JP2023525178A - Game content recommendations for video games - Google Patents

Abstract

Systems and methods are disclosed for providing game content recommendations for video games based on player performance. Prior to actual video game play, performance metrics are calculated based on stored player data and game video settings. Performance metrics are evaluated to further calculate performance metrics for video gameplay session content indicative of aspects of actual video gameplay. Metric data for each player is determined from the video gameplay session content. Player metric data is analyzed against stored player data and game video settings to determine whether to recommend video gameplay session content.

Description

TECHNICAL FIELD This disclosure relates to systems for content recommendation and, more particularly, to systems and related processes for recommending game content, such as video game play session content for video games, based on player performance.

Video gameplay content may be recommended to a user based on the user's viewing history of video games, game genres, and ratings of teams played in the video game. However, with some recommendation engines, such recommendations of gameplay can be biased. For example, a particular team may be the best in comparison to another team, but that team may not be playing the best in the game. A recommendation engine would not rate a game based on how the game was played by players or entire teams for a particular genre at a particular level.

In light of the foregoing, the present disclosure provides systems and related methods for recommending video gameplay content session content for a video game based on player and/or team performance in the game. In some embodiments, recommendations are based on a sequential order of pre-game analysis, in-game analysis, and post-game analysis of video play gameplay content.

In one embodiment, the system compares information in the post-game analysis with information in the pre-game analysis to determine whether to recommend video gameplay content. For example, the system analyzes a particular player's statistics before a game (pre-game analysis) to determine how that player played in the actual game (in-game analysis). Assuming the player played well in the actual game, the system analyzes the player's statistics (e.g., number of kills, number of saves, etc.) in the post-game analysis so that the statistics match the player's statistics in the pre-game analysis. to determine whether it is nearly identical to In other words, when the system determines whether the player's performance during the in-game analysis is close to the performance in the pre-game analysis. If determined that the player's performance is close, the system recommends video gameplay content for the actual video game.

In another embodiment, the system determines whether gameplay of a video game is worthy of attention in a pre-game analysis. For example, in pre-game analysis, the system analyzed and determined that the player performance was really good, e.g. will find out. If the gameplay is determined to be noteworthy, the system will further evaluate the gameplay of the video game in the in-game analysis. In an in-game analysis, the system would determine, for example, that the same player performed similarly well, eg, the player killed 19 dragons in 30 minutes. The system then encourages the user to watch video gameplay content of the actual video game.

The method, in one aspect, includes calculating pre-game performance metrics based on stored player data and stored settings for the video game. A determination is made that the pre-game performance metric meets a pre-game threshold. In response to determining that the pre-game performance metric meets the pre-game threshold, in-game performance metrics are calculated based on stored metadata associated with video gameplay session content indicative of aspects of gameplay. be done. A determination is also made that the in-game performance metric meets an in-game performance threshold. Aggregated statistics from the video gameplay session content are determined in response to determining that the in-game performance metric meets the in-game performance threshold. The method further analyzes the aggregated statistics for the stored player data and the stored settings for the video game, and determines whether to recommend video gameplay session content based on the analysis. .

In another aspect, the method includes evaluating video gameplay session content for a multiplayer online battle arena (MOBA) video game. Player data for each player of each team associated with the video gameplay session and stored settings for the MOBA video game are analyzed to determine evaluating the video gameplay session content. Player data includes metrics related to attack metrics, defense metrics, and damage metrics. In response to determining to evaluate the video gameplay session content, the in-game performance of each player of each team in the video gameplay session content is determined from the game map of the video gameplay session content and the video gameplay session content. Based on in-game metrics for each player on each team determined. In-game metrics include in-game attack metrics, in-game defense metrics, and in-game damage metrics. The method further determines whether to recommend video gameplay session content based on the in-game performance of each player of each team.

The above and other objects and advantages of the present disclosure will become apparent upon consideration of the following detailed description, taken in conjunction with the accompanying drawings.

FIG. 1 is an illustrative block diagram of a system for recommending game content for video games, according to some embodiments of the present disclosure.

FIG. 2 is an illustrative block diagram of a system for recommending game content for a multiplayer online battle arena (MOBA) video game, according to some embodiments of the present disclosure.

Figure 3 is an illustrative block diagram showing additional details of the system of Figures 1 and/or 2, according to some embodiments of the present disclosure.

FIG. 4 is an illustrative flowchart of a process for recommending video gameplay session content for a video game, according to some embodiments of the present disclosure.

FIG. 5 is an illustrative flowchart of pre-game analysis for analyzing data of video game content prior to video game play, according to some embodiments of the present disclosure.

FIG. 6 is an illustrative flowchart of in-game analysis for analyzing data of video game content during a video game play session, according to some embodiments of the present disclosure.

FIG. 7 is an illustrative flowchart of post-game analysis for analyzing the results of in-game analysis and recommending video gameplay session content for a video game, according to some embodiments of the present disclosure.

FIG. 8 illustrates an example table structure of pre-game performance metrics for pre-game analysis of a video game, according to some embodiments of the present disclosure.

FIG. 9 illustrates an example graphical structure of pre-game metrics for pre-game analysis of a video game, according to some embodiments of the present disclosure.

FIG. 10A illustrates an example table structure of in-game performance metrics for in-game analysis of a video game, according to some embodiments of the present disclosure.

FIG. 10B illustrates an example table structure of the aggregated in-game performance metrics of FIG. 10A.

FIG. 10C illustrates an example graphical structure of aggregated in-game performance metrics of the in-game performance metrics of FIG. 10A.

FIG. 11A illustrates an example graphical structure of a player's post-game performance metrics in post-game analysis of a video game, according to some embodiments of the present disclosure.

FIG. 11B illustrates an example graphical structure of a player's aggregated post-game performance metrics in post-game analysis of a video game, according to some embodiments of the present disclosure.

FIG. 11C illustrates an example table structure of a team's aggregated post-game performance metrics in post-game analysis of a video game, according to some embodiments of the present disclosure.

FIG. 12 is an illustrative flowchart of a process for recommending video games, according to another embodiment of the disclosure.

FIG. 13 is an illustrative flowchart of a process for analyzing video game content data prior to video game play, according to some embodiments of the present disclosure.

FIG. 14 is an illustrative flowchart of in-game analysis for analyzing data of video game content during a video game play session, according to some embodiments of the present disclosure.

FIG. 15 is an illustrative flowchart of post-game analysis of analyzing data for video game content, according to some embodiments of the present disclosure.

FIG. 1 shows an illustrative block diagram of a system 100 for recommending gameplay session content for video games, according to some embodiments of the present disclosure. System 100 includes server 102 , video game device 104 , communication network 106 , content source or database 108 , video game settings database 110 , metadata database 112 and computing device 114 . Although FIG. 1 shows content source 108, video game settings database 110, and metadata database 112 as individual components and separate from server 102, in some embodiments, some of those components are: may be combined and/or integrated with server 102 as one device. In one embodiment, server 102 communicates with video game devices 104, content sources 108, and video game settings database 110 via additional communication paths that may be included in communication network 106 or separate from communication network 106. , and the metadata database 112 . Communication network 106 may be the Internet, a cellular network, a mobile voice or data network (eg, a 4G or LTE network), a cable network, a public switched telephone network, or any combination of two or more of such communication networks. or any type of communication network. Communication network 106 may be a satellite path, a fiber optic path, a cable path, a path supporting Internet communications (eg, IPTV), a free space connection (eg, for broadcast or other wireless signals), or any other suitable network. wired or wireless communication paths, or dedicated communication paths and/or combinations of such paths such as network 103. Network 106 may, in various aspects, include the Internet, or any other suitable network or group of networks.

In one embodiment, server 102 is communicatively coupled to computing device 114 via communication network 106 . Some exemplary types of computing devices 102 include, but are not limited to, gaming devices (such as PLAYSTATION devices, XBOX® devices, or any other gaming device), smartphones, tablets, personal computers, set-tops. box (STB), digital video recorder (DVR), and/or provide various user interfaces configured to receive and view content and/or interact with server 102 and/or video game device 104 including equivalents. In some examples, computing device 102 provides a display configured to display information via a graphical user interface.

