DETAILED ACTION
This is a Non-Final Office Action in response to Applicant Arguments on 12/19/2025. Claims 1-20 are pending. The effective filling date is 05/29/2020.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/19/2025 has been entered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1- Claims 1-9 are directed to a system, which is subject eligible material. Claims 10-20 are directed to a method, which is subject eligible material. Claims 1-20 pass step 1.
Step 2A, Prong 1-Independent claim 1, and similarly claim 10, recites:
A food ordering system and method (ordering food is a mental process, as it practically can be performed in the human mind, because a person may look at a menu and decide what food to order, see MPEP 2106.04(a)(2)(III)(A) a claim to identifying head shape and applying hair designs, which is a process that can be practically performed in the human mind, In re Brown, 645 Fed. App'x 1014, 1016-17 (Fed. Cir. 2016) (non-precedential)), comprising:
an application server configured to receive order information and a physical location identifier from a user via an application running at user mobile device or a web interface accessed via the user mobile device, wherein the order information includes one or more items to be delivered to the user and the physical location identifier is associated with a physical location within a dining area of a food service establishment (receiving information about an order is a process that a person relays their order for delivery, which is a mental process of relaying information, under MPEP 2106.04(a)(2)(III)(A), and the process of receiving orders for delivery is a processing sales activities under MPEP 2106.04(a)(2)(II));
an order management system coupled with the application server (additional elements analyzed in Step 2A, Prong 2 and 2B) and configured to receive order information and the physical location identifier and provide the order information to a kitchen or service area for production, wherein the order management system uses machine learning algorithms that analyze logged historical data to continuously update item preparation times, and wherein the order management system determines an estimated completion time for the one or more items and, when the order information includes two or more items (receiving information about an order is a process that a person relays their order for delivery, which is a mental process of relaying information, under MPEP 2106.04(a)(2)(III)(A), and the process of receiving orders for delivery is a processing sales activities under MPEP 2106.04(a)(2)(II)), automatically assigns each item to one of two or more work queues based on item type, current queue capacity, and real-time kitchen conditions, and wherein the estimated completion time is dynamically calculated in real-time based at least in part on the work queue of the two or more work queues associated with each of the one or more items (making a determination is a mental process of analyzing information under MPEP 2106.04(a)(2)(III), and the determination is for the purposes of management of sales activities under MPEP 2106.05(a)(2)(II)),
the updated item preparation times for the work queue of the two or more work queues, a staffing level, an identification of personnel producing the one or more items, an equipment status of equipment used in producing the one or more items, and a quantity of other items that are in the associated work queue of the two or more work queues (historical data, the level of staffing and the identification of items are all processes used in the management of a business to maintain a steady queue based on the sales activities of the day, see MPEP 2106.04(a)(2)(II)), wherein the machine learning algorithms automatically adjust he estimated completion times based on real-time changes in the updated item preparation times, the staffing level, the identifies personnel, the equipment status, and queue conditions (algorithms are a mathematical concept, see MPEP 2106.04(a)(2)(I)); and
a payment component coupled with the order management system and the application server (additional elements analyzed under Step 2A, Prong 2 and 2B) and configured to electronically process payment for the one or more items after delivery to the user (processing payment information is an act of processing sales activities, and therefore is a certain method of organizing human activity under MPEP 2106.04(a)(2)(II)).
The configuration of the system is used to receive food order information, sending it to the kitchen, and processing the payment for the food. The process is part of everyday commercial interaction that occur between patrons and staff members, since purchasing food is a sales activity. Sales activities are described clearly in the MPEP as a method of organizing human activity, one of the enumerated groupings. Additionally, the steps describe a way to manage the interaction between people at a restaurant ordering food, a typical experience of people. The subject matter is considered a judicial exception by being part of both enumerated groupings.
Step 2A, Prong 2- The additional elements include an application server, the application running on a mobile device or web interface, order management system, the machine learning algorithm and payment component.
This judicial exception is not integrated into a practical application because an application server; the application running on a mobile device or web interface, an order management system and a payment component are not more than computer elements to implement the abstract idea. The elements are described as a server, system and component, which are presented as a tool to perform the abstract idea. Under MPEP 2106.05(a) when the elements do not integrate the judicial exception into a practical application it is not more than the abstract idea. Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations fail to improve the functioning of a computer. The limitations send and receive information, and the improvement is based on the information itself, not on how the information is manipulated in a new way by the computer, or that the information would make the computer function faster, and therefore the limitations fail to improve the functioning of a computer. Additionally, the claim use a processor that is configured to implement the abstract idea, under MPEP 2106.05(f), this is the use of a tool to perform the abstract idea, and does not provide integration into a practical application.
