DETAILED ACTION
Introduction
This Non-Final Office Action is in response to the application with serial number 18/935,356, filed on November 1, 2024.
Claims 1-20 are pending.
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.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 1-20 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Under Step 1 of the subject matter eligibility analysis, claims(s) 1-20 are all directed to one of the four statutory categories of invention. However, under step 2A, prong one, the claims recite a judicial exception: updating a status of an order (as evidenced by exemplary independent claim 1; “transmitting an update about a status of the order, wherein the status includes data indicating the estimated completion of the order”), an abstract idea. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). The limitations of exemplary claim 1 include: “receiving an order requesting a beverage;” “assigning the beverage to a chosen beverage robot;” “determining, based on [a] queue at the chosen beverage robot, an estimated completion of the order;” and “transmitting an update about a status of the order.” The steps are all steps for managing personal behavior related to the abstract idea of updating a status of an order that, when considered alone and in combination, are part of the abstract idea of updating a status of an order. The dependent claims further recite steps for managing personal behavior that are part of the abstract idea of updating a status of an order. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes providing estimated completion times of beverage orders for the purposes of delivery or pickup from a vendor.
Under step 2A, prong two, of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (no computer hardware is recited in independent claim 1; a system with processors and memories is recited in independent claim 8; and a computer readable medium is recited in independent claim 15). See MPEP §2106.04(d)[I]. The claims do recite a beverage robot, but the beverage robot is recited at a highly generalized lever with no particular structure. The beverage robot does not implement the steps of the claim, and amounts to a field of use. Therefore, the beverage robot does not amount to a particular machine in the claims. See MPEP §2106.05(b). The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). Under step 2B of the subject matter eligibility analysis, the claims do not integrate the abstract idea into a judicial exception. Referring to the additional elements provided in the analysis in step one, above, the generic computer hardware does not provide significantly more than the recited abstract idea. See MPEP §2106.05(f).
For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
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.
Claim(s) 1, 8, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210279666 A1 to Sun (hereinafter ‘SUN’) in view of US 20190056751 A1 to Ferguson et al. (hereinafter ‘FERGUSON’).
Claim 1
SUN discloses a method for operating a network having one or more beverage robots (see ¶[0020]; a beverage machine with a robotic arm), the method comprising: receiving an order requesting a beverage from the network, wherein the beverage is associated with a recipe requiring one or more ingredients (see abstract; receive order information and generate a corresponding order-recipe command list).
SUN does not specifically disclose, but FERGUSON discloses, assigning the beverage to a chosen beverage robot from the one or more beverage robots (see ¶[0081]; the best appropriate robot(s) in the fleet within the geographic region and typically closest to the service provider, is then assigned the task, and the provider of the service 204 then interacts with that robot 101 at their business (e.g., loading it with goods, if needed). The robot then travels to the customer 202 and the customer interacts with the robot to retrieve their goods or service (e.g., the goods ordered).
SUN further discloses wherein assigning the beverage to the chosen beverage robot causes the order to be added to a queue at the chosen beverage robot, (see ¶[0086]; the order is placed in a beverage making sequence) and wherein the queue is associated with beverages to be prepared by the chosen beverage robot (see ¶[0010]; deploy an ordering sequence and work simultaneously for realizing concurrent processing of a plurality of orders in the processing sequence);
determining, based on the queue at the chosen beverage robot, an estimated completion of the order (see ¶[0089]; the server is configured to feed back a current estimated completion time of the beverage to the user); and
transmitting an update about a status of the order, wherein the status includes data indicating the estimated completion of the order (see again ¶[0089]; the server is configured to feed back a current estimated completion time of the beverage to the user).
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). FERGUSON discloses a fleet of robot vehicles for food product preparation, including beverages, that includes assigning an order for a product to a best appropriate robot for preparation. It would have been obvious to include the assigning of an order to a robot as taught by FERGUSON in the system executing the method of SUN with the motivation to select a closest robot (see FERGUSON ¶[0081]).
Claim 8
SUN discloses a system comprising: one or more processors (see ¶[0011]; a master computer and controller); and one or more memories storing instructions that, when executed by the one or more processors (see ¶[0011]-[0013]; a master computer and processing devices), cause the system to perform a process for operating a network having one or more beverage robots (see ¶[0020]; a beverage machine with a robotic arm), the process comprising: receiving an order requesting a beverage from the network, wherein the beverage is associated with a recipe requiring one or more ingredients (see abstract; receive order information and generate a corresponding order-recipe command list).