In one embodiment, server 102 evaluates video gameplay session content of a video game for a particular genre at a particular level from various sources, such as video game device 104, via communication network 106. configured to aggregate content useful for Several different types of video game genres include action, adventure, horror, sports, role-playing, strategy, puzzle, board, and any combination of these genres. Server 102 evaluates video gameplay session content based on how the game was played. For example, the server 102 may provide actual gameplay challenge tutorials or video clips, etc. played on the video game devices 104 that show how the game was played by players and/or entire teams in a particular video game or segment thereof. Content may be received. In some embodiments, server 102 functions to recommend video gameplay session content to users of computing devices 114 . In one embodiment, the recommendations are based on a sequential order of pre-game, in-game, and post-game analysis of the video gameplay content. Server 102 compares the information in the post-game analysis to the information in the pre-game analysis to determine whether to recommend video gameplay content.

In some embodiments, during pre-game analysis, server 102 retrieves player data from content 108 . In one embodiment, player data includes one or more player names/characters and player metadata. In one embodiment, player metadata includes aggregated statistics of composite measures of a player's performance skills prior to game play (eg, FIG. 9). In some embodiments, the server retrieves stored video game settings 110 for the video game. Several different types of settings include physical, temporal, emotional, ethical, and environmental. Settings may vary depending on the genre of the game. In one embodiment, server 102 calculates in-game performance metrics based on player data and video game settings. In one embodiment, pre-game performance metrics use existing player data and video settings to measure team performance. Pre-game performance metrics measure a team's performance in terms of its individual player skills prior to actual game play. Pre-game performance metrics measure team performance prior to actual game play (eg, FIG. 8). The pre-game performance metric is compared to a pre-game threshold (predetermined) to determine the likelihood of a video game session of interest. In one embodiment, in response to determining the likelihood of a video game session of interest, stored player data of the video game is used for post-game analysis as described below.

In some embodiments, during in-game analysis, server 102 retrieves metadata 112 corresponding to video gameplay session content that indicates aspects of gameplay. The metadata includes gameplay metadata for the video game session. Gameplay metadata includes parameters that measure a team's performance during actual gameplay. In one embodiment, server 102 uses the gameplay metadata to calculate in-game performance metrics for gameplay based on game progress during a video game session. In-game performance metrics measure various skills performed during a video game session of actual gameplay. Some examples of in-game performance skills include character positioning (CP), route taken (RF), reaction time (RT), and the like. In-game performance metrics are compared to in-game thresholds (predetermined) to determine competitive gameplay. In one embodiment, the server determines aggregated statistics for each of the player's respective skills from the video gameplay session content. In one embodiment, the aggregated statistic is a composite measure of a player's performance skill during a video game session of actual gameplay. In one example, the aggregated statistic is a composite measure of a team's performance skill during a video game session of actual gameplay (eg, FIGS. 10A, 10B, and 10C).

In some embodiments, during the post-game analysis, the server 102 stores aggregated statistics from the video gameplay session content of the in-game analysis into the player data of the video game selected in the pre-game analysis. Compare with aggregated stats prior to game play. In one embodiment, the server determines that the aggregated statistics from the video playsession content are approximately equivalent to the aggregated statistics of the player data and recommends the video playsession content to the user of the computing device 114. .

FIG. 2 shows illustrative blocks of a system 200 for recommending content based on mobile online battle arena multiplayer (MOBA) video games, according to some embodiments of the present disclosure. In various embodiments, system 200 includes several components described above in relation to system 100 . In particular, system 200 includes server 102 , video game device 104 , communication network 106 , content source 108 , video game settings database 110 , metadata database 112 and computing device 114 . Although FIG. 2 shows content source 108, video game settings database 110, and metadata database 112 as individual components and separate from server 102, in some embodiments, some of those components are: may be combined and/or integrated with server 102 as one device. As shown, server 102 is communicatively coupled to gaming devices 104 via communications network 106 and via additional communications paths that may be included in communications network 106 or separate from communications network 106 . As such, it is communicatively coupled to content sources 108 , metadata database 110 , and game log database 202 .

As illustrated in FIG. 2, in one embodiment content 108 includes MOBA content 208, which includes player data corresponding to a MOBA video game. In one example, the MOBA content includes player data such as aggregated pre-game metrics for each of the players in the team (eg, FIG. 9). Aggregated pre-game metrics are composite measures of a player's performance skills (eg, defensive skills, offensive skills, injury skills, healing skills, control skills) prior to MOBA video game play. Some examples of pre-game metrics include pre-game damage metrics, pre-game attack metrics, pre-game defense metrics, pre-game crowd control metrics, pre-game overall performance metrics, and the like. In one embodiment, video settings 110 include MOBA video game settings 210 . In one example, the MOBA video game setting is a battlefield with two separate teams of players. In one embodiment, metadata 212 includes MOBA metadata for game maps and parameters for MOBA video gameplay. Some of the parameters include character positioning, route taken, reaction time, targets acquired, time spent in target acquisition, and targets destroyed. Character positioning includes character positioning as soon as the game starts for both teams. A route taken is a route taken by a player in the team. Reaction time is the time it takes for a player/team to reach a goal and enter the fight. Goals Captured is the number of goals captured by the player/team. Elapsed time is the time it takes for the player/team to acquire the target.

In one embodiment, the server 102 performs pre-game analysis of MOBA video gameplay and evaluates MOBA video gameplay session content. For example, server 102 determines in a pre-game analysis whether a MOBA video game is worthy of attention. The server utilizes aggregated pre-game metrics from MOBA video game settings 212 and MOBA content 208 for each of the players in the team to determine pre-game performance metrics for each of the teams. In one embodiment, server 102 utilizes graphs containing aggregated pre-game metrics (eg, FIG. 9) of pre-game analysis of three players in a team. Pre-game performance metrics measure team performance prior to actual MOBA video game play (eg, FIG. 8). The pre-game performance metric is compared to a pre-game threshold (predetermined) to determine the likelihood of a MOBA video game session of interest. In one embodiment, in response to determining the likelihood of a MOBA video game session of interest, aggregated pre-game metrics of the video game are used for post-game analysis as described below.

In one embodiment, the server 102 performs in-game analysis for MOBA video games. Server 102 utilizes parameters from game maps and MOBA metadata 212 to evaluate MOBA video game session content in an actual MOBA video game session. Specifically, server 102 utilizes game maps and parameters to calculate in-game performance metrics for video game session content. Some examples of in-game performance metrics include route calculations, number of targets captured, and the like. In one embodiment, server 102 utilizes the route taken parameters to calculate the route taken to reach the target. In another embodiment, the server 102 utilizes the goals captured parameter to calculate the number of goals captured by the player/team. In one embodiment, in-game performance metrics are compared to in-game thresholds (predetermined) to determine competitive gameplay. In one embodiment, the server 102 determines in-game performance metrics for each of the teams from the MOBA video gameplay session content. In-game performance metrics are measurements of a team's performance during actual MOBA video game play. In-game performance metrics correspond to a team's skill during a MOBA video gameplay session. In one embodiment, the server 102 calculates in-game metrics for each of the teams during the MOBA video game session of actual MOBA gameplay (eg, FIG. 10A). In-game metrics represent a team's skill during actual MOBA video gameplay. Some examples of in-game metrics include in-game damage metrics, in-game attack metrics, in-game defense metrics, in-game crowd control metrics, in-game overall ability metrics, and the like. The server combines the in-game metrics of each of the teams and computes aggregated statistics from the in-game analysis of the video gameplay session content (eg, FIGS. 10B and 10C).

In one embodiment, during post-game analysis, server 102 compares aggregated statistics from in-game analysis to aggregated statistics from pre-game analysis. In one embodiment, the server determines that the aggregated statistics of the in-game analysis from the video play session content are approximately equivalent to the aggregated statistics from the pre-game analysis and computes the MOBA video play session content. Recommend to users of device 114 .

