Content delivery system and program

Abstract

To provide a new technology that reduces the costs associated with using a generation unit when providing users with content that includes content elements generated by the generation unit. [Solution] The content provision system provides a given content to the user, which includes newly generated content elements from the generation unit. When the generation load status of the generation unit satisfies given modification conditions, the content provision system prepares generation instruction information that modifies the generation conditions for the majority of users, excluding a small number of special users, so that the generation processing load is reduced compared to the initial settings. The system then provides the modified instruction information to the generation unit to obtain new content elements and provides the user with content including those content elements.

Description

Technical Field

[0007] ,

[0001] The present invention relates to a content providing system or the like that provides given content to users.

Background Art

[0002] In services that provide content such as games to users, making users wait is a factor that reduces satisfaction with the service. Therefore, game developers are constantly required to devise ways to shorten the waiting time as much as possible and ways for users to spend their time meaningfully during the waiting time.

[0003] For example, Patent Document 1 describes a technique for reducing the waiting time by using a cache.

[0004] In recent years, AI (Artificial Intelligence) has come to be used in games. Patent Document 2 describes a technique for providing recommendations to players using an AI model.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0006] As one form of using AI in video games, it is conceivable to have a generation unit represented by a generation AI generate content elements (for example, scenarios, game stages, stage maps, characters that appear, items that appear, backgrounds, etc.) that make up the game content.

[0007] However, using the generation unit incurs costs such as the waiting time from the time generation is initiated until the generated output is received (generation waiting time), the computing resources used for the generation process, and usage fees. Hereafter, these will be collectively referred to as "generation costs." The types of content elements used in games are diverse, and reducing generation costs when using the generation unit is an important issue. In particular, generation waiting time has a significant impact on user satisfaction with the service, so countermeasures are especially necessary within the generation costs.

[0008] Furthermore, in recent years, the commercial use of generation components as paid services has become more active, and some of these services employ a pay-per-use system based on the time required for generation and the size of the generated data. In this case, reducing the time required for generation directly leads to a reduction in usage fees, making it even more important.

[0009] These problems are not limited to video games; they also apply to the use of AI in content that provides players with virtual experiences in virtual spaces.

[0010] The problem that this invention aims to solve is to provide a new technology that makes it possible to reduce the costs associated with using a generation unit when providing users with content that includes content elements generated by the generation unit. [Means for solving the problem]

[0011] The first invention for solving the above problems is a content provision system that provides a user with a given content including content elements generated by a generation unit (for example, the generation AI system 1200 in Figure 1), A generation instruction control means that determines generation instruction information, which is an instruction for the generation unit to generate the content elements, and which determines the generation instruction information by performing a change process to change the generation instruction information for when the change conditions are not met (hereinafter referred to as "primary generation instruction information") using the user information of the user when the given change conditions are met (for example, the generation instruction control unit 220 in Figure 11, and steps S14 to S46 in Figure 16), The content provision system includes a content provision control means (for example, the content provision control unit 230 in Figure 11, step S60 in Figure 17) that acquires content elements by providing the generation instruction information determined by the generation instruction control means to the generation unit, and controls the provision of the content including the acquired content elements to the user.

[0012] "User information" refers to information that can be traced based on a user's identification information (user ID, user account), and is linked to that identification information.

[0013] According to the first invention, the content provision system can determine the generation instruction information by performing a modification process that modifies the primary generation instruction information using user information when given modification conditions are met. Since the processing content, processing load, content of the generated product, and data size of the generated product are all determined by the generation instruction information, the generation waiting time will also depend on the generation instruction information. In other words, it is possible to realize a system that can reduce the costs associated with using the generation unit.

[0014] The second invention is a content provision system in which the generation instruction control means performs the modification process to change at least one of the types, number of types, and quality of the content elements to be generated by the generation unit (for example, modification pattern definition data 540 in Figure 12, step S46 in Figure 16).

[0015] According to the second invention, the content delivery system can change at least one of the types, number, and quality of content elements to be generated.