SUN does not specifically disclose, but FERGUSON discloses, assigning the beverage to a chosen beverage robot from the one or more beverage robots (see ¶[0081]; the best appropriate robot(s) in the fleet within the geographic region and typically closest to the service provider, is then assigned the task, and the provider of the service 204 then interacts with that robot 101 at their business (e.g., loading it with goods, if needed). The robot then travels to the customer 202 and the customer interacts with the robot to retrieve their goods or service (e.g., the goods ordered).
SUN further discloses, wherein assigning the beverage to the chosen beverage robot causes the order to be added to a queue at the chosen beverage robot (see ¶[0086]; the order is placed in a beverage making sequence), and wherein the queue is associated with beverages to be prepared by the chosen beverage robot (see ¶[0010]; deploy an ordering sequence and work simultaneously for realizing concurrent processing of a plurality of orders in the processing sequence);
determining, based on the queue at the chosen beverage robot, an estimated completion of the order (see ¶[0089]; the server is configured to feed back a current estimated completion time of the beverage to the user); and
transmitting an update about a status of the order, wherein the status includes data indicating the estimated completion of the order (see again ¶[0089]; the server is configured to feed back a current estimated completion time of the beverage to the user).
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). FERGUSON discloses a fleet of robot vehicles for food product preparation, including beverages, that includes assigning an order for a product to a best appropriate robot for preparation. It would have been obvious to include the assigning of an order to a robot as taught by FERGUSON in the system executing the method of SUN with the motivation to select a closest robot (see FERGUSON ¶[0081]).
Claim 15
SUN discloses a non-transitory computer-readable medium storing instructions that, when executed by a computing system (see ¶[0011]; a master computer and controller), cause the computing system to perform operations (see ¶[0011]-[0013]; a master computer and processing devices) for operating a network having one or more beverage robots (see ¶[0020]; a beverage machine with a robotic arm), the operations comprising: receiving an order requesting a beverage from the network, wherein the beverage is associated with a recipe requiring one or more ingredients (see abstract; receive order information and generate a corresponding order-recipe command list).
SUN does not specifically disclose, but FERGUSON discloses, assigning the beverage to a chosen beverage robot from the one or more beverage robots (see ¶[0081]; the best appropriate robot(s) in the fleet within the geographic region and typically closest to the service provider, is then assigned the task, and the provider of the service 204 then interacts with that robot 101 at their business (e.g., loading it with goods, if needed). The robot then travels to the customer 202 and the customer interacts with the robot to retrieve their goods or service (e.g., the goods ordered).
SUN further discloses, wherein assigning the beverage to the chosen beverage robot causes the order to be added to a queue at the chosen beverage robot (see ¶[0086]; the order is placed in a beverage making sequence), and wherein the queue is associated with beverages to be prepared by the chosen beverage robot (see ¶[0010]; deploy an ordering sequence and work simultaneously for realizing concurrent processing of a plurality of orders in the processing sequence);
determining, based on the queue at the chosen beverage robot, an estimated completion of the order (see ¶[0089]; the server is configured to feed back a current estimated completion time of the beverage to the user); and
transmitting an update about a status of the order, wherein the status includes data indicating the estimated completion of the order (see again ¶[0089]; the server is configured to feed back a current estimated completion time of the beverage to the user).
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). FERGUSON discloses a fleet of robot vehicles for food product preparation, including beverages, that includes assigning an order for a product to a best appropriate robot for preparation. It would have been obvious to include the assigning of an order to a robot as taught by FERGUSON in the system executing the method of SUN with the motivation to select a closest robot (see FERGUSON ¶[0081]).
Claim(s) 2, 3, 9, 10, 16, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210279666 A1 to SUN in view of US 20190056751 A1 to FERGUSON et al. as applied to claim 1 above, and further in view of US 20180357707 A1 to Lee (hereinafter ‘LEE’).
Claim 2
The combination of SUN and FERGUSON discloses the method as set forth in claim 1.
The combination of SUN and FERGUSON does not specifically disclose, but LEE discloses, further comprising: receiving, from a client computing device associated with the order, geographic information related to a current location of a customer (see ¶[0011]; calculate an expected arrival time of the user based on a current position of the user);
determining, based on the geographic information, an estimated time of arrival of the customer at a vending location associated with the network (see again ¶[0011]; calculate an expected arrival time of the user based on a current position of the user); and
in response to a determination that the estimated time of arrival is more than a predetermined period after the estimated completion of the order, adjusting a placement of the order in the queue to synchronize the estimated completion of the order with the estimated time of arrival (see ¶[0157]-[0159] and Fig. 8; when the expected arrival time of the user is considered, it may be determined that the automatic repeating of the order is too early. The event time is delayed so that the time the drink is provided is later. Examiner Note: delaying the event time effectively adjusts the placement of the order in the queue by changing the time the drink is provided).