FIG. 3 is an illustrative block diagram showing additional details of system 100 (FIG. 1) and/or system 200 (FIG. 2), according to some embodiments of the present disclosure. In various embodiments, system 200 includes several components described above in relation to system 100 . Although FIG. 3 shows a certain number of components, in various embodiments system 300 may include fewer components than shown and/or a plurality of one or more of the components shown. Server 102 includes control circuitry 302 and I/O paths 308 , which includes storage device 304 and processing circuitry 306 . Computing device 104, which may correspond to video game device 104 of FIGS. 1 and 2, may include gaming devices such as video game consoles, user television equipment such as set-top boxes, user computer equipment, wireless user communication devices such as smart phone devices. , or any device on which video games can be played. Computing device 104 includes control circuitry 310 , I/O paths 316 , speakers 318 , display 320 and user input interface 322 . Control circuitry 310 includes storage device 312 and processing circuitry 314 . Control circuitry 302 and/or 310 may be based on any suitable processing circuitry, such as processing circuitry 306 and/or 314 . As referred to herein, processing circuitry may be one or more microprocessors, microcontrollers, digital signal processors, programmable logic devices, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc. and may include multi-core processors (eg, dual-core, quad-core, hexa-core, or any suitable number of cores). In some embodiments, the processing circuitry includes multiple separate processors, such as multiple processors of the same type (eg, two Intel Core i9 processors) or multiple different processors (eg, an Intel Core i7 processor and an Intel Core i9 processor). Core i9 processor).

Storage 304, storage 312, and/or storage of other components of system 300 (e.g., storage of content sources 108, video game settings 110, metadata database 112, and/or the like) are each stored electronically. It may be a storage device. As referred to herein, the phrase "electronic storage device" or "storage device" includes random access memory, read-only memory, hard drives, optical drives, digital video disc (DVD) recorders, compact disc (CD) Recorder, BLU-RAY® Disc (BD) Recorder, BLU-RAY® 3D Disc Recorder, Digital Video Recorder (DVR, sometimes called Personal Video Recorder or PVR), Solid State Device , quantum storage device, game console, game media, or any other suitable fixed or removable storage device, and/or any combination of the same, for storing electronic data, computer software, or firmware. It should be understood to mean a device. Storage device 304, storage device 312, and/or storage devices of other components of system 300 each store various types of content, metadata, game data, media guidance data, and/or other types of data. may be used for Non-volatile memory may also be used (eg, to initiate boot-up routines and other instructions). Cloud-based storage may be used to supplement or replace storage 304,312. In some embodiments, control circuits 302 and/or 310 execute instructions for applications stored in memory (eg, storage devices 304 and/or 312). Specifically, control circuits 302 and/or 310 may be commanded by an application to perform the functions discussed herein. In some implementations, any actions performed by control circuitry 302 and/or 310 may be based on instructions received from an application. For example, applications may be implemented as software or sets of executable instructions that may be stored in storage devices 304 and/or 312 and executed by control circuitry 302 and/or 310 . In some embodiments, the applications may be client/server applications, where only the client application resides on computing device 104 and the server application resides on server 102 .

Applications may be implemented using any suitable architecture. For example, it may be a standalone application that is fully implemented on computing device 104 . In such an approach, instructions for an application are stored locally (e.g., in storage device 312) and data for use by the application are retrieved on a periodic basis (e.g., from out-of-band feeds to Internet resources). or using another suitable approach). Processing circuitry 314 may retrieve instructions for applications from storage device 312, process the instructions, and implement the functionality described herein. Based on the processed instructions, control circuitry 314 may determine what actions to perform when input is received from user input interface 322 .

In client/server-based embodiments, control circuitry 310 may include communication circuitry suitable for communicating with an application server (eg, server 102) or other networks or servers. Instructions for performing the functionality described herein may be stored on the application server. The communication circuitry may be a cable modem, an integrated services digital network (ISDN) modem, a digital subscriber line (DSL) modem, a telephone modem, an ethernet modem, to communicate with other equipment or any other suitable communication circuitry. card, or may include a wireless modem. Such communications may involve the Internet or any other suitable communications network or path (eg, communications network 106). In another embodiment of a client/server-based application, control circuitry 310 launches a web browser that interprets web pages served by a remote server (eg, server 102). For example, a remote server may store instructions for an application in a storage device. The remote server may use circuitry (eg, control circuitry 302) to process stored instructions and generate the displays discussed above and below. Computing device 104 may receive the display generated by the remote server and may display the content of the display locally via display 320 . In this manner, the processing of instructions is performed remotely (eg, by server 102), while the resulting display, such as a display window described elsewhere herein, resides on computing device 104. Served locally. Computing device 104 may receive inputs from a user via input interface 322 and transmit those inputs to a remote server for processing and generating a corresponding display.

A user may use user input interface 322 to send commands to control circuitry 302 and/or 310 . User input interface 322 may be any suitable user interface such as a game controller, remote control, trackball, keypad, keyboard, touchscreen, touchpad, stylus input, joystick, voice recognition interface, or other user input interface. There may be. User input interface 322 is integrated with or combined with display 320, which can be a monitor, television, liquid crystal display (LCD), electronic ink display, or any other device suitable for displaying visual images. good too.

Server 102 and computing device 104 may receive content and data via input/output (hereinafter “I/O”) paths 308 and 316, respectively. For example, I/O path 316 may include communication ports configured to receive live content streams from server 102 and/or content sources 108 via communication network 106 . The storage device 312 may be configured to buffer the received live content stream for playback, and the display 320 displays the buffered content via a primary display window and/or a secondary display window. , navigation options, alerts, and/or the like. The I/O paths 308, 316 are used for content (e.g., live streams of content, broadcast programming, on-demand programming, Internet content, content available via a local area network (LAN) or wide area network (WAN), and / or other content) and data may be provided to control circuitry 302 , 310 . Control circuits 302 , 310 may be used to send and receive commands, requests, and other suitable data using I/O paths 308 , 316 . The I/O paths 308, 316 may connect the control circuits 302, 310 (and specifically the processing circuits 306, 314) to one or more communication paths (described below). I/O functionality may be provided by one or more of these communication paths and are shown as a single path in FIG. 3 to avoid overcomplicating the drawing.

Content sources 108 may be television distribution facilities, cable system headends, satellite distribution facilities, programming sources (e.g., television broadcasters such as NBC, ABC, HBO, etc.), intermediate distribution facilities and/or servers, Internet providers, on-demand media servers. , and other content providers. NBC is a National Broadcasting Company, Inc. ABC is a trademark owned by American Broadcasting Company, INC. and HBO is a trademark owned by Home Box Office, Inc. is a trademark owned by A content source 108 may be a source of content (e.g., a television broadcaster, a webcast provider, etc.) or may not be a source of content (e.g., an on-demand content provider, a broadcast program for download). content internet providers, etc.). Content sources 108 may include cable sources, satellite providers, on-demand providers, Internet providers, over-the-top content providers, or other providers of content. Content sources 108 may also include remote media servers used to store different types of content (including user-selected video content) at locations remote from computing device 104 . Systems and methods for remote storage of content and for providing remotely stored content to user equipment are described in Ellis et al. No. 7,761,892, which is incorporated herein by reference in its entirety.

The content and/or data delivered to computing device 104 may be over-the-top (OTT) content. OTT content delivery means that an Internet-enabled user device, such as computing device 104, can receive content over the Internet, including any content described above in addition to content received over a cable or satellite connection. Allows to receive forwarded content. OTT content is delivered over Internet connections provided by Internet Service Providers (ISPs), but third parties also distribute content. ISPs may not be responsible for viewing capabilities, copyright, or redistribution of content, and may only forward IP packets provided by OTT content providers. Examples of OTT content providers include YOUTUBE, NETFLIX, and HULU, which provide audio and video over IP packets. Youtube is owned by Google LLC. Netflix is a trademark owned by Netflix, Inc. and Hulu is a trademark owned by Hulu, LLC. is a trademark owned by OTT content providers may additionally or alternatively provide the media guidance data described above. In addition to content and/or media guidance data, OTT content providers may distribute applications (e.g., web-based applications or cloud-based applications) or content may be stored on computing device 104. can be displayed by the application

Having described system 100, an illustrative flowchart of process 400 for recommending content for a video game that may be implemented by using system 300 is now depicted, according to some embodiments of the present disclosure. Then, FIG. 4 is referred. In various embodiments, process 400 or individual steps of any process described herein may be implemented by one or more components of system 300 . Although this disclosure may describe certain steps of process 400 (and of other processes described herein) as being implemented by certain components of system 300, this is for illustrative purposes only. Yes, and it should be understood that other components of system 300 may implement those steps instead.

