram 1000 of a transfer of a package from a truck 1010 to a passenger vehicle 1020 is shown according to some examples of the present disclosure. Both truck 1010 and passenger vehicle 1020 are autonomous vehicles that are in motion.”), and
the at least one third processor is configured to execute the third instructions to control autonomous driving of the delivery vehicle to adjust a traveling speed of the delivery vehicle (Healey, at least one para. 0039; “At operation 6020, the machine learning algorithm may output either one or more next states (e.g., change lanes, slow down, speed up), which are then used to determine vehicle control signals to achieve those states, or may output control signals directly. At operation 6030 these signals are then fed to the vehicle control systems to achieve the desired state.”).
Regarding claim 17, Healey teaches (Currently amended) The delivery management system according to claim 13, wherein the at least one second processor is further configured to execute the second instructions to control the user vehicle to travel autonomously (Healey, at least one para. 0022; “Turning now to FIG. 1, a diagram 1000 of a transfer of a package from a truck 1010 to a passenger vehicle 1020 is shown according to some examples of the present disclosure. Both truck 1010 and passenger vehicle 1020 are autonomous vehicles that are in motion.”), and
the at least one second processor is configured to execute the second instructions to control autonomous driving of the user vehicle to adjust a traveling speed of the user vehicle (Healey, at least one para. 0039; “At operation 6020, the machine learning algorithm may output either one or more next states (e.g., change lanes, slow down, speed up), which are then used to determine vehicle control signals to achieve those states, or may output control signals directly. At operation 6030 these signals are then fed to the vehicle control systems to achieve the desired state.”).
Regarding claim 22, Healey teaches (Original) A delivery management method (Healey, at least one para. 0039; “At”) comprising:
acquiring position information of a user vehicle in which a user who purchases a product or is provided with a service rides, and position information of a delivery vehicle configured to deliver the product or the service to the user (Healey, at least one para. 0042; “Route planner 9050 may receive a request for a goods transfer and the location of a rust vehicle 9020 of the transfer. Route planner 9050 may receive the location and/or route of the second vehicle 9030 in the transfer request or else via updates of route and position sent by the second vehicle to the network-accessible autonomous goods transfer service 9010 and stored in the directory 9040. Route planner 9050 may plan a route that brings both vehicles 9020, 9030 within a predetermined proximity of each other at a predetermined time.”); and
generating speed adjustment information for bringing the user vehicle close to the delivery vehicle based on the position information of the user vehicle and the position information of the delivery vehicle, and transmitting the generated speed adjustment information to the user vehicle and the delivery vehicle (Healey, at least one para. 0039; “At operation 6020, the machine learning algorithm may output either one or more next states (e.g., change lanes, slow down, speed up), which are then used to determine vehicle control signals to achieve those states, or may output control signals directly. At operation 6030 these signals are then fed to the vehicle control systems to achieve the desired state.”).
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
Claim(s) 6-8 and 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Healey (US 20220027852 A1) as applied to claim 1 above, and further in view of YUZAWA (US 20200117217 A1).
Regarding claim 6, The combination of Healey and YUZAWA teaches the limitations of claim 6, upon which the instant claim depends, as discussed supra. Further, Healey teaches (Currently amended) The delivery management apparatus according to claim 1, wherein the at least one processor is further configured to execute the instructions to:
receive request information from the user vehicle (Healey, at least one para. 0045; “Once a user orders the goods, the control 9090-B determines a route through communication with either the network-accessible autonomous goods transfer service (e.g., the route planner 9050) 9010 or with the other autonomous vehicle 9020 to plan a route to rendezvous with the other vehicle.”); and
specify a delivery vehicle that is able to deliver a product or a service to the user vehicle based on the received request information.
Healey does not explicitly teach that specify a delivery vehicle that is able to deliver a product or a service to the user vehicle based on the received request information.
YUZAWA, in the same field of endeavor (YUZAWA, at least one para. 0001; “The present invention relates to a delivery system, a delivery method, and a delivery processing apparatus (device) for delivering an item using a mobile body.”) teaches specify a delivery vehicle that is able to deliver a product or a service to the user vehicle based on the received request information (YUZAWA, at least one para. 0061; “In the example shown in FIG. 3, the weather is “pleasant”, the traffic condition is “normal”, the type of item 18 is “normal (not fragile)”, and the number of items 18 is “8”.”) and (YUZAWA, at least one para. 0061; “In this case, in consideration of the number (8) of items 18, the delivery vehicle 36 that has a relatively large maximum storage space is selected instead of the delivery drone 38 that has a relatively low maximum storage space.”).
Healey and YUZAWA are both considered to be analogous to the claimed invention because both of them are in the same field as delivering a package in between mobile bodies as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the order request of the Healey with teaching of YUZAWA. One of the ordinary skill in the art would have been motivated to make this modification so that the correct delivery vehicle can be selected based upon the order request details such as quantity of ordered items. For example, when a single package is ordered, a delivery drone can be used to deliver the package. When multiple packages are ordered, a delivery vehicle can be used to deliver the packages (YUZAWA; 0061).