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). LEE discloses automatic order processing based on user behavior patterns, where a determination that an order is too early results is delaying the order. It would have been obvious to delay orders based on estimated arrival time as taught by LEE in the system executing the method of SUN with the motivation to prevent ice melting or a drink cooling (see LEE ¶[0159]).
Claim 3
The combination of SUN and FERGUSON discloses the method as set forth in claim 1.
The combination of SUN and FERGUSON does not specifically disclose, but LEE discloses, further comprising: receiving, from a client computing device associated with the order, geographic information related to a current location of a customer (see ¶[0011]; calculate an expected arrival time of the user based on a current position of the user);
determining, based on the geographic information, an estimated time of arrival of the customer at a vending location associated with the network (see again ¶[0011]; calculate an expected arrival time of the user based on a current position of the user); and
in response to a determination that the estimated time of arrival is more than a predetermined period after the estimated completion of the order, pausing production of the order until a predetermined time before the estimated time of arrival (see ¶[0157]-[0159] and Fig. 8; when the expected arrival time of the user is considered, it may be determined that the automatic repeating of the order is too early. The event time is delayed so that the time the drink is provided is later).
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). LEE discloses automatic order processing based on user behavior patterns, where a determination that an order is too early results is delaying the order. It would have been obvious to delay orders based on estimated arrival time as taught by LEE in the system executing the method of SUN with the motivation to prevent ice melting or a drink cooling (see LEE ¶[0159]).
Claim 9
The combination of SUN and FERGUSON discloses the system as set forth in claim 8.
The combination of SUN and FERGUSON does not specifically disclose, but LEE discloses, wherein the process further comprises: receiving, from a client computing device associated with the order, geographic information related to a current location of a customer (see ¶[0011]; calculate an expected arrival time of the user based on a current position of the user);
determining, based on the geographic information, an estimated time of arrival of the customer at a vending location associated with the network (see again ¶[0011]; calculate an expected arrival time of the user based on a current position of the user); and
in response to a determination that the estimated time of arrival is more than a predetermined period after the estimated completion of the order, adjusting a placement of the order in the queue to synchronize the estimated completion of the order with the estimated time of arrival (see ¶[0157]-[0159] and Fig. 8; when the expected arrival time of the user is considered, it may be determined that the automatic repeating of the order is too early. The event time is delayed so that the time the drink is provided is later. Examiner Note: delaying the event time effectively adjusts the placement of the order in the queue by changing the time the drink is provided).
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). LEE discloses automatic order processing based on user behavior patterns, where a determination that an order is too early results is delaying the order. It would have been obvious to delay orders based on estimated arrival time as taught by LEE in the system executing the method of SUN with the motivation to prevent ice melting or a drink cooling (see LEE ¶[0159]).
Claim 10
The combination of SUN and FERGUSON discloses the system as set forth in claim 8.
The combination of SUN and FERGUSON does not specifically disclose, but LEE discloses, wherein the process further comprises: receiving, from a client computing device associated with the order, geographic information related to a current location of a customer (see ¶[0011]; calculate an expected arrival time of the user based on a current position of the user);
determining, based on the geographic information, an estimated time of arrival of the customer at a vending location associated with the network (see again ¶[0011]; calculate an expected arrival time of the user based on a current position of the user); and
in response to a determination that the estimated time of arrival is more than a predetermined period after the estimated completion of the order, pausing production of the order until a predetermined time before the estimated time of arrival (see ¶[0157]-[0159] and Fig. 8; when the expected arrival time of the user is considered, it may be determined that the automatic repeating of the order is too early. The event time is delayed so that the time the drink is provided is later).
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). LEE discloses automatic order processing based on user behavior patterns, where a determination that an order is too early results is delaying the order. It would have been obvious to delay orders based on estimated arrival time as taught by LEE in the system executing the method of SUN with the motivation to prevent ice melting or a drink cooling (see LEE ¶[0159]).
Claim 16
The combination of SUN and FEGUSON discloses the non-transitory computer-readable medium as set forth in claim 15.
The combination of SUN and FERGUSON does not specifically disclose, but LEE discloses, wherein the operations further comprise: receiving, from a client computing device associated with the order, geographic information related to a current location of a customer (see ¶[0011]; calculate an expected arrival time of the user based on a current position of the user);
determining, based on the geographic information, an estimated time of arrival of the customer at a vending location associated with the network (see again ¶[0011]; calculate an expected arrival time of the user based on a current position of the user); and
in response to a determination that the estimated time of arrival is more than a predetermined period after the estimated completion of the order, adjusting a placement of the order in the queue to synchronize the estimated completion of the order with the estimated time of arrival (see ¶[0157]-[0159] and Fig. 8; when the expected arrival time of the user is considered, it may be determined that the automatic repeating of the order is too early. The event time is delayed so that the time the drink is provided is later. Examiner Note: delaying the event time effectively adjusts the placement of the order in the queue by changing the time the drink is provided).
