CTFR 17/899,825 CTFR 94159 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Introduction The following is a final Office action in response to Applicant’s submission filed on 2/26/2026. Currently claims 1-20 are pending and claims 1, 11 are independent. Claims 1-3, 5-6, 10-13, 15-16, and 20 have been amended from the original claim set dated 8/31/2022. No claims have been added or cancelled. Priority 02-27 AIA Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. PCT/CN2017/078401 , filed on 3/28/2017 Response to Amendments Applicant’s amendments are acknowledged and necessitated the new grounds of rejection in this Office Action . Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea), specifically an abstract idea, without significantly more. With respect to claims 1-20, following the guidance contained within MPEP 2106, the inquiry for patent eligibility follows two steps: Step 1: Does the claimed invention fall within one of the four statutory categories of invention? Step 2A (Prong 1): Is the claim “directed to” an abstract idea? Step 2A (Prong 2): Is the claim integrated into a practical application? Step 2B: Does the claim recite additional elements that amount to “significantly more” than the abstract idea? In accordance with these steps, the Examiner finds the following: Step 1: Claim 1 and its dependent claims (claims 2-10) are directed to a statutory category, namely a method. Claim 11 and its dependent claims (claims 12-20) are directed to a statutory category, namely a system/machine. Step 2A (Prong 1): Claims 1, 11, which are substantially similar claims to one another, are directed to the abstract idea of “Certain methods of organizing human activity”, or more particularly, “Concepts relating to commercial or legal interactions (including: advertising, marketing or sales activities or behaviors; business relations) (See MPEP 2106).” In this application that refers to using a computer system to manage and analyze expense reimbursements. To clarify this further, the Applicant’s disclosed invention is a conceptual system meant to perform the same function that a finance manager might perform for a business. The abstract elements of claims 1, 11, recite in part “Detect communication…Determine variables…Map variables…Generate correlations…Identify nodes…Retrieve values…Transmit second communication…execute workflows…”. Dependent claims 2-10, 12-20, add to the abstract idea the following limitations which recite in part “Identify variable…Access rules…Restrict based on rules…Retrieve value…Transmit communications…Continuously collect data…Update model…Retrieve value for recommendation…Detect communication…Identify corresponding event…Transmit alert…Transmit communication…Users are associated with same entity…Detect parameter exceeding threshold…Access workflow…Identify documents…”. All of these additional limitations, however, only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 11. Step 2A (Prong 2): Independent claims 1, 11, which are substantially similar claims to one another, do not contain additional elements, either considered individually or in combination, that effectively integrate the exception into a practical application of the exception. These claims do include the limitation that recites in part “Processors…Non-transitory computer readable medium…Computing device…ML model…Nodes…” which limits the claims to a networked/computer based environment, but this is insufficient with respect to integration into a practical application because it is merely applying the abstract idea to a general computer (See MPEP 2106.05(f)). Additionally, dependent claims 2-10, 12-20 do not include any additional elements to conduct a further Step 2A (Prong 2) analysis. Step 2B : Independent claims 1, 11, which are substantially similar claims to one another, include additional elements, when considered both individually and as an ordered combination, which are insufficient to amount to significantly more than the judicial exception. The additional elements of these claims recite in part “Processors…Non-transitory computer readable medium…Computing device…ML model…Nodes…”. These items are not significantly more because these are merely the software and/or hardware components used to implement the abstract idea (manage and analyze expense reimbursements) on a general purpose computer (See MPEP 2106.05(f)). This is exemplified in the Applicant’s specification in [0095] – “Processing unit 604, which can be implemented as one or more integrated circuits (e.g., a conventional microprocessor or microcontroller), controls the operation of computer system 600. ” Additionally, dependent claims 2-10, 12-20 do not include any additional elements to conduct a further 2B analysis. Accordingly, whether taken individually or as an ordered combination claims 1-20 are rejected under 35 USC § 101 because the claimed invention is directed to a judicial exception, an abstract idea, without significantly more. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim s 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Farrell et al. (US 20160078566 A1) in view of Loghmani (US 20120185275 A1) further in view of Mundinger et al. (WO 2009065638 A1) Regarding claims 1, 11, Farrell discloses a computer-implemented method (Farrell ABS - Methods and apparatus for adaptive expense processing and management ) comprising: detecting a first communication transmitted from a computing device, the first communication being associated with a user of the computing device and corresponding to a request to initiate a process associated with a particular event, wherein the process corresponds to a request to define a future event or a request for reimbursement of an expense associated with a past event (Farrell ¶119 - At block 310, method 302 may transmit a resolution request indication (e.g., expense approval request) to an external electronic device associated with a reviewing entity. For example, network entity 20 (FIGS. 1A and 1B) may be configured to execute one or more modules or components to transmit a resolution request indication (e.g., expense approval request) to an external electronic device associated with a reviewing entity for one or more of the generated expense data objects ); in response to detecting the first communication, determining one or more variables from the request, each variable of the one or more variables being included in the first communication and representing a characteristic of the particular event (Farrell ¶118 - Further, at block 308, method 302 may generate an expense data object for each of the one or more datasets in response to receiving the one or more datasets…In some embodiments, the one or more transaction records forming the one or more datasets may each be processed to determine, for example, a merchant identifier, a transaction amount, a category indication, and thereby used in generating the expense data object. In some embodiments, the expense data object may include the merchant identifier, transaction amount, and category indication ); mapping each variable of the one or more variables to at least one node of a plurality of nodes (Farrell ¶135 - In some embodiments, to determine the one or more account formation characteristics, one or more expense categories may be mapped to one or more datasets based on the account formation characteristics ); retrieving one or more values associated with each node of the identified one or more nodes; and transmitting a second communication to the computing device, the second communication being responsive to the first communication and including supporting information for the particular event, the supporting information determined based on the retrieved one or more values (Farrell ¶120 - At block 312, method 302 may receive one or more resolution indications (e.g., approval/denial indications) for one or more of the generated expense data object from the external electronic device associated with the reviewing entity. For example, network entity 20 (FIGS. 1A and 1B) may be configured to execute one or more modules or components to receive one or more resolution indications (e.g., approval/denial indications) for one or more of the generated expense data object from the external electronic device associated with the reviewing entity ) and based at least in part on the supporting information, causing execution of one or more workflows to initiate the process associated with the particular event (Farrell ¶105 - FIG. 3A is a flow diagram illustrating method 300 for expense processing, management, and reporting. Specifically, method 300 provides for collecting, categorizing, approving, and delivering electronic expense data in accordance with some embodiments. In some embodiments, method 300 may be performed at network entity 20 (FIGS. 1A and 1B) including one or more electronic access devices (e.g., modules and/or servers) as part of an automated workflow system ). Farrell lacks a trained machine-learning model having been trained with a data set including one or more past events; identifying one or more nodes of the plurality of nodes corresponding to the one or more variables based at least in part on the mapping, wherein the one or more nodes are identified based on one or more correlations between at least two nodes of the plurality of nodes. Loghmani, from the same field of endeavor, teaches a trained machine-learning model having been trained with a data set including one or more past events (Loghmani ¶18 - That is, in addition to performing statistical pattern recognition on the data itself, the automated data analysis identifies hidden networks or hidden graphs in the data (e.g., topographic maps of relationships between various data fields in selected clusters of data stored and used in the system) as a first step, then expresses the graphs in quantitative terms, and finally performs statistical analysis on those hidden networks or hidden graphs to achieve more comprehensive information from the analyzed data as exemplified below ); identifying one or more nodes of the plurality of nodes corresponding to the one or more variables based at least in part on the mapping, wherein the one or more nodes are identified based on one or more correlations between at least two nodes of the plurality of nodes (Loghmani ¶ABS - Automated conversion of paper-based medical and insurance billing records to electronic data is provided, along with automatic correlation of medical services data to insurance plan policies and tax regulations for health benefits to detect errors or fraud, and to project health insurance plans for various subscribers ). It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the expense data management methodology/system of Farrell by including the automated data analysis techniques of Loghmani because Loghmani discloses “process to automatically identify interrelationships between various data fields in a system or body of data and in connection with statistical pattern analysis and machine learning to improve analyses (e.g., automated analysis of medical bills and health insurance documents) (Loghmani ¶3)”. Additionally, Farrell further details “In some embodiments, one or more modules/components of network