At step 402, control circuitry 302 calculates pre-game performance metrics based on stored player data and stored settings for the video game. Pre-game performance metrics measure a team's performance in terms of its skill. In one embodiment, the video game is a MOBA video game and the stored settings include MOBA video game settings. In one embodiment, the stored player data includes the player's respective skills. Such skills in MOBA video games include power skills, defense skills, attack skills, damage skills, healing skills, control skills, and the like. In one embodiment, the stored player data includes each player's AP Defense (AP Def) score, Ability Power (AP) score, such as Ability Power Attack (APA) score, Attack Damage (AD) score, Attack Including Injury Defense (AD Def) Score, CC Completion Score, Crowd Control (CC) Score such as CC Defense (CC Def) Score, Healing Score and the like. At step 404, control circuitry 302 determines that the pre-game performance metric meets a pre-game threshold. In one embodiment, the pre-game threshold is predetermined based on pre-game performance metrics measured for games previously played with the same team. In one embodiment, steps 402 and 404 are part of the pre-game analysis performed by control circuit 302, additional details of which are provided below in connection with FIG.

At step 406, in response to determining that the pre-game performance metric meets the pre-game threshold, control circuitry 302 updates stored metadata associated with the video gameplay session content indicative of aspects of gameplay. Calculates in-game performance metrics based on In-game performance metrics measure a team's performance in terms of its skill during actual game play. At step 408, the control circuit determines that the in-game performance metric meets the in-game performance threshold. In one embodiment, the in-game performance threshold is pre-determined based on video analysis of games played by the same player with the same team. In one embodiment, steps 406 and 408 are part of the in-game analysis performed by control circuit 302, additional details of which are provided below in connection with FIG.

At step 410, control circuitry 302 computes aggregated post-game statistics from the video gameplay session content. In one embodiment, aggregated statistics are calculated by combining each player's in-game metrics determined during in-game analysis of a video gameplay session. At step 412, the control circuit determines that the post-game aggregated statistics meet the post-game threshold. At step 414, control circuitry 302 analyzes the aggregated statistics against the stored player data and stored settings for the video game. In one embodiment, the control circuit 302 analyzes the aggregated statistics of the in-game analysis with the aggregated statistics of the pre-game analysis stored in the player data. At step 416, based on the analysis, control circuitry 302 determines whether video gameplay session content is to be recommended. In one embodiment, control circuitry 302 determines that the aggregated in-game analysis is substantially the same as the aggregated pre-game analysis. In one embodiment, steps 410, 412, 414, and 416 are part of the post-game analysis performed by control circuit 302, additional details of which are provided below in connection with FIG.

FIG. 5 depicts an illustrative flowchart of a process 500 for conducting pre-game analysis of a video game, according to some embodiments of the present disclosure. Process 500 may correspond to steps 402 and 404 of FIG. 4 in various embodiments. At step 502, the system calculates pre-game performance metrics for each of the first and second teams of the video game. An example of each team's pre-game performance metrics is shown in FIG. FIG. 8 illustrates a table 800 that includes Team A 802 and Team B 804. The table 800 also displays a plurality of scores 806, such as defensive scores, crowd control (CC) scores, ability power (AP) scores, offensive damage (AD) scores, etc., that indicate pre-game performance metrics for Team A 802 and Team B 804, respectively. include. As shown, team A 802 score 806 is approximately equal to team B 804 score 806 . In one embodiment, pre-game performance metrics are determined based on stored player data. Examples of stored player data include aggregated pre-game metrics for each player in Team A 802 as illustrated in FIG. FIG. 9 depicts a graph 900 illustrating the pre-game analysis of the three players of Team A 802. As shown, the x-axis includes three players: Player 1 902, Player 2 904, and Player 3 906, and the y-axis illustrates pre-game metric 908 scores. As shown, the aggregated pre-game metric scores are AP Def score 908a, AD Def score 908b, Attack damage (AD) score 908c, CC completion score for Player 1 902, Player 2 904, and Player 3 906, respectively. 908d, CC Def score 908e, APA score 908f, and healing score 908g. Although not shown, a similar graph can be generated that illustrates the pre-game performance of the three players of Team B 804.

At step 504, the system calculates an average aggregated pre-game performance metric for each of the first team and the second team. For example, the mean 808 for each of Team A 802 and Team B 804 is determined to be 8, ie, as illustrated in FIG. At step 506, the system compares each team's book average to the pre-game threshold average. Pre-game threshold averages are predetermined based on pre-game performance metrics measured for games previously played with the same team. At step 508, it is determined whether the average is above the pre-game threshold average.

In one embodiment, if at step 506 it is determined that the average is above the pre-game threshold average, then the pre-game threshold average is updated at step 510 with the average. At step 512, the control circuit selects stored player data of pre-game performance metrics of the video game for post-game analysis. In one embodiment, the stored player data that is selected is aggregated pre-game metrics 908 illustrated in FIG.

In one embodiment, if in step 508 it is determined that the average does not exceed the pre-game threshold average, then in step 514 the video game is discarded and another video game repeats from step 502. Selected to perform analysis. Thus, a pre-game analysis of a video game is trained on different videos of the same game over time and the database is updated with updated pre-game threshold mean values.

FIG. 6 depicts an illustrative flowchart of a process 600 for performing in-game analysis of a video game, according to some embodiments of the present disclosure. Process 600 may correspond to steps 406 and 408 of FIG. 4 in various embodiments. At step 602, control circuit 302 calculates in-game performance metrics for gameplay of each of the first and second teams of the video game. In-game performance metrics measure a team's game performance in terms of its skill during the actual gameplay session during the time interval. As discussed above, in one embodiment, in-game performance metrics for each are determined based on game maps and parameters stored in the MOBA video game's metadata. Some of the parameters are Character Positioning (CP), Route Followed (RF), Reaction Time (RT), Target Acquired (OC), Time Elapsed in Target Acquisition (TOC), Target Destroyed ( TD), Manual Management (MM), Team Fighting (TF), and Advanced Techniques (AT). In one embodiment, character positioning is analyzed as soon as the game starts for both teams and charts are created to compare the positioning. In one embodiment, a route is followed to determine a team's respective strategy. In one example, reaction time includes the time it takes to reach a goal and engage in a fight. Times for both teams may be calculated and compared. The best game may be when both teams reach the place/fight in equal time so that they can fight and gain an advantage. In one example, targets acquired is the number of targets acquired. In one example, the target acquired is the amount of time it takes to acquire the target. In one embodiment, targets destroyed include the number of enemy targets destroyed and the time taken to destroy the targets. In one embodiment, the time required to destroy the target is calculated for both teams and compared to identify the optimal time. In one embodiment, hand management includes created hands and hand targets. In one embodiment, a team game includes each team's ability to attack. In one example, advanced techniques include performing specific tasks in a game.

In one embodiment, control circuitry 302 utilizes parameters and game maps to analyze game progress during a MOBA video game session. In one example, the game map includes the target (created at a particular time) and the location of the target, different routes/paths to reach that target/goal. Such targets are towers that need protection while playing the game. The game is over when the main tower is destroyed. Each team must protect their towers while attacking enemy towers. Objectives are special items that are created at specific times, and capturing them gives the team that captured them an advantage. Thus, in route analysis, video game analysis primarily focuses on the content of the route taken by the player while reaching the target/goal. If there are multiple routes to the target/goal, the best game will be determined in which the player took the best route to the target/goal based on their current position. In another example, each MOBA video game includes objectives, which are special items that are created at specific times, and capturing them gives an advantage to the team that captured them. During the in-game analysis, the efficiency with which each team reached and captured the goal is analyzed. For example, the goal is to create too early and both teams are aware of this. Each team will reach and capture the location as quickly as possible. Data will be collected based on how individual teams have played in reaching and capturing their goals. Such data may include how players approached the current engagement (battle in progress), the route taken to reach the objective, how the team coordinated (strategy) and acquired the target, how the Includes the composition of the formation used and the time required to reach and acquire the target. Accordingly, control circuitry 302 computes in-game performance metrics based on the present analysis associated with video gameplay session content that are indicative of aspects of gameplay. Some examples of in-game performance metrics include in-game damage metrics, in-game attack metrics, in-game defense metrics, in-game crowd control metrics, in-game overall ability metrics, and the like.

