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 .
Election/Restrictions
Claims 9-15 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 1/18/26.
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.
Claims 1, 4-6, 16, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Aghdaie (US 2018/0243656) in view of Agarwal (US 20210195442).
Claim 1. Aghdaie discloses a video game environment for deploying engagement simulations, comprising:
at least one processor; a communication interface communicatively coupled to the at least one processor; and a memory device storing executable code that (paragraphs 5, 186-188), when executed, causes the at least one processor to:
train, using training data, a prediction model (paragraphs 58, 78-80) by iteratively predicting a target variable value of a churn-based event (prediction model that predict excepted churn rate; paragraphs 25, 35, 48, 61-65, 81, 91-98) associated with an online video game application (paragraph 34),
the iteratively (process is updated and repeated over time, paragraphs 78) predicting including identifying an error between a prediction and the target variable value and modifying weights of the prediction model for multiple iterations (Feedback data to correct model for instance when an anomaly exist in historical data to apply different weight to the data and or exclude certain data; paragraph 56, 58. Weight and variables are used to modify and refine the model, paragraph 84. Therefore, an error is identified in order to modify and refine the model.),
wherein the training data comprises information obtained from the online video game application (paragraphs 26-31, 37, 58),
wherein the information includes specific data that is selected from the group consisting of a duration of gameplay via the online video game application, a quantity of instances of gameplay, and a quantity of resource transactions of the virtual resource (paragraphs 26, 31, 37, 44, 47); and
deploy and apply the trained prediction model to user data of a plurality of users to predict the churn-based event (paragraphs 25, 35, 48, 61-65, 81, 91-98), wherein the user data is restricted by history date for a predefined number of days (historical data may be limited by a date criteria; i.e. 6 months; paragraphs 55).
Aghdaie discloses the claimed invention but fails to teach that the training data comprises information obtained from the partner computer application storing resource data of real-world resources that are stored to a real-world location. Nevertheless, such modification would have been obvious to one of ordinary skilled in the art. In an analogous art to monitoring users’ interaction, Agarwal discloses a system that predicts the churn of a user based on a machine learning model (paragraphs 9, 114, 116, 143) for various applications including games (paragraph 62). Agarwal discloses information obtained from the partner computer application storing resource data of real-world resources are that are stored to a real-world location (third party data, including utility data, weather data, partner APIs, which are real world resource data; paragraphs 108, 138) is stored on a device and therefore stored in a real world location.) are used to train the model and predict the churn of a user (machine learning model, paragraphs 8-9, 103, 110, 137). This allows the predative model to predict the user behavior based on external factors (paragraphs 138). It would have been obvious to one of ordinary skilled in the art before the effective filing date to modify Aghdaie’s invention and incorporate training data obtained from the partner computer application storing resource data of real-world resources that are stored to a real-world location in order to provide the predictable result of predicting the user behavior based on external factors
Claim 4. Aghdaie discloses the video game environment of claim 1, wherein the information further includes ages of one or more users of the plurality of users and genders of various users of the plurality of users (demographic including age and gender, paragraphs 64, 79).
Claim 5. Aghdaie discloses the video game environment of claim 1, wherein the information further includes geographic location data of at least some of the plurality of users (geographic location, paragraphs 79, 84), user domain information of at least a portion of the plurality of users (geographic location which is considered to be domain information, paragraphs 79, 84). Aghdaie fails to explicitly teach discloses user email addresses of one or more users of the plurality of users. However, it is implied Aghdaie discloses user profile information is used (paragraphs 43-44) and online gaming services require user email address. In addition, Agarwal discloses that customer email information is part of user profile (paragraphs 9, 44, 87, 116). It would have been obvious to one of ordinary skilled in the art before the effective filing date to modify Aghdaie’s invention and incorporate email information since Aghdaie discloses user profile information is used.
Claim 6. Aghdaie discloses the claimed invention but fails to teach that the history date for the predefined number of days include a most recent 30 days preceding a current application of the trained prediction model to the user data. However, it is implied or obvious because Abhdaie discloses that the historical data van be limited by a date criteria (paragraph 55)
Claims 16, 19. See rejection for claim 1 and 4 above.
Claims 2, 7-8, 17, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Aghdaie (US 2018/0243656) in view of Agarwal (US 20210195442 as applied to claims 1, 16 above, and further in view of Parameswar (US 2023/0139391).
Claims 2. Aghdaie discloses churn-based event includes user disengagement with the online video game application as discussed above but fails to teach that the trained prediction model is predicts, when deployed, a date that a user of the plurality of users stops engaging with the online video game application by not logging in to the online video game application. Nevertheless, such modification would have been obvious to one of ordinary skilled in the art. In an analogous art to predicting user’s action, Parameswar discloses a machine learning model which predicts a user churn rate (paragraphs 90, 119, 160). Parameswar discloses that the model predict when the user will stop engagement within a period of time such as particular day such as one or more days, weeks, etc. This allows the merchant, service provider to offer the user an incentive to stay. It would have been obvious to one of ordinary skilled in the art before the effective filing date to modify Aghdaie’s invention and predict when the user will stop engaging with the game application as claimed in order to provide the predictable result of offering the user an incentive to stay before the user stops engaging with the game.
Claims 7-8. Aghdaie discloses the claimed invention as discussed above but fails to teach (claim 7) wherein applying the trained prediction model comprises identifying users of the plurality of users that will exhibit the churn-based event within an upcoming preset number of upcoming days and (claim 8) the upcoming present number of upcoming days comprises a next seven days. However, as discussed above, Parameswar discloses a machine learning model which predicts a user churn rate (paragraphs 90, 119, 160). Parameswar discloses that the model predict when the user will stop engagement within a period of time such as particular day such as one or more days, weeks, etc. This allows the merchant, service provider to offer the user an incentive to stay. It would have been obvious to one of ordinary skilled in the art before the effective filing date to modify Aghdaie’s invention and predict when the user will stop engaging with the game application as claimed in order to provide the predictable result of offering the user an incentive to stay before the user stops engaging with the game.
Claim 17 and 20. See rejection for claim 2 and 7 above.
Claims 3, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Aghdaie (US 2018/0243656) in view of Agarwal (US 20210195442 as applied to claims 1, 16 above, and further in view Gupta (US 2019/0102820).
Claim 3, 18. Aghdaie discloses the claimed video game environment of claim 1 and method of claim 16 as discussed above but fails to explicitly teach that the churn-based event comprises deletion of the online video game application from user devices of the plurality of users. However, such modification would have been obvious to one of ordinary skilled in the art. A churn is when the user stop using the service and therefore would include when the user uninstalled or deletes the application. In addition, Gupta discloses a system that predicts a user churn associated with an action. Gupta includes that the churn is when user uninstalls or does not purchase or subscribe to the application (paragraphs 28). It would have been obvious to one of ordinary skilled in the art before the effective filing date to modify Aghdaie’s invention and incorporate a churn-based event comprising deletion of the online video game application in order to provide the predictable result of predicting when the user stops using the gaming application which including when the user deletes the game application.
Conclusion
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/JASSON H YOO/ Primary Examiner, Art Unit 3715