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). LEE discloses automatic order processing based on user behavior patterns, where a determination that an order is too early results is delaying the order. It would have been obvious to delay orders based on estimated arrival time as taught by LEE in the system executing the method of SUN with the motivation to prevent ice melting or a drink cooling (see LEE ¶[0159]).
Claim 17
The combination of SUN and FEGUSON discloses the non-transitory computer-readable medium as set forth in claim 15.
The combination of SUN and FERGUSON does not specifically disclose, but LEE discloses, wherein the operations further comprise: receiving, from a client computing device associated with the order, geographic information related to a current location of a customer (see ¶[0011]; calculate an expected arrival time of the user based on a current position of the user);
determining, based on the geographic information, an estimated time of arrival of the customer at a vending location associated with the network (see again ¶[0011]; calculate an expected arrival time of the user based on a current position of the user); and
in response to a determination that the estimated time of arrival is more than a predetermined period after the estimated completion of the order, pausing production of the order until a predetermined time before the estimated time of arrival (see ¶[0157]-[0159] and Fig. 8; when the expected arrival time of the user is considered, it may be determined that the automatic repeating of the order is too early. The event time is delayed so that the time the drink is provided is later).
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). LEE discloses automatic order processing based on user behavior patterns, where a determination that an order is too early results is delaying the order. It would have been obvious to delay orders based on estimated arrival time as taught by LEE in the system executing the method of SUN with the motivation to prevent ice melting or a drink cooling (see LEE ¶[0159]).
Claim(s) 4, 5, 11, 12, 18, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210279666 A1 to SUN in view of US 20190056751 A1 to FERGUSON et al. as applied to claim 1 above, and further in view of US 20180132507 A1 to Siegel et al. (hereinafter ‘SIEGEL’).
Claim 4
The combination of SUN and FERGUSON discloses the method as set forth in claim 1.
SUN does not specifically disclose, but FERGUSON discloses, further comprising: determining a delivery location associated with the order based on information received with the order (see ¶[0083]; in some embodiments, the robot may be requested for a pick-up of an item (e.g., a document) with the intent of delivery to another party. In this scenario, the fleet management module would assign the robot to arrive at a given location, assign a securable compartment for receipt of the item, confirm receipt from the first party to the fleet management module, then proceed to the second location where an informed receiving party would recover the item from the robot using an appropriate PIN or other recognition code to gain access to the secure compartment);
determining a reception time associated based on an estimated arrival time of a delivery person and a delivery time associated with traveling from a vending location associated with the network and to the delivery location (see ¶[0151 and [0154]; a robot vehicle is assigned an order with a delivery estimate for the food order).
The combination of SUN and FERGUSON does not specifically disclose, but SIEGEL discloses, adjusting the recipe based on a difference between the estimated completion of the order and the reception time (see ¶[0031]-[0033]; by having an understanding of the amount of ice and the quantity of water, and the temperature of the water, it may be possible to understand at a point in time the amount of ice melted during the process. With this knowledge, the amounts of water can be metered to maintain the 5:1 ratio spec.).
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). FERGUSON discloses a fleet of robot vehicles for delivery of food and beverages that includes an estimated time of arrival for delivery. It would have been obvious to include the delivery time estimate as taught by FERGUSON in the system executing the method of SUN with the motivation to communicate expected delivery of food to a customer.
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). SIEGEL discloses an intelligent beverage mixing appliance that maintains the correct ratio of components by metering ice and water based on an understanding of how much ice will melt. It would have been obvious for one of ordinary skill in the art at the time of invention to include the metering of ice and water as taught by SIEGEL in the system executing the method of SUN with the motivation to maintain the correct ratio of components in a beverage.
Claim 5
The combination of SUN, FERGUSON, and SIEGEL discloses the method as set forth in claim 4.
SUN does not specifically disclose, but SIEGEL discloses, wherein adjusting the recipe includes: estimating a volume of water from melting ice between the estimated completion of the order and the reception time, wherein the volume from melting ice will dilute the beverage (see ¶[0031]-[0033]; by having an understanding of the amount of ice and the quantity of water, and the temperature of the water, it may be possible to understand at a point in time the amount of ice melted during the process); and
adjusting an amount of water in the recipe to account for the volume of water from melting ice (see again ¶[0031]-[0033]; by having an understanding of the amount of ice and the quantity of water, and the temperature of the water, it may be possible to understand at a point in time the amount of ice melted during the process. With this knowledge, the amounts of water can be metered to maintain the 5:1 ratio spec.).