An example of in-game performance metrics for each team is shown in FIG. 10A. FIG. 10A illustrates a table 1000 that includes Team A 802 and Team B 804. Table 1000 also shows time intervals (hrs) 1004 and in-game performance metrics for Team A 802 and Team B 804 during each time interval (hrs) 1004: CP score, RF score, RT score, OC score, Also includes multiple scores 1006 such as TOC score, TD score, MM score, TF score, and AT score. An example of the time interval 1004 is 10 minutes. An average score 1008 is also calculated for each of team A 802 and team B 804 during each hour 1004 . As shown, Team A 802's score 1006 is approximately equal to Team B's 804 score 1006 during each of the time intervals. Also, Team A 802's average score 1008 is approximately equal to Team B's 804 average score 1008 during each of the time intervals. Thus, the in-game performance metric measures the team's game performance in terms of its skill during each actual gameplay session of the time interval.

In one embodiment, the average video game progress within the time interval 1004 (eg, 10 minutes) is calculated. FIG. 10B illustrates a table 1020 containing average score 1022 for team A 802 and average score 1024 for team B 804, and time of game 1004 and average/median score 1026. FIG. Average scores 1022 for each of teams A 802 and B 804 are calculated from table 1000 of FIG. 10A using average scores 1008 for teams A 802 and B 804, respectively. A mean/median score 1026 is calculated using the mean scores 1022 and 1024 of teams A 802 and B 804, respectively. In one embodiment, the mean/median score 106 determines relevant information about how long the game was most exciting. Thus, a better prediction of game quality is determined that is utilized to recommend video games. FIG. 10C depicts a graph 1030 illustrating the in-game analysis of Team A 802 and Team B 804. FIG. As shown, the x-axis includes team A 802 and team B 804 and the y-axis includes their individual average scores 1022 and 1024 during each of the hours 1004.

At step 604, control circuitry 302 compares each in-game performance metric to its corresponding in-game threshold in a plurality of in-game thresholds. The in-game threshold is predetermined based on video analysis of games played by the same player with the same team. For example, in-game thresholds for MOBA video games include route analysis thresholds. In another embodiment, in-game thresholds for MOBA video games include target acquisition thresholds. At step 606, it is determined whether at least one of the in-game performance metrics is less than its corresponding in-game threshold. If it is determined that none of the in-game performance metrics is below its corresponding in-game threshold, the player data of the in-game performance metrics of the video game is selected for post-game analysis at step 608. . In one embodiment, if at step 606 it is determined that at least one of the in-game performance metrics is less than its corresponding in-game threshold, the video game is discarded and another video game is Selected to perform in-game analysis that repeats from 602 . Thus, in-game analysis of video games is trained over time on different videos of the same game to select video games for post-game analysis.

FIG. 7 depicts an illustrative flowchart of a process 700 for conducting post-game analysis of a video game, according to some embodiments of the present disclosure. Process 700 may correspond to steps 410, 412, 414, and 416 of FIG. 4 in various embodiments.

At step 704, the in-game performance metric player data is compared to the pre-game performance metric player data. An example of post-game analysis for three players is shown in the graph of FIG. 11A. FIG. 11A shows a graph 1100. FIG. The y-axis shows the respective percentage of scores: AP Def Score 1108a, AD Def Score 1108b, Attack Damage (AD) Score 1108c, CC Completion Score 1108d, for Player 1 902, Player 2 904, and Player 3 906, respectively. CC Def Score 1108e, APA Score 1108f, and Healing Score 1108g are disclosed. Player in-game metrics are used in post-game analysis. An example of post-game analysis of each player in the first team utilizing data from the scores of FIG. 11A is shown in graph 1100 of FIG. 11B. Aggregated metric graph 1100 for each player in the first team is generated by combining the metric data of graph 1000 of FIG. 11A. Graph 1100 shows aggregated metrics for the three players of Team A 802. As shown, the x-axis includes three players, namely player 1 902, player 2 904, and player 3 906, and the y-axis illustrates the scores for the metric 1108. FIG. As shown, the aggregated metric scores are AP Def Score 1108a, AD Def Score 1108b, Attack Damage (AD) Score 1108c, CC Completion Score 1108d, Includes CC Def Score 1108e, APA Score 1108f, and Healing Score 1108g. Although not shown, a similar graph can be generated that illustrates the post-game analysis of the three players of Team B 804.

An example of post-game analysis for each team is shown in FIG. 11C. FIG. 11C illustrates a table 1130 that includes Team A 802 and Team B 804. Table 1130 also includes a plurality of scores 1136, such as AP def score, AD Def score, AD score, CC completion score, CC Def score, and Healing score, which indicate performance metrics for Team A 802 and Team B 804, respectively. As shown, Team A 802's average score 1138 is relatively comparable to Team B's 804 score 1138 . In one embodiment, the video game content of the game would be considered competitive gameplay for recommendations.

At step 704, it is determined whether the in-game performance metric is approximately equal to the pre-game performance metric. In one example, a team's respective post-game analysis, such as that shown in FIG. 11C, is used to determine whether in-game performance metrics are approximately equivalent to pre-game performance metrics. In one embodiment, a player's respective post-game analysis, such as that shown in FIG. 11B, is used to determine whether in-game performance metrics are approximately equivalent to pre-game performance metrics. If it is determined at step 706 that the in-game performance metric is approximately equal to the pre-game performance metric, video gameplay session content for the video game is recommended at step 708 . If in step 706 it is determined that the in-game performance metric is not substantially equivalent to the pre-game performance metric, then in step 710 the video game is discarded and in-game analysis of the video game session content of another video game is performed. , selected for in-game analysis. Thus, a post-game analysis of a video game is trained over time on different videos of the same game to select video gameplay sessions of the video game for recommendation.

FIG. 12 is a flowchart of a process 1200 for recommending content for video games, which may be implemented by using system 300, according to some embodiments of the present disclosure. In various embodiments, process 1200 or individual steps of any process described herein may be implemented by one or more components of system 300 . Although this disclosure may describe certain steps of process 1200 (and of other processes described herein) as being implemented by certain components of system 300, this is for illustrative purposes only. Yes, and it should be understood that other components of system 300 may implement those steps instead.

At step 1202, control circuitry 304 analyzes stored player data for each player of each team associated with the video game play session and stored settings for the MOBA video game. In one embodiment, player data includes metrics related to attack metrics, defense metrics, and damage metrics to determine to evaluate video gameplay session content. As discussed above, player data includes pre-game metrics for each of the players in each of the teams. In one embodiment, step 1202 of analyzing the stored data is performed by control circuit 302, additional details of which are provided below in connection with FIG. At step 1204 , control circuitry 304 determines each player for each team in the video gameplay session content based on in-game metrics for each player of each team determined from the game map of the video gameplay session content and the video gameplay session content. Analyze a player's in-game performance. In-game metrics include in-game attack metrics, in-game defense metrics, and in-game damage metrics. In one embodiment, in-game performance is the in-game metric discussed in detail above. In one embodiment, analyzing in-game performance step 1204 is performed by control circuitry 302, additional details of which are provided below in connection with FIG. At step 1206, control circuitry 304 analyzes the aggregated performance metrics after the end of the video game play session to determine post-game metrics in terms of player data for evaluation of video game session content. In one embodiment, player data includes attack, defense, and damage metrics. In one example, the stored player data indicates that player 1 killed 20 dragons in 30 minutes, and would therefore find the game interesting. In one example, the in-game performance indicates that Player 1 killed 19 dragons in 30 minutes, so this is also considered a good game, and control circuit 304 controls MOBA video gameplay. It will encourage the user to watch the video. At step 1208, control circuitry 304 recommends video gameplay session content based on the in-game performance of each player on each team.

FIG. 13 depicts an illustrative flowchart of a process 1300 for analyzing stored data games in pre-game analysis, according to some embodiments of the present disclosure. Process 1300 may correspond to step 1202 of FIG. 12 in various embodiments.