entity 20, and more specifically, message queuing cluster 60, may be configured to perform one or both of an autonomous expense procedure or a merchant identity procedure. For example, the autonomous expense procedure may be a procedure that receives and analyzes one or more expense data objects to determine whether to adjust a set of valid expense data used in expense processing and management (Farrell ¶70)” so it would be obvious to consider including the additional automated data analysis techniques that Loghmani discloses because it would improve the expense analysis disclosed within Farrell. Farrell further lacks a travel event and generate the plurality of nodes and one or more correlations between at least two nodes of the plurality of nodes, wherein each node of the plurality of nodes represents at least one value or travel event characteristic learned from the one or more past travel events. Mundinger, from the same field of endeavor, teaches a travel event and generate the plurality of nodes and one or more correlations between at least two nodes of the plurality of nodes, wherein each node of the plurality of nodes represents at least one value or travel event characteristic learned from the one or more past travel events (Mundinger - The metrics that will actually be used for planning a particular travel depend on the given user, or even on a particular set of preferences of this user if he has defined different sets of preferences… Moreover, 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 ). It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the expense data management methodology/system of Farrell by including the transport (logistics) management techniques of Mundinger because Mundinger discloses “The system may offer users to aggregate, track and manage various information pertaining to travel and offer further services (e.g. automatic generation of expense reports, user travel statistics, or frequent flyer miles balance). Such services may be enhanced by intelligent assistants that make specific, personalized recommendations (e.g., recommending a public transport subscription based on the users past travel) (Mundinger)”. Additionally, Farrell further details “The present disclosure generally relates to expense processing, reporting and management. Specifically, the present aspects relate to the collection, categorization, approval, and/or delivery of expense data objects to an integrated computerized system (Farrell ¶55)” so it would be obvious to consider including the additional transport (logistics) management techniques that Mundinger discloses because it would enhance the expense reporting disclosed within Farrell. Regarding claims 2, 12, Farrell in view of Loghmani further in view of Mundinger discloses identifying a client variable from the request, the client variable being one of the variables determined from the request; in response to identifying the client variable, accessing one or more rules associated with the client variable; restricting a set of nodes identified using the one or more correlations, the set of nodes being restricted to a subset of nodes, the restriction being based on the one or more rules; retrieving a value for each node of the subset of nodes; and transmitting the second communication to the computing device, the second communication including the retrieved one or more values (Farrell ¶252 - At block 345-5, method 345 may determine whether a group of expense data objects complies with one or more policies. For example, network entity 20 (FIGS. 1A and 1B) may be configured to execute one or more modules or components to determine whether a group of expense data objects complies with one or more policies. In accordance with a determination that the group of expense data object complies with one or more policies, method 345 may proceed to block 346 (FIG. 3D). Otherwise, in accordance with a determination that the single expense data object does not comply with one or more policies, method 345 may advance to block 345-6. In some embodiments, the network entity 20 (FIGS. 1A and 1B) may utilize an aggregation mechanism that may analyze variables of a company policy across a group of expense data objects. At block 345-6, method 345 may flag the expense data object or the group of expense data objects. For example, network entity 20 (FIGS. 1A and 1B) may be configured to execute one or more modules or components to flag one or more expense data objects or the group of expense data objects in accordance with a determination that the one or more data objects do not comply with one or more policies and/or a group of expense data objects does not comply with one or more policies. In some embodiments, the flag may initiate a notification procedure to the user that an expense is in violation of policy ). Regarding claims 3, 13, Farrell in view of Loghmani further in view of Mundinger discloses collecting the data set is continuously performed, such that when a new event has occurred, the new event is included in the data set (Farrell ¶104 - Consequently, in an adaptive manner, as the expense data object has been identified or determined as a valid expense, the set of expense data representative of valid data used for performing the autonomous expense procedure may be adjusted or updated. As such, subsequent expense processing and determinations (e.g., category assignment) may be more accurate and based on currently valid information ). Regarding claims 4, 14, Farrell in view of Loghmani further in view of Mundinger discloses updating the trained machine-learning model when the new event is included