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 .
DETAILED ACTION
Claims Status
Claims 1-20 are pending and have been rejected.
Response to Arguments
Applicant's arguments filed 10/29/2025 have been fully considered but they are not persuasive. Applicant’s representative asserts that the examiner suggested the amendments included in the independent claims of this response overcome the applied reference. Furthermore, for the reasons discussed during the interview and without acquiescing in the rejection, the cited sections of the applied reference do not disclose one or more features recited in the amended independent claims. The examiner did not indicate any amendments overcoming prior art of record. The amendments presented in claims filed 10/29/2025 are completely different than the proposed amendments during interview dated 10/21/2025. The prior art of record Glyman et al. (U.S. Publication 2019/0005389) in paragraph 0046 shows booking reservations include entertainment reservations (e.g., tickets to concerts, sporting events, amusement parks, meal vouchers, drink vouchers, etc.) for a reservation service purchased, selected, chosen, and/or reserved for the consumption of the good or service.
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 1-10 and 12-20 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable by Glyman et al. (U.S. Publication 2019/0005389), hereinafter ‘Glyman’.
As to claim 1, Glyman discloses a system for processing electronic messages, the system comprising: one or more processors (Glyman, see [0016], processor); and a non-transitory, computer-readable storage medium storing instructions that when executed by the one or more processors cause the one or more processors to perform operations comprising (Glyman, see [0016], a system including at least one processor and at least one non-transitory memory containing instructions): receiving, a plurality of electronic messages associated with a user (Glyman, see [0083], emails received by the user); inputting the plurality of electronic messages into a machine learning model to obtain one or more electronic messages of the plurality of electronic messages that contain one or more authorization packages for one or more entities, wherein the machine learning model is trained to identify electronic messages that contain authorization packages (Glyman, see [0044], the system then monitors the email address, identifying reservation confirmation (i.e., authorization packages) emails and using machine learning, the emails are parsed to extract reservation parameters. See [0091], a parser can be trained to only extract parameters from reservation emails sent by different booking agencies and service providers (e.g., ORBITZ.COM, HOTELS.COM, HOTWIRE.COM, etc.) (i.e., entities)); extracting, from an electronic message of the one or more electronic messages (1) an authorization package, associated with an electronic voucher, comprising an authorization identifier, and (2) entity identification data identifying an entity associated with the authorization package (Glyman, see [0046], booking reservations include entertainment reservations (e.g., tickets to concerts, sporting events, amusement parks, meal vouchers, drink vouchers, etc.) for a reservation service purchased, selected, chosen, and/or reserved. See [0104], emails containing extracted reservation parameters are parsed using machine learning to identify reservation parameters including “check in date”, “total price”, and “confirmation number”); determining, based on the entity identification data, (1) an application programming interface for communicating with an entity device and (2) user identification data used by the user to access the entity device (Glyman, see [0063-0064], rebooking system can provide the request indicating parameters with similar inventory item selected by the user on a web browser, or can provide the reservation request using an API exposed by new booking system); generating a command for executing a first operation at the entity device for inserting the authorization package into user data within the entity device, wherein the command includes the user identification data for authorizing the user and the authorization package in a format corresponding to the application programming interface (Glyman, see [0063-0064], rebooking system can indicate in the reservation request the inventory parameters of the similar inventory item selected by the user. The rebooking system can cause new booking system to reserve the similar inventory item selected by the user. The user can reply to the email message using email system); transmitting, via the application programming interface, the command to the entity device, wherein the entity device is configured to execute the first operation (Glyman, see [0063-0064], the user can select a control in the email that automatically generates and provides a response to rebooking system. Rebooking system can indicate in the reservation request the inventory parameters of the similar inventory item selected by the user); and receiving an indication of successful operation from the entity device (Glyman, see [0065], new booking engine can be configured to provide a confirmation of the reservation request, wherein the confirmation can be provided to rebooking engine).
