DETAILED ACTION
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 .
Status of Claims
The following is a final office action.
Claims 1-18 are currently pending and have been examined on their merits.
Claims 1-11 and 14-15 are newly amended see REMARKS June 25, 2026.
Claims 16-18 are newly added see REMARKS June 25, 2026.
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 1-15 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-5, and 16 recite a method (i.e. a series of steps), claims 6-10, and 17 recite an information processing apparatus, and claims 11-15, and 18 recite a non-transitory storage medium, and therefore each claim falls within one of the four statutory categories.
Step 2A prong 1 (Is a judicial exception recited?):
The representative claims 1, 6, and 11 recite: A method for providing information abound an amusement facility, the method comprising: generating event information about an event performed in an amusement facility, the event corresponding to information about a user of the amusement facility, (delivering) the generated event information, generating, map information indication a location where the event is held by using the generating event information and map information of the amusement facility; and (delivering) the generated map information to a user wherein the information about the user of the amusement facility includes at least one of information indicating an article of a character related to the amusement facility, information about a character or a living creature which the user likes, how the user spends time in the amusement facility, and a history of visits made by the user to the amusement facility.
The claims recite a certain method of organizing human activity. The claims are found to be considered a method of organizing human activity as they relate towards managing personal behavior or relationships or interactions between people. As the claims recite a method for generating and managing event information of a user booking a trip to an amusement facility and informing the amusement facility about a user’s profile and preferences. The method merely recites a series of steps for generating information pertaining to a user of an amusement facility and sending the information to an amusement facility.
Alternatively, the claims also recite a mental process. The claims recite merely generating information about a user of an amusement facility. The claims recite a method of generating and delivering user profile information to an amusement facility such as when a user is scheduled to visit and the user’s profile information. The examiner finds these limitations to be similar to concepts the courts have identified as being mental processes such as observations, evaluations, judgements, and opinions. Furthermore, the examiner finds that a user could mentally or with the aid of a “pen and paper” perform the steps of creating and providing user profile information.
Therefore, the examiner finds the claims to recite an abstract idea.
Step 2A Prong 2 (Is the exception integrated into a practical application?): The claims additionally recite;
Claim 1: A computer, a machine learning model, and transmitting by the computer, and transmitting information to a user terminal apparatus for display on a display.
Claim 6: An information processing apparatus configured to provide information about an amusement facility, the information processing apparatus comprising: at least one memory storing instructions; and at least one processor executing the instructions to: a machine learning model, and transmitting by the computer, and transmitting information to a user terminal apparatus for display on a display.
Claim 20: A non-transitory storage medium storing a program for causing a computer to: a machine learning model, and transmitting by the computer, and transmitting information to a user terminal apparatus for display on a display.
However, the limitations merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Merely utilizing a generic computer system to perform the claim limitations of generating and transmitting information is not an improvement in a technology or technical field. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B (Does the claim recite additional elements that amount to significantly more that the judicial exception?): As discussed above, the additional imitations amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Therefore, the additional elements do not integrate the judicial exception into a practical application and do not amount to significantly more.
Claims 2-5, 7-10, and 12-15 further narrowing the abstract idea of generating event information corresponding to a user of an amusement facility.
Claims 16-18 recite the additional elements of training the machine learning model by using training data including information about users of the amusement facility as input information and event information associated with the information about the users. However, the additional elements are directed to merely “apply it” or applying a generic computer elements to perform the abstract idea of generating event information corresponding to user information.
Therefore, claims 1-18 are rejected under U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-2, 5-7, 10-12, and 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Canora (US 2012/0271834) in view of Nenkova (US 2022/0027858).