in the data set, such that at least one weight that corresponds to a node of the plurality of nodes is updated (Farrell ¶181 - In some embodiments, method 342 may automatically obtain one or more expense data objects and/or triggers based on stored expense data objects. For example, method 342 may actively monitor and automatically determine whether an expense data object that was received was subsequently deleted and/or whether an expense data object that was not automatically obtained was nonetheless entered or provided manually by the user. Accordingly, method 342 may, as part of the autonomous expense procedure, identify and learn which expense data objects associated with one or more transactions are entered and/or removed directly by the user. As such, future expense data objects having similar characteristics (e.g., merchant name/identifier string) may be automatically obtained from a transaction source (e.g., credit card data feed). In some embodiments, the one or more characteristics may be a merchant identifier ). Regarding claims 5, 15, Farrell in view of Loghmani further in view of Mundinger discloses a computer-implemented method (Farrell ABS - Methods and apparatus for adaptive expense processing and management ). Loghmani further teaches the retrieved one or more values correspond to one or more recommended values provided as a recommendation associated with the particular event (Loghmani ¶80 - In the next step (58), the algorithm uses data about each subscriber's health trajectory and correlates that data to each insurance company's plans, as well as the upcoming changes in tax laws, to identify the most appropriate insurance and tax planning advice for each subscriber. In the next step (69), the system contacts each subscriber to provide them the advice before exiting the periodic process (56) as illustrated at (59) ). It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the expense data management methodology/system of Farrell by including the automated data analysis techniques of Loghmani because Loghmani discloses “process to automatically identify interrelationships between various data fields in a system or body of data and in connection with statistical pattern analysis and machine learning to improve analyses (e.g., automated analysis of medical bills and health insurance documents) (Loghmani ¶3)”. Additionally, Farrell further details “In some embodiments, one or more modules/components of network entity 20, and more specifically, message queuing cluster 60, may be configured to perform one or both of an autonomous expense procedure or a merchant identity procedure. For example, the autonomous expense procedure may be a procedure that receives and analyzes one or more expense data objects to determine whether to adjust a set of valid expense data used in expense processing and management (Farrell ¶70)” so it would be obvious to consider including the additional automated data analysis techniques that Loghmani discloses because it would improve the expense analysis disclosed within Farrell. Regarding claims 9, 19, Farrell in view of Loghmani further in view of Mundinger discloses detecting that a particular node of the one or more nodes is associated with a predicted occurrence, the predicted occurrence corresponding to an event parameter exceeding a defined threshold (Farrell ¶254 - At block 345-7, method 345 may determine whether a violated policy involves a pre-defined restricted reimbursement threshold. For example, network entity 20 (FIGS. 1A and 1B) may be configured to execute one or more modules or components to determine whether a violated policy involves a pre-defined restricted reimbursement threshold. In accordance with a determination that the violated policy involves a pre-defined restricted reimbursement threshold, method 345 may proceed to block 345-8. Otherwise, in accordance with a determination that the single expense data object does not comply with one or more policies, method 345 may advance to block 345-9 ); and accessing a workflow associated with the predicted occurrence, the workflow including an identification of one or more documents associated with the predicted occurrence, the one or more documents identifying a procedure for obtaining an offset associated with the particular node (Farrell ¶150 - In some embodiments, when specifying policies across a group of expenses, network entity 20 (FIGS. 1A and 1B) may be configured to execute one or more modules or components to utilize an aggregation mechanism to analyze variables associated with one or more policy specifications... Additionally, one or more rules engines {i.e. workflows} may be used to adjust policy specification requirements based on the external factors ). Regarding claims 10, 20, Farrell in view of Loghmani further in view of Mundinger discloses at least one node of the one or more nodes corresponds to a workflow for identifying one or more documents that identify a procedure for obtaining an offset associated with the particular event (Farrell ¶150 - In some embodiments, when specifying policies across a group of expenses, network entity 20 (FIGS. 1A and 1B) may be configured to execute one or more modules or components to utilize an aggregation mechanism to analyze variables associated with one or more policy specifications... Additionally, one or more rules engines {i.e. workflows} may be used to adjust policy specification requirements based on the external factors ). Regarding claims 6, 16, Farrell in view of Loghmani further in view of Mundinger discloses a computer-implemented method (Farrell ABS - Methods and apparatus