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). SIEGEL discloses an intelligent beverage mixing appliance that maintains the correct ratio of components by metering ice and water based on an understanding of how much ice will melt. It would have been obvious for one of ordinary skill in the art at the time of invention to include the metering of ice and water as taught by SIEGEL in the system executing the method of SUN with the motivation to maintain the correct ratio of components in a beverage.
Claim 11
The combination of SUN and FERGUSON discloses the system as set forth in claim 8.
SUN does not specifically disclose, but FERGUSON discloses, wherein the process further comprises: determining a delivery location associated with the order based on information received with the order (see ¶[0083]; in some embodiments, the robot may be requested for a pick-up of an item (e.g., a document) with the intent of delivery to another party. In this scenario, the fleet management module would assign the robot to arrive at a given location, assign a securable compartment for receipt of the item, confirm receipt from the first party to the fleet management module, then proceed to the second location where an informed receiving party would recover the item from the robot using an appropriate PIN or other recognition code to gain access to the secure compartment); and
determining a reception time associated based on an estimated arrival time of a delivery person and a delivery time associated with traveling from a vending location associated with the network and to the delivery location (see ¶[0151 and [0154]; a robot vehicle is assigned an order with a delivery estimate for the food order).
The combination of SUN and FERGUSON does not specifically disclose, but SIEGEL discloses, adjusting the recipe based on a difference between the estimated completion of the order and the reception time (see ¶[0031]-[0033]; by having an understanding of the amount of ice and the quantity of water, and the temperature of the water, it may be possible to understand at a point in time the amount of ice melted during the process. With this knowledge, the amounts of water can be metered to maintain the 5:1 ratio spec.).
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). FERGUSON discloses a fleet of robot vehicles for delivery of food and beverages that includes an estimated time of arrival for delivery. It would have been obvious to include the delivery time estimate as taught by FERGUSON in the system executing the method of SUN with the motivation to communicate expected delivery of food to a customer.
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). SIEGEL discloses an intelligent beverage mixing appliance that maintains the correct ratio of components by metering ice and water based on an understanding of how much ice will melt. It would have been obvious for one of ordinary skill in the art at the time of invention to include the metering of ice and water as taught by SIEGEL in the system executing the method of SUN with the motivation to maintain the correct ratio of components in a beverage.
Claim 12
The combination of SUN, FERGUSON, and SIEGEL discloses the system as set forth in claim 11.
SUN does not specifically disclose, but SIEGEL discloses, wherein adjusting the recipe includes: estimating a volume of water from melting ice between the estimated completion of the order and the reception time, wherein the volume from melting ice will dilute the beverage (see ¶[0031]-[0033]; by having an understanding of the amount of ice and the quantity of water, and the temperature of the water, it may be possible to understand at a point in time the amount of ice melted during the process); and
adjusting an amount of water in the recipe to account for the volume of water from melting ice (see again ¶[0031]-[0033]; by having an understanding of the amount of ice and the quantity of water, and the temperature of the water, it may be possible to understand at a point in time the amount of ice melted during the process. With this knowledge, the amounts of water can be metered to maintain the 5:1 ratio spec.).
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). SIEGEL discloses an intelligent beverage mixing appliance that maintains the correct ratio of components by metering ice and water based on an understanding of how much ice will melt. It would have been obvious for one of ordinary skill in the art at the time of invention to include the metering of ice and water as taught by SIEGEL in the system executing the method of SUN with the motivation to maintain the correct ratio of components in a beverage.
Claim 18
The combination of SUN and FEGUSON discloses the non-transitory computer-readable medium as set forth in claim 15.
SUN does not specifically disclose, but FERGUSON discloses, wherein the operations further comprise: determining a delivery location associated with the order based on information received with the order (see ¶[0083]; in some embodiments, the robot may be requested for a pick-up of an item (e.g., a document) with the intent of delivery to another party. In this scenario, the fleet management module would assign the robot to arrive at a given location, assign a securable compartment for receipt of the item, confirm receipt from the first party to the fleet management module, then proceed to the second location where an informed receiving party would recover the item from the robot using an appropriate PIN or other recognition code to gain access to the secure compartment);
determining a reception time associated based on an estimated arrival time of a delivery person and a delivery time associated with traveling from a vending location associated with the network and to the delivery location (see ¶[0151 and [0154]; a robot vehicle is assigned an order with a delivery estimate for the food order).