[0016] The third invention is a content provision system in which, in the above-described content provision system, the user information includes privacy information such as age information and / or gender information, and the generation instruction control means performs the modification process using the privacy information.

[0017] According to the third invention, the content delivery system can set the content generation instructions to be modified according to the user's age and gender.

[0018] The fourth invention is a content provision system in which the generation instruction control means includes an analysis means for analyzing the user information (for example, the analysis unit 224 in Figure 11, step S14 in Figure 16), and performs the modification process based on the analysis results of the analysis means.

[0019] According to the fourth invention, the content provision system becomes capable of changing the generation instruction information based on the results of user information analysis.

[0020] The fifth invention is a content provision system in which, in the above-described content provision system, the user information includes past generation information which is information about when the content element was generated for that user (for example, past generation information 761 in Figure 15 which can be traced from the content element ID list of the play history data 610 in Figure 13), and the analysis means analyzes the past generation information.

[0021] According to the fifth invention, the content provision system becomes capable of changing the generation instruction information based on the results of analyzing previously generated information.

[0022] The sixth invention is a content providing system in which, in the above content providing system, the past generation information includes information indicating the content of the generated content element (for example, information 769 indicating the content of the content element in FIG. 15), and the analysis means analyzes the information indicating the content.

[0023] According to the sixth invention, the content providing system can change the generation instruction information based on the analysis result of the content of the content element.

[0024] The seventh invention is a content providing system in which, in the above content providing system, the past generation information includes system status information (for example, generation-time system status information 766 in FIG. 15) when the content element was generated, and the analysis means analyzes the system status information.

[0025] According to the seventh invention, the content providing system can change the generation instruction information based on the analysis result of the system status information.

[0026] The eighth invention is a content providing system in which, in the above content providing system, the analysis means estimates the estimated satisfaction degree of the user with respect to the content element to be generated based on the past generation information (for example, estimated satisfaction degree determination reference data 580 in FIG. 10, step S14 in FIG. 16), and the generation instruction control means performs the change process using the estimated satisfaction degree.

[0027] According to the eighth invention, the content providing system can change the generation instruction information based on the estimated satisfaction degree of the user.

[0028] The ninth invention is a content provision system in which, when the estimated satisfaction level satisfies a given high satisfaction condition (for example, when it proceeds to NO in step S20B of Figure 22), the generation instruction control means performs the modification process to modify the primary generation instruction information so that the content element satisfies a given poor content condition compared to the content element that would be generated by providing the primary generation instruction information to the generation unit (for example, the poor content condition in Figure 7).

[0029] "Poor" content elements refer to content elements that are of relatively low quality and lacking in substance. For example, if the content element is a character, "poor" content elements include: "few details in the character model," "few polygons used in the character model," "few colors used in the color scheme," "no emissive color or emissive material settings," and "omission or rough setting of skin weights." In other words, it means that the data for the content element is limited.

[0030] The "poor content conditions" refer to the ranges and thresholds of these parameter values ​​that result in "poor" content, as well as the specified values ​​for those ranges and thresholds.

[0031] According to the ninth invention, the content provision system can modify the quality of content elements provided to users who meet the high satisfaction criteria so that they are of lower quality and less desirable than those provided to users who do not meet the high satisfaction criteria.

[0032] The tenth invention is a content provision system in which, when the estimated satisfaction level satisfies a given low satisfaction condition, the system performs the modification process to modify the primary generation instruction information so that the content element satisfies a given rich content condition compared to the content element that would be generated by providing the primary generation instruction information to the generation unit (for example, modification of the generation instruction information for a preferred user in Figure 21).

[0033] For a content element to be "rich," it means that the quality of that content element is relatively high and abundant. For example, if the content element is a game stage, then "rich" would include things like "a large number and variety of character models," "diverse character and background motions," and "a high level of complexity in the stage space." In other words, it means that the content element is meticulously and complexly constructed, and the data for that content element is large.