The machine learning algorithm is presented as a tool to implement the abstract idea. When information is input into the machine learning algorithm, it then output a solution, with no details to how this solution is accomplished. Therefore, under MPEP 2106.05(f)(1) where the claim only recites the odes of a solution, and fails to provide any details on how the solution is accomplished. Naming a tool, the machine learning algorithm, is not descriptive language to explain how the solution is accomplished.
Step 2B- The newly added limitations to the independent claims adds information about the factors used to estimate delivery times. The act of using an order management system to make a calculation, or decision, about how long it should take to prepare food is a decision that is made every day by employees of restaurants, and therefore is a commercial interaction. The fact that the decision is accomplished on a management system, but the management system is drafted as a tool to make a determination, and the decision does not change how the management system would operate, it indicates to the management system the order of completion. The calculation and decision making on a computer does not indicate more than using the computer as a tool to implement the abstract idea, and therefore, remains abstract according to MPEP 2106.05(f).
Dependent Claims
Dependent claims 2 and 11, add a physical location identifier, which adds another system to send and receive information, but fails to provide a limitation that would alter the system to better function, and therefore does not integrate the application into a practical application.
Dependent claims 3-5, 9, 12-20 further describe the order management system, and do not add additional elements, but merely add steps to the financial transaction, and the method of people ordering food. The sending and receiving of information do not provide more than application of the abstract idea within a computer environment, and therefore, they remain rejected under 101.
Dependent claims 6-7, further describe the application server, and do not add additional elements, but merely add steps to the financial transaction, and the method of people ordering food. The sending and receiving of information do not provide more than application of the abstract idea within a computer environment, and therefore, they remain rejected under 101.
Dependent claims 8 further describe the order management system and application server combined. The two elements are described in further details, but together do not provide more than information being sent and received, but does not alter the computer functionality.
117231B2
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6, 10-13, 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2013/0191229 A1 Rodgers et al. (hereinafter Rodgers) in view of US 2017/0290345 A1 Garden et al. (hereinafter Garden) and in further view of US 2020/0249660 A1 Rao et al. (hereinafter Rao).
Regarding claim 1, Rodgers teaches a food ordering system (Rodgers Abstract, ordering menu items), comprising:
an application server configured to receive order information and a physical location identifier from a user via an application running at user mobile device or a web interface accessed via the user mobile device, wherein the order information includes one or more items to be delivered to the user and the physical location identifier is associated with a physical location within a dining area of a food service establishment (Rodgers [0055-0057] customer uses the mobile phone application to scan QR code with table location number and the menu to place an order; [0047] the system includes a server that transmits the order);
an order management system coupled with the application server and configured to receive order information and the physical location identifier and provide the order information to a kitchen or service area for production (Rodgers [0060] the order is directly sent to the restaurant kitchen; [0099] the user ordering application is sent to a central platform, the central platform receives the order information and send to kitchen area); and
a payment component coupled with the order management system and the application server and configured to electronically process payment for the one or more items after delivery to the user (Rodgers [0068-0069] user may use the app to complete payment using a credit card).
Rodgers fails to explicitly disclose wherein the order management system uses machine learning algorithms that analyze logged historical data to continuously update item preparation times, and wherein the order management system determines an estimated completion time for the one or more items and, when the order information includes two or more items, automatically assigns each item to one of two or more work queues based on item type, current queue capacity, and real-time kitchen conditions, and wherein the estimated completion time is dynamically calculated in real-time based at least in part on the work queue of the two or more work queues associated with each of the one or more items; the updated item preparation times for the work queue of the two or more work queues, a staffing level, an identification of personnel producing the one or more items, an equipment status of equipment used in producing the one or more items, and a quantity of other items that are in the associated work queue of the two or more work queues; wherein the machine learning algorithms automatically adjust he estimated completion times based on real-time changes in the updated item preparation times, the staffing level, the identifies personnel, the equipment status, and queue conditions.