for adaptive expense processing and management ). Mundinger further teaches detecting a third communication from an additional computing device, wherein the third communication corresponds to another request to initiate another process associated with the particular event, wherein the third communication is received after the first communication is received and before the particular event occurs; identifying that the first communication and the third communication each correspond to the particular event; and transmitting an alert message to the additional computing device, the alert message including a notification that the user associated with the first communication is also associated with the particular event (Mundinger - in one possible implementation the system is aware of other users, for example users belonging to the same community as the travelling use… In such an implementation, the system may include for example methods to: Notify users that a friend or colleague is planning a similar trip at approximately the same time and propose convenient solutions (e.g., car pooling). Notify users during booking, check-in, or during transport that a friend or colleague has booked or is taking the same flight and may propose convenient solutions (e.g. reserving joint seating )). It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the expense data management methodology/system of Farrell by including the transport (logistics) management techniques of Mundinger because Mundinger discloses “The system may offer users to aggregate, track and manage various information pertaining to travel and offer further services (e.g. automatic generation of expense reports, user travel statistics, or frequent flyer miles balance). Such services may be enhanced by intelligent assistants that make specific, personalized recommendations (e.g., recommending a public transport subscription based on the users past travel) (Mundinger)”. Additionally, Farrell further details “The present disclosure generally relates to expense processing, reporting and management. Specifically, the present aspects relate to the collection, categorization, approval, and/or delivery of expense data objects to an integrated computerized system (Farrell ¶55)” so it would be obvious to consider including the additional transport (logistics) management techniques that Mundinger discloses because it would enhance the expense reporting disclosed within Farrell. Regarding claims 7, 17, Farrell in view of Loghmani further in view of Mundinger discloses a computer-implemented method (Farrell ABS - Methods and apparatus for adaptive expense processing and management ). Mundinger further teaches transmitting a fourth communication to the additional computing device, the fourth communication being responsive with the third communication and including at least one of the retrieved one or more values (Mundinger - in one possible implementation the system is aware of other users, for example users belonging to the same community as the travelling use… In such an implementation, the system may include for example methods to: Notify users that a friend or colleague is planning a similar trip at approximately the same time and propose convenient solutions (e.g., car pooling). Notify users during booking, check-in, or during transport that a friend or colleague has booked or is taking the same flight and may propose convenient solutions (e.g. reserving joint seating )). It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the expense data management methodology/system of Farrell by including the transport (logistics) management techniques of Mundinger because Mundinger discloses “The system may offer users to aggregate, track and manage various information pertaining to travel and offer further services (e.g. automatic generation of expense reports, user travel statistics, or frequent flyer miles balance). Such services may be enhanced by intelligent assistants that make specific, personalized recommendations (e.g., recommending a public transport subscription based on the users past travel) (Mundinger)”. Additionally, Farrell further details “The present disclosure generally relates to expense processing, reporting and management. Specifically, the present aspects relate to the collection, categorization, approval, and/or delivery of expense data objects to an integrated computerized system (Farrell ¶55)” so it would be obvious to consider including the additional transport (logistics) management techniques that Mundinger discloses because it would enhance the expense reporting disclosed within Farrell. Regarding claims 8, 18, Farrell in view of Loghmani further in view of Mundinger discloses a computer-implemented method (Farrell ABS - Methods and apparatus for adaptive expense processing and management ). Mundinger further teaches the user associated with the first communication and a different user associated with the third communication are each associated with a same entity (Mundinger - in one possible implementation the system is aware of other users, for example users belonging to the same community as the travelling use {i.e. same entity} … In such an implementation, the system may include for example methods to: Notify users that a friend or colleague is planning a similar trip at approximately the same time and propose convenient solutions (e.g., car pooling). Notify users during booking, check-in, or during transport that a friend or colleague has booked or is taking the same flight and may propose convenient solutions (e.g. reserving joint seating )). It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the