[0034] "Rich content conditions" refer to the conditions that must be met for the range or threshold values ​​of these parameter values ​​that result in "rich" content, as well as the specified values ​​for those ranges or thresholds.

[0035] According to the tenth invention, the content delivery system can provide higher quality and richer content elements to users whose estimated satisfaction level meets the low satisfaction criteria.

[0036] The eleventh invention is a content provision system in which, in the above-described content provision system, the content is a game, the analysis means analyzes the user's proficiency and / or fondness for the game based on the user information (for example, the fondness determination criterion data 582 in Figure 10, step S14 in Figure 16), and the generation instruction control means performs the modification process based on the analysis results of the analysis means.

[0037] According to the eleventh invention, the content delivery system becomes capable of making changes based on the results of an analysis of the user's proficiency and / or enjoyment of the game.

[0038] The twelfth invention is a content provision system in which, in the above-mentioned content provision system, the user information includes one of the following: the user's billing information, the user's item information, and the usage information of the content elements generated for the user (for example, the billing performance data 608, item information 612, and usage status information 618 in Figure 13).

[0039] According to the twelfth invention, the content provision system can perform modification processing based on any one of the following: user billing information, user item information, or usage information of content elements generated for that user.

[0040] The 13th invention is a content provision system in which, in the above-described content provision system, the generation instruction control means performs the modification process based on whether or not the special user conditions based on the user information are met (for example, the flow from branching to NO in step S20 of Figure 16 to step S46).

[0041] According to the 13th invention, the content delivery system becomes capable of performing modification processing based on whether or not the user meets special user conditions.

[0042] The fourteenth invention is a content provision system in which, in the above-described content provision system, the user information includes the user's billing information, and the generation instruction control means determines whether or not the special user conditions are met based on the billing information (for example, a high-spending user in Figure 8).

[0043] According to the 14th invention, the content provision system can determine whether or not special user conditions are met based on the user's billing information, and perform modification processing based on the determination result.

[0044] The 15th invention is a content provision system in which, in the above-described content provision system, the user information includes the user's item information, and the generation instruction control means determines whether or not the special user conditions are met based on the item information (for example, in the case of a high-spending user in Figure 8 who falls under the category of "number of items owned and / or number of items consumed is greater than the standard").

[0045] According to the 15th invention, the content provision system can determine whether or not special user conditions are met based on the user's item information, and perform modification processing based on the determination result.

[0046] The sixteenth invention is a content provision system in which, in the above-described content provision system, the generation instruction control means performs the modification process to modify the primary generation instruction information so that, if the special user conditions are not met, the content element becomes one that satisfies a given poor content condition compared to the content element that would be generated by providing the primary generation instruction information to the generation unit (for example, the poor content condition in Figure 7, and the flow from NO in step S20 in Figure 16 to step S46).

[0047] According to the 16th invention, the content provision system can modify the quality of content elements provided to users who meet special user conditions so that they are of lower quality and less desirable than when provided to users who do not meet the special user conditions.

[0048] The 17th invention is a content provision system in which, when the special user conditions are met, the generation instruction control means performs the modification process to modify the primary generation instruction information so that the content element becomes one that satisfies given rich content conditions compared to the content element that is generated by providing the primary generation instruction information to the generation unit (for example, modification of generation instruction information for a preferred user in Figure 21).

[0049] According to the 17th invention, the content provision system can provide higher quality and richer content elements to users who meet special user conditions.

[0050] The 18th invention is a content provision system in which the generation instruction control means performs the modification process based on the generation processing status of the generation unit (for example, the sub-conditions that the generation processing status must satisfy in the applicable generation level determination criterion data 530 in Figure 10, and the flow from YES in steps S14 and S44 to step S46 in Figure 16).

[0051] According to the 18th invention, the content provision system becomes capable of performing modification processing based on the generation processing status of the generation unit.

[0052] The 19th invention is a content provision system in which the generation processing status includes information related to the processing load of the generation unit (for example, processing load information of the generation processing status monitoring data 680 in Figure 10).