Garden is in the field of food assembly (Garden Abstract, robotic food assembly) and teaches wherein the order management system uses machine learning algorithms that analyze logged historical data to continuously update item preparation times, and wherein the order management system determines an estimated completion time for the one or more items (Garden [0181-0183] a machine learning algorithm to control the computer, which uses the estimated delivery time is based on cook time and traffic factors; [0200] the food may be ordered through an application, and placed within a delivery vehicle, and that delivery vehicle may include the process of finishing cooking so the food is completed cooking at the exact time of delivery; this means that the system must determine an estimated time for the food to be finished cooking and coordinate with the time it will take to get to the destination, the destination may be a persons home or a persons dining table) and, when the order information includes two or more items, automatically assigns each item to one of two or more work queues based on item type, current queue capacity, and real-time kitchen conditions (Garden [0183] the system includes an order control system, that creates a fulfillment queue based on conditions; the order control system is exactly cited as system(s) and therefore showcases that there may be more than one order control system, and shown in Fig. 2A indicates that there are multiple stations and ways to cook and assemble food; Fig. 2A showcases different workstations (shown as item 124a-j) and the items are assigned based on the order, as an example the pizza may not have toppings, and therefore would not need some work stations; [0013] plural orders, plural robots; [0075-0076] the system may include one or more assembly conveyors, and if there are more than one conveyor, the decision would be made for which conveyor to place the item on), and wherein the estimated completion time is dynamically calculated in real-time based at least in part on the work queue of the two or more work queues associated with each of the one or more items (Garden [0182-0183] the estimated delivery time is based on cook time, order in queue and traffic factors; orders in a queue vary, and therefore the estimated time is dynamic based on that factor; [0200] the food may be ordered through an application, and placed within a delivery vehicle, and that delivery vehicle include the process of finishing cooking so the food is completed cooking at the exact time of delivery; this means that the system must determine an estimated time for the food to be finished cooking and coordinate with the time it will take to get to the destination, making the delivery time and completion time the same time; the destination is not specified but may be a persons home or a persons dining table) and the updated item preparation times for the work queue of the two or more work queues, a staffing level, an identification of personnel producing the one or more items, an equipment status of equipment used in producing the one or more items, and a quantity of other items that are in the associated work queue of the two or more work queues (Garden [0182] the estimated delivery time, which is also the completion time, is based on a plurality of factors including: time to produce the food by the defined cooking process, which is the past timing for item preparation in the work queue; the factors associated with the en route cooking control systems, including congestion and time of day, which falls under the broadest reasonable interpretation of an equipment status of the equipment used to produce the items and the availability of food on the delivery vehicle, and therefore can be a quantity of other foods; [0103] the historical data associated with order will be used to make determinations about staffing; adding staff during historical peak times will reduce queue times).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the food ordering system, which delivers food to a specific table, of Rodgers with the time estimation teaching of Garden. The estimation provided in Garden is based on the totality of circumstances, and if there is no road traffic, but rather the distance within a restaurant to travel, it still may accomplish the task of estimating the final time for delivery, which is the same time as completion is Garden. The motivation for doing so would be to minimize the amount of time between completion and consumption to provide the most consistent consumer experience (Garden [0003] passage of time minimized to provide best consumer experience).
Rao is in the field of restaurant operations (Rao Abstract, restaurant system management) and teaches wherein the machine learning algorithms automatically adjust the estimated completion times based on real-time changes in the updated item preparation times, the staffing level, the identified personnel, the equipment status, and queue conditions (Rao [0156] the machine monitors the coking and is able to estimate the time of completion based on different factors; [0104] station, equipment and speed of cooking racks is used to determine time for completion; [0194] machine learning and Ai techniques are being used within the system for real-time analysis). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the management of Rodgers with the machine learning modification of completion times as taught by Rao. The motivation for doing so would be to streamline a variety of operations within a single restaurant in order to improve efficiency (Rao [0002-0003] improving efficiency by streamlining every process).
Regarding claim 2, modified Rodgers teaches the food ordering system of claim 1, wherein: the physical location identifier comprises one or more of a table number that is marked on a table and entered into the mobile device by the user, an identification from an optical marker on the table that is scanned into the mobile device, an electronic identification based on an electronic marker on the table that read by the mobile device, or any combinations thereof (Rodgers [0055] the app scans a QR code to determine table location).
Regarding claim 3, modified Rodgers teaches the food ordering system of claim 1. Rodgers fails to explicitly disclose wherein: the order management system is further configured to perform metering of received orders based on a rate of production of the kitchen or service area, and to provide the estimated completion time to the user for delivery of the one or more items based at least in part on the rate of production associated with the work queue associated with each of the one or more items.
Garden teaches wherein: the order management system is further configured to perform metering of received orders based on a rate of production of the kitchen or service area (Garden [0183] production of food items may be placed in a queue, and scheduled and moved based on production time), and to provide the estimated completion time to the user for delivery of the one or more items based at least in part on the rate of production associated with the work queue associated with each of the one or more items (Garden [0181-0183] the user may obtain a time estimation based on the time to produce the food, and delivery of the food item; [0183] the system includes an order control system, that creates a fulfillment queue based on conditions; [0200] the food may be ordered through an application, and placed within a delivery vehicle, and that delivery vehicle include the process of finishing cooking so the food is completed cooking at the exact time of delivery; this means that the system must determine an estimated time for the food to be finished cooking and coordinate with the time it will take to get to the destination, making the delivery time and completion time the same time; the destination is not specified but may be a persons home or a persons dining table).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the food ordering system of Rodgers with the production management of Garden. The motivation for doing so would be to deliver food in its optimum condition, the preparation control may alter when food is being made in order to deliver the entire tables food at the same time, based on different production times, without items sitting and changing in quality (Garden [0003-0005] improving timing of food preparation to ensure top quality products).
