DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This communication is responsive to the amendment to the original application. This action is Final. Claims 1 – 3, 5 – 13 and 15 – 22 are pending and have been examined.
Response to Amendments
In the reply filed 2/6/25, claims 1 – 3, 7 – 9, 11 – 13 and 17 were amended. New claims 18 – 22 were added. Accordingly, claims 1 – 3, 5 – 13 and 15 – 22 are pending.
Response to Arguments
Applicant's arguments with respect to claims 1 – 3, 5 – 13 and 15 – 22 have been carefully considered but are not deemed persuasive in view of rejections below.
Examiner respectfully disagrees with applicant’s arguments on pages 10 – 12, that prior art fails to teach, “wherein the historic gameplay data includes: a plurality of past gameplay attributes of a past interactive content title of the plurality of past interactive content titles;” Benzatti [0041] specifically teaches, “… historical data for each of a plurality of users of computing device(s) 102. … a game broadcast, a community manager or message board post, playing a video game, etc.” It would have been obvious that based on user preferences and gameplay history data, games are recommended to users or players. Therefore, examiner is not persuaded.
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 – 3, 5 – 13 and 15 – 21 are rejected under 35 U.S.C. 103 as being unpatentable over Benzatti et al., U.S. Patent Application Publication No.: 2019/0373331 (Hereinafter “Benzatti”), and further in view of Lalonde et al., U.S. Patent Application Publication No.: 2019/0308099 (Hereinafter “Lalonde”).
Regarding claim 1, Benzatti teaches, a method for selecting new interactive content titles for new gameplay sessions, the method comprising:
storing historic gameplay data (Benzatti [0123]: “… storing user event records…”) of a user device from a plurality of past interactive sessions of a plurality of past interactive content titles in memory (Benzatti [0043]: “… a log of each session in which a user played a multiplayer game across a plurality of different game titles (e.g., by logging a start time, end time, duration, the identification of the content type, and/or the game title), … generate features corresponding to the number of hours a user played multiplayer games in the past 7 days, 30 days, 90 days, etc.),
wherein the historic gameplay data includes (Benzatti [0041]: Event catalog 114 may include, for instance, historical data for each of a plurality of users of computing device(s) 102. Each user event record 128 may include any action performed by a user on computing device(s) 102, including but not limited to viewing or accessing certain types of content, such as accessing a screenshot, a game broadcast, a community manager or message board post, playing a video game, etc.):
a plurality of past gameplay attributes of a past interactive content title of the plurality of past interactive content titles (Benzatti [0041]: “For instance, with reference to FIG. 3, event cataloger 301 is configured to obtain user event records 128 associated with a plurality of media items (e.g., video games) and a plurality of content types … Event catalog 114 may include, for instance, historical data for each of a plurality of users of computing device(s) 102. Each user event record 128 may include any action performed by a user on computing device(s) 102, including but not limited to viewing or accessing certain types of content, such as accessing a screenshot, a game broadcast, a community manager or message board post, playing a video game, etc. Each user event record 128 may also include a time associated with a performed action (e.g., a start time, an end time, a duration), one or more navigation actions performed on a graphical user interface of computing device (e.g., interface elements with which a user interacted, menus or pages that were accessed), etc.”); and
a set of past audiovisual in-game media elements that are presented in a past virtual environment during gameplay of one or more interactive content titles of the plurality of past interactive content titles (Benzatti [0050]: In other implementations, the user-content score model may comprise a heuristics model configured to analyze a frequency of event records from event logger 114. For instance, a heuristics model may analyze a number of times a user accessed a particular type of content in a given time period (e.g., in the past day, week, month, etc.). For instance, if a particular user accessed a first content type (e.g., screenshots) for one hour, and another content type (e.g., joining a multiplayer game) for another hour in a particular time period (e.g., the past day), a heuristics model may determine that the user scores may assign 0.5 to the first and second content types, while assigning a lower score (e.g., zero) to the remaining content types for the given time period.);
applying a machine learning model to the historic gameplay data to identify one or more common gameplay attributes of the plurality of past gameplay attributes that are shared across a subset of the plurality of past interactive content titles played by the user device (Benzatti [0049]: In some implementations, as described above, the user-content score model may comprise a machine-learning based model based on one or more machine-learning based features 314 (e.g., from feature catalog 116). User score generator 310 may apply the user-content score model using a machine-learning based algorithm to identity and/or determine one or more clusters, patterns, etc., to generate a set of user scores for a particular user. For instance, the user-content score model may be configured to analyze patterns based on user behaviors (e.g., which types of a content a user is likely to access) and generate user scores based on such behaviors. Machine-learning based model may implement one or more unsupervised approaches, including collaborative filtering and/or content based recommendations to predict how likely a user may interact with certain content types despite no prior interaction by the user. In other instances, machine-learning based model may also be trained based on a supervised approach to predict how likely a user may interact with a type of content based on a variety of learned behavioral patterns at a certain time (e.g., a certain day) or over a particular period of time.);