[0053] According to the 19th invention, the content provision system becomes capable of performing modification processing based on information related to the processing load of the generation unit.

[0054] The 20th invention is a content provision system in which the generation processing status includes information related to the time required from the time the generation instruction information is given to the generation unit until the content element is acquired (for example, communication quality information of the generation processing status monitoring data 680 in Figure 10).

[0055] According to the 20th invention, the content provision system can perform modification processing based on information related to the time required from the time generation instruction information is given until the generated content elements are obtained.

[0056] The 21st invention is a content provision system in which, in the above-described content provision system, the generation unit is an external system that requires payment for the generation of the content elements, and the generation processing status includes information on the payment (for example, the payment information in the generation processing status monitoring data 680 in Figure 10).

[0057] According to the 21st invention, the content provision system becomes capable of performing modification processing based on information regarding the compensation for generating content elements.

[0058] The 22nd invention relates to the above-mentioned content provision system, wherein the generation unit has a plurality of generation units corresponding to the type and / or quality of the content elements that can be generated, The generation instruction control means determines, among the plurality of types of generation units, which generation unit to instruct to generate by including the instruction destination information in the generation instruction information (for example, the instruction destination information in FIG. 19). It is a content providing system.

[0059] According to the 22nd invention, in the content providing system, it is possible to indicate the instruction destination information as to which generation unit among the plurality of types of generation units to instruct to generate.

[0060] The 23rd invention is the above content providing system, wherein the user information includes past generation information which is information at the time of generating the content element for the user, the generation instruction control means changes the generation instruction information so as to reuse the content element that has been generated in the past based on the past generation information (for example, the example in FIG. 7 with a mark of white characters "補" on a black background; addition to the generation instruction information of the data of the base content element). It is a content providing system.

[0061] According to the 23rd invention, the content providing system can change the generation instruction information so as to reuse the content element that has been generated in the past.

[0062] The 24th invention is the above content providing system, wherein the content is a game, the user information is information that is updated at any time according to the play of the game, and different content elements are generated according to changes in the user information to provide the user with the content that realizes various game progressions.

[0063] According to the 24th invention, the content providing system creates diversity in the content as a service by changing the generation of content elements according to user information, and can provide more and more frequent generation opportunities for content elements. By increasing the generation opportunities, the opportunity for change processing by the generation instruction control means increases, and accordingly the opportunity to reduce the cost associated with the use of the generation unit increases, and the effect can be enhanced.

[0064] The 25th invention is a program for causing a computer system to perform control to provide a given content, including the content elements generated by a content generation unit, to a user. A generation instruction control means for determining generation instruction information, which is an instruction for the generation unit to generate the content elements, wherein, when a given change condition is met, the generation instruction control means performs a change process to change the generation instruction information for when the change condition is not met (hereinafter referred to as "primary generation instruction information") using the user information of the user to determine the generation instruction information, Content provision control means that acquires content elements by providing the generation instruction information determined by the generation instruction control means to the generation unit, and controls the provision of the content including the acquired content elements to the user. This is a program for making the aforementioned computer system function.

[0065] According to the 25th invention, it is possible to realize a program that can enable a computer system to perform the same functions as the first invention. [Brief explanation of the drawing]

[0066] [Figure 1] A system configuration diagram showing an example of a content delivery system. [Figure 2] A diagram illustrating an example of a user interface in the process of a user selecting content to play. [Figure 3] A diagram illustrating examples of content elements in an action RPG. [Figure 4] A diagram showing an example of the data structure for initial content settings data. [Figure 5] A diagram illustrating the process of generating content elements using an AI generation system. [Figure 6] A diagram illustrating the settings for content elements to be generated, generation levels, and quality, based on the generation process status. [Figure 7] A table showing examples of change processing. [Figure 8] A diagram illustrating the change processing for special users and non-special users. [Figure 9] A diagram illustrating satisfaction surveys and estimated satisfaction levels. [Figure 10] A diagram showing examples of programs and data stored by a server system. [Figure 11] A diagram showing an example configuration of the functional components of a server system. [Figure 12] A diagram showing an example of the data structure for change pattern definition data. [Figure 13] A diagram showing an example of the data structure of user registration data. [Figure 14] A diagram showing an example of the data structure of play data. [Figure 15] A diagram showing an example of the data structure of previously generated content element data. [Figure 16] A flowchart illustrating the processing flow related to the delivery of user-facing content by a server system. [Figure 17] Flowchart continuing from Figure 16. [Figure 18] A diagram illustrating a modified example. [Figure 19] A diagram illustrating a modified example. [Figure 20] A diagram illustrating a modified example. [Figure 21] A diagram illustrating a modified example. [Figure 22] A diagram illustrating a modified example. [Modes for carrying out the invention]