Regarding claim 4, modified Rodgers teaches the food ordering system of claim 3. Rodgers fails to explicitly disclose wherein: the rate of production is determined based at least in part on one or more of a historical rate of production for the one or more items, a number of staff that are available for producing the one or more items, one or more particular staff members working in the kitchen or service area, or any combinations thereof.
Garden teaches wherein: the rate of production is determined based at least in part on one or more of a historical rate of production for the one or more items, a number of staff that are available for producing the one or more items, one or more particular staff members working in the kitchen or service area, or any combinations thereof (Garden [0182-0183] assembly estimation may use a variety of factors in real time to determine the production time; the factors include time to produce the specific food item and time to cook the food items, those times are altered based on known time to cook each item. To know how long an item must be cooked it must have already been tested, and is based on historical data).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the food ordering system of Rodgers with the production management of Garden. The motivation for doing so would be to deliver food in its optimum condition, the preparation control may alter when food is being made in order to deliver the entire tables food at the same time, based on different production times, without items sitting and changing in quality (Garden [0003-0005] improving timing of food preparation to ensure top quality products).
Regarding claim 5, modified Rodgers teaches the food ordering system of claim 1, wherein: the order management system is further configured to receive input from a user and to initiate payment processing responsive to the received input (Rodgers [0088-0090] the order can be reviewed by the user, confirmed as correct and then payment for the order is initiated; Fig. 5, food product delivered to the table by a waiter). Rodgers fails to explicitly disclose the input from the user is indicating that the one or more items have been delivered to the user.
Garden teaches input from the user is indicating that the one or more items have been delivered to the user (Garden [0192] upon delivery, the operator can scan a symbol to confirm delivery). It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the user confirmation of a correct order to initiate payment as taught by Rodgers with the confirmation of delivery as taught by Garden. The motivation for doing so would be to close out the order in the food preparation queue, give finality to the control system and allowing more accurate data for the control system to better manage food preparation (Garden [0192] the cooking control system may be informed of the correct delivery, ending the ticket item).
Regarding claim 6, modified Rodgers teaches the food ordering system of claim 1, wherein: the application server is further configured to provide menu information to the mobile device, wherein the menu information is determined based at least in part on one or more of a list of available items, a restaurant site of the physical location, a time of day, an age of the user, or any combinations thereof (Rodgers 0057] the menu displayed may change based on the time of day, specific time for breakfast; [0047] the system includes a server that transmits the order).
Regarding claim 10, Rodgers teaches a method for restaurant or bar order management at a customer device (Rodgers Abstract, ordering menu items), comprising:
receiving an indication of a physical location identifier at an application running at the customer device, the physical location identifier associated with a physical location at a restaurant or bar (Rodgers [0055-0057] customer uses the mobile phone application to scan QR code with table location number and the menu to place an order);
receiving order information from a user of the customer device via the application, wherein the order information includes one or more items to be delivered to the physical location (Rodgers [0055] ordering menu items to be delivered to their location; [0060] the order is directly sent to the restaurant kitchen);
providing the physical location identifier and order information to an order management system (Rodgers [0056] location of table identified);
receiving, at the customer device from the order management system, a confirmation of the order information (Rodgers [0059] user can review before payment); and
prompting the user of the customer device to authorize payment for the one or more items upon delivery to the user (Rodgers [0068-0069] user may use the app to complete payment using a credit card).
Rodgers fails to explicitly disclose an estimated completion time for the one or more items, wherein the order management system automatically assigns each item to a specific work queue of two or more work queues based on item type, current queue capacity, and real-time kitchen conditions, and wherein the estimated completion time is dynamically calculated in real-time based at least in part on an associated work queue of the two or more work queues associate with each item, an updated item preparation time based on logged historical data for item preparation of items in the associated work queue of each item, a staffing level, an identification of personnel producing the one or more items, an equipment status of equipment used in producing the one or more items, and a quantity of other items that are in the associated work queue of each item, wherein machine learning algorithm automatically adjust the estimated completion times based on real-time changes in the updated item preparation time, the staffing level, the identified personnel, the equipment status, and queue conditions.