selecting a new interactive content title that is not one of a past interactive content title of the plurality of past interactive content titles and that include the one or more common gameplay attributes (Benzatti [0072]: Because publishing pipeline 322 is configured to continuously receive updated user scores 318 and title scores 320, in addition to near real-time signals 330, content recommendation score ranker 326 may combine scores 336 (e.g., mathematically or using any other technique) in real-time in response to receiving request 124. As a result, content recommendation score ranker 326 may generate a combined set of scores along with any biasing factors or filters, thus enabling computing device(s) 102 to receive a set of content recommendations 126 with minimal delay or latency. Furthermore, such quick decision-making capabilities enables the selection of content to recommend in high performance synchronous scenarios, while also minimizing a footprint in multi-tier asynchronous integrations.),
Benzatti does not clearly teach, wherein the new interactive content title includes a first set of audiovisual in-game media elements; and However, Lalonde [0189]: Specifically, in some implementations, a sample set of sessions are selected from all online gaming sessions associated with a gaming title. … In some implementations, a shader pipeline optimizer 534 samples selectively within an online gaming session, e.g., samples particular shaders, or a subset of image frames, to reduce the impact on game content rendering in the foreground. We then use these timings to select good candidate shaders for further optimization and invoke manual review or automated systems to refine the shaders. Alternatively, in some implementations, power consumption is monitored and used to optimize a shader pipeline (i.e., a sequence of compiled shaders). Specifically, the shader pipeline optimizer 534 is instrumented to measure power draw instead of timing performance.
selecting a custom audiovisual in-game media element from among the set of past audiovisual in-game media elements, wherein the custom audiovisual in-game media element is different than each audiovisual in-game media element of the first set of audiovisual in-game media elements (Lalonde [0053] teaches, “If a shader program is selected for use by a gaming title, it is retrieved from the shader library server 124, compiled by the game server 118, and stored locally in the game servers 118 for use to render the plurality of image frames for each online gaming session. In some implementations, each image frame of an online gaming session is rendered by an ordered sequence of compiled shaders (e.g., including a vertex shader, a tessellation shader, a geometry shader and a fragment shader) each of which implements one or more specific image rendering operations during the course of creating the respective image frame.”
initiating a new interactive session to present a new virtual environment of the new interactive content title on the user device; Lalonde [0087] teaches, “In some situations, the modified sequence of compiled shaders is optionally stored in place of the sequence of compiled shaders and used by subsequent online gaming sessions. In some situations, the modified sequence of compiled shaders is stored as a new version of the sequence of compiled shaders corresponding to the usage statistics, and is used when subsequent game states of the online gaming sessions are consistent with the usage statistics.”
wherein the new virtual environment presents the first set of audiovisual in-game media elements associated with the new interactive content title (Lalonde [0059]: In some implementations, gaming content corresponding to a gaming title are rendered by the GPUs 140 for online gaming sessions according to a graphics pipeline process (e.g., an OpenGL rendering pipeline). The graphics pipeline process includes an ordered sequence of graphics operations, and each graphics operation is implemented based on one or more user-defined shader programs. … The graphics pipeline process renders a sequence of image frames for each online gaming session, and each image frame includes an array of pixels (also called a page) having a resolution. In some implementations, a static data item corresponding to the graphics pipeline process (e.g., a texture object) includes one or more images having the same resolution as the image frames of the online gaming sessions.) and the custom audiovisual in-game media element of the set of past audiovisual in-game media elements (Lalonde [0134]: “Game application(s) settings 638 for storing information associated with user accounts of the game application(s), including one or more of account access information, in-game user preferences, gameplay history data, and information on other players.” Here, the new virtual gaming environment is similar to the game applications using in-game media elements based on in-game user preferences and gameplay historical data.).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to incorporate the teaching of Benzatti et al. to the Lalonde’s system by adding the feature of in-game elements. The references (Benzatti and Lalonde) teach features that are analogous art and they are directed to the same field of endeavor, such as multi-media content. Ordinary skilled artisan would have been motivated to do so to provide Benzatti’s system with enhanced data. (See Lalonde [Abstract], [0006 – 0008], [0041], [0059], [0134]). One of the biggest advantages of network machine learning database algorithms is their ability to improve over time. Machine learning technology typically improves efficiency and accuracy thanks to the ever-increasing amounts of data that are processed.