The combination of SUN and FERGUSON does not specifically disclose, but SIEGEL discloses, adjusting the recipe based on a difference between the estimated completion of the order and the reception time (see ¶[0031]-[0033]; by having an understanding of the amount of ice and the quantity of water, and the temperature of the water, it may be possible to understand at a point in time the amount of ice melted during the process. With this knowledge, the amounts of water can be metered to maintain the 5:1 ratio spec.).
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). FERGUSON discloses a fleet of robot vehicles for delivery of food and beverages that includes an estimated time of arrival for delivery. It would have been obvious to include the delivery time estimate as taught by FERGUSON in the system executing the method of SUN with the motivation to communicate expected delivery of food to a customer.
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). SIEGEL discloses an intelligent beverage mixing appliance that maintains the correct ratio of components by metering ice and water based on an understanding of how much ice will melt. It would have been obvious for one of ordinary skill in the art at the time of invention to include the metering of ice and water as taught by SIEGEL in the system executing the method of SUN with the motivation to maintain the correct ratio of components in a beverage.
Claim 19
The combination of SUN, FERGUSON, and SIEGEL discloses the non-transitory computer-readable medium as set forth in claim 18.
SUN does not specifically disclose, but SIEGEL discloses, wherein adjusting the recipe includes: estimating a volume of water from melting ice between the estimated completion of the order and the reception time, wherein the volume from melting ice will dilute the beverage (see ¶[0031]-[0033]; by having an understanding of the amount of ice and the quantity of water, and the temperature of the water, it may be possible to understand at a point in time the amount of ice melted during the process); and
adjusting an amount of water in the recipe to account for the volume of water from melting ice (see again ¶[0031]-[0033]; by having an understanding of the amount of ice and the quantity of water, and the temperature of the water, it may be possible to understand at a point in time the amount of ice melted during the process. With this knowledge, the amounts of water can be metered to maintain the 5:1 ratio spec.).
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). SIEGEL discloses an intelligent beverage mixing appliance that maintains the correct ratio of components by metering ice and water based on an understanding of how much ice will melt. It would have been obvious for one of ordinary skill in the art at the time of invention to include the metering of ice and water as taught by SIEGEL in the system executing the method of SUN with the motivation to maintain the correct ratio of components in a beverage.
Claim(s) 6, 13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210279666 A1 to SUN in view of US 20190056751 A1 to FERGUSON et al. as applied to claim 1 above, and further in view of US 20230237428 A1 to Francis (hereinafter ‘FRANCIS’) and US 20180132507 A1 to SIEGEL et al.
Claim 6
The combination of SUN and FERGUSON discloses the method as set forth in claim 1.
The combination of SUN and FERGUSON does not specifically disclose, but FRANCIS discloses, further comprising: receiving, from a client computing device associated with the order, geographic information related to a current location of a customer (see ¶[0703]; the computing system may determine an estimated pickup time for a customer order. For example, the computing system may determine an estimated pickup time based on a current customer location relative to the store);
determining, based on the geographic information, an estimated time of arrival of the customer at a vending location associated with the network (see again ¶[0703]; the computing system may determine an estimated pickup time for a customer order. For example, the computing system may determine an estimated pickup time based on a current customer location relative to the store).
The combination of SUN, FERGUSON, and FRANCIS does not specifically disclose, but SIEGEL discloses, adjusting a ratio of the one or more ingredients in the recipe based on a difference between the estimated completion of the order and the estimated time of arrival (see again ¶[0031]-[0033]; by having an understanding of the amount of ice and the quantity of water, and the temperature of the water, it may be possible to understand at a point in time the amount of ice melted during the process. With this knowledge, the amounts of water can be metered to maintain the 5:1 ratio spec.).
SUN discloses a system with a beverage making robot. FRANCIS discloses order fulfillment using routes, where an estimated pickup time for an order is determined based on a current customer location. It would have been obvious to determine estimated pickup time for an order as taught by FRANCIS in the system executing the method of SUN with the motivation to manage a beverage making business that takes orders.
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). FRANCIS discloses order fulfillment using routes, where an estimated pickup time for an order is determined based on a current customer location. SIEGEL discloses an intelligent beverage mixing appliance that maintains the correct ratio of components by metering ice and water based on an understanding of how much ice will melt. It would have been obvious for one of ordinary skill in the art at the time of invention to include the metering of ice and water as taught by SIEGEL in the system executing the method of SUN and FRANCIS with the motivation to maintain the correct ratio of components in a beverage.
Claim 13
The combination of SUN and FERGUSON discloses the system as set forth in claim 8.