Garden teaches an estimated completion time for the one or more items wherein the order management system automatically assigns each item to a specific work queue of two or more work queues based on item type, current queue capacity, and real-time kitchen conditions, and wherein the estimated completion time is dynamically calculated in real-time based at least in part on an associated work queue of the two or more work queues associate with each item (Garden [0181-0183] the user may obtain a time estimation based on the time to produce the food, and delivery of the food item; [0183] the system includes an order control system, that creates a fulfillment queue based on conditions; [0200] the food may be ordered through an application, and placed within a delivery vehicle, and that delivery vehicle include the process of finishing cooking so the food is completed cooking at the exact time of delivery; this means that the system must determine an estimated time for the food to be finished cooking and coordinate with the time it will take to get to the destination, making the delivery time and completion time the same time; the destination is not specified but may be a persons home or a persons dining table), an updated item preparation time based on logged historical data for item preparation of items in the associated work queue of each item, a staffing level, an identification of personnel producing the one or more items, an equipment status of equipment used in producing the one or more items, and a quantity of other items that are in the associated work queue of each item (Garden [0182] the estimated delivery time, which is also the completion time, is based on a plurality of factors including: time to produce the food by the defined cooking process, which is the past timing for item preparation in the work queue; the factors associated with the en route cooking control systems, including congestion and time of day, which falls under the broadest reasonable interpretation of an equipment status of the equipment used to produce the items and the availability of food on the delivery vehicle, and therefore can be a quantity of other foods; [0103] the historical data associated with order will be used to make determinations about staffing; adding staff during historical peak times will reduce queue times).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the food ordering system of Rodgers with the production management of Garden. The estimation provided in Garden is based on the totality of circumstances, and if there is no road traffic, but rather the distance within a restaurant to travel, it still may accomplish the task of estimating the final time for delivery, which is the same time as completion is Garden. The motivation for doing so would be to deliver food in its optimum condition, the preparation control may alter when food is being made in order to deliver the entire tables food at the same time, based on different production times, without items sitting and changing in quality (Garden [0003-0005] improving timing of food preparation to ensure top quality products).
Rao teaches wherein the machine learning algorithms automatically adjust the estimated completion times based on real-time changes in the updated item preparation times, the staffing level, the identified personnel, the equipment status, and queue conditions (Rao [0156] the machine monitors the coking and is able to estimate the time of completion based on different factors; [0104] station, equipment and speed of cooking racks is used to determine time for completion; [0194] machine learning and Ai techniques are being used within the system for real-time analysis). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the management of Rodgers with the machine learning modification of completion times as taught by Rao. The motivation for doing so would be to streamline a variety of operations within a single restaurant in order to improve efficiency (Rao [0002-0003] improving efficiency by streamlining every process).
Regarding claim 11, modified Rodgers teaches the method of claim 10, wherein: the physical location identifier comprises one or more of a table number that is marked on a table and entered into the customer device by the user, an identification from an optical marker on the table that is scanned into the customer device, an electronic identification based on an electronic marker on the table that read by the customer device, or any combinations thereof (Rodgers [0055] the app scans a QR code to determine table location).
Regarding claim 12, modified Rodgers teaches the method of claim 10. Rodgers fails to explicitly disclose further comprising: receiving, from the order management system, a time estimate for delivery of the one or more items based at least in part on the estimated completion time for the one or more items, wherein the estimated completion time is based at least in part on which of two or more work queues is associated with each of the one or more items.
Garden teaches further comprising: receiving, from the order management system, a time estimate for delivery of the one or more items based at least in part on the estimated completion time for the one or more items, wherein the estimated completion time is based at least in part on which of two or more work queues is associated with each of the one or more items (Garden [0182-0183] assembly estimation may use a variety of factors in real time to determine the production time; [0183] the system includes an order control system, that creates a fulfillment queue based on conditions; [0200] the food may be ordered through an application, and placed within a delivery vehicle, and that delivery vehicle include the process of finishing cooking so the food is completed cooking at the exact time of delivery; this means that the system must determine an estimated time for the food to be finished cooking and coordinate with the time it will take to get to the destination, making the delivery time and completion time the same time; the destination is not specified but may be a persons home or a persons dining table; the order control system is exactly cited as system(s) and therefore showcases that there may be more than one order control system, and shown in Fig. 2A indicates that there are multiple stations and ways to cook and assemble food; [0013] plural orders, plural robots).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the food ordering system of Rodgers with the time estimation of Garden. The estimation provided in Garden is based on the totality of circumstances, and if there is no road traffic, but rather the distance within a restaurant to travel, it still may accomplish the task of estimating the final time for delivery, which is the same time as completion is Garden. The motivation for doing so would be improve customer experience by keeping them informed of food delivery (Garden [0003-0005] improving timing of food preparation to ensure top quality products).
Regarding claim 13, modified Rodgers teaches the method of claim 10, further comprising: receiving, from the order management system, menu information that indicates available items for order by the user (Rodgers [0057] the menu displayed may change based on the time of day, specific time for breakfast; [0055-0057] customer uses the mobile phone application to scan QR code with table location number and the menu to place an order; [0047] the system includes a server that transmits the order).
Regarding claim 15, modified Rodgers teaches the method of claim 13, wherein: the available items for order by the user are based at least in part on a list of available items, a restaurant site of the physical location, a time of day, a to-go or dine-in order type, or any combinations thereof (Rodgers 0057] the menu displayed may change based on the time of day, specific time for breakfast).
Regarding claim 20, modified Rodgers teaches the method of claim 10. Rodgers fails to explicitly disclose further comprising: receiving, from the user, a selection of a plurality of items and a timing for delivery of each of the plurality of items; and providing the selection and the timing to the order management system.