Regarding claim 2, the method of clam 1, further comprising generating the subset by filtering the plurality of past interactive content titles based at least in part on the common gameplay attributes of the plurality of past gameplay attributes (Benzatti [0066]: In such examples, content recommendation score ranker 326 may implement a filter (e.g., a high-pass filter or the like) to filter out content types for games that are not relevant. In an implementation, the high-pass filter may comprise a multiplier of 1 for each content type that is available for a game, and 0 for content types that are not available for the game).
Regarding claim 3, the method of claim 1, wherein selecting a new interactive content title is based at least in part on determining that one or more gameplay attributes exhibited by a selected activity within the new interactive content title meets a predetermined threshold (Benzatti [0051]: In some implementations, a user interface may be implemented that enables one or more attributes of user score generator 310 to be configured. For instance, a user interface may enable the types of content for which a score is to be determined, may assign time durations for which a user's propensity to a particular content type are analyzed, may assign a weighting factor or exponential decay for any one or more content types, or any enable any other configuration of the manner in which user score generator 310 generates a set of user scores for a given user.).
Regarding claim 5, the method of claim 1, wherein the audiovisual in-game media elements integrated into the interactive session, include one or more randomly selected game elements or media elements associated with the historic gameplay data (Lalonde [0151-0152]: Gaming user interface module 728 for accessing user content (e.g., profile, avatar, purchased games, game catalog, friends, messaging) and optimized images for display, and for receiving inputs from a client device 102 during gameplay; Interface control module 730 for interfacing communications between the gaming application 726 and the gaming user interface module 728, and for initiating a session pairing request with the server system 114 during a game launch; ).
Regarding claim 6, the method of claim 1, further comprising recording the historic gameplay data by:
recording object data onto an object ring-buffer; and storing the recorded object data in one or more object files that store data regarding one or more activities associated with past interaction by the user with one or more past interactive content titles, wherein a stored user profile associated with the user device includes at least one object file (Lalonde [0059]: In some implementations, gaming content corresponding to a gaming title are rendered by the GPUs 140 for online gaming sessions according to a graphics pipeline process (e.g., an OpenGL rendering pipeline). … For example, the static data items used in a graphics operation of the OpenGL rendering pipeline include one or more texture objects, a vertex data buffer object, and a constant data object. The graphics pipeline process renders a sequence of image frames for each online gaming session, and each image frame includes an array of pixels (also called a page) having a resolution. In some implementations, a static data item corresponding to the graphics pipeline process (e.g., a texture object) includes one or more images having the same resolution as the image frames of the online gaming sessions.).
Regarding claim 7, the method of claim 1, further comprising:
evaluating the plurality of past interactive content titles to identify the subset, wherein each of the past interactive content titles exhibit one or more of the common gameplay attributes (Benzatti [0032]: Feature catalog 116 is configured to store data regarding one or more features used by recommender system 108, as described below, to generate content recommendations 126. Feature catalog 116 may store, for instance, machine – learning based features generated or derived from user event records 128 stored in event catalog 114. In some implementations, feature catalog 116 may comprise machine- learning based features based on an aggregation of user event records obtained from event catalog 114.).
Regarding claim 8, the method of claim 7, further comprising updating the machine learning model associated with the user based on the common gameplay attributes, the machine learning model specifying one or more past gameplay attributes and an associated prioritization (Benzatti [0051]: In some implementations, a user interface may be implemented that enables one or more attributes of user score generator 310 to be configured. For instance, a user interface may enable the types of content for which a score is to be determined, may assign time durations for which a user's propensity to a particular content type are analyzed, may assign a weighting factor or exponential decay for any one or more content types, or any enable any other configuration of the manner in which user score generator 310 generates a set of user scores for a given user.).