Claims 1, 6, and 11: Canora discloses (Claim 1) a method for providing information about an amusement facility, the method comprising: (Claim 6) An information processing apparatus configured to provide information about an amusement facility, the information processing apparatus comprising: at least one memory storing instructions; and at least one processor executing the instructions to: (Claim 11) A non-transitory storage medium storing a program for causing a computer to: generating, by a computer, event information about an event performed in the amusement facility with a machine learning model, the event corresponding to information about a user of the amusement facility,
wherein the information about the user of the amusement facility includes at least one of information indicating an article of a character related to the amusement facility, information about a character or a living creature which the user likes, how the user spends time in the amusement facility, and a history of visits made by the user to the amusement facility (Paragraph [0010-0012]; [0014]; [0033-0035]; the present invention provides a method for managing the distribution of personalization and exclusive experience enhancements. The method includes tracking which personalization or experiences visitors to a destination (e.g. an amusement park) are entitled to participate in or to receive. The method includes ranking experiences on an individual basis and storing such as individual ranked list in storage. The method involves the delivery of experiences or personalization when two or more visitors are in a delivery zone (e.g. a station on a ride that personalizes the ride, such as a character that is instructed to personalize a greeting to a visitor). After an experience is provided, a feedback loop is performed to update the recipient’s managed experience state. The system includes a profile generation module, and the module may function to create a visitor profile for each of these tracked visitors. The visitor profile may include personal information such as their name, their photograph, their birth date, and whether they have earned a special status. The profile may include purchase information such as if they have paid more to be treated as a preferred customer. A history may be associated with these entitlements that may be used in later decisions regarding which visitors should next receive an experience or personalization. For example, the history may include a count of the number of times the visitor has received or missed an opportunity when eligible).
Canora discloses a system of managing the experience of a user at a destination using an application for distributing personalized experiences to a user. However, Canora does not specifically disclose the following claim limitations: transmitting, by the computer, the generated event information generating, by the computer, map information indicating a location where the event is held by using the generated event information and map information of the amusement facility; and transmitting, by the computer, the generated map information to a user terminal apparatus for display on a display.
In the same field of endeavor of managing a user’s experience visiting an amusement park Nenkova teaches transmitting, by the computer, the generated event information generating, by the computer, map information indicating a location where the event is held by using the generated event information and map information of the amusement facility; and transmitting, by the computer, the generated map information to a user terminal apparatus for display on a display (Paragraph [0004]; [0014-0016]; [0020]; [0023-0024]; [0041] in some embodiments, one or more processors may receive user specific data and location data. In some embodiments, a recommendation machine learning algorithm may be utilized to analyze the user specific data and location data. A visit recommendation may be generated using the recommendation machine learning algorithm. The one or more recommended locations may be arranged utilizing a scheduling algorithm into a visit schedule. The visit schedule may be revised using real-time user data. The present disclosure is directed to customized schedules for people who visit locations such as theme parks. The system uses data points from visitors to provide personalized recommendations as they navigate the theme park. The specific user data may include data about the user and the user’s preferences, including current and historical information. In some embodiments, the recommendation machine learning algorithm may be any machine learning algorithm capable of utilizing the user specific data, and changes to the user specific data to provide a recommendation score and ranking for each location or item at the location. The algorithm may be a recurrent neural network. The real-time user data may include real-time weather data. In some embodiments, directions and/or a map showing the locations of places and routes to them may be provided to the user on a graphical user interface. The graphical user interface may also provide a visit map showing locations of past and future locations on the optimized visit schedule).
Before the effective filing date of the invention it would have been obvious to modify the system of creating and managing a personalized experience for a user at a destination such as a theme park as disclosed by Canora with the system of transmitting, by the computer, the generated event information generating, by the computer, map information indicating a location where the event is held by using the generated event information and map information of the amusement facility; and transmitting, by the computer, the generated map information to a user terminal apparatus for display on a display as taught by Nenkova (Nenkova [0041]). With the motivation of helping to create and display recommendations of events and attractions to a visitor of a theme park based on their preferences (Nenkova [0002]).
Claims 2, 7, and 12: Modified Canora discloses the method as per claim 1, the information processing apparatus according as per claim 6, and the non-transitory storage medium as per claim 11. However, Canora does not disclose further comprising generating, by the computer, generates the event information corresponding to at least one of weather of a day on which the user uses the amusement facility and a date on which the user uses the amusement facility.
