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 .
Response to Arguments
Regarding the 35 USC 101 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “Applicant notes that claim 21 constructs a computer network including a first, second and third computing devices, each computing device associated with a different respective entity. According to a non-limiting embodiment shown in FIG. 1, the claim recited first computing device is an exemplary airline server (20); the claim recited second computing device is an exemplary authorizing entity (AE) server (e.g., 25A); the third computing device is an exemplary food packing server (15). The vast computer network coherently performs the function of minimizing the amount of food supplied to a flight.,” Examiner respectfully asserts that the three devices of the claims, as drafted, under consideration of broadest reasonable interpretation of the claim, are nothing more than mere use of a computer as a tool in its ordinary capacity. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Regarding Applicant’s assertion of “The claim recited "food packing system" is a specific device that is coherently integrated with the claim recited "food packing instruction". Such integration limits the claim to a specific practical implementation of a specific technological process that make an airplane fly more efficiently by minimizing food weight,” Examiner respectfully disagrees. The claim merely recites transmitting instructions “by the processor to a third computing device associated with a food packing system.” Examiner suggests amending the claim to more explicitly recite the food packing system actively performing functions within the claim.
Regarding Applicant’s assertion of “Further, the claim does not recite a mental process because the functions performed by the first, second and third computing devices, as recited in claim 21, cannot be practically performed in the human mind. For example, the first computing device receives flight information from a payment card purchase information. In practice, payment card purchase information are shielded from human operators.,” Examiner respectfully asserts this additional element is mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Regarding Applicant’s assertion of “As detailed above with respect to Step 2A, Prong 1, claim 25 recites a method of using computer networking to make complex decisions of minimizing food weight for a flight. The decisions are complex because the claimed method decides which food to stock in an airport lounge based on food preference which in turn is determined from flight information obtained through flight purchase. The claimed method also decides which food to exclude from inventory based on pre-flight food purchase which in turn is obtained from payment card transactions.,” Examiner respectfully asserts that Applicant’s argued improvements, such as complex decision making, is an improvement related to the abstract limitations of the claim for consideration under Step 2A, Prong 1. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”
Regarding Applicant’s assertion of “In addition, the coordination of three computing devices (the first, second and third computing devices) provides a specific technological improvement in computer technology by, at least, improving the technical field of flying an aircraft by managing in real-time its weight (minimizing food weight for a flight) and thus providing a specific practical impact. This technological improvement imposes meaningful limit on any alleged abstract idea, such that the claim is more than a drafting effort designed to monopolize the alleged judicial exception, as required under MPEP §2106.04(d). Moreover, Applicant's claims, as now clarified, do not merely present the so-called "general linking" but a specific practical implementation to a specific technological process, since they are not similar to any of enumerated examples listed by MPEP §2106.05(h).,” Examiner respectfully disagrees. Primarily, the present claims do not recite performing these steps “in real time.” However, even assuming arguendo, “[M]erely adding computer functionality to increase the speed or efficiency of the process does not confer patent eligibility on an otherwise abstract idea.”); Alice, 573 U.S. at 223 (“Thus, if a patent’s recitation of a computer amounts to a mere instruction to implement an abstract idea on a computer, that addition cannot impart patent eligibility.”). Furthermore, the minimizing of food weight for a flight is an abstract process that, as drafted, is an improvement related to the abstract limitations of the claim for consideration under Step 2A, Prong 1. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”
Accordingly, the present claims are rejected under 35 USC 101.
Regarding the 35 USC 103 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “Villa does not teach and/or suggest that the exclusion of a flight-specific food item is determined based on "transaction information indicating a charge for a meal by the payment card occurred within a predetermined time before a departure time of the flight" as recited in each of the claims.,” Applicant’s arguments with respect to the previous prior art combination of the record have been considered but are moot because the new grounds of rejection does not rely on any reference applied in the prior art rejection for any teachings or matter specifically challenged in the argument. The claims are rejected under a new grounds of rejection, which was necessitated by amendment. Examiner has introduced the DiGioacchino reference to cure the deficiencies of the prior art combination of the record. See the detailed rejection below.
Accordingly, the present claims are rejected under 35 USC 103.
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 21-23, 25-28, 30-33, and 35-40 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 21-23 and 25-26 are directed to a method, claims 27-28 are directed to a method, and 30-33 and 35-40 are directed to a method. Therefore, the claims are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 21, 27, and 30 recite limitations related to determining an exclusion of a food related item, constituting an abstract idea based on “Certain Methods of Organizing Human Activity” related to commercial interactions including advertising or marketing sales activities or behaviors. Claim 21 recites limitations including “receiving, flight information associated with a flight purchased using a payment card associated with the card authorizing entity, wherein the flight information comprises a flight identifier and data associated with a flight passenger; receiving, a food preference for the flight passenger; determining, a type of food to stock in an airport lounge; receiving, transaction information indicating a charge for a meal by the payment card occurred within a predetermined time before a departure time of the flight; determining, an exclusion of a flight-specific food item from a flight-specific food inventory of the flight.” Claim 27 recites limitations including “receiving, flight information associated with a flight purchased using a payment card associated with the card authorizing entity by a flight passenger, wherein the flight information comprises a flight identifier and data associated with a flight passenger; receiving, a food preference for the flight passenger; determining, based on the food preference, a type of food to stock in an airport lounge; receiving, transaction information indicating a charge for a meal by the payment card occurred within a predetermined time before a departure time of the flight; determining, based on the transaction information, an exclusion of a flight-specific food item from a flight-specific food inventory of the flight.” Claim 30 recites limitations including “receiving, flight information associated with a flight purchased using a payment card associated with the card authorizing entity, wherein the flight information comprises a flight identifier and data associated with a flight passenger; receiving, a food preference for the flight passenger; wherein the data is indicative of an incentive to purchase a food item related to the food preference in an airport prior to the flight; determining, an acceptance of the incentive based at least in part on a purchase of the food item by the flight passenger using the payment card within a predetermined time before a departure of the flight; determining, based on the purchase of the food item, an exclusion of a flight-specific food item from a flight-specific food inventory of the flight.” These limitations, as drafted, is a process that, under its broadest reasonable interpretation, but for the language of “by the processor,” covers an abstract idea but for the recitation of generic computer components. That is, other than reciting “by the processor,” nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the “by the processor” language, the claim steps in the context of the claim encompass an abstract idea directed to “Certain Methods of Organizing Human Activity.”
Dependent claims 22, 28, 31-32, 35-36, and 39-40 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claims 23, 25-26, 33, and 37-38 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Claims 21, 27, and 20 do not integrate the judicial exception into a practical application. Claim 21 is a method that recites “receiving, by a processor of a first computing device from a second computing device associated with a card authorizing entity,…” “receiving, by the processor from the second computing device,…” “by the processor…” and “and transmitting, by the processor to a third computing device associated with a food packing system, a food packing instruction for the flight indicative of the flight-specific food inventory, to facilitate the food packing system to automatically load the flight-specific food inventory into an airline meal transport device based on the food packing instruction.” Claim 27 is a method that recites “receiving, by a processor of a first computing device from a second computing device associated with a card authorizing entity,…” “receiving, by the processor from the second computing device, …” “by the processor,” and “and transmitting, by the processor to a third computing device associated with a food packing system, a food packing instruction for the flight indicative of the flight-specific food inventory to facilitate the food packing system to automatically load the flight-specific food inventory into an airline meal transport device based on the food packing instruction.” Claim 30 is a method performed “receiving, by a processor of a first computing device from a second computing device associated with a card authorizing entity,…” “receiving, by the processor from the second computing device,…” “causing, by the processor, data to be presented to a third computing device associated with the flight passenger based on the food preference,” “ by the processor,” and “and transmitting, by the processor to a fourth computing device associated with a food packing system, a food packing instruction for the flight indicative of the flight-specific food inventory, to facilitate the food packing system to automatically load the flight-specific food inventory into an airline meal transport device based on the food packing instruction.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 22, 28, 31-32, 35-36, and 39-40 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claim 23 introduces the additional element of “wherein the food packing instruction is transmitted to at least one packing-related machine.” Dependent claim 25 introduces the additional element of “wherein the food packing instruction is transmitted to the at least one packing-related machine in a predefined time interval before a departure time of the flight.” Dependent claim 33 introduces the additional element of “wherein the food packing instruction is transmitted to at least one packing-related machine.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claim 26 introduces the additional element of “wherein the second computing device associated with the card authorizing entity further comprises a trained machine learning model, and further wherein the second computing device associated with the card authorizing entity is configured to determine the food preference for the flight passenger based at least in part on historical payment card transaction data of the flight passenger using the trained machine learning model.” Dependent claim 37 introduces the additional element of “wherein the second computing device associated with the card authorizing entity further comprises a trained machine learning model, and further wherein the second computing device associated with the card authorizing entity is configured to determine the food preference for the flight passenger based at least in part on historical payment card transaction data of the flight passenger using the trained machine learning model.” Dependent claim 38 introduces the additional element of “wherein the trained machine learning model is configured to determine, from the historical payment card transaction data, a type of cuisine or type of dish associated with each of a plurality of transactions of the historical payment card transaction data.” These limitations merely utilize a trained machine learning model to perform limitations including determining a food preference for the passenger. These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not sufficient to prove integration into a practical application.
Step 2B: Claims 21, 27, and 20 do not comprise anything significantly more than the judicial exception. Claim 21 is a method that recites “receiving, by a processor of a first computing device from a second computing device associated with a card authorizing entity,…” “receiving, by the processor from the second computing device,…” “by the processor…” and “and transmitting, by the processor to a third computing device associated with a food packing system, a food packing instruction for the flight indicative of the flight-specific food inventory, to facilitate the food packing system to automatically load the flight-specific food inventory into an airline meal transport device based on the food packing instruction.” Claim 27 is a method that recites “receiving, by a processor of a first computing device from a second computing device associated with a card authorizing entity,…” “receiving, by the processor from the second computing device, …” “by the processor,” and “and transmitting, by the processor to a third computing device associated with a food packing system, a food packing instruction for the flight indicative of the flight-specific food inventory to facilitate the food packing system to automatically load the flight-specific food inventory into an airline meal transport device based on the food packing instruction.” Claim 30 is a method performed “receiving, by a processor of a first computing device from a second computing device associated with a card authorizing entity,…” “receiving, by the processor from the second computing device,…” “causing, by the processor, data to be presented to a third computing device associated with the flight passenger based on the food preference,” “ by the processor,” and “and transmitting, by the processor to a fourth computing device associated with a food packing system, a food packing instruction for the flight indicative of the flight-specific food inventory, to facilitate the food packing system to automatically load the flight-specific food inventory into an airline meal transport device based on the food packing instruction.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f). Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not anything significantly more than the judicial exception.
