Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR
1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/29/2025 has been entered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-9, 11, and 13-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claims 1 and 11 each recite a method of organizing human activity because the claims recite methods that include receiving a query from a user, wherein the query corresponds to an itinerary; receiving personal data associated with the user; aggregating data from one or more external sources for generating a travel itinerary plan based on the query and the personal data gathered, wherein the travel itinerary plan is a directed graph, wherein the directed graph comprises a set of preferred nodes and a set of preferred edges connecting the set of preferred nodes, wherein each preferred node corresponds to an intermediate destination in the travel itinerary plan, wherein each preferred edge from the set of preferred edges corresponds to a transportation option between two adjacent preferred nodes in the set of preferred nodes in the travel itinerary plan, wherein the preferred nodes and preferred edges are selected based on the personal data; analysing the travel itinerary plan and identifying a set of possible points of failure in the travel itinerary plan, wherein the set of possible points of failure correspond to one or more preferred edges or one or more preferred nodes from the set of preferred nodes and set of preferred edges, wherein the set of possible points of failure are identified based on real-time data and/or historical data associated with the set of preferred nodes and/or the set of preferred edges; generating an updated travel itinerary plan based on the set of possible points of failure, wherein the updated travel itinerary plan comprises a set of alternate nodes and a set of alternate edges along with the set of preferred nodes and the set of preferred edges; consuming the travel itinerary plan and updated travel itinerary plan to generate a final travel itinerary; and consuming the final travel itinerary and generating an Al package to assist the user in a journey based on the final travel itinerary, wherein the AI package, when deployed on a user device, renders a graphical user interface over the user device to assist the user in a journey based on the final travel itinerary and suggest one or more alternate nodes and/or alternative edges even in offline mode based on real-time inputs received from the user during the travel or changes encountered during the journey, and wherein the Itinerary Recalibration Module is an Artificial Intelligence engine that implements machine learning algorithms to separately rank the alternate nodes independently from the alternate edges based on a combination of a probability of occurrence of unexpected situations as well as user preferences/selections, wildcard options, and users historical travel data or changes encountered during the journey, wherein the machine learning algorithms learn from past data to predict future disruptions and rank potential points of failure based on their likelihood and impact on the user's itinerary, and wherein the AI package operates independently on the user device with or without requiring server
connectivity during itinerary recalibration. This is a method of managing interactions between people (e.g., the shipper, the recipient, customs). The mere nominal recitation of a memory, a processor coupled to the memory wherein the processor is configured to execute programmed instructions stored in the memory, a Requirement Analysis Module, an Itinerary Planning Module, an Itinerary Recalibration Module, an Itinerary Generation Module, and an Onboard Al Modelling Module does not take the claims out of the method of organizing human activity grouping. Thus, the claims fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
This judicial exception is not integrated into a practical application. Each of the claims as a whole merely describe how to generally “apply” the concepts of receiving, receiving, aggregating, analysing, identifying, generating, consuming, consuming, and generating in a computer environment. The claimed memory, processor, Requirement Analysis Module, Itinerary Planning Module, Itinerary Recalibration Module, Itinerary Generation Module, and Onboard Al Modelling Module are merely invoked as tools to perform the claimed method, whether viewed individually or in combination. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A, each of the claims as a whole merely describe how to generally “apply” the concepts of receiving, receiving, aggregating, analysing, identifying, generating, consuming, consuming, and generating in a computer environment. Thus, even when viewed as a whole, nothing in the claims add significantly more (i.e., an inventive concept) to the abstract idea. The claims are ineligible.
Dependent claims 3-9, 11, and 13-19 are directed to substantially the same abstract idea as claims 1 and 11 and are rejected for substantially the same reasons. Claims 3, 4, 13, and 14 further narrow the abstract idea of claims 1 and 11, respectively, by e.g., further defining the query and the personal data. Claims 5 and 15 further narrow the abstract idea of claims 1 and 11, respectively, by e.g., further defining the one or more external sources. Claims 6 and 16 further narrow the abstract idea of claims 1 and 11, respectively, by e.g., further defining the intermediate destination. Claims 7 and 17 further narrow the abstract idea of claims 1 and 11, respectively, by e.g., further defining the transportation option. Claims 8 and 18 further narrow the abstract idea of claims 1 and 11, respectively, by e.g., further defining the real- time data and/or the historical data. Claims 9 and 19 further narrow the abstract idea of claims 1 and 11, respectively, by e.g., further defining the real- time inputs. The dependent claims do not add any additional elements to evaluate at Steps 2A prong two or 2B and thus describe neither a practical application of nor significantly more than the abstract idea.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis
for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3-9, 11, and 13-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mundinger US 9261374.