In the same field of endeavor of managing a user’s experience visiting an amusement park Nenkova teaches further comprising generating, by the computer, generates the event information corresponding to at least one of weather of a day on which the user uses the amusement facility and a date on which the user uses the amusement facility (Paragraph [0004]; [0014-0016]; [0020]; [0023-0024]; [0041] in some embodiments, one or more processors may receive user specific data and location data. In some embodiments, a recommendation machine learning algorithm may be utilized to analyze the user specific data and location data. A visit recommendation may be generated using the recommendation machine learning algorithm. The one or more recommended locations may be arranged utilizing a scheduling algorithm into a visit schedule. The visit schedule may be revised using real-time user data. The present disclosure is directed to customized schedules for people who visit locations such as theme parks. The system uses data points from visitors to provide personalized recommendations as they navigate the theme park. The specific user data may include data about the user and the user’s preferences, including current and historical information. In some embodiments, the recommendation machine learning algorithm may be any machine learning algorithm capable of utilizing the user specific data, and changes to the user specific data to provide a recommendation score and ranking for each location or item at the location. The algorithm may be a recurrent neural network. The real-time user data may include real-time weather data. In some embodiments, directions and/or a map showing the locations of places and routes to them may be provided to the user on a graphical user interface. The graphical user interface may also provide a visit map showing locations of past and future locations on the optimized visit schedule).
Before the effective filing date of the invention it would have been obvious to modify the system of creating and managing a personalized experience for a user at a destination such as a theme park as disclosed by Canora with the system of transmitting, by the computer, the generated event information generating, by the computer, map information indicating a location where the event is held by using the generated event information and map information of the amusement facility; and transmitting, by the computer, the generated map information to a user terminal apparatus for display on a display as taught by Nenkova (Nenkova [0041]). With the motivation of helping to create and display recommendations of events and attractions to a visitor of a theme park based on their preferences (Nenkova [0002]).
Claims 5, 10, and 15: Modified Canora discloses the method as per claim 1, the information processing apparatus according as per claim 6, and the non-transitory storage medium as per claim 11. However, Canora does not disclose wherein the map information includes a plan view showing positions of structures in the amusement park.
In the same field of endeavor of managing a user’s experience visiting an amusement park Nenkova teaches wherein the map information includes a plan view showing positions of structures in the amusement park (Paragraph [0004]; [0014-0016]; [0020]; [0023-0024]; [0041] in some embodiments, one or more processors may receive user specific data and location data. In some embodiments, a recommendation machine learning algorithm may be utilized to analyze the user specific data and location data. A visit recommendation may be generated using the recommendation machine learning algorithm. The one or more recommended locations may be arranged utilizing a scheduling algorithm into a visit schedule. The visit schedule may be revised using real-time user data. The present disclosure is directed to customized schedules for people who visit locations such as theme parks. The system uses data points from visitors to provide personalized recommendations as they navigate the theme park. The specific user data may include data about the user and the user’s preferences, including current and historical information. In some embodiments, the recommendation machine learning algorithm may be any machine learning algorithm capable of utilizing the user specific data, and changes to the user specific data to provide a recommendation score and ranking for each location or item at the location. The algorithm may be a recurrent neural network. The real-time user data may include real-time weather data. In some embodiments, directions and/or a map showing the locations of places and routes to them may be provided to the user on a graphical user interface. The graphical user interface may also provide a visit map showing locations of past and future locations on the optimized visit schedule).
Before the effective filing date of the invention it would have been obvious to modify the system of creating and managing a personalized experience for a user at a destination such as a theme park as disclosed by Canora with the system of transmitting, by the computer, the generated event information generating, by the computer, map information indicating a location where the event is held by using the generated event information and map information of the amusement facility; and transmitting, by the computer, the generated map information to a user terminal apparatus for display on a display as taught by Nenkova (Nenkova [0041]). With the motivation of helping to create and display recommendations of events and attractions to a visitor of a theme park based on their preferences (Nenkova [0002]).
Claims 16, 17, and 18: Modified Canora discloses the method as per claim 1, the information processing apparatus according as per claim 6, and the non-transitory storage medium as per claim 11. However, Canora does not disclose further comprising training the machine learning model by using training data including information about users of the amusement facility as input information and event information associated with the information about the users.