Dependent claims 22, 28, 31-32, 35-36, and 39-40 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which are not anything significantly more than the judicial exception.
Dependent claim 23 introduces the additional element of “wherein the food packing instruction is transmitted to at least one packing-related machine.” Dependent claim 25 introduces the additional element of “wherein the food packing instruction is transmitted to the at least one packing-related machine in a predefined time interval before a departure time of the flight.” Dependent claim 33 introduces the additional element of “wherein the food packing instruction is transmitted to at least one packing-related machine.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claim 26 introduces the additional element of “wherein the second computing device associated with the card authorizing entity further comprises a trained machine learning model, and further wherein the second computing device associated with the card authorizing entity is configured to determine the food preference for the flight passenger based at least in part on historical payment card transaction data of the flight passenger using the trained machine learning model.” Dependent claim 37 introduces the additional element of “wherein the second computing device associated with the card authorizing entity further comprises a trained machine learning model, and further wherein the second computing device associated with the card authorizing entity is configured to determine the food preference for the flight passenger based at least in part on historical payment card transaction data of the flight passenger using the trained machine learning model.” Dependent claim 38 introduces the additional element of “wherein the trained machine learning model is configured to determine, from the historical payment card transaction data, a type of cuisine or type of dish associated with each of a plurality of transactions of the historical payment card transaction data.” These limitations merely utilize a trained machine learning model to perform limitations including determining a food preference for the passenger. These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not anything significantly more than the judicial exception.
Accordingly, 21-23, 25-28, 30-33, and 35-40 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 21-23, 25, 27-28, 30-33, 35, and 39-40 are rejected under 35 U.S.C. 103 as being unpatentable over Villa et al. (US 20220405653 A1) in view of Sekar et al. (US 20220092056 A1) in view of Digby-Jones et al. (US 20150294227 A1) in view of DiGioacchino et al. (US 20220405653 A1).
Regarding claim 21, Villa teaches a method comprising (Figs. 7-9):
receiving, by a processor of a first computing device from a second computing device associated with a card authorizing entity, flight information associated with a flight purchased using a payment card associated with the card authorizing entity ([0050] teaches a passenger can be scheduled for a number of aviation services, such as flight services and food offerings, wherein the system can obtain passenger data associated with the user, wherein [0054] teaches one or more passenger characteristics include itinerary data including an arrival time, a current location, flight information, a mode of transportation, and more; see also: [0055-0056]; Examiner’s Note: See the 35 USC 103 combination below for teachings pertaining to the unbolded claim language),
wherein the flight information comprises a flight identifier and data associated with a flight passenger ([0050] teaches a passenger can be scheduled for a number of aviation services, such as flight services and food offerings, wherein the system can obtain passenger data associated with the user, wherein [0052] teaches the passenger data includes passenger characteristics including historical data such as preferences, statuses, and other user behaviors, wherein [0054] teaches one or more passenger characteristics include itinerary data including an arrival time, a current location, flight information, a mode of transportation, and more, wherein [0055] teaches the computing system associated with the aviation service provider can determine, based on the passenger data, one or more aviation services to be provided to at least one of the users of the multimodal transportation service, wherein the aviation services can identify the aviation services that are scheduled for the one or more users based on the user data, wherein the aviation services can be provided to the flight facility operator and/or airline operator; see also: [0056]);
receiving, by the processor from the second computing device, a food preference for the flight passenger ([0027] teaches one or more user characteristics including historical user data that may indicate that a user prefers or avoids certain types of food, e.g. dietary preferences, as well as in [0030] teaches obtaining user preference data including preferred dietary choices and more; see also: [0050, 0052]);
determining, by the processor based on the food preference, a type of food to stock in an airport lounge ([0105] teaches the flight facility mapping data includes additional information regarding the services provided by the flight facility operator including menus for restaurants located in the flight facility and products offered in various shops in the flight facility, wherein the user can interact with the displayed mapping data to communicate service requests to the multimodal transportation service provider or service providers associated with the multi modal transportation service provider including food ordering services, wherein the user request to see the menu at the transportation service provider restaurant can be communicated to the flight facility operator, as well as in [0023] teaches the aviation data can include aviation service characteristics associated with the aviation service providers including food offerings and the airport terminal map, as well as in [0024] teaches the aviation service modifications include onboard food options and removing food offerings from the food products offered on-board the flight service, wherein [0033-0034] teach communicating service requests to the multi modal transportation service provider through an associated application with food offering services, such as restaurants within the flight facility, including a menu; see also: [0027, 0034-0035, 0143]);
receiving, by the processor from the second computing device, transaction information indicating a charge for a meal by the payment card occurred within a predetermined time before a departure time of the flight ([0048] teaches determining a user prefers vegetarian food offerings and generating a display for user service optimizations, wherein [0050] teaches determining modifications for the aviation service provider in response to the change in food offering based on passenger food preferences, wherein [0051] teaches the aviation service provider can make a food purchasing determination for passengers, wherein the computing system can utilize the passenger status data to determine whether the passenger has recently purchased food, such as if the passenger purchased food at a transportation service facility, wherein in response to this determination, the computing system can modify one or more characteristics of the aviation services offered, wherein if the user is determined to have recently eaten, a flight service provider can reduce the amount of food loaded onto the airplane performing the user’s flight service, wherein [0131] teaches that the user service optimizations can include providing an offer from an airline service to a user device of a user for display, wherein an aviation service provider can provide offers related to any provided service including food service discounts, wherein the historical user data can indicate that the user prefers vegetarian food offerings, wherein in response a flight facility operator can offer a coupon for vegetarian-oriented restaurants located inside of the flight facility; see also: [0051-0055, 0143]);
determining, by the processor based on the transaction information, an exclusion of a flight-specific food item from a flight-specific food inventory of the flight ([0050] teaches in response to obtaining passenger data associated with the user, the aviation service provider can determine modifications to their services including food offerings, wherein [0051] teaches the aviation service provider can make a food purchasing determination for passengers, wherein the computing system can utilize the passenger status data to determine whether the passenger has recently purchased food, such as if the passenger purchased food at a transportation service facility, wherein in response to this determination, the computing system can modify one or more characteristics of the aviation services offered, wherein if the user is determined to have recently eaten, a flight service provider can reduce the amount of food loaded onto the airplane performing the user’s flight service, as well as in [0024] teaches the aviation service modifications include onboard food options and removing food offerings from the food products offered on-board the flight service; see also: [0033-0035, 0143]).
However, Villa does not explicitly teach receiving, by a processor of a first computing device from a second computing device associated with a card authorizing entity, flight information associated with a flight purchased using a payment card associated with the card authorizing entity, receiving, by the processor from the second computing device, transaction information indicating a charge for a meal by the payment card occurred within a predetermined time before a departure time of the flight; and transmitting, by the processor to a third computing device associated with a food packing system, a food packing instruction for the flight indicative of the flight-specific food inventory, to facilitate the food packing system to automatically load the flight-specific food inventory into an airline meal transport device based on the food packing instruction.
From the same or similar field of endeavor, Sekar teaches receiving, by a processor of a first computing device from a second computing device associated with a card authorizing entity, flight information associated with a flight purchased using a payment card associated with the card authorizing entity ([0061] teaches end users may desire to purchase airline tickets and can provide payment information to an airline enterprise, wherein [0057-0059] teach users can set data permission levels for enterprises, such as an airline, to access their user preferences including travel meal preferences, as well as in [0130] teaches a user can trigger an interaction with an airline to book a flight for a business trip, wherein this booking can trigger an automatic interaction with an airline indicating that the user’s meal preferences may be obtained from their Credit Card Company, wherein the airline may gain meal preferences in the Credit Card Company’s data regarding the user, and wherein [0085] teaches maintain a history of end users’ preferences and interaction history; see also: [0051, 0085, 0159-0160]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Villa to incorporate the teachings of Sekar to include receiving, by a processor of a first computing device from a second computing device associated with a card authorizing entity, flight information associated with a flight purchased using a payment card associated with the card authorizing entity. One would have been motivated to do so in order to provide predictive and proactive services that better understand the needs of users by intelligently mining the real-time behavior of the user (Sekar, [0159]). By incorporating the teachings of Sekar, one would have been able to provide rich personalization and proactive communications with end users by considering user purchases that add insights into the habitual behavior or proclivities of the individual (Sekar, [0001]).
However, the combination of Villa and Sekar does not explicitly teach receiving, by the processor from the second computing device, transaction information indicating a charge for a meal by the payment card occurred within a predetermined time before a departure time of the flight; and transmitting, by the processor to a third computing device associated with a food packing system, a food packing instruction for the flight indicative of the flight-specific food inventory, to facilitate the food packing system to automatically load the flight-specific food inventory into an airline meal transport device based on the food packing instruction.