At step 1302, control circuitry 304 reads the stored players for each of the two teams of the video game play session of the MOBA video game. The player data includes pre-game metrics for each player in each of the first team and the second team. For example, the player data are pre-game metrics for each of the three players in the first team, as illustrated in the graph of FIG. At step 1304, the control circuit compares the pre-game metrics of each of the players in the first team with the pre-game metrics of each player in the second team. At step 1306, it is determined whether the pre-game metrics of each of the players in the first team are equal to the pre-game metrics of each of the players in the second team. If at step 1306 it is determined that the pre-game metrics of each of the players in the first team are equivalent to the pre-game metrics of each of the players in the second team, then process 1300 performs the pre-game metrics of each player in the second team. Leads to in-game analytics where metrics are compared to in-game metrics. However, if in step 1306 it is determined that the pre-game metrics of each of the players in the first team are not equal to the pre-game metrics of each of the players in the second team, then in step 1308 the control circuit , select the stored player data of the video game session of another MOBA video game, and process 1300 is repeated starting at step 1302 . Thus, pre-game analysis of video games is trained over time on different videos of the same game to select stored player data of video game content for in-game analysis.

FIG. 14 depicts an illustrative flowchart of a process 1400 for analyzing in-game performance in an in-game analysis, according to some embodiments of the present disclosure. Process 1400 may correspond to step 1204 of FIG. 12 in various embodiments.

At step 1402, the control circuit 304 compares the in-game metrics of each player in the first team and the second team to the pre-game metrics of each corresponding player in the first team and the second team. . In one example, such comparisons are performed during post-game analysis, as discussed above with respect to FIGS. 11A, 11B, and 11C. At step 1404, it is determined whether the in-game metric is approximately equal to the stored in-game threshold metric. If at step 1404 it is determined that the in-game metric is approximately equal to the stored in-game threshold metric, the process 1400 leads to further post-game analysis. If it is determined at step 1404 that the in-game metric is not substantially equal to the stored in-game threshold metric, then at step 1406 control circuitry 304 selects an in-game metric for another MOBA video game, and process 1400 is repeated starting at step 1402 . Therefore, an in-game analysis of video games was trained over time on different videos of the same game to select in-game metrics for video gameplay session content and recommend video gameplay session content for MOBA video games. be done.

FIG. 15 depicts an illustrative flowchart of a process 1500 for post-game analysis of analyzing data for video game content, according to some embodiments of the present disclosure.

At step 1502, control circuitry 304 retrieves real-time calculated aggregated player data for each of the two teams upon completion of the video game play session. At step 1504, it is determined whether the post-game metric is comparable to the pre-game metric and approximately equal to the stored post-game threshold metric. If it is determined at step 1504 that the post-game metric is comparable to the pre-game metric and is approximately equal to the stored post-game threshold metric, then at step 1506 the video gameplay session content of the MOBA video game is recommended. be done. However, if it is determined at step 1504 that the post-game metric is not comparable to the pre-game metric and is not approximately equal to the stored post-game threshold metric, then at step 1508 a different MOBA video game is selected and Process 1500 is repeated starting at step 1502 .