Regarding claim 7, The combination of Healey and YUZAWA teaches the limitations of claim 6, upon which the instant claim depends, as discussed supra. Further, YUZAWA teaches (Currently amended) The delivery management apparatus according to claim 6, wherein the request information includes information specifying a product or a service desired by a user, and the at least one processor is configured to execute the instructions to specify a delivery vehicle that handles the product or the service desired by the user and is not delivering a product or a service to another user as the delivery vehicle that is able to deliver a product or a service to the user vehicle (YUZAWA, at least one para. 0049; “At step S8, the delivery mobile body 42 performs a negotiation with the customer vehicle 16 concerning the pick-up method for the product 18. As a result, the occupant of the customer vehicle 16 can pick up the product 18 from the delivery mobile body 42.”) and (YUZAWA, at least one para. 0073; “the delivery drone 38 may select an approach direction and/or position of the opening portion 44R (L) or 46 according to at least one of the content of the product 18, the traffic conditions on the road, and the weather conditions. By considering the change in the burden on the occupant or the difficulty of the pick-up according to the content of the product 18, the traffic conditions on the road, or the weather conditions, it is possible to perform a pick-up operation suitable for the situation.”, in other words, drone 38 is able to identify the correct position of the occupant who ordered the package through S8 and deliver the package to the correct occupant based on their position through 44R or 44L).
Regarding claim 8, The combination of Healey and YUZAWA teaches the limitations of claim 6, upon which the instant claim depends, as discussed supra. Further, YUZAWA teaches (Currently amended) The delivery management apparatus according to claim 6, wherein the at least one processor is configured to execute the instructions to specify the delivery vehicle that is able to deliver a product or a service to the user vehicle based on the request information (YUZAWA, at least one para. 0061; “In this case, in consideration of the number (8) of items 18, the delivery vehicle 36 that has a relatively large maximum storage space is selected instead of the delivery drone 38 that has a relatively low maximum storage space. ”), the position information of the user vehicle (YUZAWA, at least one para. 0063; “In the example shown in FIG. 4, the weather is “rainy”, the traffic condition is “normal”, the type of item 18 is “normal (not fragile)”, and the number of items 18 is “1”. As an example, if an attempt is being made to minimize the delivery time, it is preferable to select the delivery drone 38 of the shop “SP1”.”), and the position information of the delivery vehicle (YUZAWA, at least one para. 0065; “In the example shown in FIG. 5, the weather is “clear”, the traffic condition is “congested”, the type of item 18 is “normal (not fragile)”, and the number of items 18 is “1”. In this case, in consideration of the traffic condition being “congested”, the delivery drone 38 that has relatively high mobility is selected instead of the delivery vehicle 36 that has relatively low mobility. Furthermore, in consideration of the prediction result that the customer vehicle 16 will reach a position near a midway point between the branch points P1 and P2 when 30 minutes have passed, it is judged that the position of the customer vehicle 16 cannot be reached if the delivery drone 38 leaves from the shop “SP2”.”) and (YUZAWA, at least one para. 0066; “As a result, the delivery arrangement server 12 or the shop server 34 selects the delivery drone 38 assigned to the shop “SP1” as the delivery mobile body 42.”).
Regarding claim 10, The combination of Healey and YUZAWA teaches the limitations of claim 6, upon which the instant claim depends, as discussed supra. Further, YUZAWA teaches (Currently amended) The delivery management apparatus according to claim 6, wherein the at least one processor is configured to execute the instructions to estimate at least one of a time required for the specified delivery vehicle to deliver a product or a service to the user vehicle and a place where the specified delivery vehicle delivers a product or a service to the user vehicle (YUZAWA, at least one para. 0060; “For each of the situations shown FIGS. 3 to 6, a scheme (i.e. a delivery scheme) is determined for realizing the delivery of the product 18 within a limited time (e.g. within 30 minutes from the delivery request timing) and without interfering with the travelling of the customer vehicle 16.”).
Regarding claim 11, The combination of Healey and YUZAWA teaches the limitations of claim 1, upon which the instant claim depends, as discussed supra. Further, YUZAWA teaches (Currently amended) The delivery management apparatus according to claim 1, the at least one processor is further configured to execute the instructions to receive order information from the user vehicle (YUZAWA, at least one para. 0043; “At step S2, the communicating unit 20 of the delivery arrangement server 12 receives the request signal transmitted from the customer vehicle 16 at step S1. Then, the information acquiring unit 26 acquires the various types of information (e.g. the order information and the delivery destination information) included in the received request signal.”) and transmit the received order information to the delivery vehicle (YUZAWA, at least one para. 0046; “At step S5, the transmission processing unit 30 of the delivery arrangement server 12 makes a delivery instruction request to the shop 14b that has received the delivery request. Specifically, the transmission processing unit 30 transmits a request signal including the order information and the delivery destination information to the shop server 34 in the shop 14b. [0047] At step S6, the shop server 34 issues delivery instructions for the product 18 to the delivery mobile body 42, which is in a usable state in the shop 14b. Specifically, the shop server 34 transmits an instruction signal including delivery destination information to the delivery drone 38 in the shop 14b.”).