From the same or similar field of endeavor, Digby-Jones teaches and transmitting, by the processor to a third computing device associated with a food packing system, a food packing instruction for the flight indicative of the flight-specific food inventory (Fig. 2 and [0044] teaches generating an optimal subset of containers for a loading plan report that includes information about the containers to be loaded for a particular trip, wherein the loading plan may include information that may conveyed to a caterer or other individual in order to obtain an optimal set of containers that may be loaded for a particular trip, wherein [0045] teaches the loading plan report may be transmitted to an entity that may be responsible for loading containers onto an aircraft, wherein the server may transmit an electronic copy of the loading plan report to a client computer accessible by a caterer responsible for loading containers onto the vehicle, as well as in [0046] teaches additionally transmitting the generated loading plan report from the client or the server to the extended services server that is at a location accessible to an entity that may be responsible for loading containers onto the vehicle; see also: [0047]),
to facilitate the food packing system to automatically load the flight-specific food inventory into an airline meal transport device based on the food packing instruction ([0045] teaches the loading plant report may be transmitted to a fully autonomous individual, such as a robot, including instructions for one or more machines to load the identified optimal subset of containers onto the vehicle, as well as in [0016] teaches automated devices, such as robots or robotic hardware, that are utilized by an airline caterer that is responsible for loading containers on an aircraft, wherein [0024-0025] teach a carrier may only allow modifications during a predetermined time period that ends one hour before the schedule departure time, wherein the predetermined time period is established by the carrier in order to provide a sufficient amount of time to load the necessary containers onto the vehicle, as well as in Fig. 3 and [0050] teaches the server may generate a new exchange rule upon receiving modifications to customer meals that may be submitted within a predetermined time period of departure, as specific by the carrier, wherein [0051-0053] teach generating an optimal subset of containers in order to generate a loading plan report, wherein the loading plan report is transmitted to an entity responsible for loading containers onto the vehicle, wherein an autonomous individual may receive the instructions that represent the loading plan and instruct one or more machines to load the identified optimal subset of containers onto the vehicle; see also: [0027, 0030]).
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 combination of Villa and Sekar to incorporate the teachings of Digby-Jones to include and transmitting, by the processor to a third computing device associated with a food packing system, a food packing instruction for the flight indicative of the flight-specific food inventory, to facilitate the food packing system to automatically load the flight-specific food inventory into an airline meal transport device based on the food packing instruction. One would have been motivated to do so in order to accurately perform weight calculations for aircraft balancing by managing aircraft inventory, thus avoiding issues at the time the galley is loaded (Digby-Jones, [0004]). By incorporating the teachings of Digby-Jones, one would have been able to identify an optimal subset of containers for inclusion in the loading plan for loading into the aircraft prior to departure, thus configuring the proper types of containers based on space and weight limitations of the aircraft (Digby-Jones, [0003, 0043]). Furthermore, the teachings of Digby-Jones would have allowed a person having ordinary skill to more accurately identify an optimal set of containers to be loaded onto the plane, thus avoiding waste by utilizing historical passenger data (Digby-Jones, [0049]).
However, the combination of Villa, Sekar, and Digby-Jones does not explicitly teach receiving, by the processor from the second computing device, transaction information indicating a charge for a meal by the payment card occurred within a predetermined time before a departure time of the flight.
From the same or similar field of endeavor, DiGioacchino teaches receiving, by the processor from the second computing device, transaction information indicating a charge for a meal by the payment card occurred within a predetermined time before a departure time of the flight ([0022] teaches a customer may purchase products or services using a credit card while flying from one city to another, wherein [0037] teaches an airline may want to know what purchases their customers are making while at the airport before a flight, wherein [0045] teaches a temporal filter may specify one or more predetermined time ranges relative to the service date, wherein the filter may retrieve transaction data to include only transaction records occurring before the service day, such as a particular time frame during the day before the service, wherein the merchant user may specify a predetermined time range of interest, wherein [0069] teaches identifying transactions in the airport including food and other purchases, wherein [0072] teaches the analytics categories include airline bookings as event triggers, wherein [0081] teaches identifying other transaction records comprising an account identifier that satisfy the temporal filter, wherein the server may query the transaction database to obtain other transaction that include transaction data associated with a purchase of another service to be provided within a first predetermined amount of time of the service date, and purchase of a second product within a second predetermined amount of time of the service date, wherein the filters are used to filter transaction records; see also: [0038-0039]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Villa, Sekar, and Digby-Jones to incorporate the teachings of DiGioacchino to include receiving, by the processor from the second computing device, transaction information indicating a charge for a meal by the payment card occurred within a predetermined time before a departure time of the flight. One would have been motivated to do so in order to allow an airline to know what other purchases their customers are making while at the airport before a flight in order to provide analytics on what other products and services a merchant’s customers are purchasing near the same time the merchant is scheduled to provide a service to the customers (DiGioacchino, [0037]). By incorporating the teachings of DiGioacchino, one would have been able to allow the airline company to make better business decisions by providing them with information on what products their customer may purchase while in the flight origination city before their flight (DiGioacchino, [0022]).
Regarding claim 22, the combination of Villa, Sekar, Digby-Jones, and DiGioacchino teaches all the limitations of claim 21 above.
Villa further teaches wherein the airport lounge is at a departure airport of the flight ([0033] teaches the flight mapping facility data includes menus for restaurants located in the flight facility, wherein [0036] teaches the flight facility includes of a terminal from which the user will depart, as well as in [0105] teaches the flight facility mapping data includes additional information regarding the services provided by the flight facility operator including menus for restaurants located in the flight facility and products offered in various shops in the flight facility, wherein the user can interact with the displayed mapping data to communicate service requests to the multimodal transportation service provider or service providers associated with the multi modal transportation service provider including food ordering services, wherein the user request to see the menu at the transportation service provider restaurant can be communicated to the flight facility operator, as well as in [0023] teaches the aviation data can include aviation service characteristics associated with the aviation service providers including food offerings and the airport terminal map, as well as in [0024] teaches the aviation service modifications include onboard food options and removing food offerings from the food products offered on-board the flight service; see also: [0027, 0034-0035, 0143]).
Regarding claim 23, the combination of Villa, Sekar, Digby-Jones, and DiGioacchino teaches all the limitations of claim 21 above.
However, Villa fails to explicitly teach wherein the food packing instruction is transmitted to at least one packing-related machine.
From the same or similar field of endeavor, Digby-Jones further teaches wherein the food packing instruction is transmitted to at least one packing-related machine ([0045] teaches the loading plant report may be transmitted to a fully autonomous individual, such as a robot, including instructions for one or more machines to load the identified optimal subset of containers onto the vehicle, as well as in [0016] teaches automated devices, such as robots or robotic hardware, that are utilized by an airline caterer that is responsible for loading containers on an aircraft, wherein [0024-0025] teach a carrier may only allow modifications during a predetermined time period that ends one hour before the schedule departure time, wherein the predetermined time period is established by the carrier in order to provide a sufficient amount of time to load the necessary containers onto the vehicle, as well as in Fig. 3 and [0050] teaches the server may generate a new exchange rule upon receiving modifications to customer meals that may be submitted within a predetermined time period of departure, as specific by the carrier, wherein [0051-0053] teach generating an optimal subset of containers in order to generate a loading plan report, wherein the loading plan report is transmitted to an entity responsible for loading containers onto the vehicle, wherein an autonomous individual may receive the instructions that represent the loading plan and instruct one or more machines to load the identified optimal subset of containers onto the vehicle; see also: [0027, 0030]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Villa, Sekar, Digby-Jones, and DiGioacchino to incorporate the further teachings of Digby-Jones to include wherein the food packing instruction is transmitted to at least one packing-related machine. One would have been motivated to do so in order to accurately perform weight calculations for aircraft balancing by managing aircraft inventory, thus avoiding issues at the time the galley is loaded (Digby-Jones, [0004]). By incorporating the teachings of Digby-Jones, one would have been able to identify an optimal subset of containers for inclusion in the loading plan for loading into the aircraft prior to departure, thus configuring the proper types of containers based on space and weight limitations of the aircraft (Digby-Jones, [0003, 0043]).
Regarding claim 25, the combination of Villa, Sekar, Digby-Jones, and DiGioacchino teaches all the limitations of claim 23 above.
However, Villa fails to explicitly teach wherein the food packing instruction is transmitted to the at least one packing-related machine in a predefined time interval before a departure time of the flight.
From the same or similar field of endeavor, Digby-Jones further teaches wherein the food packing instruction is transmitted to the at least one packing-related machine in a predefined time interval before a departure time of the flight ([0045] teaches the loading plant report may be transmitted to a fully autonomous individual, such as a robot, including instructions for one or more machines to load the identified optimal subset of containers onto the vehicle, as well as in [0016] teaches automated devices, such as robots or robotic hardware, that are utilized by an airline caterer that is responsible for loading containers on an aircraft, wherein [0024-0025] teach a carrier may only allow modifications during a predetermined time period that ends one hour before the schedule departure time, wherein the predetermined time period is established by the carrier in order to provide a sufficient amount of time to load the necessary containers onto the vehicle, as well as in Fig. 3 and [0050] teaches the server may generate a new exchange rule upon receiving modifications to customer meals that may be submitted within a predetermined time period of departure, as specific by the carrier, wherein [0051-0053] teach generating an optimal subset of containers in order to generate a loading plan report, wherein the loading plan report is transmitted to an entity responsible for loading containers onto the vehicle, wherein an autonomous individual may receive the instructions that represent the loading plan and instruct one or more machines to load the identified optimal subset of containers onto the vehicle; see also: [0027, 0030]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Villa, Sekar, Digby-Jones, and DiGioacchino to incorporate the further teachings of Digby-Jones to include wherein the food packing instruction is transmitted to the at least one packing-related machine in a predefined time interval before a departure time of the flight. One would have been motivated to do so in order to accurately perform weight calculations for aircraft balancing by managing aircraft inventory, thus avoiding issues at the time the galley is loaded (Digby-Jones, [0004]). By incorporating the teachings of Digby-Jones, one would have been able to identify an optimal subset of containers for inclusion in the loading plan for loading into the aircraft prior to departure, thus configuring the proper types of containers based on space and weight limitations of the aircraft (Digby-Jones, [0003, 0043]).