Regarding claim 9, the method of claim 7, further comprising updating the machine learning model over time based on feedback from the user, the feedback indicating an updated prioritization for one or more of the past gameplay attributes (Benzatti [0064]: In other instances, the biasing factor may be configured to bias an entire set of scores (e.g., the user scores), such that the user scores are weighted heavier than the title scores during generation of the combined scores. As one illustrative example, because it may be inferred that experienced users of computing device(s) 102 may prefer content recommendations based more heavily on personalized historical behavior (e.g., user scores), content recommendation score ranker 326 may implement a biasing factor enabling the user scores associated with the particular user to have a heavier weight. In other instances, such as where a user is relatively new, behavior patterns across the general population (e.g., using title scores) may be weighted stronger. In yet another example, where event catalog 114 comprises insufficient data for a given title (such as where a title is newly released), biasing factor may result in user scores being weighted stronger.).
Regarding claim 10, the method of clam 9, where updating the machine learning model includes adjusting one or more weighting factors associated with the updated prioritization (Benzatti [0050]: The heuristics model may be configured to perform such an analysis on a plurality of different time periods to generate an aggregated frequency mapping of a particular user's interaction with each of a plurality of content types. In implementations, an exponential decay (or other weighting factor) may also be applied to user interactions, such that a user's more recent interactions are assigned higher scores than the user's older interactions with the plurality of content types. In this manner, user score generator 310 may generate a set of user scores for a particular user's affinity for accessing a plurality of content types.).
Regarding claim 11, Benzatti teaches, a system for selecting new interactive content titles for new gameplay sessions, comprising:
memory that stores historic gameplay data (Benzatti [0123]: “… storing user event records…”) of a user device during a plurality of interactive sessions in memory (Benzatti [0043]: “… a log of each session in which a user played a multiplayer game across a plurality of different game titles (e.g., by logging a start time, end time, duration, the identification of the content type, and/or the game title), … generate features corresponding to the number of hours a user played multiplayer games in the past 7 days, 30 days, 90 days, etc.);
wherein the historic gameplay data includes (Benzatti [0041]: Event catalog 114 may include, for instance, historical data for each of a plurality of users of computing device(s) 102. Each user event record 128 may include any action performed by a user on computing device(s) 102, including but not limited to viewing or accessing certain types of content, such as accessing a screenshot, a game broadcast, a community manager or message board post, playing a video game, etc.):
a plurality of past gameplay attributes of a past interactive content title of the plurality of past interactive content titles (Benzatti [0041]: “For instance, with reference to FIG. 3, event cataloger 301 is configured to obtain user event records 128 associated with a plurality of media items (e.g., video games) and a plurality of content types … Event catalog 114 may include, for instance, historical data for each of a plurality of users of computing device(s) 102. Each user event record 128 may include any action performed by a user on computing device(s) 102, including but not limited to viewing or accessing certain types of content, such as accessing a screenshot, a game broadcast, a community manager or message board post, playing a video game, etc. Each user event record 128 may also include a time associated with a performed action (e.g., a start time, an end time, a duration), one or more navigation actions performed on a graphical user interface of computing device (e.g., interface elements with which a user interacted, menus or pages that were accessed), etc.”); and
a set of past audiovisual in- game media elements that are presented in a past virtual environment during gameplay of one or more interactive content titles of the plurality of past interactive content titles (Benzatti [0050]: In other implementations, the user-content score model may comprise a heuristics model configured to analyze a frequency of event records from event logger 114. For instance, a heuristics model may analyze a number of times a user accessed a particular type of content in a given time period (e.g., in the past day, week, month, etc.). For instance, if a particular user accessed a first content type (e.g., screenshots) for one hour, and another content type (e.g., joining a multiplayer game) for another hour in a particular time period (e.g., the past day), a heuristics model may determine that the user scores may assign 0.5 to the first and second content types, while assigning a lower score (e.g., zero) to the remaining content types for the given time period.); and
apply a machine learning model to the historic gameplay data to identify one or more common gameplay attributes of the plurality of past gameplay attributes that are shared across a subset of the plurality of past interactive content titles played by the user device (Benzatti [0049]: In some implementations, as described above, the user-content score model may comprise a machine-learning based model based on one or more machine-learning based features 314 (e.g., from feature catalog 116). User score generator 310 may apply the user-content score model using a machine-learning based algorithm to identity and/or determine one or more clusters, patterns, etc., to generate a set of user scores for a particular user. For instance, the user-content score model may be configured to analyze patterns based on user behaviors (e.g., which types of a content a user is likely to access) and generate user scores based on such behaviors. Machine-learning based model may implement one or more unsupervised approaches, including collaborative filtering and/or content based recommendations to predict how likely a user may interact with certain content types despite no prior interaction by the user. In other instances, machine-learning based model may also be trained based on a supervised approach to predict how likely a user may interact with a type of content based on a variety of learned behavioral patterns at a certain time (e.g., a certain day) or over a particular period of time.);