Regarding Claim 1, Mundinger discloses an Itinerary planning system, wherein the itinerary planning system comprises: a memory; and a processor coupled to the memory wherein the processor is configured to execute programmed instructions stored in the memory for (Claim 2 . An Information Technology system for transportation planning comprising a server configured to execute computer-executable components, the server comprising: at least one computer processor coupled to a non-transitory, computer-readable medium):
receiving, by a Requirement Analysis Module, a query from a user, wherein the query corresponds to an itinerary (Col. 3:17-36 means for deconstructing a user enquiry specifying the journey into a plurality of information requests, each specifying a part of the journey using a single mode of transport, such as rail, car or coach. This document also describes a Navigator having means for sending each request to an appropriate one of a plurality of local and on-line databases, which each hold travel information regarding a different mode of transport. Further means are provided for reconstructing the responses to the requests received from the plurality of local and on-line databases into at least one multi-modal travel option, for the user specified journey, incorporating different modes of transport. Usually, the multi-modal travel option incorporates timetable travel information such as train timetables and non-timetable travel information. The Navigator can also implement uni-modal point to point travel where the user can specify any geographical location and a mode of transport with the most suitable terminals and services being determined; Col. 12:55-58 two users unexpectedly meet during a travel, decide to travel together, and send a request to the system for re-organizing the common part of the journey; FIG. 4);
receiving, by the Requirement Analysis Module, personal data associated with the user (6:60-67 User preferences may be stored in the database 32 accessible by the IT system 30, as illustrated, and/or locally stored in the user's device (for example as a cookie). At least some of the preferences can be displayed and/or edited via a web interface from the computer 10 or from the personal travel assistant 40. The use of messages such as SMS, voice message and text to speech, e-mail, etc. for editing the user's preferences can be foreseen as well. Suppose you have booked a trip from Lausanne to Amsterdam for example by using a web interface to enter your home address in Lausanne and the address of your destination in Amsterdam as well as an approximate desired time for your trip, then choosing one of the routes proposed and then booking that route with the different travel providers for train, bus and air travel online);
aggregating, by an Itinerary Planning Module, data from one or more external sources for
generating a travel itinerary plan based on the query and the personal data gathered by the Requirement Analysis Module, wherein the travel itinerary plan is a directed graph, wherein the directed graph comprises a set of preferred nodes and a set of preferred edges connecting the set of preferred nodes, wherein each preferred node corresponds to an intermediate destination in the travel itinerary plan, wherein each preferred edge from the set of preferred edges corresponds to a transportation option between two adjacent preferred nodes in the set of preferred nodes in the travel itinerary plan, wherein the preferred nodes and preferred edges are selected based on the personal data (Col. 4:63 – 5:5 FIG. 2 illustrates a schematic map of a geographic region covered by different networks for different travels using different mode of transportation. In this application, a route segment is a branch between two nodes (or points) of a network that can be travelled by a user, using one specific transportation mode. A route is a set of one or several mutually connected route segments by which one user can travel from one one departure point to one destination point. A multimodal route is a route made up of different route segments that are travelled using different transportation modes);
analysing, by an Itinerary Recalibration Module, the travel itinerary plan and identifying a set of possible points of failure in the travel itinerary plan, wherein the set of possible points of failure correspond to one or more preferred edges or one or more preferred nodes from the set of preferred nodes and set of preferred edges (Col. 12:59-67 Unexpected events that may occur during planned joint travel comprise for example situation where one of the joint travellers miss a connection, or is delayed, or decides to change his itinerary; in those situations, the system may automatically generate an electronic message informing all concerned users of this unexpected event, reorganize the route of the other users in the joint travel, and/or propose to renounce to the joint travel or to split the group of users on some or all route segments),
wherein the set of possible points of failure are identified based on real-time data and/or historical data associated with the set of preferred nodes and/or the set of preferred edges (Col. 6:1-15 the personal travel assistants include a data sending part for sending data to the IT system 30. This data sending part may comprise for example a cellular equipment able to send data over the cellular network 50, a Bluetooth connection for connecting the personal travel assistant to a user's mobile equipment in the vicinity, etc. This allows the personal travel assistant 40 to transmit its position and other data in real time, for example while travelling, to the IT system 30 which can use this information for adapting the route (when required), sending suggestions etc. This connection can also be used by the IT system for verifying the displacements of the user, checking delays, and using this information for planning further travels of other users);