Regarding claim 27, Villa teaches a method comprising (Figs. 7-9): receiving, by a processor of a first computing device from a second computing device associated with a card authorizing entity, flight information associated with a flight purchased using a payment card associated with the card authorizing entity by a flight passenger ([0050] teaches a passenger can be scheduled for a number of aviation services, such as flight services and food offerings, wherein the system can obtain passenger data associated with the user, wherein [0054] teaches one or more passenger characteristics include itinerary data including an arrival time, a current location, flight information, a mode of transportation, and more; see also: [0055-0056]; Examiner’s Note: See the 35 USC 103 combination below for teachings pertaining to the unbolded claim language),
wherein the flight information comprises a flight identifier and data associated with a flight passenger ([0050] teaches a passenger can be scheduled for a number of aviation services, such as flight services and food offerings, wherein the system can obtain passenger data associated with the user, wherein [0052] teaches the passenger data includes passenger characteristics including historical data such as preferences, statuses, and other user behaviors, wherein [0054] teaches one or more passenger characteristics include itinerary data including an arrival time, a current location, flight information, a mode of transportation, and more, wherein [0055] teaches the computing system associated with the aviation service provider can determine, based on the passenger data, one or more aviation services to be provided to at least one of the users of the multimodal transportation service, wherein the aviation services can identify the aviation services that are scheduled for the one or more users based on the user data, wherein the aviation services can be provided to the flight facility operator and/or airline operator; see also: [0056]);
receiving, by the processor from the second computing device, a food preference for the flight passenger ([0027] teaches one or more user characteristics including historical user data that may indicate that a user prefers or avoids certain types of food, e.g. dietary preferences, as well as in [0030] teaches obtaining user preference data including preferred dietary choices and more; see also: [0050, 0052]);
determining, by the processor based on the food preference, a type of food to stock in an airport lounge ([0105] teaches the flight facility mapping data includes additional information regarding the services provided by the flight facility operator including menus for restaurants located in the flight facility and products offered in various shops in the flight facility, wherein the user can interact with the displayed mapping data to communicate service requests to the multimodal transportation service provider or service providers associated with the multi modal transportation service provider including food ordering services, wherein the user request to see the menu at the transportation service provider restaurant can be communicated to the flight facility operator, as well as in [0023] teaches the aviation data can include aviation service characteristics associated with the aviation service providers including food offerings and the airport terminal map, as well as in [0024] teaches the aviation service modifications include onboard food options and removing food offerings from the food products offered on-board the flight service, wherein [0033-0034] teach communicating service requests to the multi modal transportation service provider through an associated application with food offering services, such as restaurants within the flight facility, including a menu; see also: [0027, 0034-0035, 0143]);
receiving, by the processor from the second computing device, transaction information indicating a charge for a meal by the payment card occurred within a predetermined time before a departure time of the flight ([0048] teaches determining a user prefers vegetarian food offerings and generating a display for user service optimizations, wherein [0050] teaches determining modifications for the aviation service provider in response to the change in food offering based on passenger food preferences, wherein [0051] teaches the aviation service provider can make a food purchasing determination for passengers, wherein the computing system can utilize the passenger status data to determine whether the passenger has recently purchased food, such as if the passenger purchased food at a transportation service facility, wherein in response to this determination, the computing system can modify one or more characteristics of the aviation services offered, wherein if the user is determined to have recently eaten, a flight service provider can reduce the amount of food loaded onto the airplane performing the user’s flight service, wherein [0131] teaches that the user service optimizations can include providing an offer from an airline service to a user device of a user for display, wherein an aviation service provider can provide offers related to any provided service including food service discounts, wherein the historical user data can indicate that the user prefers vegetarian food offerings, wherein in response a flight facility operator can offer a coupon for vegetarian-oriented restaurants located inside of the flight facility; see also: [0051-0055, 0143]);
determining, by the processor based on the transaction information, an exclusion of a flight-specific food item from a flight-specific food inventory of the flight ([0050] teaches in response to obtaining passenger data associated with the user, the aviation service provider can determine modifications to their services including food offerings, wherein [0051] teaches the aviation service provider can make a food purchasing determination for passengers, wherein the computing system can utilize the passenger status data to determine whether the passenger has recently purchased food, such as if the passenger purchased food at a transportation service facility, wherein in response to this determination, the computing system can modify one or more characteristics of the aviation services offered, wherein if the user is determined to have recently eaten, a flight service provider can reduce the amount of food loaded onto the airplane performing the user’s flight service, as well as in [0024] teaches the aviation service modifications include onboard food options and removing food offerings from the food products offered on-board the flight service; see also: [0033-0035, 0143]).
However, Villa does not explicitly teach receiving, by a processor of a first computing device from a second computing device associated with a card authorizing entity, flight information associated with a flight purchased using a payment card associated with the card authorizing entity by a flight passenger, receiving, by the processor from the second computing device, transaction information indicating a charge for a meal by the payment card occurred within a predetermined time before a departure time of the flight; and transmitting, by the processor to a third computing device associated with a food packing system, a food packing instruction for the flight indicative of the flight-specific food inventory to facilitate the food packing system to automatically load the flight-specific food inventory into an airline meal transport device based on the food packing instruction.
From the same or similar field of endeavor, Sekar teaches receiving, by a processor of a first computing device from a second computing device associated with a card authorizing entity, flight information associated with a flight purchased using a payment card associated with the card authorizing entity by a flight passenger ([0061-0062] teach one or more devices associated with an organization, e.g. companies, that exchange data via a technological platform, wherein Fig. 9 and [0130] teaches a user can trigger an automatic interaction by booking a flight, wherein the airline can request the user’s meal preferences from their Credit Card Company, as well as in [0058] teaches an airline company may receive data related to a user’s travel meal preferences, wherein [0085] teaches a CRM database that maintains a history of end users’ preferences and interaction history, wherein [0001] teaches user interactions are associated with a purchase or payment; see also: [0051, 0057, 0085, 0140, 0159-0160]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Villa to incorporate the teachings of Sekar to include receiving, by a processor of a first computing device from a second computing device associated with a card authorizing entity, flight information associated with a flight purchased using a payment card associated with the card authorizing entity by a flight passenger. One would have been motivated to do so in order to provide predictive and proactive services that better understand the needs of users by intelligently mining the real-time behavior of the user (Sekar, [0159]). By incorporating the teachings of Sekar, one would have been able to provide rich personalization and proactive communications with end users by considering user purchases that add insights into the habitual behavior or proclivities of the individual (Sekar, [0001]).
However, the combination of Villa and Sekar does not explicitly teach receiving, by the processor from the second computing device, transaction information indicating a charge for a meal by the payment card occurred within a predetermined time before a departure time of the flight; and transmitting, by the processor to a third computing device associated with a food packing system, a food packing instruction for the flight indicative of the flight-specific food inventory to facilitate the food packing system to automatically load the flight-specific food inventory into an airline meal transport device based on the food packing instruction.
From the same or similar field of endeavor, Digby-Jones teaches and transmitting, by the processor to a third computing device associated with a food packing system, a food packing instruction for the flight indicative of the flight-specific food inventory (Fig. 2 and [0044] teaches generating an optimal subset of containers for a loading plan report that includes information about the containers to be loaded for a particular trip, wherein the loading plan may include information that may conveyed to a caterer or other individual in order to obtain an optimal set of containers that may be loaded for a particular trip, wherein [0045] teaches the loading plan report may be transmitted to an entity that may be responsible for loading containers onto an aircraft, wherein the server may transmit an electronic copy of the loading plan report to a client computer accessible by a caterer responsible for loading containers onto the vehicle, as well as in [0046] teaches additionally transmitting the generated loading plan report from the client or the server to the extended services server that is at a location accessible to an entity that may be responsible for loading containers onto the vehicle; see also: [0047]),
to facilitate the food packing system to automatically load the flight-specific food inventory into an airline meal transport device based on the food packing instruction ([0045] teaches the loading plant report may be transmitted to a fully autonomous individual, such as a robot, including instructions for one or more machines to load the identified optimal subset of containers onto the vehicle, as well as in [0016] teaches automated devices, such as robots or robotic hardware, that are utilized by an airline caterer that is responsible for loading containers on an aircraft, wherein [0024-0025] teach a carrier may only allow modifications during a predetermined time period that ends one hour before the schedule departure time, wherein the predetermined time period is established by the carrier in order to provide a sufficient amount of time to load the necessary containers onto the vehicle, as well as in Fig. 3 and [0050] teaches the server may generate a new exchange rule upon receiving modifications to customer meals that may be submitted within a predetermined time period of departure, as specific by the carrier, wherein [0051-0053] teach generating an optimal subset of containers in order to generate a loading plan report, wherein the loading plan report is transmitted to an entity responsible for loading containers onto the vehicle, wherein an autonomous individual may receive the instructions that represent the loading plan and instruct one or more machines to load the identified optimal subset of containers onto the vehicle; see also: [0027, 0030]).
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 combination of Villa and Sekar to incorporate the teachings of Digby-Jones to include and transmitting, by the processor to a third computing device associated with a food packing system, a food packing instruction for the flight indicative of the flight-specific food inventory to facilitate the food packing system to automatically load the flight-specific food inventory into an airline meal transport device based on the food packing instruction. One would have been motivated to do so in order to accurately perform weight calculations for aircraft balancing by managing aircraft inventory, thus avoiding issues at the time the galley is loaded (Digby-Jones, [0004]). By incorporating the teachings of Digby-Jones, one would have been able to identify an optimal subset of containers for inclusion in the loading plan for loading into the aircraft prior to departure, thus configuring the proper types of containers based on space and weight limitations of the aircraft (Digby-Jones, [0003, 0043]). Furthermore, the teachings of Digby-Jones would have allowed a person having ordinary skill to more accurately identify an optimal set of containers to be loaded onto the plane, thus avoiding waste by utilizing historical passenger data (Digby-Jones, [0049]).