expense data management methodology/system of Farrell by including the transport (logistics) management techniques of Mundinger because Mundinger discloses “The system may offer users to aggregate, track and manage various information pertaining to travel and offer further services (e.g. automatic generation of expense reports, user travel statistics, or frequent flyer miles balance). Such services may be enhanced by intelligent assistants that make specific, personalized recommendations (e.g., recommending a public transport subscription based on the users past travel) (Mundinger)”. Additionally, Farrell further details “The present disclosure generally relates to expense processing, reporting and management. Specifically, the present aspects relate to the collection, categorization, approval, and/or delivery of expense data objects to an integrated computerized system (Farrell ¶55)” so it would be obvious to consider including the additional transport (logistics) management techniques that Mundinger discloses because it would enhance the expense reporting disclosed within Farrell . Response to Arguments 07-37 AIA Applicant's arguments filed 2/26/2026 have been fully considered but they are not persuasive and/or are moot in light of the new rejections addressed above . Regarding the arguments related to the 35 USC § 101 rejections, as addressed above according to the 2019 USPTO guidance for 35 USC § 101 rejections contained within MPEP 2106, the Examiner maintains that the claimed invention is an abstract idea, without significantly more, and not integrated into a practical application. Applicant first argues that that the claimed invention is patent eligible because it provides an improvement to a technology, however, Examiner does not find this persuasive. Specifically, Examiner interprets the claimed invention as an improvement to the business process of reimbursing expenses that happens to be facilitated through the use of a computer. Applicant next argues that the claimed invention overcome the 101 rejection within the Step 2A (Prong 2) and/or Step 2B analysis. Examiner does not find this persuasive. Specifically, the identified additional elements (“Processors…Non-transitory computer readable medium…Computing device…ML model…Nodes…) neither integrate the abstract idea into a practical application nor are significantly more because they merely apply the abstract idea to a general computer and are the software/hardware used to facilitate the abstract idea on a general computer (See MPEP 2106.05 (f)). Examiner will note, however, that the inclusion of the limitation “based at least in part on the supporting information, causing execution of one or more workflows to initiate the process associated with the particular travel event” brings the claims closer to overcoming the 101 rejection. It is unclear to the Examiner which particular workflows are being executed, but if it is clear that these workflows control some functionality (e.g. financial transactions/reimbursements) and the system initiates the action (as opposed to a user) that would potentially overcome the 101 rejection within the Step 2A (Prong 1 analysis). Regarding the 35 USC § 103 rejections on the previous Office action, Applicant amended the independent claims to further limit the claims with respect to correlations between travel event characteristics. In light of this amendment, Examiner agrees that the original references did not teach this, however the amendment necessitated further search and consideration. As a result of this further search and consideration, previously cited prior art was found to teach these limitations (Mundinger as discussed above) and is now cited. As such, Applicant’s arguments (with respect to the independent claims and their respective dependent claims) are unpersuasive. Conclusion 07-39 AIA THIS ACTION IS MADE FINAL. 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. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael R Koester whose telephone number is (313)446-4837. The examiner can normally be reached Monday thru Friday 8:00AM-5:00 PM EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached at (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MICHAEL R KOESTER/Examiner, Art Unit 3624 /Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624 Application/Control Number: 17/899,825 Page 2 Art Unit: 3624 Application/Control Number: 17/899,825 Page 3 Art Unit: 3624 Application/Control Number: 17/899,825 Page 4 Art Unit: 3624 Application/Control Number: 17/899,825 Page 5 Art Unit: 3624 Application/Control Number: 17/899,825 Page 6 Art Unit: 3624 Application/Control Number: 17/899,825 Page 7 Art Unit: 3624 Application/Control Number: 17/899,825 Page 8 Art Unit: 3624 Application/Control Number: 17/899,825 Page 9 Art Unit: 3624 Application/Control Number: 17/899,825 Page 10 Art Unit: 3624 Application/Control Number: 17/899,825 Page 11 Art Unit: 3624 Application/Control Number: 17/899,825 Page 12 Art Unit: 3624 Application/Control Number: 17/899,825 Page 13 Art Unit: 3624 Application/Control Number: 17/899,825 Page 14 Art Unit: 3624 Application/Control Number: 17/899,825 Page 15 Art Unit: 3624 Application/Control Number: 17/899,825 Page 16 Art Unit: 3624 Application/Control Number: 17/899,825 Page 17 Art Unit: 3624 Application/Control Number: 17/899,825 Page 18 Art Unit: 3624 Application/Control Number: 17/899,825 Page 19 Art Unit: 3624 Application/Control Number: 17/899,825 Page 20 Art Unit: 3624 Application/Control Number: 17/899,825 Page 21 Art Unit: 3624
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