a processor that executes instructions stored in memory (Benzatti [0100]: processor … memory… ), wherein the processor executes the instructions to:
select a new interactive content title that is not one of a past interactive content title of the plurality of past interactive content titles and that include the one or more common gameplay attributes (Benzatti [0072]: Because publishing pipeline 322 is configured to continuously receive updated user scores 318 and title scores 320, in addition to near real-time signals 330, content recommendation score ranker 326 may combine scores 336 (e.g., mathematically or using any other technique) in real-time in response to receiving request 124. As a result, content recommendation score ranker 326 may generate a combined set of scores along with any biasing factors or filters, thus enabling computing device(s) 102 to receive a set of content recommendations 126 with minimal delay or latency. Furthermore, such quick decision-making capabilities enables the selection of content to recommend in high performance synchronous scenarios, while also minimizing a footprint in multi-tier asynchronous integrations.),
Benzatti does not clearly teach, wherein the new interactive content title includes a first set of audiovisual in-game media elements; and However, Lalonde [0189]: Specifically, in some implementations, a sample set of sessions are selected from all online gaming sessions associated with a gaming title. … In some implementations, a shader pipeline optimizer 534 samples selectively within an online gaming session, e.g., samples particular shaders, or a subset of image frames, to reduce the impact on game content rendering in the foreground. We then use these timings to select good candidate shaders for further optimization and invoke manual review or automated systems to refine the shaders. Alternatively, in some implementations, power consumption is monitored and used to optimize a shader pipeline (i.e., a sequence of compiled shaders). Specifically, the shader pipeline optimizer 534 is instrumented to measure power draw instead of timing performance.
select a custom audiovisual in-game media elements from among the set of past audiovisual in-game media elements, wherein the custom audiovisual in-game media element is different than each audiovisual in-game media element of the first set of audiovisual in-game media elements (Lalonde [0053] teaches, “If a shader program is selected for use by a gaming title, it is retrieved from the shader library server 124, compiled by the game server 118, and stored locally in the game servers 118 for use to render the plurality of image frames for each online gaming session. In some implementations, each image frame of an online gaming session is rendered by an ordered sequence of compiled shaders (e.g., including a vertex shader, a tessellation shader, a geometry shader and a fragment shader) each of which implements one or more specific image rendering operations during the course of creating the respective image frame.”);
initiating a new interactive session to present a new virtual environment of the new interactive content title on the user device (Lalonde [0087]: “In some situations, the modified sequence of compiled shaders is optionally stored in place of the sequence of compiled shaders and used by subsequent online gaming sessions. In some situations, the modified sequence of compiled shaders is stored as a new version of the sequence of compiled shaders corresponding to the usage statistics, and is used when subsequent game states of the online gaming sessions are consistent with the usage statistics.”);
wherein the new virtual environment presents the first set of audiovisual in-game media elements associated with the new interactive content title (Lalonde [0059]: In some implementations, gaming content corresponding to a gaming title are rendered by the GPUs 140 for online gaming sessions according to a graphics pipeline process (e.g., an OpenGL rendering pipeline). The graphics pipeline process includes an ordered sequence of graphics operations, and each graphics operation is implemented based on one or more user-defined shader programs. … The graphics pipeline process renders a sequence of image frames for each online gaming session, and each image frame includes an array of pixels (also called a page) having a resolution. In some implementations, a static data item corresponding to the graphics pipeline process (e.g., a texture object) includes one or more images having the same resolution as the image frames of the online gaming sessions.) and the custom audiovisual in-game media element of the set of past audiovisual in-game media elements (Lalonde [0134]: “Game application(s) settings 638 for storing information associated with user accounts of the game application(s), including one or more of account access information, in-game user preferences, gameplay history data, and information on other players.” Here, the new virtual gaming environment is similar to the game applications using in-game media elements based on in-game user preferences and gameplay historical data.).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to incorporate the teaching of Benzatti et al. to the Lalonde’s system by adding the feature of in-game elements. The references (Benzatti and Lalonde) teach features that are analogous art and they are directed to the same field of endeavor, such as multi-media content. Ordinary skilled artisan would have been motivated to do so to provide Benzatti’s system with enhanced data. (See Lalonde [Abstract], [0006 – 0008], [0041], [0059], [0134]). One of the biggest advantages of network machine learning database algorithms is their ability to improve over time. Machine learning technology typically improves efficiency and accuracy thanks to the ever-increasing amounts of data that are processed.