As to claim 2, Glyman discloses a method for processing electronic messages, the method comprising: receiving, a plurality of electronic messages associated with a user (Glyman, see [0083], emails received by the user); inputting the plurality of electronic messages into a machine learning model to obtain one or more electronic messages of the plurality of electronic messages that contain one or more authorization packages for one or more entities, wherein the machine learning model is trained to identify electronic messages that contain authorization packages (Glyman, see [0044], the system then monitors the email address, identifying reservation confirmation (i.e., authorization packages) emails and using machine learning, the emails are parsed to extract reservation parameters. See [0091], a parser can be trained to only extract parameters from reservation emails sent by different booking agencies and service providers (e.g., ORBITZ.COM, HOTELS.COM, HOTWIRE.COM, etc.) (i.e., entities)); determining based on the one or more electronic messages (1) an authorization package, associated with a gift card, comprising a token identifier, and (2) entity identification data identifying an entity associated with the authorization package (Glyman, see [0046], booking reservations include entertainment reservations (e.g., meal vouchers, drink vouchers, etc.) for a reservation service purchased, selected, chosen, and/or reserved for consumption of the good or service. See [0104], emails containing extracted reservation parameters are parsed using machine learning to identify reservation parameters including “check in date”, “total price”, and “confirmation number”); determining, based on the entity identification data, (1) an application programming interface for communicating with an entity device and (2) user identification data used by the user to access the entity device (Glyman, see [0063-0064], rebooking system can provide the request indicating parameters with similar inventory item selected by the user on a web browser, or can provide the reservation request using an API exposed by new booking system); and transmitting a command for executing a first operation at the entity device, wherein the command includes the user identification data and the authorization package in a format corresponding to the application programming interface (Glyman, see [0063-0064], the user can select a control in the email that automatically generates and provides a response to rebooking system. Rebooking system can indicate in the reservation request the inventory parameters of the similar inventory item selected by the user).
As to claim 3, Glyman discloses everything discloses in claim 2, wherein the command comprises an instruction for inserting the authorization package into user data of the user within the entity device and wherein the method further comprises generating the command for executing the first operation (Glyman, see [0063-0064], the user can reply using the email, which would automatically generate and provide a response to rebooking system. Rebooking system can indicate in the reservation request the inventory parameters of the similar inventory item selected by the user).
As to claim 4, Glyman discloses everything discloses in claim 3, receiving, from the entity device, a message indicating that execution of the command was successful, wherein the message comprises a validity indicator for the authorization package (Glyman, see [0065], a reservation email can be sent to the email account of the user hosted on email system); and generating for display at a user device, the validity indicator for the authorization package (Glyman, see [0088], reservation confirmation email as it would be displayed to a human user).
As to claim 5, Glyman discloses everything discloses in claim 3, initiating a storage application on a user device associated with the user, wherein the storage application is configured to store authorization package data of a plurality of authorization packages associated with the user (Glyman, see [0064-0065], providing confirmation of the new booking to the user. See [0144], rebooking system can be configured to store the user identity within rebooking system); and updating the storage application with the authorization package (Glyman, see [0064-0065], providing confirmation of the new booking to the user).
As to claim 6, Glyman discloses everything discloses in claim 5, generating for display, using the storage application, (1) authorization package data of the plurality of authorization packages associated with the user (Glyman, see [0088] and fig. 4, reservation confirmation email as it would be displayed to a human user) and (2) one or more actions corresponding to one or more authorization packages (Glyman, see [0088] and fig. 4, reservation parameters presented must be identified and the correct values of these reservation parameters must be assigned by the automated rebooking system).
As to claim 7, Glyman discloses everything discloses in claim 6, receiving one or more attributes corresponding to each of the one or more authorization packages of the storage application, wherein the one or more attributes include a period of time during which the user is allowed to use the authorization package (Glyman, see [0088], reservation confirmation email contains reservation parameters (e.g., check-in date and time, check-out date and time, hotel address, confirmation number). See [0146], rebooking system is configured to use reservation parameters to locate the stored user identity of the user); and in response to determining that a value of the period of time is less than a threshold period of time, generating for display a recommendation for using the authorization package (Glyman, see [0136], graphical user interface for displaying a rebooking option to be selected with the rebooking control).
As to claim 8, Glyman discloses everything discloses in claim 2, receiving an output of the machine learning model, wherein the output of the machine learning model comprises (1) a probability for each electronic message that each electronic message of the plurality of electronic messages includes at least one authorization package (Glyman, see [0072], rebooking system can be configured to monitor the probability that an email is a reservation email) and (2) entity identification data associated with the at least one authorization package (Glyman, see [0050], new booking system provide reservation booking capabilities that indicate booking agency); and in response to determining that the probability exceeds a threshold probability for an electronic message, determining that the electronic message of the plurality of electronic messages includes the at least one authorization package (Glyman, see [0072], rebooking system is configured to monitor metrics concerning reservation email identification, reservation email parsing, and rebooking opportunity identification).
As to claim 9, Glyman discloses everything discloses in claim 2, determining that a user device corresponding with the user is interacting with the entity device (Glyman, see [0055], rebooking system can be configured to access the account of the user hosted on email system); and in response to determining that the user device is interacting with the entity device, generating for display, a recommendation to the user for using the authorization package (Glyman, see [0136], graphical user interface for displaying a rebooking option).