generating, by the Itinerary Recalibration Module, an updated travel itinerary plan based on the set of possible points of failure, wherein the updated travel itinerary plan comprises a set of alternate nodes and a set of alternate edges along with the set of preferred nodes and the set of preferred edges; consuming, by an Itinerary Generation Module, the travel itinerary plan and updated travel itinerary plan to generate a final travel itinerary (Col. 14:9-47 Unexpected (including predicted) events may comprise for example delays, cancellations, bad weather, users wanting to make a change. Unexpected events may also concern other users of the system; for example, in case of joint planning, the IT system 30 may detect that one user in a group has missed his bus, and propose an alternative route to all users to avoid cancellation of the meeting. Voting mechanisms may be proposed if the users disagree on the new route to select; the personal travel assistant may also help the different users to establish a voice and/or data communication between themselves in case of unexpected event, and help all users to rearrange the travel. Rerouting may be triggered by any one of the following events: Comparing in the IT system and/or in the user's personal travel assistant 40 the scheduled time and location with the current time and location measured by the assistant; Information on delays retrieved from the transportation providers, from news services, or from different sources including current weather and weather forecasts Information from other users collected by other users of the system. For example, the central IT system may detect that the plane XY expected to start in 60 minutes from Geneva is still in Amsterdam and thus will be delayed. Computation and display of a probability of catching the next segment or transportation means in a route; User input, for example if the user indicates during its travel that he wants to make a change Current location of other users for example in the case of joint travel. At least some of those events are detected by the central IT system in which the route selected by the user has been stored and which follows the displacements of the user during travel. Other events may be detected locally in the user personal travel assistant and possibly transmitted to the central IT system 30. As already indicated, a reaction, for example a new route or new segments, triggered by those events may be proposed by the central system 30 and/or by the user's personal travel assistant 30; Claim 20 proposing a new route to a traveler based on unexpected events during a travel made by a second user); and
consuming, by an Onboard Al Modelling Module, the final travel itinerary and generate an Al package to assist the user in a journey based on the final travel itinerary (Col. 12:8-15 The search engine that computes the first list and the second list thus uses different metrics and considers different user preferences at each stage. Moreover, a different algorithm may be used at each stage. For example, the first list of criteria, such as candidate routes, may be based on a simple computation of a score for each route, while the second stage may use more complex algorithms based on artificial intelligence for example).
wherein the AI package, when deployed on a user device, renders a graphical user interface over the user device to assist the user in a journey based on the final travel itinerary and suggest one or more alternate nodes and/or alternative edges even in offline mode based on real-time inputs received from the user during the travel (Col. 4:48-49 FIG. 4 shows a screenshot of one possible implementation of a user interface displaying a sample search result; Col. 6:60-67 User preferences may be stored in the database 32 accessible by the IT system 30, as illustrated, and/or locally stored in the user's device (for example as a cookie). At least some of the preferences can be displayed and/or edited via a web interface from the computer 10 or from the personal travel assistant 40; Col. 11:1-6 a new updated list is displayed only after a user action, for example if the user clicks on an “update” button on the interface. The new list may also be automatically updated based on choices made by other users), and
wherein the Itinerary Recalibration Module is an Artificial Intelligence engine that implements machine learning algorithms to separately rank the alternate nodes independently from the alternate edges based on a combination of a probability of occurrence of unexpected situations as well as user preferences/selections, wildcard options, and users historical travel data or changes encountered during the journey, wherein the machine learning algorithms learn from past data to predict future disruptions and rank potential points of failure based on their likelihood and impact on the user's itinerary, and wherein the AI package operates independently on the user device with or without requiring server connectivity during itinerary recalibration (Col. 7:1-10 the system is self learning and automatically learns at least some of the user preferences, based for example on explicitly indicated preferences, on selection among list of possible routes, on feedback and/or on observations during the journeys. For example, the system may detect that a particular user systematically prefers the train to the plane, unless the travel time difference exceeds two hours. Neural networks or Hidden Markov Models (HMMs) can be used for analyzing the behaviour of the users and for classifying their preferences in predefined classes. Col. 9:1-6 Artificial intelligence (e.g., reinforcement learning or Kohonen maps) may be used for combining objective and subjective metrics and creating an optimal metric for personalized route planning. Col. 9:38-50 the system may be self learning and automatically determine which criteria are important for each user, based for example on previous selections and/or on user feedback from previous travels).