Regarding claim 12, the system of claim 11, wherein the processor executes further instructions to generate the subset by filtering the plurality of past interactive content titles based at least in part on the common gameplay attributes of the plurality of past gameplay attributes (Benzatti [0066]: In such examples, content recommendation score ranker 326 may implement a filter (e.g., a high-pass filter or the like) to filter out content types for games that are not relevant. In an implementation, the high-pass filter may comprise a multiplier of 1 for each content type that is available for a game, and 0 for content types that are not available for the game).
Regarding claim 13, the system of claim 11, wherein the processor selecting a new interactive content title is based at least in part on determining that one or more gameplay attributes exhibited by a selected activity within the new interactive content title meets a predetermined threshold (Benzatti [0051]: In some implementations, a user interface may be implemented that enables one or more attributes of user score generator 310 to be configured. For instance, a user interface may enable the types of content for which a score is to be determined, may assign time durations for which a user's propensity to a particular content type are analyzed, may assign a weighting factor or exponential decay for any one or more content types, or any enable any other configuration of the manner in which user score generator 310 generates a set of user scores for a given user.).
Regarding claim 15, the system of claim 11, where the audiovisual in-game media elements integrated into the interactive session, include one or more randomly selected game elements or media elements associated with the historic gameplay data (Lalonde [0151-0152]: Gaming user interface module 728 for accessing user content (e.g., profile, avatar, purchased games, game catalog, friends, messaging) and optimized images for display, and for receiving inputs from a client device 102 during gameplay; Interface control module 730 for interfacing communications between the gaming application 726 and the gaming user interface module 728, and for initiating a session pairing request with the server system 114 during a game launch; ).
Regarding claim 16, the system of claim 11, wherein the processor records the historic gameplay data by:
recording object data onto an object ring-buffer; and storing to memory the recorded object data in one or more object files that store data regarding one or more activities associated with past interaction by the user with one or more past interactive content tithes, wherein a stored user profile associated with the user device includes at least one object file (Lalonde [0059]: In some implementations, gaming content corresponding to a gaming title are rendered by the GPUs 140 for online gaming sessions according to a graphics pipeline process (e.g., an OpenGL rendering pipeline). … For example, the static data items used in a graphics operation of the OpenGL rendering pipeline include one or more texture objects, a vertex data buffer object, and a constant data object. The graphics pipeline process renders a sequence of image frames for each online gaming session, and each image frame includes an array of pixels (also called a page) having a resolution. In some implementations, a static data item corresponding to the graphics pipeline process (e.g., a texture object) includes one or more images having the same resolution as the image frames of the online gaming sessions.).
Regarding claim 17, the system of claim 11, wherein the processor executes further instructions to:
evaluate the plurality of past interactive content titles to identity the subset, wherein each of the past interactive content titles exhibit one or more of the common gameplay attributes (Benzatti [0032]: Feature catalog 116 is configured to store data regarding one or more features used by recommender system 108, as described below, to generate content recommendations 126. Feature catalog 116 may store, for instance, machine – learning based features generated or derived from user event records 128 stored in event catalog 114. In some implementations, feature catalog 116 may comprise machine- learning based features based on an aggregation of user event records obtained from event catalog 114.).