In the same field of endeavor of managing a user’s experience visiting an amusement park Nenkova teaches further comprising training the machine learning model by using training data including information about users of the amusement facility as input information and event information associated with the information about the users (Paragraph [0004]; [0014-0016]; [0020]; [0023-0024]; [0041] in some embodiments, one or more processors may receive user specific data and location data. In some embodiments, a recommendation machine learning algorithm may be utilized to analyze the user specific data and location data. A visit recommendation may be generated using the recommendation machine learning algorithm. The one or more recommended locations may be arranged utilizing a scheduling algorithm into a visit schedule. The visit schedule may be revised using real-time user data. The present disclosure is directed to customized schedules for people who visit locations such as theme parks. The system uses data points from visitors to provide personalized recommendations as they navigate the theme park. The specific user data may include data about the user and the user’s preferences, including current and historical information. In some embodiments, the recommendation machine learning algorithm may be any machine learning algorithm capable of utilizing the user specific data, and changes to the user specific data to provide a recommendation score and ranking for each location or item at the location. The algorithm may be a recurrent neural network. The real-time user data may include real-time weather data. In some embodiments, directions and/or a map showing the locations of places and routes to them may be provided to the user on a graphical user interface. The graphical user interface may also provide a visit map showing locations of past and future locations on the optimized visit schedule).
Before the effective filing date of the invention it would have been obvious to modify the system of creating and managing a personalized experience for a user at a destination such as a theme park as disclosed by Canora with the system of transmitting, by the computer, the generated event information generating, by the computer, map information indicating a location where the event is held by using the generated event information and map information of the amusement facility; and transmitting, by the computer, the generated map information to a user terminal apparatus for display on a display as taught by Nenkova (Nenkova [0041]). With the motivation of helping to create and display recommendations of events and attractions to a visitor of a theme park based on their preferences (Nenkova [0002]).
Claims 3-4, 8-9, and 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Canora (US 2012/0271834) in view of Nenkova (US 2022/0027858) further in view of Heaven (US 2017/0169449).
Claims 3, 8, and 13: Modified Canora discloses the method as per claim 1, the information processing apparatus according as per claim 6, and the non-transitory storage medium as per claim 11. However, Canora does not disclose further comprising: specifying, by the computer, an article corresponding to an answer of the user to a questionnaire related to the generated event information, and transmitting, by the computer information about the specified article.
In the same field of endeavor of managing a user’s experience at an amusement facility Heaven teaches further comprising: specifying, by the computer, an article corresponding to an answer of the user to a questionnaire related to the generated event information, and transmitting, by the computer information about the specified article (Paragraph [0010-0011]; [0073]; [0080-0081] the mobile application determines or sets one or more preselected rides for the user. The user profile may contain information identifying the types of rides most enjoyed by the user, characteristics of the user, previously stored rides indicated by the user as desirable, etc. The mobile application determines a type of guest representative of the user. This may be accomplished by querying the user to answer one or more questions. In another embodiment, a plurality of guest types may be defined and shown to the user for user self-identification and selection. After determining the type of guest, operation continues to determine the amount of time expected that the user will be available to participate in the various attraction by querying a user to answer one or more questions (e.g. expected time to enter the park, expected time to exit the park, how many meals are expected, etc.)).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of managing a user profile to determine a user’s preferences and interests at an amusement facility as disclosed by Canora with the system of wherein the computer specifies an article corresponding to an answer of the user to a questionnaire related to the generated event information, and the computer transmits information about the specified article as taught by Heaven (Heaven [0080]). With the motivation of helping to personalize and manage a user’s experience at an amusement facility (Heaven [0006]).
Claims 4, 9, and 14: Modified Canora discloses the method as per claim 1, the information processing apparatus according as per claim 6, and the non-transitory storage medium as per claim 11. However, Canora does not disclose further comprising associating, by the computer, a point that can be used to purchase an article sold by the amusement facility with identification information of the user who has participated in the event indicated by the generated event information, and registering the associated identification information.