Regarding Claim 3, Mundinger teaches the limitations of claim 1 as discussed above. Mundinger further teaches wherein the query comprises data corresponding to basic information, associated with the itinerary, including source, destination, date, time, and other information associated with itinerary (FIGS. 3-4; Col. 10:18-24 This example corresponds to a travel trip from Bern to Nurnberg; different possible routes, using different transportation modes and via different intermediate cities such as Geneva, Basel or Zurich are displayed and selectable by the user).
Regarding Claim 4, Mundinger teaches the limitations of claim 1 as discussed above. Mundinger further teaches wherein the personal data is captured from user device as well as online sources, wherein the personal data comprises at least user preferences and users historical travel data (Col. 6:60-67 User preferences may be stored in the database 32 accessible by the IT system 30, as illustrated, and/or locally stored in the user's device (for example as a cookie). At least some of the preferences can be displayed and/or edited via a web interface from the computer 10 or from the personal travel assistant 40. The use of messages such as SMS, voice message and text to speech, e-mail, etc. for editing the user's preferences can be foreseen as well; Col. 9:51-67 A user who wants to plan a travel first needs to indicate the departure point, destination point and desired travelling time. This can be done with a web page accessed with the computer 10 or with the personal travel assistant 40, in a conventional manner. The user preferably identifies himself, for example with a login/password, with a SIM card in the case of a cell phone, etc. This identification is used by the IT system 30 for retrieving the corresponding set of user preferences in database 32 that will be used for selecting and sorting the best routes that suit this particular user. The user may also indicate over the web page preferences that will only apply to the specific travel he is currently planning, and which may be different than his general preferences. The user may for example indicate a category for the travel (for example “professional”, “family holidays” etc) in order to retrieve a suitable set of preferences if several sets of preferences have been stored for this user).
Regarding Claim 5, Mundinger teaches the limitations of claim 1 as discussed above. Mundinger further teaches wherein the one or more external sources include ticket booking, hotel booking, transportation booking website and applications (Col. 8:16-41 information furnished by previous users in the form of comments, suggestions, feedbacks, notes or score on a travel segment, etc; Transport provider, terminal, services, hotels, or other information relevant to the user or the algorithm. Information on each segment retrieved from travel literature, websites, forums, and publications; Col. 15:36-43 an airline might use a similar bidding system to resolve rebooking for an overbooked flight).
Regarding Claim 6, Mundinger teaches the limitations of claim 1 as discussed above. Mundinger further teaches wherein the intermediate destination corresponds to at least one of hotels, lodges, places, and destination points (Col. 10:18-24 a list of candidate routes that may be displayed on a computer equipment 10 or 40, is illustrated on FIG. 3. This example corresponds to a travel trip from Bern to Nurnberg; different possible routes, using different transportation modes and via different intermediate cities such as Geneva, Basel or Zurich are displayed and selectable by the user).
Regarding Claim 7, Mundinger teaches the limitations of claim 1 as discussed above. Mundinger further teaches wherein the transportation option includes flight, boat, train, car, cab, car-pooling and all other travel options between two preferred nodes (Col. 4:54-62 The present invention generally relates to multimodal transportation, i.e., transportation over a network using different transportation modes, such as plane, train, car, bus, metro, taxi, car sharing, foot, etc proposed by various transportation providers such as airline companies, train operating companies, etc. Each company and each provider may have its own schedules, timetables, and travel conditions etc. which make planning of a travel through this network a difficult task).
Regarding Claim 8, Mundinger teaches the limitations of claim 1 as discussed above. Mundinger further teaches wherein the real-time data and/or the historical data is captured from the online sources and/or the user device (Col. 6:26-42 The IT system 30 includes a database 31, or can access such a database, with data used for planning the travels of the different users. It includes for example timetables, tariffs and schedules of different transportation providers, conditions of those providers (luggage, etc), geographic information including maps or distances between geographic points, weather and traffic forecasts, etc. The database 31 preferably also includes data entered or retrieved by the users, including statistics and data retrieved from previous travels over different travels segments, user comments, notes and scores etc as will be described later. The database 31 may also be a set of different databases in one or different machines, and include for example data stored in remote servers and retrieved over the Internet, for example using SOAP or another suitable technology. Data available in the database 31 may be imported from various sources and converted in a common format that can be used by a software routing engine).