The systems and processes discussed above are intended to be illustrative rather than limiting. One skilled in the art will appreciate that the actions of the processes discussed herein may be omitted, modified, combined and/or rearranged and any additional actions taken without departing from the scope of the invention. you will understand that it can be done. More generally, the above disclosure is intended to be illustrative rather than restrictive. Only the claims that follow are intended to define boundaries as to what this disclosure includes. Moreover, features and limitations described in any one embodiment may apply to any other embodiment herein, and a flow chart or example relating to one embodiment may be used in any suitable manner to describe any other embodiment. Note that embodiments may be combined, performed in a different order, or performed in parallel. Additionally, the systems and methods described herein can be implemented in real time. It should also be noted that the systems and/or methods described above may be applied to or used in accordance with other systems and/or methods.
This specification discloses embodiments including, but not limited to, the following.
1. A computer-implemented method for evaluating video gameplay session content for a video game, comprising:
calculating pre-game performance metrics based on stored player data and stored settings for the video game;
determining that the pre-game performance metric meets a pre-game threshold;
Calculating an in-game performance metric based on stored metadata associated with video gameplay session content indicative of aspects of gameplay in response to determining that the pre-game performance metric meets a pre-game threshold. and,
determining that the in-game performance metric meets an in-game performance threshold;
determining statistics aggregated from the video gameplay session content in response to determining that the in-game performance metric meets the in-game performance threshold;
analyzing aggregated statistics against stored player data and stored settings for the video game;
determining, based on the analysis, whether the video gameplay session content is to be recommended;
A method, including
2. The computer-implemented method of item 1, further comprising determining a genre of the video game.
3. 3. The computer-implemented method of item 2, further comprising calculating pre-game performance metrics and post-game performance metrics based on genre.
4. Determining that the pre-game performance metric meets a pre-game threshold includes:
calculating a plurality of pre-game performance metrics for each of the first team and the second team;
averaging pre-game performance metrics for each of the first team and the second team;
comparing the average value to the average pre-game threshold of pre-game thresholds;
The computer-implemented method of item 1, comprising:
5. determining that the average value is above the average pre-game threshold;
updating the average pre-game threshold with the average in response to determining that the average exceeds the average pre-game threshold;
5. The computer-implemented method of item 4, further comprising:
6. Determining that the in-game performance metric meets the in-game performance threshold comprises:
calculating a plurality of in-game performance metrics for each of the first team and the second team over a plurality of time intervals;
comparing each in-game performance metric for each of the first team and the second team to a corresponding in-game performance threshold of the plurality of in-game thresholds;
The computer-implemented method of item 1, comprising:
7. 2. The computer-implemented method of item 1, wherein the player data comprises aggregated pre-game performance metrics for each player of the plurality of players of the video game prior to actual gameplay of the video game.
8. The step of determining aggregated statistics includes:
8. The computer-implemented method of item 7, comprising determining an aggregated in-game performance metric for each player in a plurality of players from video gameplay session content.
9. The step of analyzing the aggregated statistics is
9. The computer-implemented method of item 8, comprising comparing aggregated pre-game performance metrics to aggregated in-game performance metrics.
10. 10. The computer-implemented method of item 9, further comprising determining whether the video gameplay session is to be recommended based on the comparison.
11. A system for evaluating video gameplay session content for a video game, comprising:
remember player data and settings for video games;
a memory configured to
bound to memory,
Calculate pre-game performance metrics based on player data and settings for the video game;
determine that the pre-game performance metric meets the pre-game threshold;
responsive to determining that the pre-game performance metric meets the pre-game threshold, calculating an in-game performance metric based on stored metadata associated with video gameplay session content indicative of aspects of gameplay;
determining that the in-game performance metric meets the in-game performance threshold;
determining aggregated statistics from the video gameplay session content in response to determining that the in-game performance metric meets the in-game performance threshold;
analyzing aggregated statistics against stored player data and stored settings for video games;
determine, based on the analysis, whether video game play session content is to be recommended;
a control circuit configured to
A system comprising:
12. 12. The system of item 11, wherein the control circuit is further configured to determine the genre of the video game.
13. 13. The system of item 12, wherein the control circuit is further configured to calculate pre-game performance metrics and post-game performance metrics based on genre.
14. To determine that the pre-game performance metric meets the pre-game threshold, the control circuit further:
calculating a plurality of pre-game performance metrics for each of the first team and the second team;
calculating an average pre-game performance metric for each of the first team and the second team;
comparing the average value to the average pre-game threshold of the pre-game threshold;
12. The system of item 11, wherein the system is configured to:
15. The control circuit further
determining that the mean is above the mean pre-game threshold;
updating the average pre-game threshold with the average value in response to determining that the average value exceeds the average pre-game threshold;
15. The system of item 14, wherein the system is configured to:
16. To determine that the in-game performance metric meets the in-game performance threshold, the control circuitry includes:
calculating a plurality of in-game performance metrics for each of the first team and the second team at the plurality of time intervals;
comparing each of the in-game performance metrics for each of the first team and the second team to a corresponding in-game performance threshold of the plurality of in-game thresholds;
12. The system of item 11, wherein the system is configured to:
17. 12. The system of claim 11, wherein the player data comprises aggregated pre-game performance metrics for each player among the plurality of players of the video game prior to actual gameplay of the video game.
18. To determine the aggregated statistics, the control circuit:
18. The system of claim 17, wherein the system is configured to determine an aggregate in-game performance metric for each player in a plurality of players from video gameplay session content.
19. To analyze the aggregated statistics, the control circuit
19. The system of item 18, configured to compare aggregated pre-game performance metrics with aggregated in-game performance metrics.
20. 20. The system of item 19, wherein the control circuit is configured to determine whether a video gameplay session is to be recommended based on the comparison.
21. A non-transitory computer-readable medium which, when executed by the control circuit, causes the control circuit to:
Calculate pre-game performance metrics based on player data and settings for the video game;
determine that a pre-game performance metric meets a pre-game threshold;
responsive to determining that the pre-game performance metric meets the pre-game threshold, causing a mid-game performance metric based on stored metadata associated with the video gameplay session content indicative of aspects of gameplay to be calculated;
determining that an in-game performance metric meets an in-game performance threshold;
determining statistics aggregated from the video gameplay session content in response to determining that the in-game performance metric meets the in-game performance threshold;
having aggregated statistics analyzed against stored player data and stored settings for a video game;
let us determine, based on the analysis, whether video game play session content is to be recommended;
A non-transitory computer-readable medium having instructions encoded thereon.
22. The computer-readable medium further, when executed by the control circuit, causes the control circuit to:
22. The non-transitory computer readable medium of item 21, having instructions encoded thereon to determine the genre of the video game.
23. The computer-readable medium further, when executed by the control circuit, causes the control circuit to:
23. The non-transitory computer-readable medium of item 22, having instructions encoded thereon to cause pre-game and post-game performance metrics to be calculated based on genre.
24. The computer-readable medium further, when executed by the control circuit, causes the control circuit to:
cause a plurality of pre-game performance metrics for each of the first team and the second team to be calculated;
calculating an average pre-game performance metric for each of the first team and the second team;
having the average value compared to the average pre-game threshold of the pre-game threshold;
22. The non-transitory computer-readable medium of item 21, having instructions encoded thereon.
25. The computer-readable medium further, when executed by the control circuit, causes the control circuit to:
having determined that the average is above the average pre-game threshold;
in response to determining that the average value exceeds the average pre-game threshold, causing the average value to be used to update the average pre-game threshold;
22. The non-transitory computer-readable medium of item 21, having instructions encoded thereon.
26. The computer-readable medium further, when executed by the control circuit, causes the control circuit to:
calculating a plurality of in-game performance metrics for each of the first team and the second team over a plurality of time intervals;
comparing each in-game performance metric for each of the first team and the second team to a corresponding in-game performance threshold of the plurality of in-game thresholds;
22. The non-transitory computer-readable medium of item 21, having instructions encoded thereon.
27. 22. The non-transitory computer-readable medium of item 21, wherein the player data comprises aggregated pre-game performance metrics for each player among the plurality of players of the video game prior to actual gameplay of the video game. .
28. The computer-readable medium further, when executed by the control circuit, causes the control circuit to:
28. The non-transitory computer-readable medium of item 27, having instructions encoded thereon to cause determination of an aggregated in-game performance metric for each player in a plurality of players from video gameplay session content. .
29. The computer-readable medium further, when executed by the control circuit, causes the control circuit to:
29. The non-transitory computer-readable medium of item 28, having instructions encoded thereon to cause the aggregated pre-game performance metrics to be compared to the aggregated in-game performance metrics.
30. The computer-readable medium further, when executed by the control circuit, causes the control circuit to:
30. The non-transitory computer-readable medium of item 29, having instructions encoded thereon to cause a determination whether a video gameplay session is to be recommended based on the comparison.
31. A computer-implemented method for evaluating video gameplay session content for a multiplayer online battle arena (MOBA) video game, comprising:
analyzing stored player data for each player of each team associated with a video game play session and stored settings for a MOBA video game, wherein the player data includes attack metrics, defense metrics, and damage metrics; determining to evaluate the video gameplay session content, comprising a metric associated with
video based on in-game metrics for each player of each team determined from the game map of the video gameplay session content and the video gameplay session content, in response to determining to evaluate the video gameplay session content; analyzing the in-game performance of each player of each team in the gameplay session content, the in-game metrics comprising an in-game attack metric, an in-game defense metric, and an in-game damage metric;
determining to recommend video gameplay session content based on the in-game performance of each player of each team;
A method, including
32. 32. The computer-implemented method of clause 31, wherein the stored player data comprises pre-game metrics for each player among the plurality of players in the first team and the second team.
33. The step of analyzing the stored player data includes:
comparing pre-game metrics of each player in the first team to pre-game metrics of each corresponding player in the second team, wherein the comparing comprises pre-game metrics of each player in the first team. determining whether the metric is approximately equal to the pre-game metric of each corresponding player in the second team;
33. The computer-implemented method of item 32.
34. 34. The computer-implemented method of item 33, further comprising determining to rate the video gameplay session content based on the comparison.
35. The step of analyzing in-game performance further includes:
comparing an in-game metric of each player among the plurality of players in the first team with an in-game metric of each corresponding player in the second team, the comparing step comprising: 32. The computer-implemented method of item 31, comprising determining whether the in-game metric of each player in the second team is approximately equal to the in-game metric of each corresponding player in the second team.
36. 36. The computer-implemented method of item 35, further comprising determining to recommend a video gameplay session based on the comparison.
37. 32. The computer-implemented method of item 31, further comprising calculating in-game metrics based on metadata associated with video gameplay session content indicative of aspects of gameplay.
38. 38. The computer-implemented method of item 37, wherein the metadata comprises MOBA video game character metadata, item metadata, and overall ability metadata.
39. 32. The computer-implemented method of item 31, wherein the game map comprises routes for locations of the targets and goals, different routes for reaching the targets and goals, or a combination thereof.
40. 32. The computer-implemented method of item 31, wherein the setting for the MOBA video game comprises a battlefield including two separate teams, each team including multiple players.
41. A system for evaluating video gameplay session content for a multiplayer online battle arena (MOBA) video game, comprising:
store player data and settings for MOBA video games;
a memory configured to
bound to memory,
Analyzing the stored player data for each player of each team associated with the video game play session and the stored settings for the MOBA video game, the player data including metrics related to attack metrics, defense metrics, and damage metrics and decides to evaluate video game play session content;
video based on in-game metrics for each player of each team determined from the game map of the video gameplay session content and the video gameplay session content, in response to determining to evaluate the video gameplay session content; analyzing the in-game performance of each player of each team in the gameplay session content, the in-game metrics comprising in-game attack metrics, in-game defense metrics, and in-game damage metrics;
deciding to recommend video gameplay session content based on the in-game performance of each player on each team;
a control circuit configured to
A system comprising:
42. 42. The system of clause 41, wherein the stored player data comprises pre-game metrics for each player among the plurality of players in the first team and the second team.
43. To analyze the stored player data, the control circuit further:
configured to compare pre-game metrics of each player in the first team with pre-game metrics of each corresponding player in the second team; 43. The system of clause 42, comprising determining whether the pre-game metric is approximately equal to the pre-game metric of each corresponding player in the second team.
44. 44. The system of clause 43, wherein the control circuit is further configured to determine to evaluate the video gameplay session content based on the comparison.
45. To analyze in-game performance, the control circuit also:
configured to compare an in-game metric of each player among the plurality of players in the first team with an in-game metric of each corresponding player in the second team; 42. The system of clause 41, comprising determining whether the in-game metric of each player in the team is approximately equal to the in-game metric of each corresponding player in the second team.
46. 46. The system of clause 45, wherein the control circuit is further configured to determine to recommend a video game play session based on the comparison.
47. 42. The system of clause 41, wherein the control circuitry is further configured to calculate in-game metrics based on metadata associated with the video gameplay session content indicative of aspects of gameplay.
48. 48. The system of clause 47, wherein the metadata comprises MOBA video game character metadata, item metadata, and overall ability metadata.
49. 42. The system of item 41, wherein the game map comprises routes for locations of targets and targets, different routes for reaching targets and targets, or combinations thereof.
50. 42. The system of item 41, wherein the setting for the MOBA video game comprises a battlefield including two separate teams, each team including multiple players.
51. A non-transitory computer-readable medium which, when executed by the control circuit, causes the control circuit to:
Analyzing the stored player data for each player of each team associated with the video game play session and the stored settings for the MOBA video game, the player data including metrics related to attack metrics, defense metrics, and damage metrics and decide to rate video game play session content;
video based on in-game metrics for each player of each team determined from the game map of the video gameplay session content and the video gameplay session content, in response to determining to evaluate the video gameplay session content; having the in-game performance of each player of each team analyzed in the gameplay session content, the in-game metrics comprising an in-game attack metric, an in-game defense metric, and an in-game damage metric;
making decisions to recommend video gameplay session content based on the in-game performance of each player on each team;
A non-transitory computer-readable medium having instructions encoded thereon.
52. 52. The non-transitory computer-readable medium of clause 51, wherein the stored player data comprises pre-game metrics for each player among the plurality of players in the first team and the second team.
53. The computer-readable medium further, when executed by the control circuit, causes the control circuit to:
having instructions encoded thereon causing the pre-game metrics of each player in the first team to be compared with the pre-game metrics of each corresponding player in the second team; 53. The non-transitory computer of item 52, comprising determining whether the pre-game metric of each player in the team of is approximately equal to the pre-game metric of each corresponding player in the second team. readable medium.
54. The computer-readable medium further, when executed by the control circuit, causes the control circuit to:
54. The non-transitory computer readable medium of item 53, having instructions encoded thereon for determining to evaluate video gameplay session content based on the comparison.
55. The computer-readable medium further, when executed by the control circuit, causes the control circuit to:
having instructions encoded thereon causing the in-game metrics of each player among the plurality of players in the first team to be compared with the in-game metrics of each corresponding player in the second team; 52. The method of item 51 further comprising determining whether the in-game metric of each player in the first team is approximately equal to the in-game metric of each corresponding player in the second team. non-transitory computer-readable medium;
56. The computer-readable medium further, when executed by the control circuit, causes the control circuit to:
56. The non-transitory computer readable medium of item 55, having instructions encoded thereon to cause a determination to recommend a video gameplay session based on the comparison.
57. The computer-readable medium further, when executed by the control circuit, causes the control circuit to:
52. The non-transitory computer-readable medium of item 51, having instructions encoded thereon to cause calculation of in-game metrics based on metadata associated with video gameplay session content indicative of aspects of gameplay.
58. 58. The non-transitory computer-readable medium of item 57, wherein the metadata comprises MOBA video game character metadata, item metadata, and overall ability metadata.
59. 52. The non-transitory computer-readable medium of item 51, wherein the game map comprises routes for locations of targets and goals, different routes for reaching targets and goals, or combinations thereof.
60. 52. The non-transitory computer-readable medium of item 51, wherein a setting for a MOBA video game comprises a battlefield including two separate teams, each team including multiple players.