Regarding claim 12, The combination of Healey and YUZAWA teaches the limitations of claim 11, upon which the instant claim depends, as discussed supra. Further, YUZAWA teaches (Original) The delivery management apparatus according to claim 11, wherein when the user purchases a product (YUZAWA, at least one para. 0042; “At step S1, the customer vehicle 16 makes a delivery request for the product 18 in response to an ordering operation by an occupant (a manipulation of an in-vehicle device).”), the order information includes a product purchased by the user, the number of the products (YUZAWA, at least one para. 0054; “Examples of the content of the product 18 include size, weight, number of items, durability, value, and environmental resistance.”),
YUZAWA does not explicitly teach that and a delivery and receipt method used for delivery and receipt of the product.
Healey, in the same field of endeavor (Healey, at least one para. 0002; “Embodiments pertain to autonomous or semi-autonomous vehicles. Some embodiments relate to transferring of goods between autonomous road vehicles.”) teaches and a delivery and receipt method used for delivery and receipt of the product (Healey, at least one para. 0025; “Upon selecting the order now button, the user may order the goods and the vehicle of the user and the truck may maneuver themselves to transfer the package. To order, in some examples, the user may navigate to a website that is provided by the merchant operating truck 2015. The website may relay the order to the truck 2015, providing it with identification of the car that the customer is ordering from. In other examples, the ordering may be peer-to-peer over short range wireless technologies. The order may contain information on the car of the customer to enable the merchant to find the car to transfer the goods to (e.g., description, license plate, GPS coordinates, and the like). In other examples, a token is sent to the customer, and the customer may broadcast the token over short range wireless technologies. The truck 2015 then determines the customer's vehicle by locating the token.”).
YUZAWA and Healey are both considered to be analogous to the claimed invention because both of them are in the same field as delivering a package in between mobile bodies as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the order request of the YUZAWA with teaching of Healey. One of the ordinary skill in the art would have been motivated to make this modification so that the correct customer vehicle can be identified to complete the delivery of product and to confirm the completion of the delivery process (Healey; 0025)
Claim(s) 9 is rejected under 35 U.S.C. 103 as being unpatentable over Healey (US 20220027852 A1) and YUZAWA (US 20200117217 A1) as applied to claim 6 above, and further in view of Javidan (US 20210256472 A1).
Regarding claim 9, The combination of Healey and YUZAWA teaches the limitations of claim 6, upon which the instant claim depends, as discussed supra. Further, YUZAWA teaches (Currently amended) The delivery management apparatus according to claim 6, the at least one processor is configured to execute the instructions to specify the delivery vehicle that is able to deliver a product or a service to the user vehicle based on a destination of the user vehicle (YUZAWA, at least one para. 0049; “At step S8, the delivery mobile body 42 performs a negotiation with the customer vehicle 16 concerning the pick-up method for the product 18. As a result, the occupant of the customer vehicle 16 can pick up the product 18 from the delivery mobile body 42.”, wherein the package is delivered to the user vehicle)
Even though YUZAWA teaches about delivering a package to the user vehicle, YUZAWA does not explicitly teach that wherein the delivery vehicle is circulating along a predetermined circulating route, and the at least one processor is configured to execute the instructions to specify the delivery vehicle that is able to deliver a product or a service to the user vehicle based on a destination of the user vehicle and the circulating route of the delivery vehicle.
Javidan, in the same field of endeavor (Javidan, at least one para. 0019; “The techniques discussed herein may relate to item access and delivery systems including autonomous delivery vehicles used to provide access to and/or deliver physical items to recipients.”) teaches wherein the delivery vehicle is circulating along a predetermined circulating route, and the at least one processor is configured to execute the instructions to specify the delivery vehicle that is able to deliver a product or a service to the user vehicle based on a destination of the user vehicle and the circulating route of the delivery vehicle (Javidan, at least one para. 0102; “When dispatching a delivery vehicle 608 to a delivery location (e.g., directly to a recipient and/or to a shared delivery location along a predetermined route), the item delivery system 606 and/or the delivery vehicle 608 may transmit notifications to recipients. Some notifications may indicate that the delivery vehicle 608 has arrived at the delivery location and may provide the precise location (e.g., street address or GPS coordinates) of the delivery vehicle 608. Other notifications may indicate that the delivery vehicle 608 is on-route and within a time or distance threshold of arriving at the delivery location.”).
The combination of Healey, YUZAWA, and Javidan is considered to be analogous to the claimed invention because all of them are in the same field as delivering a package in between mobile bodies as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the delivering process of the YUZAWA with teaching of Javidan. One of the ordinary skill in the art would have been motivated to make this modification so that the recipient can order a product or service on the go and meet the delivery vehicle at predetermined location and time thus dynamically modifying the delivering process (Javidan; 0102-0104).
Conclusion
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/U.P.C./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665