The combination of SUN and FERGUSON does not specifically disclose, but FRANCIS discloses, wherein the process further comprises: receiving, from a client computing device associated with the order, geographic information related to a current location of a customer (see ¶[0703]; the computing system may determine an estimated pickup time for a customer order. For example, the computing system may determine an estimated pickup time based on a current customer location relative to the store);
determining, based on the geographic information, an estimated time of arrival of the customer at a vending location associated with the network (see again ¶[0703]; the computing system may determine an estimated pickup time for a customer order. For example, the computing system may determine an estimated pickup time based on a current customer location relative to the store).
The combination of SUN, FERGUSON, and FRANCIS does not specifically disclose, but SIEGEL discloses, adjusting a ratio of the one or more ingredients in the recipe based on a difference between the estimated completion of the order and the estimated time of arrival (see again ¶[0031]-[0033]; by having an understanding of the amount of ice and the quantity of water, and the temperature of the water, it may be possible to understand at a point in time the amount of ice melted during the process. With this knowledge, the amounts of water can be metered to maintain the 5:1 ratio spec.).
SUN discloses a system with a beverage making robot. FRANCIS discloses order fulfillment using routes, where an estimated pickup time for an order is determined based on a current customer location. It would have been obvious to determine estimated pickup time for an order as taught by FRANCIS in the system executing the method of SUN with the motivation to manage a beverage making business that takes orders.
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). FRANCIS discloses order fulfillment using routes, where an estimated pickup time for an order is determined based on a current customer location. SIEGEL discloses an intelligent beverage mixing appliance that maintains the correct ratio of components by metering ice and water based on an understanding of how much ice will melt. It would have been obvious for one of ordinary skill in the art at the time of invention to include the metering of ice and water as taught by SIEGEL in the system executing the method of SUN and FRANCIS with the motivation to maintain the correct ratio of components in a beverage.
Claim 20
The combination of SUN and FEGUSON discloses the non-transitory computer-readable medium as set forth in claim 15.
The combination of SUN and FERGUSON does not specifically disclose, but FRANCIS discloses, wherein the operations further comprise: receiving, from a client computing device associated with the order, geographic information related to a current location of a customer (see ¶[0703]; the computing system may determine an estimated pickup time for a customer order. For example, the computing system may determine an estimated pickup time based on a current customer location relative to the store);
determining, based on the geographic information, an estimated time of arrival of the customer at a vending location associated with the network (see again ¶[0703]; the computing system may determine an estimated pickup time for a customer order. For example, the computing system may determine an estimated pickup time based on a current customer location relative to the store).
The combination of SUN, FERGUSON, and FRANCIS does not specifically disclose, but SIEGEL discloses, adjusting a ratio of the one or more ingredients in the recipe based on a difference between the estimated completion of the order and the estimated time of arrival (see again ¶[0031]-[0033]; by having an understanding of the amount of ice and the quantity of water, and the temperature of the water, it may be possible to understand at a point in time the amount of ice melted during the process. With this knowledge, the amounts of water can be metered to maintain the 5:1 ratio spec.).
SUN discloses a system with a beverage making robot. FRANCIS discloses order fulfillment using routes, where an estimated pickup time for an order is determined based on a current customer location. It would have been obvious to determine estimated pickup time for an order as taught by FRANCIS in the system executing the method of SUN with the motivation to manage a beverage making business that takes orders.
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). FRANCIS discloses order fulfillment using routes, where an estimated pickup time for an order is determined based on a current customer location. SIEGEL discloses an intelligent beverage mixing appliance that maintains the correct ratio of components by metering ice and water based on an understanding of how much ice will melt. It would have been obvious for one of ordinary skill in the art at the time of invention to include the metering of ice and water as taught by SIEGEL in the system executing the method of SUN and FRANCIS with the motivation to maintain the correct ratio of components in a beverage.
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210279666 A1 to SUN in view of US 20190056751 A1 to FERGUSON et al. as applied to claim 1 above, and further in view of US 20200273089 A1 to Siefken et al. (hereinafter ‘SIEFKEN’) and US 20230191615 A1 to Creusot et al. (hereinafter ‘CREUSOT’).
Claim 7
The combination of SUN and FERGUSON discloses the method as set forth in claim 1.
The combination of SUN and FERGUSON does not specifically disclose, but SIEFKEN discloses, wherein the update is a first update (see ¶[0092]; send the order update to the order submission API 123 [S129] which in turn updates the order [S131]), and wherein the method further comprises: determining an availability of the one or more ingredients at the chosen beverage robot (see ¶[0094] and Fig. 4; editing an order (S143) may occur when a customer voluntarily chooses to modify the order on the customer's own accord or may occur when the store 109 informs the customer that a particular item is unavailable or that an expected wait time may exceed a threshold).