Regarding claim 18, Benzatti teaches, one or more non-transitory computer-readable media comprising computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
storing historic gameplay data (Benzatti [0123]: “… storing user event records…”) of a user device from a plurality of past interactive sessions of a plurality of past interactive content titles in memory (Benzatti [0043]: “… a log of each session in which a user played a multiplayer game across a plurality of different game titles (e.g., by logging a start time, end time, duration, the identification of the content type, and/or the game title), … generate features corresponding to the number of hours a user played multiplayer games in the past 7 days, 30 days, 90 days, etc.); and
a set of past audiovisual in-game media elements that are presented in a past virtual environment during gameplay of one or more interactive content titles of the plurality of past interactive content titles (Benzatti [0050]: In other implementations, the user-content score model may comprise a heuristics model configured to analyze a frequency of event records from event logger 114. For instance, a heuristics model may analyze a number of times a user accessed a particular type of content in a given time period (e.g., in the past day, week, month, etc.). For instance, if a particular user accessed a first content type (e.g., screenshots) for one hour, and another content type (e.g., joining a multiplayer game) for another hour in a particular time period (e.g., the past day), a heuristics model may determine that the user scores may assign 0.5 to the first and second content types, while assigning a lower score (e.g., zero) to the remaining content types for the given time period.);
applying a machine learning model to the historic gameplay data to identify one or more common gameplay attributes of the plurality of past gameplay attributes that are shared across a subset of the plurality of past interactive content titles played by the user device (Benzatti [0049]: In some implementations, as described above, the user-content score model may comprise a machine-learning based model based on one or more machine-learning based features 314 (e.g., from feature catalog 116). User score generator 310 may apply the user-content score model using a machine-learning based algorithm to identity and/or determine one or more clusters, patterns, etc., to generate a set of user scores for a particular user. For instance, the user-content score model may be configured to analyze patterns based on user behaviors (e.g., which types of a content a user is likely to access) and generate user scores based on such behaviors. Machine-learning based model may implement one or more unsupervised approaches, including collaborative filtering and/or content based recommendations to predict how likely a user may interact with certain content types despite no prior interaction by the user. In other instances, machine-learning based model may also be trained based on a supervised approach to predict how likely a user may interact with a type of content based on a variety of learned behavioral patterns at a certain time (e.g., a certain day) or over a particular period of time.);
selecting a new interactive content title that is not one of a past interactive content title of the plurality of past interactive content titles and that include the one or more common gameplay attributes, wherein the new interactive content title includes a first set of audiovisual in-game media element (Benzatti [0072]: Because publishing pipeline 322 is configured to continuously receive updated user scores 318 and title scores 320, in addition to near real-time signals 330, content recommendation score ranker 326 may combine scores 336 (e.g., mathematically or using any other technique) in real-time in response to receiving request 124. As a result, content recommendation score ranker 326 may generate a combined set of scores along with any biasing factors or filters, thus enabling computing device(s) 102 to receive a set of content recommendations 126 with minimal delay or latency. Furthermore, such quick decision-making capabilities enables the selection of content to recommend in high performance synchronous scenarios, while also minimizing a footprint in multi-tier asynchronous integrations.),
Benzatti does not clearly teach, wherein the new interactive content title includes a first set of audiovisual in-game media element; and However, Lalonde [0189]: Specifically, in some implementations, a sample set of sessions are selected from all online gaming sessions associated with a gaming title. … In some implementations, a shader pipeline optimizer 534 samples selectively within an online gaming session, e.g., samples particular shaders, or a subset of image frames, to reduce the impact on game content rendering in the foreground. We then use these timings to select good candidate shaders for further optimization and invoke manual review or automated systems to refine the shaders. Alternatively, in some implementations, power consumption is monitored and used to optimize a shader pipeline (i.e., a sequence of compiled shaders). Specifically, the shader pipeline optimizer 534 is instrumented to measure power draw instead of timing performance.