Regarding Claim 9, Mundinger teaches the limitations of claim 1 as discussed above. Mundinger further teaches wherein the real-time inputs are received from the user during a travel as per the final travel itinerary (Col. 12:46-67 Another possible use of the system relates to dynamic rerouting in the case of changes due to unexpected (including predicted) events or changes in the route schedule. In one aspect, the invention may allow users to plan, book and dynamically re-route routes in real-time and while they are travelling. This may allow users to be informed about and to accurately react to travel events both unexpected and those that can be anticipated from a certain point on). Unexpected events that may occur during planned joint travel comprise for example situation where one of the joint travellers miss a connection, or is delayed, or decides to change his itinerary; in those situations, the system may automatically generate an electronic message informing all concerned users of this unexpected event, reorganize the route of the other users in the joint travel, and/or propose to renounce to the joint travel or to split the group of users on some or all route segments).
Regarding Claim 11, Mundinger teaches a method for itinerary planning, wherein the method comprises steps of: receiving, by a Requirement Analysis Module, a query from a user, wherein the query corresponds to an itinerary (Col. 3:17-36 means for deconstructing a user enquiry specifying the journey into a plurality of information requests, each specifying a part of the journey using a single mode of transport, such as rail, car or coach. This document also describes a Navigator having means for sending each request to an appropriate one of a plurality of local and on-line databases, which each hold travel information regarding a different mode of transport. Further means are provided for reconstructing the responses to the requests received from the plurality of local and on-line databases into at least one multi-modal travel option, for the user specified journey, incorporating different modes of transport. Usually, the multi-modal travel option incorporates timetable travel information such as train timetables and non-timetable travel information. The Navigator can also implement uni-modal point to point travel where the user can specify any geographical location and a mode of transport with the most suitable terminals and services being determined; Col. 12:55-58 two users unexpectedly meet during a travel, decide to travel together, and send a request to the system for re-organizing the common part of the journey; FIG. 4);
receiving, by the Requirement Analysis Module, personal data associated with the user (6:60-67 User preferences may be stored in the database 32 accessible by the IT system 30, as illustrated, and/or locally stored in the user's device (for example as a cookie). At least some of the preferences can be displayed and/or edited via a web interface from the computer 10 or from the personal travel assistant 40. The use of messages such as SMS, voice message and text to speech, e-mail, etc. for editing the user's preferences can be foreseen as well. Suppose you have booked a trip from Lausanne to Amsterdam for example by using a web interface to enter your home address in Lausanne and the address of your destination in Amsterdam as well as an approximate desired time for your trip, then choosing one of the routes proposed and then booking that route with the different travel providers for train, bus and air travel online);
aggregating, by an Itinerary Planning Module, data from one or more external sources for generating a travel itinerary plan based on the query and the personal data gathered by the Requirement Analysis Module, wherein the travel itinerary plan is a directed graph, wherein the directed graph comprises a set of preferred nodes and a set of preferred edges connecting the set of preferred nodes, wherein each preferred node corresponds to an intermediate destination in the travel itinerary plan, wherein each preferred edge from the set of preferred edges corresponds to a transportation option between two preferred nodes in the set of preferred nodes in the travel itinerary plan, wherein the preferred nodes and preferred edges are selected based on the personal data (Col. 4:63 – 5:5 FIG. 2 illustrates a schematic map of a geographic region covered by different networks for different travels using different mode of transportation. In this application, a route segment is a branch between two nodes (or points) of a network that can be travelled by a user, using one specific transportation mode. A route is a set of one or several mutually connected route segments by which one user can travel from one one departure point to one destination point. A multimodal route is a route made up of different route segments that are travelled using different transportation modes);
analysing, by an Itinerary Recalibration Module, the travel itinerary plan and identify a set of possible points of failure in the travel itinerary plan, wherein the set of possible points of failure correspond to one or more preferred edges or one or more preferred nodes from the set of preferred nodes and set of preferred edges (Col. 12:59-67 Unexpected events that may occur during planned joint travel comprise for example situation where one of the joint travellers miss a connection, or is delayed, or decides to change his itinerary; in those situations, the system may automatically generate an electronic message informing all concerned users of this unexpected event, reorganize the route of the other users in the joint travel, and/or propose to renounce to the joint travel or to split the group of users on some or all route segments),
wherein the set of possible points of failure are identified based on real-time data and/or historical data associated with the set of preferred nodes and/or the set of preferred edges (Col. 6:1-15 the personal travel assistants include a data sending part for sending data to the IT system 30. This data sending part may comprise for example a cellular equipment able to send data over the cellular network 50, a Bluetooth connection for connecting the personal travel assistant to a user's mobile equipment in the vicinity, etc. This allows the personal travel assistant 40 to transmit its position and other data in real time, for example while travelling, to the IT system 30 which can use this information for adapting the route (when required), sending suggestions etc. This connection can also be used by the IT system for verifying the displacements of the user, checking delays, and using this information for planning further travels of other users);