Garden teaches further comprising: receiving, from the user, a selection of a plurality of items and a timing for delivery of each of the plurality of items (Garden [0182-0183] assembly estimation may use a variety of factors in real time to determine the production time); and providing the selection and the timing to the order management system (Garden [0181-0183] the user may obtain a time estimation based on the time to produce the food, and delivery of the food item).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the food ordering system of Rodgers with the production management of Garden. The estimation provided in Garden is based on the totality of circumstances, and if there is no road traffic, but rather the distance within a restaurant to travel, it still may accomplish the task of estimating the final time for delivery, which is the same time as completion is Garden. The motivation for doing so would be to deliver food in its optimum condition, the preparation control may alter when food is being made in order to deliver the entire tables food at the same time, based on different production times, without items sitting and changing in quality (Garden [0003-0005] improving timing of food preparation to ensure top quality products).
Claims 7-9, 14, 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Rodgers, Garden, and Rao and in further view of WO 2021/222975 A1 Posch.
Regarding claim 7, modified Rodgers teaches the food ordering system of claim 1, wherein: the application server is further configured to provide menu information to the mobile device (Rodgers [0055-0057] customer uses the mobile phone application to scan QR code with table location number and the menu to place an order; [0047] the system includes a server that transmits the order). Rodgers fails to explicitly disclose wherein the menu information is determined based at least in part on allergen information provided by the user.
Posch is in the field of finding different restaurants (Posch Abstract, finding specific restaurants) and teaches wherein the menu information is determined based at least in part on allergen information provided by the user (Posch [0042] user profile includes allergens, in order to find detailed restaurant information that fits their dietary needs).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the ordering system of Rodgers with the allergen specification of Posch. The motivation for doing so would be to give user specific menu details that directly relate to their own personal preferences, especially in relation to possible life threatening allergies (Posch [0007] keeping guests and owners safe by giving the user full allergy information, and preventing accidental allergen contamination).
Regarding claim 8, modified Rodgers teaches the food ordering system of claim 1. Rodgers fails to explicitly disclose wherein: the application server is further configured to receive a request from the user to be added to a wait list for a table and provide the request to the order management system; and the order management system is further configured to place the user into a queue for an available table, provide an estimated wait time to the application server, and provide a notification to the application server when a table is available for the user.
Posch teaches wherein: the application server is further configured to receive a request from the user to be added to a wait list for a table and provide the request to the order management system (Posch [0038] the user may request a table reservation); and the order management system is further configured to place the user into a queue for an available table, provide an estimated wait time to the application server, and provide a notification to the application server when a table is available for the user (Posch [0038] the restaurant may give the user available table for their criteria, and the time in which the table is ready; [0045] when the criteria is met, a confirmation I sent to the user to detail when the table will be available).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the food ordering system of Rodgers with the table reservation of Posch. The motivation for doing so would be to give customers a clear idea of the menu, and the ability to find a restaurant that meets their needs nutritionally, but also timing and spacing (Posch [0006-0007] vetting menus for dietary restrictions, in combination with available dining times).
Regarding claim 9, modified Rodgers teaches the food ordering system of claim 8, wherein: the order information is received prior to the table being available for the user (Rodgers [0098] customer can place an order before being seated). Rodgers fails to explicitly disclose wherein the order management system provides the order information to the kitchen or service area for production based on a projected preparation time of the order and the estimated wait time to provide delivery of the order within a predetermined period after the estimated wait time.
Posch teaches wherein the order management system provides the order information to the kitchen or service area for production based on a projected preparation time of the order and the estimated wait time to provide delivery of the order within a predetermined period after the estimated wait time (Posch [0054-0055] when a restaurant reservation is made, the participating members may pre-order food and drink, to prepare the staff to deliver the food efficiently, and allow for a smaller wait period).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the food ordering system of Rodgers with the ability to pre-order their food before the table reservation is ready as taught by Posch. The motivation for doing so would be to provide patrons with increased flexibility to review menu choices and reduce error from quick service constraints (Posch [0054] allowing for more time to review menu, and the kitchen to properly prepares, reduce mistakes).
Regarding claim 14, modified Rodgers teaches the method of claim 13. Rodgers fails to explicitly disclose further comprising: receiving, from the user, one or more of age information, allergen information, or combinations thereof, and wherein the available items for order by the user are based at least in part on the age information, the allergen information, or any combinations thereof.
Posch teaches further comprising: receiving, from the user, one or more of age information, allergen information, or combinations thereof, and wherein the available items for order by the user are based at least in part on the age information, the allergen information, or any combinations thereof (Posch [0042] user profile includes allergens, in order to find detailed restaurant information that fits their dietary needs).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the ordering system of Rodgers with the allergen specification of Posch. The motivation for doing so would be to give user specific menu details that directly relate to their own personal preferences, especially in relation to possible life threatening allergies (Posch [0007] keeping guests and owners safe by giving the user full allergy information, and preventing accidental allergen contamination).