[0067] Examples of embodiments of the present invention will be described below, but it goes without saying that the embodiments to which the present invention can be applied are not limited to the following embodiments.

[0068] [First Embodiment] Figure 1 is a system configuration diagram showing an example of the configuration of a content provision system according to this embodiment. The content provision system 1000 is a computer system that provides content offering virtual experiences in a virtual space, such as video games, virtual activities, and shopping, to multiple registered users in parallel. The content provided by the content provision system 1000 changes each time content is selected to be played. Therefore, the content provision system 1000 is able to always provide users with a new virtual experience.

[0069] The content provision system 1000 is a computer system including a server system 1100 and user terminals 1500 for each user, all connected via a network 9 for data communication. The server system 1100 is capable of communicating with an external system, the generation AI system 1200, via the network 9.

[0070] Network 9 refers to a communication path capable of data transmission. In other words, Network 9 includes not only LANs (Local Area Networks) using dedicated lines (dedicated cables) or Ethernet (registered trademark) for direct connections, but also telephone networks, cable networks, and the Internet.

[0071] The server system 1100 is a computer system that performs various processes such as managing and controlling registered user information and various controls related to content provision (for example, controlling the progress of a game).

[0072] The server system 1100 has a control board 1150 mounted on the main unit 1101. The control board 1150 is equipped with various microprocessors such as a CPU (Central Processing Unit) 1151, a GPU (Graphics Processing Unit), and a DSP (Digital Signal Processor), various IC memories 1152 such as VRAM, RAM, and ROM, and a communication device 1153. Some or all of the functions mounted on the control board 1150 may be implemented using an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a SoC (System on a Chip).

[0073] Although the server system 1100 is depicted as a single server device, it may also be configured with multiple devices. For example, the server system 1100 may consist of multiple servers, each handling a different function, connected to each other via an internal bus or network 9 for data communication. Furthermore, the server system 1100 may include a database and online storage.

[0074] The generation AI system 1200 is a computer system having a generation AI that generates content elements based on generation instructions, and functionally corresponds to the content element generation unit 1202 included in the content provided by the content provision system 1000. The generation AI system 1200 employs a multi-core architecture and performs dynamic load balancing, such as automatically allocating cores and memory space.

[0075] The generation AI system 1200 may be implemented as a single generation AI model, or it may be configured as a group of AIs having one or more element-specific generation AIs 1204 (1204a, 1204b, ...) for each type of content element.

[0076] Content elements can include, for example, if the content is a video game, the game's scenario, game stage maps, characters appearing in the game, character dialogue and actions, etc. Other elements could include the game's background music, background images, background objects, etc.

[0077] For example, if the content is a virtual underwater dive experience (one of the virtual activities), the types of content elements may include the diving space (stage), the types of creatures that appear in the water, and artificial objects that appear in the water (e.g., ships, submarines, shipwrecks, etc.). The generation AI system 1200 may be configured to have element-specific generation AIs 1204 for each of these content elements.

[0078] Furthermore, a configuration may have multiple element-specific generation AIs 1204 for a single type of content element. For example, the types of content elements may be further subdivided, and a configuration may be provided with element-specific generation AIs 1204 for each subdivision. Specifically, the element-specific generation AIs 1204 for generating characters may include an element-specific generation AI 1204 for generating monster-like characters, an element-specific generation AI 1204 for generating cute, pop-style characters, an element-specific generation AI 1204 for generating robot-like characters, and so on.