As to claim 10, Glyman discloses everything discloses in claim 2, receiving a training dataset comprising electronic messages and label data for each electronic message indicating (1) whether each electronic message includes a corresponding authorization package and (2) corresponding entity identification data (Glyman, see [0071], system can be configured to use neural networks to identify reservation emails, parse reservation emails, and identify rebooking opportunities. The neural networks used for each of these processes can be trained using supervised learning, wherein emails can be labeled. See [0157], rebooking system can determine from the reservation email the online booking agency or service provider used to create the reservation); and training the machine learning model using the training dataset (Glyman, see [0091-0092], system can use a machine learning to develop a parser capable of extracting reservation parameters from reservation emails with dynamically generated content, wherein a parser can be trained to accommodate one or more different booking agencies and service providers. The system can use a specific implementation of machine learning to extract reservation parameters from a reservation email).
As to claim 12, Glyman discloses everything discloses in claim 10, wherein determining the entity identification data comprises: generating an image from an electronic message (Glyman, see [0088] and fig. 4A, reservation confirmation email as it would be displayed to a human user, wherein the computing device of the user, generates the image displayed); inputting the image into an entity recognition machine learning model to obtain the entity identification data, wherein the entity recognition machine learning model has been trained to identify entities associated with electronic messages (Glyman, see [0044], the system then monitors the email address, identifying reservation confirmation (i.e., authorization packages) emails and using machine learning, the emails are parsed to extract reservation parameters. See [0091], a parser can be trained to only extract parameters from reservation emails sent by different booking agencies and service providers (e.g., ORBITZ.COM, HOTELS.COM, HOTWIRE.COM, etc.) (i.e., entities)); and receiving the entity identification data from the entity recognition machine learning model (Glyman, see [0116], systems uses machine learning to identify retrieved inventory equivalent to the reserved inventory item, wherein this system relies upon supervised learning to determine whether retrieved inventory matches the reserved inventory item).
As to claim 13, Glyman discloses a non-transitory, computer readable medium storing instructions, the method comprising: receiving, a plurality of electronic messages associated with a user (Glyman, see [0083], emails received by the user); inputting the plurality of electronic messages into a machine learning model to obtain one or more electronic messages of the plurality of electronic messages that contain one or more authorization packages for one or more entities, wherein the machine learning model is trained to identify electronic messages that contain authorization packages (Glyman, see [0044], the system then monitors the email address, identifying reservation confirmation (i.e., authorization packages) emails and using machine learning, the emails are parsed to extract reservation parameters. See [0091], a parser can be trained to only extract parameters from reservation emails sent by different booking agencies and service providers (e.g., ORBITZ.COM, HOTELS.COM, HOTWIRE.COM, etc.) (i.e., entities)); determining based on the one or more electronic messages (1) an authorization package, associated with an electronic voucher, comprising a token identifier, and (2) entity identification data identifying an entity associated with the authorization package (Glyman, see [0046], booking reservations include entertainment reservations (e.g., tickets to concerts, sporting events, amusement parks, meal vouchers, drink vouchers, etc.) for a reservation service purchased, selected, chosen, and/or reserved. See [0104], emails containing extracted reservation parameters are parsed using machine learning to identify reservation parameters including “check in date”, “total price”, and “confirmation number”); determining, based on the entity identification data, (1) an application programming interface for communicating with an entity device and (2) user identification data used by the user to access the entity device (Glyman, see [0063-0064], rebooking system can provide the request indicating parameters with similar inventory item selected by the user on a web browser, or can provide the reservation request using an API exposed by new booking system); and transmitting a command for executing a first operation at the entity device, wherein the command includes the user identification data and the authorization package in a format corresponding to the application programming interface (Glyman, see [0063-0064], the user can select a control in the email that automatically generates and provides a response to rebooking system. Rebooking system can indicate in the reservation request the inventory parameters of the similar inventory item selected by the user).
As to claim 14, Glyman discloses everything discloses in claim 13, wherein the command comprises an instruction for inserting the authorization package into user data of the user within the entity device and wherein the instructions cause the one or more processors to perform generating the command for executing the first operation (Glyman, see [0063-0064], the user can reply using the email, which would automatically generate and provide a response to rebooking system. Rebooking system can indicate in the reservation request the inventory parameters of the similar inventory item selected by the user).
As to claim 15, Glyman discloses everything discloses in claim 14, wherein the instructions cause the one or more processors to perform operations comprising: receiving, from the entity device, a message indicating that execution of the command was successful, wherein the message comprises a validity indicator for the authorization package (Glyman, see [0065], a reservation email can be sent to the email account of the user hosted on email system); and generating for display at a user device, the validity indicator for the authorization package (Glyman, see [0088], reservation confirmation email as it would be displayed to a human user).