generating, by the Itinerary Recalibration Module, an updated travel itinerary plan based on the set of possible points of failure, wherein the updated travel itinerary plan comprises a set of alternate nodes and a set of alternate edges along with the set of preferred nodes and the set of preferred edges; consuming, by an Itinerary Generation Module, the travel itinerary plan and updated travel itinerary plan to generate a final travel itinerary (Col. 14:9-47 Unexpected (including predicted) events may comprise for example delays, cancellations, bad weather, users wanting to make a change. Unexpected events may also concern other users of the system; for example, in case of joint planning, the IT system 30 may detect that one user in a group has missed his bus, and propose an alternative route to all users to avoid cancellation of the meeting. Voting mechanisms may be proposed if the users disagree on the new route to select; the personal travel assistant may also help the different users to establish a voice and/or data communication between themselves in case of unexpected event, and help all users to rearrange the travel. Rerouting may be triggered by any one of the following events: Comparing in the IT system and/or in the user's personal travel assistant 40 the scheduled time and location with the current time and location measured by the assistant; Information on delays retrieved from the transportation providers, from news services, or from different sources including current weather and weather forecasts Information from other users collected by other users of the system. For example, the central IT system may detect that the plane XY expected to start in 60 minutes from Geneva is still in Amsterdam and thus will be delayed. Computation and display of a probability of catching the next segment or transportation means in a route; User input, for example if the user indicates during its travel that he wants to make a change Current location of other users for example in the case of joint travel. At least some of those events are detected by the central IT system in which the route selected by the user has been stored and which follows the displacements of the user during travel. Other events may be detected locally in the user personal travel assistant and possibly transmitted to the central IT system 30. As already indicated, a reaction, for example a new route or new segments, triggered by those events may be proposed by the central system 30 and/or by the user's personal travel assistant 30; Claim 20 proposing a new route to a traveler based on unexpected events during a travel made by a second user); and
consuming, by an Onboard Al Modelling Module, the final travel itinerary and generate an Al package to assist the user in a journey based on the final travel itinerary (Col. 12:8-15 The search engine that computes the first list and the second list thus uses different metrics and considers different user preferences at each stage. Moreover, a different algorithm may be used at each stage. For example, the first list of criteria, such as candidate routes, may be based on a simple computation of a score for each route, while the second stage may use more complex algorithms based on artificial intelligence for example).
wherein the AI package, when deployed on a user device, renders a graphical user interface over the user device to assist the user in a journey based on the final travel itinerary and suggest one or more alternate nodes and/or alternative edges even in offline mode based on real-time inputs received from the user during the travel (Col. 4:48-49 FIG. 4 shows a screenshot of one possible implementation of a user interface displaying a sample search result; Col. 6:60-67 User preferences may be stored in the database 32 accessible by the IT system 30, as illustrated, and/or locally stored in the user's device (for example as a cookie). At least some of the preferences can be displayed and/or edited via a web interface from the computer 10 or from the personal travel assistant 40; Col. 11:1-6 a new updated list is displayed only after a user action, for example if the user clicks on an “update” button on the interface. The new list may also be automatically updated based on choices made by other users), and
wherein the Itinerary Recalibration Module is an Artificial Intelligence engine that implements machine learning algorithms to separately rank the alternate nodes independently from the alternate edges based on a combination of a probability of occurrence of unexpected situations as well as user preferences/selections, wildcard options, and users historical travel data or changes encountered during the journey, wherein the machine learning algorithms learn from past data to predict future disruptions and rank potential points of failure based on their likelihood and impact on the user's itinerary, and wherein the AI package operates independently on the user device with or without requiring server connectivity during itinerary recalibration (Col. 7:1-10 the system is self learning and automatically learns at least some of the user preferences, based for example on explicitly indicated preferences, on selection among list of possible routes, on feedback and/or on observations during the journeys. For example, the system may detect that a particular user systematically prefers the train to the plane, unless the travel time difference exceeds two hours. Neural networks or Hidden Markov Models (HMMs) can be used for analyzing the behaviour of the users and for classifying their preferences in predefined classes. Col. 9:1-6 Artificial intelligence (e.g., reinforcement learning or Kohonen maps) may be used for combining objective and subjective metrics and creating an optimal metric for personalized route planning. Col. 9:38-50 the system may be self learning and automatically determine which criteria are important for each user, based for example on previous selections and/or on user feedback from previous travels).