In the same field of endeavor of managing a user’s experience at an amusement facility Heaven teaches further comprising associating, by the computer, a point that can be used to purchase an article sold by the amusement facility with identification information of the user who has participated in the event indicated by the generated event information, and registering the associated identification information (Paragraph [0010-0011]; [0073]; [0097]; [0105] the mobile application determines or sets one or more preselected rides for the user. The user profile may contain information identifying the types of rides most enjoyed by the user, characteristics of the user, previously stored rides indicated by the user as desirable, etc. In one embodiment, the system may be configured to offer purchasing options to guests via the one or more guest interfaces. Via the one or more guest interfaces, guests may be provided with options to purchase photographs and/or videos, customized souvenirs, and/or any of a variety of other items sold throughout the park).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of managing a user profile to determine a user’s preferences and interests at an amusement facility as disclosed by Canora with the system of wherein the computer associates a point that can be used to purchase an article sold by the amusement facility with identification information of the user who has participated in the event indicated by the generated event information and registers the associated information as taught by Heaven (Heaven [0097]). With the motivation of helping to personalize and manage a user’s experience at an amusement facility (Heaven [0006]).
Therefore, claim 1-18 are rejected under U.S.C. 103.
Response to arguments
Applicant’s arguments, see REMARKS, filed June 25, 2026, with respect to the rejections of Claim(s) 1-18 is/are rejected under 35 U.S.C. 101 are considered and not persuasive.
Claims 1, 6, and 13: Representative argues that the currently amended claim limitations are directed to a practical application as they recite an improvement by generating personalized event information using a machine learning model and integrating that event information with map information indication a location where the event is held by using the generated event information and the map information. However, the examiner respectfully disagrees as the claims recite a method for providing information about an amusement park by generating event information about an event in the amusement facility corresponding to information about a user. Merely receiving user information including a user’s preferences and historical information with an amusement facility and generating event information corresponding to the user information is an abstract idea. As a person is mentally, or using simple tools such as pen and paper, capable of determining event information for a user based on their personal information. Such as making suggestions to a person of a meet and greet for their favorite character in a park on a particular day. The claims recite concepts the courts have identified as being mental processes such as observation, evaluation, judgement, and opinions. The examiner further finds that the additional elements of a computer and a machine learning model being used to perform the abstract idea as well as transmitting information to a terminal apparatus to display information are directed to merely “apply it” or applying generic computer elements to perform the abstract idea of receiving and presenting information to a user. Merely using a computer to receive and process user information to generate an output such as event information a user may be interested in and presenting it in a display are not improvement to a technology or technical field. Therefore, the additional elements do not direct the claims to a practical application.
The examiner maintains the current 101 rejection.
Claims 2-5, 7-10, and 12-18 were dependent on claims 1, 6, and 11. Therefore, they are also rejected under the same rejection as above.
Applicant’s arguments, see REMARKS, filed June 25, 2026, with respect to the rejections of 1-2, 5-7, 10-12, and 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Canora (US 2012/0271834) in view of Nenkova (US 2022/0027858) are not persuasive as the claims were amended which required further search and consideration and new art was applied.
Claims 1, 6, and 11: Applicant argues that the current prior art does not disclose the amended claim limitations. However, upon further search and consideration the examiner finds that Canora can be used in combination with Nenkova to teach the newly amended claim limitations. Canora discloses a system of creating personalized experiences for a patron of a theme park based on their personal information and historical information. Which can be used in combination with Nenkova which teaches a system of using a machine learning model to generate a customized schedule or suggested itinerary for an attendee of a theme park. Nenkova further teaches generating a map to guide a user to various facilities and events in a theme park based on user information such as preferences. Nenkova additionally teaches updating information in real time such as the current weather or changing in availability of facilities and events in a theme park while providing a graphical display on a user device of recommendations.
Therefore, the examiner finds that the combination of Canora and Nenkova as capable of teaching the currently amended claimed limitations. Claims 1-18 are newly rejected under U.S.C. 103.
Claims 2-5, 7-10, and 12-18 were dependent on claims 1, 6, and 11. Therefore, they are also newly rejected under the same rejection as above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Filatoff (US 2017/0270565) Facility mapping and interactive tracking.
Chanick (US 2009/0319306) System and method for venue attendance management.
Klappert (US 2021/0097893) technical solutions for customized tours.
Jadav (US 2020/0154235) Cognitive location and navigation services for custom applications.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to COREY RUSS whose telephone number is (571)270-5902. The examiner can normally be reached on M-F 7:30-4:30.
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, Lynda Jasmin can be reached on 5712726782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/COREY RUSS/Primary Examiner, Art Unit 3629
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