However, the combination of Villa, Sekar, and Digby-Jones does not explicitly teach receiving, by the processor from the second computing device, transaction information indicating a charge for a meal by the payment card occurred within a predetermined time before a departure time of the flight;
From the same or similar field of endeavor, DiGioacchino teaches receiving, by the processor from the second computing device, transaction information indicating a charge for a meal by the payment card occurred within a predetermined time before a departure time of the flight ([0022] teaches a customer may purchase products or services using a credit card while flying from one city to another, wherein [0037] teaches an airline may want to know what purchases their customers are making while at the airport before a flight, wherein [0045] teaches a temporal filter may specify one or more predetermined time ranges relative to the service date, wherein the filter may retrieve transaction data to include only transaction records occurring before the service day, such as a particular time frame during the day before the service, wherein the merchant user may specify a predetermined time range of interest, wherein [0069] teaches identifying transactions in the airport including food and other purchases, wherein [0072] teaches the analytics categories include airline bookings as event triggers, wherein [0081] teaches identifying other transaction records comprising an account identifier that satisfy the temporal filter, wherein the server may query the transaction database to obtain other transaction that include transaction data associated with a purchase of another service to be provided within a first predetermined amount of time of the service date, and purchase of a second product within a second predetermined amount of time of the service date, wherein the filters are used to filter transaction records; see also: [0038-0039]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Villa, Sekar, and Digby-Jones to incorporate the teachings of DiGioacchino to include receiving, by the processor from the second computing device, transaction information indicating a charge for a meal by the payment card occurred within a predetermined time before a departure time of the flight. One would have been motivated to do so in order to allow an airline to know what other purchases their customers are making while at the airport before a flight in order to provide analytics on what other products and services a merchant’s customers are purchasing near the same time the merchant is scheduled to provide a service to the customers (DiGioacchino, [0037]). By incorporating the teachings of DiGioacchino, one would have been able to allow the airline company to make better business decisions by providing them with information on what products their customer may purchase while in the flight origination city before their flight (DiGioacchino, [0022]).
Regarding claim 28, the combination of Villa, Sekar, Digby-Jones, and DiGioacchino teaches all the limitations of claim 27 above.
However, Villa fails to explicitly disclose wherein the card authorizing entity determines the food preference based at least in part on historical payment card transaction data of the flight passenger.
From the same or similar field of endeavor, Sekar further teaches wherein the card authorizing entity determines the food preference based at least in part on historical payment card transaction data of the flight passenger ([0061-0062] teach one or more devices associated with an organization, e.g. companies, that exchange data via a technological platform, wherein Fig. 9 and [0130] teaches a user can trigger an automatic interaction by booking a flight, wherein the airline can request the user’s meal preferences from their Credit Card Company, as well as in [0058] teaches an airline company may receive data related to a user’s travel meal preferences, wherein [0085] teaches a CRM database that maintains a history of end users’ preferences and interaction history, wherein [0001] teaches user interactions are associated with a purchase or payment; see also: [0051, 0057, 0085, 0140, 0159-0160]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Villa, Sekar, Digby-Jones, and DiGioacchino to incorporate the further teachings of Sekar to include wherein the card authorizing entity determines the food preference based at least in part on historical payment card transaction data of the flight passenger. One would have been motivated to do so in order to provide predictive and proactive services that better understand the needs of users by intelligently mining the real-time behavior of the user (Sekar, [0159]). By incorporating the teachings of Sekar, one would have been able to provide rich personalization and proactive communications with end users by considering user purchases that add insights into the habitual behavior or proclivities of the individual (Sekar, [0001]).
Regarding claim 30, Villa teaches a method comprising (Figs. 7-9)
receiving, by a processor of a first computing device from a second computing device associated with a card authorizing entity, flight information associated with a flight purchased using a payment card associated with the card authorizing entity ([0050] teaches a passenger can be scheduled for a number of aviation services, such as flight services and food offerings, wherein the system can obtain passenger data associated with the user, wherein [0054] teaches one or more passenger characteristics include itinerary data including an arrival time, a current location, flight information, a mode of transportation, and more; see also: [0055-0056]; Examiner’s Note: See the 35 USC 103 combination below for teachings pertaining to the unbolded claim language),
wherein the flight information comprises a flight identifier and data associated with a flight passenger ([0050] teaches a passenger can be scheduled for a number of aviation services, such as flight services and food offerings, wherein the system can obtain passenger data associated with the user, wherein [0052] teaches the passenger data includes passenger characteristics including historical data such as preferences, statuses, and other user behaviors, wherein [0054] teaches one or more passenger characteristics include itinerary data including an arrival time, a current location, flight information, a mode of transportation, and more, wherein [0055] teaches the computing system associated with the aviation service provider can determine, based on the passenger data, one or more aviation services to be provided to at least one of the users of the multimodal transportation service, wherein the aviation services can identify the aviation services that are scheduled for the one or more users based on the user data, wherein the aviation services can be provided to the flight facility operator and/or airline operator; see also: [0056]);
receiving, by the processor from the second computing device, a food preference for the flight passenger ([0027] teaches one or more user characteristics including historical user data that may indicate that a user prefers or avoids certain types of food, e.g. dietary preferences, as well as in [0030] teaches obtaining user preference data including preferred dietary choices and more; see also: [0050, 0052]);
causing, by the processor, data to be presented to a third computing device associated with the flight passenger based on the food preference ([0048] teaches determining a user prefers vegetarian food offerings and generating a display for user service optimizations, wherein [0050] teaches determining modifications for the aviation service provider in response to the change in food offering based on passenger food preferences, wherein [0051] teaches the aviation service provider can make a food purchasing determination for passengers, wherein the computing system can utilize the passenger status data to determine whether the passenger has recently purchased food, such as if the passenger purchased food at a transportation service facility, wherein in response to this determination, the computing system can modify one or more characteristics of the aviation services offered, wherein if the user is determined to have recently eaten, a flight service provider can reduce the amount of food loaded onto the airplane performing the user’s flight service, wherein [0131] teaches that the user service optimizations can include providing an offer from an airline service to a user device of a user for display, wherein an aviation service provider can provide offers related to any provided service including food service discounts, wherein the historical user data can indicate that the user prefers vegetarian food offerings, wherein in response a flight facility operator can offer a coupon for vegetarian-oriented restaurants located inside of the flight facility; see also: [0051-0055]),
wherein the data is indicative of an incentive to purchase a food item related to the food preference in an airport prior to the flight ([0048] teaches determining a user prefers vegetarian food offerings and generating a display for user service optimizations, wherein [0050] teaches determining modifications for the aviation service provider in response to the change in food offering based on passenger food preferences, wherein [0051] teaches the aviation service provider can make a food purchasing determination for passengers, wherein the computing system can utilize the passenger status data to determine whether the passenger has recently purchased food, such as if the passenger purchased food at a transportation service facility, wherein in response to this determination, the computing system can modify one or more characteristics of the aviation services offered, wherein if the user is determined to have recently eaten, a flight service provider can reduce the amount of food loaded onto the airplane performing the user’s flight service, wherein [0131] teaches that the user service optimizations can include providing an offer from an airline service to a user device of a user for display, wherein an aviation service provider can provide offers related to any provided service including food service discounts, wherein the historical user data can indicate that the user prefers vegetarian food offerings, wherein in response a flight facility operator can offer a coupon for vegetarian-oriented restaurants located inside of the flight facility; see also: [0051-0055]);
determining, by the processor, an acceptance of the incentive based at least in part on a purchase of the food item by the flight passenger using the payment card within a predetermined time before a departure of the flight ([0048] teaches determining a user prefers vegetarian food offerings and generating a display for user service optimizations, wherein [0050] teaches determining modifications for the aviation service provider in response to the change in food offering based on passenger food preferences, wherein [0051] teaches the aviation service provider can make a food purchasing determination for passengers, wherein the computing system can utilize the passenger status data to determine whether the passenger has recently purchased food, such as if the passenger purchased food at a transportation service facility, wherein in response to this determination, the computing system can modify one or more characteristics of the aviation services offered, wherein if the user is determined to have recently eaten, a flight service provider can reduce the amount of food loaded onto the airplane performing the user’s flight service, wherein [0131] teaches that the user service optimizations can include providing an offer from an airline service to a user device of a user for display, wherein an aviation service provider can provide offers related to any provided service including food service discounts, wherein the historical user data can indicate that the user prefers vegetarian food offerings, wherein in response a flight facility operator can offer a coupon for vegetarian-oriented restaurants located inside of the flight facility; see also: [0051-0055, 0143]);
determining, by the processor based on the purchase of the food item, an exclusion of a flight-specific food item from a flight-specific food inventory of the flight ([0048] teaches determining a user prefers vegetarian food offerings and generating a display for user service optimizations, wherein [0050] teaches determining modifications for the aviation service provider in response to the change in food offering based on passenger food preferences, wherein [0051] teaches the aviation service provider can make a food purchasing determination for passengers, wherein the computing system can utilize the passenger status data to determine whether the passenger has recently purchased food, such as if the passenger purchased food at a transportation service facility, wherein in response to this determination, the computing system can modify one or more characteristics of the aviation services offered, wherein if the user is determined to have recently eaten, a flight service provider can reduce the amount of food loaded onto the airplane performing the user’s flight service, wherein [0131] teaches that the user service optimizations can include providing an offer from an airline service to a user device of a user for display, wherein an aviation service provider can provide offers related to any provided service including food service discounts, wherein the historical user data can indicate that the user prefers vegetarian food offerings, wherein in response a flight facility operator can offer a coupon for vegetarian-oriented restaurants located inside of the flight facility; see also: [0051-0055, 0143]).
However, Villa does not explicitly teach receiving, by a processor of a first computing device from a second computing device associated with a card authorizing entity, flight information associated with a flight purchased using a payment card associated with the card authorizing entity, determining, a purchase of the food item by the flight passenger using the payment card within a predetermined time before a departure of the flight; and transmitting, by the processor to a fourth computing device associated with a food packing system, a food packing instruction for the flight indicative of the flight-specific food inventory, to facilitate the food packing system to automatically load the flight-specific food inventory into an airline meal transport device based on the food packing instruction.
From the same or similar field of endeavor, Sekar teaches receiving, by a processor of a first computing device from a second computing device associated with a card authorizing entity, flight information associated with a flight purchased using a payment card associated with the card authorizing entity ([0061] teaches end users may desire to purchase airline tickets and can provide payment information to an airline enterprise, wherein [0057-0059] teach users can set data permission levels for enterprises, such as an airline, to access their user preferences including travel meal preferences, as well as in [0130] teaches a user can trigger an interaction with an airline to book a flight for a business trip, wherein this booking can trigger an automatic interaction with an airline indicating that the user’s meal preferences may be obtained from their Credit Card Company, wherein the airline may gain meal preferences in the Credit Card Company’s data regarding the user, and wherein [0085] teaches maintain a history of end users’ preferences and interaction history; see also: [0051, 0085, 0159-0160]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Villa to incorporate the teachings of Sekar to include receiving, by a processor of a first computing device from a second computing device associated with a card authorizing entity, flight information associated with a flight purchased using a payment card associated with the card authorizing entity. One would have been motivated to do so in order to provide predictive and proactive services that better understand the needs of users by intelligently mining the real-time behavior of the user (Sekar, [0159]). By incorporating the teachings of Sekar, one would have been able to provide rich personalization and proactive communications with end users by considering user purchases that add insights into the habitual behavior or proclivities of the individual (Sekar, [0001]).