Regarding Claim 13, Mundinger teaches the limitations of claim 11 as discussed above. Mundinger further teaches wherein the query comprises data corresponding to basic information, associated with the itinerary, including source, destination, date, time, and other information associated with itinerary (FIGS. 3-4; Col. 10:18-24 This example corresponds to a travel trip from Bern to Nurnberg; different possible routes, using different transportation modes and via different intermediate cities such as Geneva, Basel or Zurich are displayed and selectable by the user).
Regarding Claim 14, Mundinger teaches the limitations of claim 11 as discussed above. Mundinger further teaches wherein the personal data is captured from user device as well as online sources, wherein the personal data comprises at least user preferences and users historical travel data (Col. 6:60-67 User preferences may be stored in the database 32 accessible by the IT system 30, as illustrated, and/or locally stored in the user's device (for example as a cookie). At least some of the preferences can be displayed and/or edited via a web interface from the computer 10 or from the personal travel assistant 40. The use of messages such as SMS, voice message and text to speech, e-mail, etc. for editing the user's preferences can be foreseen as well; Col. 9:51-67 A user who wants to plan a travel first needs to indicate the departure point, destination point and desired travelling time. This can be done with a web page accessed with the computer 10 or with the personal travel assistant 40, in a conventional manner. The user preferably identifies himself, for example with a login/password, with a SIM card in the case of a cell phone, etc. This identification is used by the IT system 30 for retrieving the corresponding set of user preferences in database 32 that will be used for selecting and sorting the best routes that suit this particular user. The user may also indicate over the web page preferences that will only apply to the specific travel he is currently planning, and which may be different than his general preferences. The user may for example indicate a category for the travel (for example “professional”, “family holidays” etc) in order to retrieve a suitable set of preferences if several sets of preferences have been stored for this user).
Regarding Claim 15, Mundinger teaches the limitations of claim 11 as discussed above. Mundinger further teaches wherein the one or more external sources include ticket booking, hotel booking, transportation booking website and applications (Col. 8:16-41 information furnished by previous users in the form of comments, suggestions, feedbacks, notes or score on a travel segment, etc; Transport provider, terminal, services, hotels, or other information relevant to the user or the algorithm. Information on each segment retrieved from travel literature, websites, forums, and publications; Col. 15:36-43 an airline might use a similar bidding system to resolve rebooking for an overbooked flight).
Regarding Claim 16, Mundinger teaches the limitations of claim 11 as discussed above. Mundinger further teaches wherein the intermediate destination corresponds to at least one of hotels, lodges, places, and destination points (Col. 10:18-24 a list of candidate routes that may be displayed on a computer equipment 10 or 40, is illustrated on FIG. 3. This example corresponds to a travel trip from Bern to Nurnberg; different possible routes, using different transportation modes and via different intermediate cities such as Geneva, Basel or Zurich are displayed and selectable by the user).
Regarding Claim 17, Mundinger teaches the limitations of claim 11 as discussed above. Mundinger further teaches wherein the transportation option includes flight, boat, train, car, cab, car-pooling and all other travel options between two preferred nodes (Col. 4:54-62 The present invention generally relates to multimodal transportation, i.e., transportation over a network using different transportation modes, such as plane, train, car, bus, metro, taxi, car sharing, foot, etc proposed by various transportation providers such as airline companies, train operating companies, etc. Each company and each provider may have its own schedules, timetables, and travel conditions etc. which make planning of a travel through this network a difficult task).
Regarding Claim 18, Mundinger teaches the limitations of claim 11 as discussed above. Mundinger further teaches wherein the real-time data and/or the historical data is captured from the online sources and/or user device (Col. 6:26-42 The IT system 30 includes a database 31, or can access such a database, with data used for planning the travels of the different users. It includes for example timetables, tariffs and schedules of different transportation providers, conditions of those providers (luggage, etc), geographic information including maps or distances between geographic points, weather and traffic forecasts, etc. The database 31 preferably also includes data entered or retrieved by the users, including statistics and data retrieved from previous travels over different travels segments, user comments, notes and scores etc as will be described later. The database 31 may also be a set of different databases in one or different machines, and include for example data stored in remote servers and retrieved over the Internet, for example using SOAP or another suitable technology. Data available in the database 31 may be imported from various sources and converted in a common format that can be used by a software routing engine).