As to claim 16, Glyman discloses everything discloses in claim 14, wherein the instructions cause the one or more processors to perform operations comprising: initiating a storage application on a user device associated with the user, wherein the storage application is configured to store authorization package data of a plurality of authorization packages associated with the user (Glyman, see [0064-0065], providing confirmation of the new booking to the user. See [0144], rebooking system can be configured to store the user identity within rebooking system); and updating the storage application with the authorization package (Glyman, see [0064-0065], providing confirmation of the new booking to the user).
As to claim 17, Glyman discloses everything discloses in claim 16, wherein the instructions cause the one or more processors to perform operations comprising: generating for display, using the storage application, (1) authorization package data of the plurality of authorization packages associated with the user (Glyman, see [0088] and fig. 4, reservation confirmation email as it would be displayed to a human user) and (2) one or more actions corresponding to one or more authorization packages (Glyman, see [0088] and fig. 4, reservation parameters presented must be identified and the correct values of these reservation parameters must be assigned by the automated rebooking system).
As to claim 18, Glyman discloses everything discloses in claim 17, wherein the instructions cause the one or more processors to perform operations comprising: receiving one or more attributes corresponding to each of the one or more authorization packages of the storage application, wherein the one or more attributes include a period of time during which the user is allowed to use the authorization package (Glyman, see [0088], reservation confirmation email contains reservation parameters (e.g., check-in date and time, check-out date and time, hotel address, confirmation number). See [0146], rebooking system is configured to use reservation parameters to locate the stored user identity of the user); and in response to determining that a value of the period of time is less than a threshold period of time, generating for display a recommendation for using the authorization package (Glyman, see [0136], graphical user interface for displaying a rebooking option to be selected with the rebooking control).
As to claim 19, Glyman discloses everything discloses in claim 13, wherein the instructions cause the one or more processors to perform operations comprising: receiving an output of the machine learning model, wherein the output of the machine learning model comprises (1) a probability for each electronic message that each electronic message of the plurality of electronic messages includes at least one authorization package (Glyman, see [0072], rebooking system can be configured to monitor the probability that an email is a reservation email) and (2) entity identification data associated with the at least one authorization package (Glyman, see [0050], new booking system provide reservation booking capabilities that indicate booking agency); and in response to determining that the probability exceeds a threshold probability for an electronic message, determining that the electronic message of the plurality of electronic messages includes the at least one authorization package (Glyman, see [0072], rebooking system is configured to monitor metrics concerning reservation email identification, reservation email parsing, and rebooking opportunity identification).
As to claim 20, Glyman discloses everything discloses in claim 13, wherein the instructions cause the one or more processors to perform operations comprising: determining that a user device corresponding with the user is interacting with the entity device (Glyman, see [0055], rebooking system can be configured to access the account of the user hosted on email system); and in response to determining that the user device is interacting with the entity device, generating for display, a recommendation to the user for using the authorization package (Glyman, see [0136], graphical user interface for displaying a rebooking option).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Glyman et al. (U.S. Publication 2019/0005389), hereinafter ‘Glyman’ in view of Edwards et al. (U.S. Publication 2024/0232730), hereinafter ‘Edwards’.
As to claim 11, Glyman discloses everything discloses in claim 10, wherein determining the entity identification data comprises: extracting textual data from an electronic message (Glyman, see [0092-0093], system can use a specific implementation of machine learning to extract reservation parameters from a reservation email); and Glyman is silent to performing optical character recognition on the textual data. However, Edwards discloses performing optical character recognition on the textual data (Edwards, see [0013], the system can access and interface with different websites and data sources (e.g., a user's emails), using optical character recognition, machine learning models, etc. in order to determine selected accommodation). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Glyman in view of Edwards in order to further modify the method of provided for automatically performing a task on a remote computer from the teachings of Glyman with the method for curating online vehicle reservations from the teachings of Edwards.
One of ordinary skill in the art would have been motivated because it would allow to prevent mismatched reservations or at least warning the user of the mismatch and possibly providing the user with suggested modifications to the reservations to rectify the mismatch (Edwards – 0013).
Conclusion
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 TANIA M PENA-SANTANA whose telephone number is (571)270-0627. The examiner can normally be reached Monday - Friday 8am to 4pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nicholas R Taylor can be reached at 5712723889. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TANIA M PENA-SANTANA/Examiner, Art Unit 2443
/NICHOLAS R TAYLOR/Supervisory Patent Examiner, Art Unit 2443