Regarding claim 16, modified Rodgers teaches the method of claim 10. Rodgers fails to explicitly disclose further comprising: receiving, from the user, a request to be added to a wait list for a table; and receiving, from the order management system, an estimated wait time for an available table; receiving, from the order management system, a notification that a table is available for the user; and providing a notice to the user responsive to the notification that the table is available.
Posch teaches further comprising: receiving, from the user, a request to be added to a wait list for a table (Posch [0038] the user may request a table reservation); and receiving, from the order management system, an estimated wait time for an available table; receiving, from the order management system, a notification that a table is available for the user; and providing a notice to the user responsive to the notification that the table is available (Posch [0038] the restaurant may give the user available table for their criteria, and the time in which the table is ready; [0045] when the criteria is met, a confirmation I sent to the user to detail when the table will be available).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the food ordering system of Rodgers with the table reservation of Posch. The motivation for doing so would be to give customers a clear idea of the menu, and the ability to find a restaurant that meets their needs nutritionally, but also timing and spacing (Posch [0006-0007] vetting menus for dietary restrictions, in combination with available dining times).
Regarding claim 17, modified Rodgers teaches the method of claim 16, wherein: the order information is received from the user and provided to the order management system prior to the receiving the notification that the table is available (Rodgers [0098] customer can place an order before being seated).
Regarding claim 18, modified Rodgers teaches the method of claim 10. Rodgers fails to explicitly disclose further comprising: receiving, from a different application at a different customer device, a request to join a party of the user; and authorizing, with the order management system responsive to an approval provided by the user, the different application at the different customer device to join the party of the user, and wherein items ordered through the different application at the different customer device are added to a tab associated with the party of the user.
Posch teaches further comprising: receiving, from a different application at a different customer device, a request to join a party of the user (Posch [0045] user may invite registered guests to join the table); and authorizing, with the order management system responsive to an approval provided by the user, the different application at the different customer device to join the party of the user, and wherein items ordered through the different application at the different customer device are added to a tab associated with the party of the user (Posch [0045]The guests may accept or decline the invitation; [0041] the accepted guests may divide payment for the bill in anyway that fits the parties).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the ordering system of Rodgers with the bill splitting of Posch. The motivation for doing so would be to allow for customer justifiable payments by not taking up time of the staff by needing to input multiple payment options, and allowing the settlement to be determined prior to arrival (Posch [0008] some large groups are not allowed to split checks because of time and money related to processing multiple cards, and gives better satisfaction to patrons and staff).
Regarding claim 19, modified Rodgers teaches the method of claim 18. Rodgers fails to explicitly disclose further comprising: prompting the user with a plurality of payment options for the tab, wherein the plurality of payment options include one or more of a splitting option to divide the tab between two or more users that agree to split the tab, a random selection option to randomly select one of two or more users to pay the tab, or an exclusion option that excludes one or more users and splits the tab between remaining users.
Posch teaches further comprising: prompting the user with a plurality of payment options for the tab, wherein the plurality of payment options include one or more of a splitting option to divide the tab between two or more users that agree to split the tab, a random selection option to randomly select one of two or more users to pay the tab, or an exclusion option that excludes one or more users and splits the tab between remaining users (Posch [0045]The guests may accept or decline the invitation; [0041] the accepted guests may divide payment for the bill in anyway that fits the parties).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the ordering system of Rodgers with the bill splitting of Posch. The motivation for doing so would be to allow for customer justifiable payments by not taking up time of the staff by needing to input multiple payment options, and allowing the settlement to be determined prior to arrival (Posch [0008] some large groups are not allowed to split checks because of time and money related to processing multiple cards, and gives better satisfaction to patrons and staff).
Response to Arguments
Applicant's arguments filed 12/19/2025 have been fully considered but they are not persuasive.