However, the combination of Villa and Sekar does not explicitly teach determining, a purchase of the food item by the flight passenger using the payment card within a predetermined time before a departure of the flight; and transmitting, by the processor to a fourth computing device associated with a food packing system, a food packing instruction for the flight indicative of the flight-specific food inventory, to facilitate the food packing system to automatically load the flight-specific food inventory into an airline meal transport device based on the food packing instruction.
From the same or similar field of endeavor, Digby-Jones teaches and transmitting, by the processor to a fourth computing device associated with a food packing system, a food packing instruction for the flight indicative of the flight-specific food inventory (Fig. 2 and [0044] teaches generating an optimal subset of containers for a loading plan report that includes information about the containers to be loaded for a particular trip, wherein the loading plan may include information that may conveyed to a caterer or other individual in order to obtain an optimal set of containers that may be loaded for a particular trip, wherein [0045] teaches the loading plan report may be transmitted to an entity that may be responsible for loading containers onto an aircraft, wherein the server may transmit an electronic copy of the loading plan report to a client computer accessible by a caterer responsible for loading containers onto the vehicle, as well as in [0046] teaches additionally transmitting the generated loading plan report from the client or the server to the extended services server that is at a location accessible to an entity that may be responsible for loading containers onto the vehicle; see also: [0047]),
to facilitate the food packing system to automatically load the flight-specific food inventory into an airline meal transport device based on the food packing instruction ([0045] teaches the loading plant report may be transmitted to a fully autonomous individual, such as a robot, including instructions for one or more machines to load the identified optimal subset of containers onto the vehicle, as well as in [0016] teaches automated devices, such as robots or robotic hardware, that are utilized by an airline caterer that is responsible for loading containers on an aircraft, wherein [0024-0025] teach a carrier may only allow modifications during a predetermined time period that ends one hour before the schedule departure time, wherein the predetermined time period is established by the carrier in order to provide a sufficient amount of time to load the necessary containers onto the vehicle, as well as in Fig. 3 and [0050] teaches the server may generate a new exchange rule upon receiving modifications to customer meals that may be submitted within a predetermined time period of departure, as specific by the carrier, wherein [0051-0053] teach generating an optimal subset of containers in order to generate a loading plan report, wherein the loading plan report is transmitted to an entity responsible for loading containers onto the vehicle, wherein an autonomous individual may receive the instructions that represent the loading plan and instruct one or more machines to load the identified optimal subset of containers onto the vehicle; see also: [0027, 0030]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Villa and Sekar to incorporate the teachings of Digby-Jones to include and transmitting, by the processor to a fourth computing device associated with a food packing system, a food packing instruction for the flight indicative of the flight-specific food inventory, to facilitate the food packing system to automatically load the flight-specific food inventory into an airline meal transport device based on the food packing instruction. One would have been motivated to do so in order to accurately perform weight calculations for aircraft balancing by managing aircraft inventory, thus avoiding issues at the time the galley is loaded (Digby-Jones, [0004]). By incorporating the teachings of Digby-Jones, one would have been able to identify an optimal subset of containers for inclusion in the loading plan for loading into the aircraft prior to departure, thus configuring the proper types of containers based on space and weight limitations of the aircraft (Digby-Jones, [0003, 0043]).
However, the combination of Villa, Sekar, and Digby-Jones does not explicitly teach determining, a purchase of the food item by the flight passenger using the payment card within a predetermined time before a departure of the flight.
From the same or similar field of endeavor, DiGioacchino teaches determining, a purchase of the food item by the flight passenger using the payment card within a predetermined time before a departure of the flight ([0022] teaches a customer may purchase products or services using a credit card while flying from one city to another, wherein [0037] teaches an airline may want to know what purchases their customers are making while at the airport before a flight, wherein [0045] teaches a temporal filter may specify one or more predetermined time ranges relative to the service date, wherein the filter may retrieve transaction data to include only transaction records occurring before the service day, such as a particular time frame during the day before the service, wherein the merchant user may specify a predetermined time range of interest, wherein [0069] teaches identifying transactions in the airport including food and other purchases, wherein [0072] teaches the analytics categories include airline bookings as event triggers, wherein [0081] teaches identifying other transaction records comprising an account identifier that satisfy the temporal filter, wherein the server may query the transaction database to obtain other transaction that include transaction data associated with a purchase of another service to be provided within a first predetermined amount of time of the service date, and purchase of a second product within a second predetermined amount of time of the service date, wherein the filters are used to filter transaction records; see also: [0038-0039]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Villa, Sekar, and Digby-Jones to incorporate the teachings of DiGioacchino to include determining, a purchase of the food item by the flight passenger using the payment card within a predetermined time before a departure of the flight. One would have been motivated to do so in order to allow an airline to know what other purchases their customers are making while at the airport before a flight in order to provide analytics on what other products and services a merchant’s customers are purchasing near the same time the merchant is scheduled to provide a service to the customers (DiGioacchino, [0037]). By incorporating the teachings of DiGioacchino, one would have been able to allow the airline company to make better business decisions by providing them with information on what products their customer may purchase while in the flight origination city before their flight (DiGioacchino, [0022]).
Regarding claim 31, the combination of Villa, Sekar, Digby-Jones, and DiGioacchino teaches all the limitations of claim 30 above.
However, Villa fails to explicitly disclose wherein the card authorizing entity determines the food preference based at least in part on historical payment card transaction data of the flight passenger.
From the same or similar field of endeavor, Sekar further teaches wherein the card authorizing entity determines the food preference based at least in part on historical payment card transaction data of the flight passenger ([0061-0062] teach one or more devices associated with an organization, e.g. companies, that exchange data via a technological platform, wherein Fig. 9 and [0130] teaches a user can trigger an automatic interaction by booking a flight, wherein the airline can request the user’s meal preferences from their Credit Card Company, as well as in [0058] teaches an airline company may receive data related to a user’s travel meal preferences, wherein [0085] teaches a CRM database that maintains a history of end users’ preferences and interaction history, wherein [0001] teaches user interactions are associated with a purchase or payment; see also: [0051, 0057, 0085, 0140, 0159-0160]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Villa, Sekar, Digby-Jones, and DiGioacchino to incorporate the further teachings of Sekar to include wherein the card authorizing entity determines the food preference based at least in part on historical payment card transaction data of the flight passenger. One would have been motivated to do so in order to provide predictive and proactive services that better understand the needs of users by intelligently mining the real-time behavior of the user (Sekar, [0159]). By incorporating the teachings of Sekar, one would have been able to provide rich personalization and proactive communications with end users by considering user purchases that add insights into the habitual behavior or proclivities of the individual (Sekar, [0001]).
Regarding claim 32, the combination of Villa, Sekar, Digby-Jones, and DiGioacchino teaches all the limitations of claim 31 above.
However, Villa fails to explicitly disclose wherein the historical payment card transaction data is from within a predefined time period before the flight.
From the same or similar field of endeavor, DiGioacchino teaches wherein the historical payment card transaction data is from within a predefined time period before the flight ([0044-0045] teach the server may query the transaction database to retrieve some or all of the transaction records having the account identifier of the merchant’s customer, wherein the server may filter the transaction records based on a temporal filter that specifies one or more predetermined time ranges relative to the date of service, wherein the retrieved transaction records may be filtered to include transaction records that are specified for a predetermined time range of interest before the service date, wherein [0041-0042] teach the service date may be the date of a flight, wherein [0033] teaches the transaction records include restaurants, wherein [0022] teaches aggregating transaction data of an airline’s customer to determine what their customers are purchasing in the flight origination city before the flight, wherein the system may then generate analytics data based on customer behavior so the airline company may make better business decisions, and wherein [0068-0069] teach the merchant may specify what categories of analytics data are to be analyzed including food transactions and other category spend breakdowns of the customer, as well as in [0072] teaches identifying dining trends of customers; see also: [0051-0053, 0059, 0081]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Villa, Sekar, Digby-Jones, and DiGioacchino to incorporate the further teachings of DiGioacchino to include wherein the historical payment card transaction data is from within a predefined time period before the flight. One would have been motivated to do so in order to allow an airline to make better business decisions using analytics gathered from customer transaction data before their flight (DiGioacchino, [0022]). By incorporating the teachings of DiGioacchino, one would have been able to permit merchants to better understand their customer base by providing transaction record searching tools that can aid airlines that may be interested in what purchases a customer makes before their outgoing flight (DiGioacchino, [0006]).
Regarding claim 33, the combination of Villa, Sekar, Digby-Jones, and DiGioacchino teaches all the limitations of claim 30 above.
However, Villa fails to explicitly disclose wherein the food packing instruction is transmitted to at least one packing-related machine.
From the same or similar field of endeavor, Digby-Jones further teaches wherein the food packing instruction is transmitted to at least one packing-related machine ([0045] teaches the loading plant report may be transmitted to a fully autonomous individual, such as a robot, including instructions for one or more machines to load the identified optimal subset of containers onto the vehicle, as well as in [0016] teaches automated devices, such as robots or robotic hardware, that are utilized by an airline caterer that is responsible for loading containers on an aircraft, wherein [0024-0025] teach a carrier may only allow modifications during a predetermined time period that ends one hour before the schedule departure time, wherein the predetermined time period is established by the carrier in order to provide a sufficient amount of time to load the necessary containers onto the vehicle, as well as in Fig. 3 and [0050] teaches the server may generate a new exchange rule upon receiving modifications to customer meals that may be submitted within a predetermined time period of departure, as specific by the carrier, wherein [0051-0053] teach generating an optimal subset of containers in order to generate a loading plan report, wherein the loading plan report is transmitted to an entity responsible for loading containers onto the vehicle, wherein an autonomous individual may receive the instructions that represent the loading plan and instruct one or more machines to load the identified optimal subset of containers onto the vehicle; see also: [0027, 0030]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Villa, Sekar, Digby-Jones, and DiGioacchino to incorporate the further teachings of Digby-Jones to include wherein the food packing instruction is transmitted to at least one packing-related machine. One would have been motivated to do so in order to accurately perform weight calculations for aircraft balancing by managing aircraft inventory, thus avoiding issues at the time the galley is loaded (Digby-Jones, [0004]). By incorporating the teachings of Digby-Jones, one would have been able to identify an optimal subset of containers for inclusion in the loading plan for loading into the aircraft prior to departure, thus configuring the proper types of containers based on space and weight limitations of the aircraft (Digby-Jones, [0003, 0043]).