Claims (24)

A computer-implemented method for evaluating video gameplay session content for a video game, the method comprising:
calculating pre-game performance metrics based on stored player data and stored settings for the video game;
determining that the pre-game performance metric meets a pre-game threshold;
calculating an in-game performance metric based on stored metadata associated with the video gameplay session content indicative of aspects of gameplay in response to determining that the pre-game performance metric meets a pre-game threshold; and
determining that the in-game performance metric meets an in-game performance threshold;
Determining statistics aggregated from the video gameplay session content in response to determining that the in-game performance metric meets the in-game performance threshold;
analyzing the aggregated statistics against the stored player data and stored settings for the video game;
determining whether the video gameplay session content is to be recommended based on the analysis. 2. The computer-implemented method of claim 1, further comprising determining a genre of said video game. 3. The computer-implemented method of claim 2, further comprising calculating the pre-game performance metric and the post-game performance metric based on the genre. Determining that the pre-game performance metric meets a pre-game threshold comprises:
calculating a plurality of pre-game performance metrics for each of the first team and the second team;
calculating an average pre-game performance metric for each of the first team and the second team;
2. The computer-implemented method of claim 1, comprising: comparing the average value to an average pre-game threshold of the pre-game thresholds. determining that the average value is above the average pre-game threshold;
5. The computer-implemented method of claim 4, further comprising: responsive to determining that the average value exceeds the average pre-game threshold, updating the average pre-game threshold using the average value. . Determining that the in-game performance metric meets an in-game performance threshold comprises:
calculating a plurality of in-game performance metrics for each of the first team and the second team over a plurality of time intervals;
comparing each in-game performance metric for each of the first team and the second team to a corresponding in-game performance threshold among a plurality of in-game thresholds. How to implement. 2. The computer-implemented method of claim 1, wherein the player data comprises aggregated pre-game performance metrics for each player of the plurality of players of the video game prior to actual gameplay of the video game. Determining the aggregated statistics includes:
8. The computer-implemented method of claim 7, comprising determining an aggregated in-game performance metric for each player of the plurality of players from the video gameplay session content. Analyzing the aggregated statistics includes:
9. The computer-implemented method of claim 8, comprising comparing the aggregated pre-game performance metrics to the aggregated in-game performance metrics. 10. The computer-implemented method of claim 9, further comprising determining whether the video gameplay session is to be recommended based on the comparison. A system comprising means for performing the steps of the method according to any of claims 1-10. A non-transitory computer-readable medium having instructions encoded thereon, the instructions being executed by a control circuit to cause the control A non-transitory computer readable medium enabling a circuit to perform the steps of the method of any of claims 1-10. 1. A computer-implemented method for evaluating video gameplay session content for a multiplayer online battle arena (MOBA) video game, said method comprising:
analyzing stored player data for each player of each team associated with the video gameplay session and stored settings for the MOBA video game, wherein the player data includes offensive metrics, defensive metrics, and a metric related to a damage metric, determining to evaluate the video gameplay session content;
based on in-game metrics for each player of each team determined from a game map of the video gameplay session content and the video gameplay session content, in response to determining to evaluate the video gameplay session content. analyzing in-game performance of each player of each team in said video gameplay session content, said in-game metrics comprising in-game attack metrics, in-game defense metrics, and in-game damage metrics. be prepared and
determining to recommend said video gameplay session content based on in-game performance of each player of each team. 14. The computer-implemented method of claim 13, wherein the stored player data comprises pre-game metrics for each player among multiple players in a first team and a second team. Analyzing the stored player data includes:
Comparing pre-game metrics of each player in the first team with pre-game metrics of each corresponding player in the second team, wherein the comparing comprises: determining whether a player's pre-game metric is approximately equal to the pre-game metric of each corresponding player in said second team;
15. The computer-implemented method of claim 14. 16. The computer-implemented method of claim 15, further comprising determining to evaluate the video gameplay session content based on the comparison. Analyzing the in-game performance further comprises:
comparing an in-game metric of each player among the plurality of players in the first team with an in-game metric of each corresponding player in the second team, said comparing comprising: determining whether the in-game metric of each player in the team is approximately equal to the in-game metric of each corresponding player in the second team;
14. The computer-implemented method of claim 13. 18. The computer-implemented method of claim 17, further comprising determining to recommend the video gameplay session based on the comparison. 14. The computer-implemented method of claim 13, further comprising calculating the in-game metrics based on metadata associated with the video gameplay session content indicative of aspects of gameplay. 20. The computer-implemented method of Claim 19, wherein the metadata comprises character metadata, item metadata, and overall ability metadata for the MOBA video game. 14. The computer-implemented method of claim 13, wherein the game map comprises routes for locations of targets and goals, different routes to reach the targets and goals, or combinations thereof. 14. The computer-implemented method of claim 13, wherein the setting for the MOBA video game comprises a battlefield including two separate teams, each of the teams including multiple players. A system comprising means for performing the steps of the method according to any of claims 13-22. A non-transitory computer-readable medium having instructions encoded thereon, the instructions being executed by a control circuit to cause the control A non-transitory computer readable medium enabling a circuit to perform the steps of the method of any of claims 13-22.

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