Regarding 101, under Step 2A, Prong one, the claims are analyzed to determine if the claim as a whole recites an exception, and if they do, they then are analyzed to determine if they qualify based on further analysis of their integration into practical application or provide significantly more. Applicant points to an estimation of completion time, and this calculation is based on features that are regularly known in a business, such as the current queue, staffing, and historically busy times, which are all elements used to analyze data to make a determining about completion time, all of which are different abstract ideas. Calculations can be a mathematical concept of comparing data points, management of orders is the management of sales activities under a certain method of organizing human activity, and the analysis of data can be performed in the human mind, See MPEP 2106.04(a)(2)(!-III). Every day in restaurants, the cooking staff receives orders from waitstaff, and has to make a determination on what to cook first based on a variety of factors, such as amount of time to cook the food, the necessary equipment to cook the food, etc, in order for all the plates at a single table to be delivered at the same time. Therefore, the management system takes the information and makes a decision based on factors, which are making decision about everyday business decision, but the only difference is that a management system is making the decision. The addition of machine learning algorithms and automation of task assignments, and performing the calculation in real time does not remove the claim from an abstract idea. The claim is still using the machine learning to make decision, a mental process, to assign tasks, a business decision, and performing the calculation in real time is still a calculation, mathematical equation. Example 45 and 46 specially recite how the decision made by the computer, is mechanically linked to the injection molded to start a physical process, and the livestock is able to connect to a physical process of a gate. This is distinctly different from the instant claims because the “physical” changes being performed are where to perform the actions, which is a decision, and is grouped as a mental process, or an economic principle under MPEP 2106.04(a)(2). Therefore, the management system is to be analyzed under Prong Two to determine if the feature integrate the judicial exception into a practical application.
Under Step 2A, Prong two, the claims are evaluated to determine whether the claim as a whole integrates the abstract idea into a practical application. The applicant asserts that the improvement to the functioning of a food ordering system is accomplished by providing more accurate estimates of completion times of items. Under MPEP 2106.05(a) there needs to be some improvement to the functioning of a computer system, that is accomplished by more than the speed of completion because of its application on a computer. The server and management system are coupled and used to receive information and make determination. The completion time is more accurate because of the information being evaluated about the work queue, and therefore the information itself is the reason for an improved knowledge, not that the information is sent, received or evaluated any differently to showcase how the computer would be improved. Improving speed because of the capabilities of a computer, or improvement to information, but not how the system and server are configured to obtain or send information beyond being used as the tool to perform the abstract idea, it fails to showcase an improvement to the computer-functionality. Therefore, when claim limitations are to the improvement of the information, by adding more factors, that improvement is not based on its connection to a computer, or how that information would improve things on a computer, such as search or database structure, the claims fail to integrate the judicial exception into a practical application. Example 45 describes the use of a mathematical calculation, and how it can be integrated into a practical application by controlling when the mold door opens. However, as described above, this is distinct from the instant application because the machine learning algorithm provides queue selection, which is a choice/decision, NOT a physical opening of a door, or physical movement to a conveyor, and therefore, the improvement is used for the decision making process, not the physical movement of parts. The same reasoning follows for Example 46. The use of a machine learning algorithm, as recited in general terms, does not provide a technology based solution, as there is no details to that technology to indicate what this physical solution would even contain. See new arguments under section 101 relating to the machine learning.
Providing an estimation based on a calculation on a computer element does not transform the computer, but it does transform the calculation, which is the equivalence to the determination made by employees within a restaurant.
Step 2B- For similar reasons as state above, reciting additional elements in general terms does not provide significantly more than the abstract idea. The “other meaningful limitation” claimed by the applicant relates to the machine learning, but when the limitation is so broad, it cannot possible create a meaningful limitation because there is no claim language that is actually limiting.
Regarding 103, Rodger does not teach machine learning. However, Garden and new reference Rao both teach using machine learning to perform a variety of different calculations. Garden showcases how the machine learning algorithm is used to determine delivery time, and that delivery time is based the amount of time needed to prepare the food, and therefore is used to update final completion time. The items are tracked, and the database is updated, and therefore this occurs in real time, and if taking longer to cook (whether because of prep time, staffing levels), the completion is calculated. Garden also teaches multiple conveyors, and each conveyor has multiple workstations, and therefore when the management system makes a decision about where to place the order, it chooses the location from different types of queues. There is an assembly queue, a cooking queue, and a packaging queue, and therefore the item type (does it need assembly, or can it go straight to cooking), can determine where to place the item. Garden does not teach the machine learning algorithm adjusting the estimated completion, and therefore new reference Rao. Therefore, the combination of the two different types of machine learning are combined to create an amendment to the management system of Rodgers.
Claims 2-9 and 11-10 do not rely on allowable independent claims, and therefore remain rejected.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 8,498,900 B1 Spirin et al. teaches splitting bill payments (Abstract); US 2012/0173350 A1 Robson teaches facilitating restaurants (Abstract); US 2022/0058723 A1 Swett et al. teaches remote location identification of merchants (Abstract); US 2016/0350837 A1 Williams et al. teaches mobile food ordering (Abstract); and US 2015/0046271 A1 Scholl et al. teaches online ordering (Abstract); US 2013/0332208 A1 Mehta teaches processing wait times at restaurants (Abstract); US 2021/0374669 A1 Neuman teaches creating an optimized delivery path (Abstract); US 2022/0292834 A1 DeSantola et al. teaches kitchen management system (Abstract).
Conclusion
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/JESSICA E SULLIVAN/Examiner, Art Unit 3627
/AARON TUTOR/Primary Examiner, Art Unit 3627