Regarding claim 35, the combination of Villa, Sekar, Digby-Jones, and DiGioacchino teaches all the limitations of claim 33 above.
However, Villa fails to explicitly disclose wherein the food packing instruction is transmitted to the at least one packing-related machine in a predefined time interval before a departure time of the flight.
From the same or similar field of endeavor, Digby-Jones further teaches wherein the food packing instruction is transmitted to the at least one packing-related machine in a predefined time interval before a departure time of the flight ([0045] teaches the loading plant report may be transmitted to a fully autonomous individual, such as a robot, including instructions for one or more machines to load the identified optimal subset of containers onto the vehicle, as well as in [0016] teaches automated devices, such as robots or robotic hardware, that are utilized by an airline caterer that is responsible for loading containers on an aircraft, wherein [0024-0025] teach a carrier may only allow modifications during a predetermined time period that ends one hour before the schedule departure time, wherein the predetermined time period is established by the carrier in order to provide a sufficient amount of time to load the necessary containers onto the vehicle, as well as in Fig. 3 and [0050] teaches the server may generate a new exchange rule upon receiving modifications to customer meals that may be submitted within a predetermined time period of departure, as specific by the carrier, wherein [0051-0053] teach generating an optimal subset of containers in order to generate a loading plan report, wherein the loading plan report is transmitted to an entity responsible for loading containers onto the vehicle, wherein an autonomous individual may receive the instructions that represent the loading plan and instruct one or more machines to load the identified optimal subset of containers onto the vehicle; see also: [0027, 0030]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Villa, Sekar, Digby-Jones, and DiGioacchino to incorporate the further teachings of Digby-Jones to include wherein the food packing instruction is transmitted to the at least one packing-related machine in a predefined time interval before a departure time of the flight. One would have been motivated to do so in order to accurately perform weight calculations for aircraft balancing by managing aircraft inventory, thus avoiding issues at the time the galley is loaded (Digby-Jones, [0004]). By incorporating the teachings of Digby-Jones, one would have been able to identify an optimal subset of containers for inclusion in the loading plan for loading into the aircraft prior to departure, thus configuring the proper types of containers based on space and weight limitations of the aircraft (Digby-Jones, [0003, 0043]).
Regarding claim 39, the combination of Villa, Sekar, Digby-Jones, and DiGioacchino teaches all the limitations of claim 30 above.
However, Villa fails to explicitly disclose wherein the food preference is determined based on historical payment card transaction data from a predefined time period before the flight
From the same or similar field of endeavor, DiGioacchino teaches wherein the food preference is determined based on historical payment card transaction data from a predefined time period before the flight ([0044-0045] teach the server may query the transaction database to retrieve some or all of the transaction records having the account identifier of the merchant’s customer, wherein the server may filter the transaction records based on a temporal filter that specifies one or more predetermined time ranges relative to the date of service, wherein the retrieved transaction records may be filtered to include transaction records that are specified for a predetermined time range of interest before the service date, wherein [0041-0042] teach the service date may be the date of a flight, wherein [0033] teaches the transaction records include restaurants, wherein [0022] teaches aggregating transaction data of an airline’s customer to determine what their customers are purchasing in the flight origination city before the flight, wherein the system may then generate analytics data based on customer behavior so the airline company may make better business decisions, and wherein [0068-0069] teach the merchant may specify what categories of analytics data are to be analyzed including food transactions and other category spend breakdowns of the customer, as well as in [0072] teaches identifying dining trends of customers; see also: [0051-0053, 0059, 0081]).
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 combination of Villa, Sekar, Digby-Jones, and DiGioacchino to incorporate the further teachings of DiGioacchino to include wherein the food preference is determined based on historical payment card transaction data from a predefined time period before the flight. One would have been motivated to do so in order to allow an airline to make better business decisions using analytics gathered from customer transaction data before their flight (DiGioacchino, [0022]). By incorporating the teachings of DiGioacchino, one would have been able to permit merchants to better understand their customer base by providing transaction record searching tools that can aid airlines that may be interested in what purchases a customer makes before their outgoing flight (DiGioacchino, [0006]).
Regarding claim 40, the combination of Villa, Sekar, Digby-Jones, and DiGioacchino teaches all the limitations of claim 39 above.
However, Villa fails to explicitly disclose wherein the predefined time period comprises six months before the flight.
From the same or similar field of endeavor, DiGioacchino further teaches wherein the predefined time period comprises six months before the flight ([0044-0045] teach the server may query the transaction database to retrieve some of the transaction records having the account identifier of the merchant’s customer, wherein the server may filter the transaction records based on a temporal filter that specifies one or more predetermined time ranges relative to the date of service, wherein the retrieved transaction records may be filtered to include transaction records that are specified for a predetermined time range of interest before the service date, wherein [0067] teaches the period of time for analysis may be the last six months, wherein [0041-0042] teach the service date may be the date of a flight, wherein [0033] teaches the transaction records include restaurants, wherein [0022] teaches aggregating transaction data of an airline’s customer to determine what their customers are purchasing in the flight origination city before the flight, wherein the system may then generate analytics data based on customer behavior so the airline company may make better business decisions, and wherein [0068-0069] teach the merchant may specify what categories of analytics data are to be analyzed including food transactions and other category spend breakdowns of the customer, as well as in [0072] teaches identifying dining trends of customers; see also: [0051-0053, 0059, 0068, 0070, 0081]).
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 combination of Villa, Sekar, Digby-Jones, and DiGioacchino to incorporate the further teachings of DiGioacchino to include wherein the predefined time period comprises six months before the flight. One would have been motivated to do so in order to allow an airline to make better business decisions using analytics gathered from customer transaction data before their flight (DiGioacchino, [0022]). By incorporating the teachings of DiGioacchino, one would have been able to permit merchants to better understand their customer base by providing transaction record searching tools that can aid airlines that may be interested in what purchases a customer makes before their outgoing flight (DiGioacchino, [0006]).
Claim(s) 26 and 37-38 are rejected under 35 U.S.C. 103 as being unpatentable over Villa et al. (US 20220405653 A1) in view of Sekar et al. (US 20220092056 A1) in view of Digby-Jones et al. (US 20150294227 A1) in view of DiGioacchino et al. (US 20220405653 A1) in view of Raviv et al. (US 11205196 B1).
Regarding claim 26, the combination of Villa, Sekar, Digby-Jones, and DiGioacchino teaches all the limitations of claim 21 above.
However, Villa fails to explicitly teach wherein the second computing device associated with the card authorizing entity further comprises a trained machine learning model, and further wherein the second computing device associated with the card authorizing entity is configured to determine the food preference for the flight passenger based at least in part on historical payment card transaction data of the flight passenger using the trained machine learning model.
From the same or similar field of endeavor, Sekar further teaches wherein the second computing device associated with the card authorizing entity further comprises a trained machine learning model ([0133] teaches leveraging a machine learning algorithm to ascertain patterns and relevant information related to underlying data, which may be used to facilitate predictions of events, as well as in [0164-0165] teach a trained predictive AI model of an enterprise, wherein [0129-0130] teach a credit card company provisioning information related to an individual, which may be added to the blockchain, and wherein [0085] teaches maintain a history of end users’ preferences and interaction history; see also: [0051, 0061, 0159-0160]),
and further wherein the second computing device associated with the card authorizing entity is configured to determine the food preference for the flight passenger based at least in part on historical payment card transaction data of the flight passenger using the trained machine learning model ([0118] teaches an end user may share specific preferences with an enterprise, such as an airline, wherein [0130] teaches a user can trigger an interaction with an airline to book a flight for a business trip, wherein this booking can trigger an automatic interaction with an airline indicating that the user’s meal preferences may be obtained from their Credit Card Company, wherein the airline may gain meal preferences in the Credit Card Company’s data regarding the user, wherein [0085] teaches a CRM database that maintains a history of end users’ preferences and interaction history, wherein [0001] teaches user interactions are associated with a purchase or payment; Examiner’s Note: See the 35 USC 103 combination below for teachings pertaining to the unbolded claim language.).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Villa, Sekar, Digby-Jones, and DiGioacchino to incorporate the further teachings of Sekar to include wherein the second computing device associated with the card authorizing entity further comprises a trained machine learning model, and further wherein the computing device associated with the card authorizing entity is configured to determine the food preference for the flight passenger. One would have been motivated to do so in order to provide predictive and proactive services that better understand the needs of users by intelligently mining the real-time behavior of the user (Sekar, [0159]). By incorporating the teachings of Sekar, one would have been able to provide rich personalization and proactive communications with end users by considering user purchases that add insights into the habitual behavior or proclivities of the individual (Sekar, [0001]).
As can be seen above, Sekar discloses a computing device associated with the card authorizing entity determining a food preference for the passenger; however, the combination of Villa, Sekar, Digby-Jones, and DiGioacchino fails to explicitly disclose and further wherein the second computing device associated with the card authorizing entity is configured to determine the food preference for the flight passenger based at least in part on historical payment card transaction data of the flight passenger using the trained machine learning model.