selecting a custom audiovisual in-game media element from among the set of past audiovisual in-game media elements, wherein the custom audiovisual in-game media element is different than each audiovisual in-game media element of the first set of audiovisual in-game media elements (Lalonde [0053] teaches, “If a shader program is selected for use by a gaming title, it is retrieved from the shader library server 124, compiled by the game server 118, and stored locally in the game servers 118 for use to render the plurality of image frames for each online gaming session. In some implementations, each image frame of an online gaming session is rendered by an ordered sequence of compiled shaders (e.g., including a vertex shader, a tessellation shader, a geometry shader and a fragment shader) each of which implements one or more specific image rendering operations during the course of creating the respective image frame.”);
initiating a new interactive session to present a new virtual environment of the new interactive content title on the user device (Lalonde [0087] teaches, “In some situations, the modified sequence of compiled shaders is optionally stored in place of the sequence of compiled shaders and used by subsequent online gaming sessions. In some situations, the modified sequence of compiled shaders is stored as a new version of the sequence of compiled shaders corresponding to the usage statistics, and is used when subsequent game states of the online gaming sessions are consistent with the usage statistics.”);
wherein the new virtual environment presents the first set of audiovisual in-game media elements associated with the new interactive content title (Lalonde [0059]: In some implementations, gaming content corresponding to a gaming title are rendered by the GPUs 140 for online gaming sessions according to a graphics pipeline process (e.g., an OpenGL rendering pipeline). The graphics pipeline process includes an ordered sequence of graphics operations, and each graphics operation is implemented based on one or more user-defined shader programs. … The graphics pipeline process renders a sequence of image frames for each online gaming session, and each image frame includes an array of pixels (also called a page) having a resolution. In some implementations, a static data item corresponding to the graphics pipeline process (e.g., a texture object) includes one or more images having the same resolution as the image frames of the online gaming sessions.) and the custom audiovisual in-game media element of the set of past audiovisual in-game media elements (Lalonde [0134]: “Game application(s) settings 638 for storing information associated with user accounts of the game application(s), including one or more of account access information, in-game user preferences, gameplay history data, and information on other players.” Here, the new virtual gaming environment is similar to the game applications using in-game media elements based on in-game user preferences and gameplay historical data.).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to incorporate the teaching of Benzatti et al. to the Lalonde’s system by adding the feature of in-game elements. The references (Benzatti and Lalonde) teach features that are analogous art and they are directed to the same field of endeavor, such as multi-media content. Ordinary skilled artisan would have been motivated to do so to provide Benzatti’s system with enhanced data. (See Lalonde [Abstract], [0006 – 0008], [0041], [0059], [0134]). One of the biggest advantages of network machine learning database algorithms is their ability to improve over time. Machine learning technology typically improves efficiency and accuracy thanks to the ever-increasing amounts of data that are processed.
Regarding claim 19, the method of claim 1, wherein the audiovisual in-game media elements include at least one of virtual interactive gameplay elements, music, gameplay sounds, or colors.
Regarding claim 20, the method of claim 19, wherein: the virtual interactive gameplay elements include peer players, animation styles, or virtual objects (Lalonde [0009]: “…multiple players …”); and the virtual objects include in-game weapons, vehicles, or non-player characters (Lalonde [0069]: The static memory pool 202 is configures to store one or more static data items used to render image frames of online gaming sessions of a gaming title. Examples of the one or more static data items include, but are not limited to, texture objects 312, vertex data buffer objects 314, constant data objects 316. When the plurality of game servers 118 are assigned to execute a plurality of online gaming sessions, each game server 118 obtains one or more static data items from the static memory pool 202, and one or more dynamic data items 306 from its corresponding main dynamic memory 144.).
Regarding claim 21, the method of claim 1, wherein the gameplay attributes includes at least one of a game genre, a date and time of the plurality of past interactive content titles, historical viewing data of non-interactive media titles, or peer gameplay attributes of other user devices (Benzatti [00092]: In this manner, because the modified title scores comprise title scores updated at a more frequent interval than title scores 320, runtime engine 324 is enabled to provide content recommendations 126 taking into account time sensitive content that may not have existed at the time title scores 320 were generated. As a result, content recommendations 126 comprise up-to-date content recommendations, thereby increasing the likelihood of interaction with such recommended content and enhancing the user experience.).
Regarding claim 22 , the method of claim 1, wherein selecting the custom audiovisual in-game media element includes randomly selecting an audiovisual in-game media element from among the set of past audiovisual in-game media elements (Benzatti [0109]: : an event cataloger configured to store user event records associated with a plurality of media items and a plurality of content types in an event catalog; a content score generator that includes: a user score generator configured to generate a set of user scores for a user based on a user-content score model and the user event records, each generated user score indicating an affinity between the user and a corresponding content type of the plurality of content types, and a title score generator configured to generate a set of title scores for a media item based on a title-content score model and the user event records, each generated title score indicating an affinity between the media item and a corresponding content type of the plurality of content types; and a runtime engine configured to provide a set of content recommendations to a requestor based on combinations of the user scores and the title scores.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Kaufman, US 2022/0249957, Automatic detection of prohibited gaming content
Dzjind, US 2021/0260475, Management of Provisioning of video game during game preview
Puniyani, US 2021/0029419, Systems and Methods for providing contextual information
Marr, US 2016/0005270, System and Method for simulating gameplay of nonplayer characters distributed across networked end user devices
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 extension fee 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 SABA AHMED whose telephone number is (571) 270-0236. The examiner can normally be reached on MON – FRI: 8AM – 5PM EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached on 571-270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SABA AHMED/
Examiner, Art Unit 2154
/SYED H HASAN/Primary Examiner, Art Unit 2154