The combination of SUN and FERGUSON does not specifically disclose, but CREUSOT discloses, adjusting a placement of the order in the queue based on the availability of the one or more ingredients (see ¶[0022]; a robot can fill a container and dynamically infer which containers to skip based on available ingredient capacity and ingredient availability).
SUN further discloses checking an updated status of the order at the chosen beverage robot, wherein checking the updated status comprises identifying a remaining queue at the chosen beverage robot ahead of the order and determining an updated estimated completion of the order based on the remaining queue (see again ¶[0089]; the server is configured to feed back a current estimated completion time of the beverage to the user).
The combination of SUN and FERGUSON does not specifically disclose, but SIEFKEN discloses transmitting a second update about the order, wherein the second update comprises information related to the remaining queue and the estimated completion of the order (see again ¶[0010] & [0094] and Fig. 4; editing an order (S143) may occur when a customer voluntarily chooses to modify the order on the customer's own accord or may occur when the store 109 informs the customer that a particular item is unavailable or that an expected wait time may exceed a threshold); and
receiving a response from a customer, the response indicating an approval to complete the order at the chosen beverage robot (see again ¶[0094] and Fig. 4; after receiving notification that the order will take 15 minutes S147, the confirmation message is repeated S151).
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). SIEFKEN discloses eatery ordering that informs customers regarding unavailability of items and allows customers to confirm orders. It would have been obvious to include the order availability and confirmation as taught by SIEFKEN in the system executing the method of SUN with the motivation to process and sequence beverage orders.
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). CREUSOT discloses robots inserting ingredients into food containers dynamically and determining to skip containers based on available ingredients. It would have been obvious to include the skipping of containers as taught by CREUSOT in the system executing the method of SUN with the motivation to optimize scheduling and picking (see CREUSOT ¶[0022]).
Claim 14
The combination of SUN and FERGUSON discloses the system as set forth in claim 8.
The combination of SUN and FERGUSON does not specifically disclose, but SIEFKEN discloses, wherein the update is a first update (see ¶[0092]; send the order update to the order submission API 123 [S129] which in turn updates the order [S131]), and wherein the process further comprises: determining an availability of the one or more ingredients at the chosen beverage robot (see ¶[0094] and Fig. 4; editing an order (S143) may occur when a customer voluntarily chooses to modify the order on the customer's own accord or may occur when the store 109 informs the customer that a particular item is unavailable or that an expected wait time may exceed a threshold).
The combination of SUN and FERGUSON does not specifically disclose, but CREUSOT discloses, adjusting a placement of the order in the queue based on the availability of the one or more ingredients (see ¶[0022]; a robot can fill a container and dynamically infer which containers to skip based on available ingredient capacity and ingredient availability).
SUN further discloses checking an updated status of the order at the chosen beverage robot, wherein checking the updated status comprises identifying a remaining queue at the chosen beverage robot ahead of the order and determining an updated estimated completion of the order based on the remaining queue (see again ¶[0089]; the server is configured to feed back a current estimated completion time of the beverage to the user).
The combination of SUN and FERGUSON does not specifically disclose, but SIEFKEN discloses, transmitting a second update about the order, wherein the second update comprises information related to the remaining queue and the estimated completion of the order (see again ¶[0010] & [0094] and Fig. 4; editing an order (S143) may occur when a customer voluntarily chooses to modify the order on the customer's own accord or may occur when the store 109 informs the customer that a particular item is unavailable or that an expected wait time may exceed a threshold); and
receiving a response from a customer, the response indicating an approval to complete the order at the chosen beverage robot (see again ¶[0094] and Fig. 4; after receiving notification that the order will take 15 minutes S147, the confirmation message is repeated S151).
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). SIEFKEN discloses eatery ordering that informs customers regarding unavailability of items and allows customers to confirm orders. It would have been obvious to include the order availability and confirmation as taught by SIEFKEN in the system executing the method of SUN with the motivation to process and sequence beverage orders.
SUN discloses a beverage making robot that receives beverage orders and adds the orders to an ordering sequence (see abstract). CREUSOT discloses robots inserting ingredients into food containers dynamically and determining to skip containers based on available ingredients. It would have been obvious to include the skipping of containers as taught by CREUSOT in the system executing the method of SUN with the motivation to optimize scheduling and picking (see CREUSOT ¶[0022]).
Conclusion
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/RICHARD N SCHEUNEMANN/ Primary Examiner, Art Unit 3624
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