From the same or similar field of endeavor, Raviv teaches and further wherein the second computing device associated with the card authorizing entity is configured to determine the food preference for the flight passenger based at least in part on historical payment card transaction data of the flight passenger using the trained machine learning model (Col 10 lines 49-61 teach updating a user profile of user information using one or more machine learning models, wherein Col 9 line 52 Col 10 line 32 teach maintaining user information including restaurant preferences, as well as in Col 8 lines 14-43 teaches extracting travel-related data from aggregated external data including historical travel information including group information related to a single person, couple, family, or group, airline information including company name and meal preference, restaurant information including type of food and costs, and wherein the extracted data includes non-travel related data based on prior user transactions, wherein Col 8 line 44 to Col 9 teaches filtering vendor services, such as credit card company and airline information, and historical travel information corresponding to a user in order to identify service offerings pertaining to air travel, wherein a user profile can be generated and maintained based on historical information including previous travel, prior purchases, and travel corresponding to friends/relationships, wherein the user profile can be updated based on machine learning models, and wherein Col 7 lines 42-58 teach the machine learning module can perform the steps described).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Villa, Sekar, Digby-Jones, and DiGioacchino to incorporate the teachings of Raviv to include and further wherein the second computing device associated with the card authorizing entity is configured to determine the food preference for the flight passenger based at least in part on historical payment card transaction data of the flight passenger using the trained machine learning model. One would have been motivated to do so in order to avoid each newly booked provider being unaware of the preferences and past behaviors of the user, wherein the details of user’s travel plans are typically scattered among several service provider that each hold a piece of information regarding any given trip (Raviv, Col 1 lines 30-37).
Regarding claim 37, the combination of Villa, Sekar, Digby-Jones, and DiGioacchino teaches all the limitations of claim 30 above.
However, Villa fails to explicitly disclose wherein the second computing device associated with the card authorizing entity further comprises a trained machine learning model, and further wherein the computing device associated with the card authorizing entity is configured to determine the food preference for the flight passenger based at least in part on historical payment card transaction data of the flight passenger using the trained machine learning model.
From the same or similar field of endeavor, Sekar further teaches wherein the second computing device associated with the card authorizing entity further comprises a trained machine learning model ([0133] teaches leveraging a machine learning algorithm to ascertain patterns and relevant information related to underlying data, which may be used to facilitate predictions of events, as well as in [0164-0165] teach a trained predictive AI model of an enterprise, wherein [0129-0130] teach a credit card company provisioning information related to an individual, which may be added to the blockchain, and wherein [0085] teaches maintain a history of end users’ preferences and interaction history; see also: [0051, 0061, 0159-0160]),
and further wherein the second computing device associated with the card authorizing entity is configured to determine the food preference for the flight passenger based at least in part on historical payment card transaction data of the flight passenger using the trained machine learning model ([0118] teaches an end user may share specific preferences with an enterprise, such as an airline, wherein [0130] teaches a user can trigger an interaction with an airline to book a flight for a business trip, wherein this booking can trigger an automatic interaction with an airline indicating that the user’s meal preferences may be obtained from their Credit Card Company, wherein the airline may gain meal preferences in the Credit Card Company’s data regarding the user, wherein [0085] teaches a CRM database that maintains a history of end users’ preferences and interaction history, wherein [0001] teaches user interactions are associated with a purchase or payment; Examiner’s Note: See the 35 USC 103 combination below for teachings pertaining to the unbolded claim language.).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Villa, Sekar, Digby-Jones, and DiGioacchino to incorporate the further teachings of Sekar to include wherein the second computing device associated with the card authorizing entity further comprises a trained machine learning model, and further wherein the second computing device associated with the card authorizing entity is configured to determine the food preference for the flight passenger. One would have been motivated to do so in order to provide predictive and proactive services that better understand the needs of users by intelligently mining the real-time behavior of the user (Sekar, [0159]). By incorporating the teachings of Sekar, one would have been able to provide rich personalization and proactive communications with end users by considering user purchases that add insights into the habitual behavior or proclivities of the individual (Sekar, [0001]).
As can be seen above, Sekar discloses a computing device associated with the card authorizing entity determining a food preference for the passenger; however, the combination of Villa, Sekar, Digby-Jones, and DiGioacchino fails to explicitly disclose and further wherein the second computing device associated with the card authorizing entity is configured to determine the food preference for the flight passenger based at least in part on historical payment card transaction data of the flight passenger using the trained machine learning model.
From the same or similar field of endeavor, Raviv teaches and further wherein the second computing device associated with the card authorizing entity is configured to determine the food preference for the flight passenger based at least in part on historical payment card transaction data of the flight passenger using the trained machine learning model (Col 10 lines 49-61 teach updating a user profile of user information using one or more machine learning models, wherein Col 9 line 52 Col 10 line 32 teach maintaining user information including restaurant preferences, as well as in Col 8 lines 14-43 teaches extracting travel-related data from aggregated external data including historical travel information including group information related to a single person, couple, family, or group, airline information including company name and meal preference, restaurant information including type of food and costs, and wherein the extracted data includes non-travel related data based on prior user transactions, wherein Col 8 line 44 to Col 9 teaches filtering vendor services, such as credit card company and airline information, and historical travel information corresponding to a user in order to identify service offerings pertaining to air travel, wherein a user profile can be generated and maintained based on historical information including previous travel, prior purchases, and travel corresponding to friends/relationships, wherein the user profile can be updated based on machine learning models, and wherein Col 7 lines 42-58 teach the machine learning module can perform the steps described).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Villa, Sekar, Digby-Jones, and DiGioacchino to incorporate the teachings of Raviv to include and further wherein the second computing device associated with the card authorizing entity is configured to determine the food preference for the flight passenger based at least in part on historical payment card transaction data of the flight passenger using the trained machine learning model. One would have been motivated to do so in order to avoid each newly booked provider being unaware of the preferences and past behaviors of the user, wherein the details of user’s travel plans are typically scattered among several service provider that each hold a piece of information regarding any given trip (Raviv, Col 1 lines 30-37).
Regarding claim 38, the combination of Villa, Sekar, Digby-Jones, DiGioacchino, and Raviv teaches all the limitations of claim 37 above.
However, Villa fails to explicitly teach wherein the trained machine learning model is configured to determine, from the historical payment card transaction data, a type of cuisine or type of dish associated with each of a plurality of transactions of the historical payment card transaction data.
From the same or similar field of endeavor, Raviv further teaches wherein the trained machine learning model is configured to determine (Col 10 lines 49-61 teach updating a user profile of user information using one or more machine learning models; see also: Col 7 lines 42-58),
from the historical payment card transaction data (Col 10 lines 49-61 teach updating a user profile of user information using one or more machine learning models, wherein Col 8 lines 14-43 teaches extracting travel-related data from aggregated external data including historical travel information and non-travel related data based on prior user transactions, as well as in Col 8 line 44 to Col 9 teaches filtering information from credit card companies and historical travel corresponding to a user in order to identify service offerings pertaining to air travel, wherein a user profile can be generated and maintained based on historical information including previous travel, prior purchases, wherein the user profile can be updated based on machine learning models, and wherein Col 7 lines 42-58 teach the machine learning module can perform the steps described),
a type of cuisine or type of dish associated with each of a plurality of transactions of the historical payment card transaction data (Col 9 line 52 Col 10 line 32 teach maintaining user information including restaurant preferences, as well as in Col 8 lines 14-43 teaches extracting travel-related data from aggregated external data including historical travel information including group information related to a single person, couple, family, or group, airline information including company name and meal preference, restaurant information including type of food and costs, and wherein the extracted data includes non-travel related data based on prior user transactions; see also: Col 3 lines 33-63, Col 7 lines 42-58, Col 8 line 44 to Col 9).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Villa, Sekar, Digby-Jones, DiGioacchino, and Raviv to incorporate the further teachings of Raviv to include wherein the trained machine learning model is configured to determine, from the historical payment card transaction data, a type of cuisine or type of dish associated with each of a plurality of transactions of the historical payment card transaction data. One would have been motivated to do so in order to avoid each newly booked provider being unaware of the preferences and past behaviors of the user, wherein the details of user’s travel plans are typically scattered among several service provider that each hold a piece of information regarding any given trip (Raviv, Col 1 lines 30-37).
Claim(s) 36 is rejected under 35 U.S.C. 103 as being unpatentable over Villa et al. (US 20220405653 A1) in view of Sekar et al. (US 20220092056 A1) in view of Digby-Jones et al. (US 20150294227 A1) in view of DiGioacchino et al. (US 20220405653 A1) in view of Browne et al. (US 20210024215 A1).
Regarding claim 36, the combination of Villa, Sekar, Digby-Jones, and DiGioacchino teaches all the limitations of claim 30 above.
However, Villa fails to explicitly disclose further comprising using the food preference to negotiate a price for purchasing food for the flight-specific food inventory.
From the same or similar field of endeavor, Browne teaches further comprising using the food preference to negotiate a price for purchasing food for the flight-specific food inventory ([0045] teaches a flight plan is determined including meal planning, wherein the meal plan is determined and appropriate orders are made to obtain the desired types and quantities of consumables, wherein the quantities are tracked in-flight in order to determine food trends, wherein food trends may be calculated and meal planning can be optimized to reflect food utilization during trips based on collected data in order to improve meal planning to provide a reduce cost of non-perishable goods due to a lower quantity of unused items and reduced fuel cost, wherein [0034] teaches one or more passengers may have previously known preferences based on orders made by that particular passenger on previous trips, which can be used to modify the general meal plan, and wherein [0069] teaches efficiently planning the meal plan for the trip can reduce the costs and reduce waste; see also: [0016-0018]).
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 combination of Villa, Sekar, Digby-Jones, and DiGioacchino to incorporate the teachings of Browne to include further comprising using the food preference to negotiate a price for purchasing food for the flight-specific food inventory. One would have been motivated to do so in order to reduce food waste and reduce weight, thus reducing the amount of fuel costs by tracking passenger consumption of inflight consumables for future meal planning (Browne, [0016]). By incorporating the teachings of Browne, one would have been able to improve meal planning efficiency and reduce cost of non-perishable goods due to lower quantities of unused items (Browne, [0045]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Li et al. (US 20210294485 A1) discloses suggesting a user get a meal before boarding the plane
Anderson et al. (US 20180330307 A1) discloses identifying a group of people who are stopping at a restaurant within the transportation hub prior to going to the departure location and boarding gate for their flight
Thogersen et al. (US 20160080913 A1) discloses informing the passenger of nearby restaurants in case it determines that the passenger has time for a meal before schedule gate opening of the passenger’s departure gate
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SARA GRACE BROWN/Primary Examiner, Art Unit 3625