Regarding Claim 19, Mundinger teaches the limitations of claim 11 as discussed above. Mundinger further teaches wherein the real-time inputs are received from the user during a travel as per the final travel itinerary (Col. 12:46-67 Another possible use of the system relates to dynamic rerouting in the case of changes due to unexpected (including predicted) events or changes in the route schedule. In one aspect, the invention may allow users to plan, book and dynamically re-route routes in real-time and while they are travelling. This may allow users to be informed about and to accurately react to travel events both unexpected and those that can be anticipated from a certain point on). Unexpected events that may occur during planned joint travel comprise for example situation where one of the joint travellers miss a connection, or is delayed, or decides to change his itinerary; in those situations, the system may automatically generate an electronic message informing all concerned users of this unexpected event, reorganize the route of the other users in the joint travel, and/or propose to renounce to the joint travel or to split the group of users on some or all route segments).
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive. Regarding the 35 U.S.C. 101 rejections, Applicant argues that “the proposed invention cannot facilitate interaction between people at all while in Offline mode” (p. 12). The Examiner disagrees and notes that people often interact with each other when Offline.
Applicant argues that there is:
a clearly defined technological limitation of existing computer-implemented travel planning systems that prevents them from functioning effectively in connectivity-constrained environments, creating a concrete technological problem that demands a technological solution. The claimed invention provides a technological solution to this technological problem … this separate component ranking architecture represents a fundamental technological advancement that enables sophisticated AI computation on resource constrained user devices without server connectivity … The technical effect of ranking nodes and edges independently creates a computationally efficient system that can dynamically recombine travel components in real-time during offline operation, solving the specific technological challenge of enabling complex AI decision-making in environments where traditional systems fail due to connectivity constraints
(p. 13). The Examiner disagrees. Providing a solution to the problem of “connectivity-constrain[ts]” and being Offline does not provide an improvement to the functioning of a computer, or to any other technology or technical field – see MPEP 2106.05(a).
Applicant argues that “[t]he claimed invention's technical advancement through separate component ranking also enables predictive failure analysis at the component level … The separate ranking of alternate nodes and edges represents a technological solution that addresses a technological problem through specific technical means, implementing machine learning algorithms in an unconventional architecture that enables capabilities not achievable with conventional route-based systems” (pp. 14-15). The Examiner disagrees. Providing a ranking of travel itineraries does not provide an improvement to the functioning of a computer, or to any other technology or technical field – see MPEP 2106.05(a).
Applicant argues that
The Claims Amount to Significantly More [because] … The claimed combination of elements represents an unconventional technical approach to the problem of offline travel planning through the specific innovation of ranking alternate nodes and edges separately and independently, which provides dramatic memory utilization improvements and computational efficiency advantages that enable sophisticated AI deployment on resource-constrained user devices
(pp. 16-19). The Examiner disagrees. A claim that recites additional elements that amount to an inventive concept (aka “significantly more” than the recited abstract idea) is eligible. Therefore, any purported inventive concept has to be an additional element that is not part of the abstract idea. In the present claims, there is no inventive concept that is in addition to (i.e., not a part of) the abstract idea. For instance, the “sophisticated AI deployment on resource-constrained user devices” discussed by Applicant on pages 16-19 are part of the abstract idea (i.e., the process steps are directed to a method of managing interactions between people). If the purported inventive concept is part of the abstract idea, it is not an “additional element” under Step 2B. Therefore, even assuming arguendo that the abstract limitations were novel/non-obvious, “a claim for a new abstract idea is still an abstract idea.” Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016).
Regarding the prior art rejections, Applicant argues that Mundinger fails to teach the claimed recitation:
consuming, by an Onboard AI Modelling Module, the final travel itinerary and generate an AI package to assist the user in a journey based on the final travel itinerary, wherein the AI package, when deployed on a user device, renders a graphical user interface over the user device to assist the user in a journey based on the final travel itinerary and suggest one or more alternate nodes and/or alternative edges even in offline mode based on real-time inputs received from the user during the travel, and wherein the
Itinerary Recalibration Module is an Artificial Intelligence engine that implements machine learning algorithms to separately rank the alternate nodes independently from the alternate edges rank the alte