[0079] User terminal 1500 serves as a Man-Machine Interface (MMIF) for user 2 to play content as a player. For example, if the content to be played is a game, the user terminal 1500 for user 2, who is the player, functions as a gameplay terminal. Although only one user terminal 1500 is depicted, in actual operation, it is common for multiple user terminals 1500 to communicate and connect to the server system 1100 simultaneously.

[0080] The user terminal 1500 is a computer system that can connect to the network 9, such as a personal computer, smartphone, wearable computer, portable game console, home game console, or tablet computer.

[0081] The user terminal 1500 is a computer comprising an operation input device, an image display device, and a control board 1550 that performs calculations. Examples of operation input devices include a touch panel 1506, a keyboard, a game controller, and a mouse. Examples of image display devices include a touch panel 1506, a head-mounted display, and a glasses-type display.

[0082] The control board 1550 is equipped with a CPU 1551, various microprocessors such as a GPU and DSP, various IC memories 1552 such as VRAM, RAM, and ROM, and a communication module 1553 that connects to the network 9. These elements mounted on the control board 1550 are electrically connected via bus circuits and the like, enabling data reading and writing, and signal transmission and reception. Part or all of the control board 1550 may be an ASIC, FPGA, or SoC.

[0083] The control board 1550 stores programs and various data necessary to realize the functions of the user terminal 1500 in the IC memory 1552. The user terminal 1500 realizes its functions as a play terminal by executing a predetermined application program.

[0084] Figure 2 shows an example of a user interface in the content provision system 1000, illustrating the process by which a user selects content to play. The content provision system 1000 provides various types of content for enjoying virtual experiences. These types of content can be categorized as, for example, games, virtual activities, shopping, and other virtual experiences.

[0085] A game is content that allows players to play games using game characters or their own avatars in a game space constructed in a virtual environment.

[0086] Virtual activities include, for example, content that involves using avatars to engage in activities in a virtual space, such as various sports, trekking, fishing, hunting, horseback riding, gathering, mining, courage tests, raising virtual creatures, and exploring virtual worlds.

[0087] Shopping is content that provides a shopping experience in a commercial facility built in a virtual space. It may also allow users to purchase goods from the real world.

[0088] Other virtual experiences include visiting virtual museums and art galleries, participating in virtual events and concerts, and raising virtual creatures.

[0089] The server system 1100 displays a category selection screen W2 on the user terminal 1500 of the logged-in user and accepts the user's selection of which category of content to play. In the example in Figure 2, the "Games" category is selected.

[0090] Once a category is selected, the server system 1100 displays the genre selection screen W4 on the user terminal 1500 and accepts the user to select one of several genres included in the selected category. The number and types of genres can be set as appropriate. In the example in Figure 2, the "Action RPG (role playing game)" genre is selected.

[0091] Once a genre is selected, the server system 1100 displays a type selection screen W6 on the user terminal 1500 and accepts the user to select one of several types (or game titles) set for the game content. In the example in Figure 2, since the "Action RPG" genre has been selected first, an example of selecting an Action RPG type is shown, and the "Leveling Up Focused" type is shown. The number and types of types can be set as appropriate.

[0092] After selecting a type, a screen could be displayed showing a list of more specific scenario names (content IDs) for the user to select from.

[0093] In other words, the server system 1100 prompts the user to sequentially select the category of content to play, from higher-level categories to lower-level categories. Hereafter, we will assume that the user has selected the "Action RPG" genre within the "Games" category, as shown in Figure 2.

[0094] Figure 3 shows an example of content elements in an action RPG. In the case of game content, content elements include game scenarios, characters, items, background objects, and background music (BGM).

[0095] Regarding the game scenario, further setup parameters related to the content and quality of the content elements include the time required to complete the game, the number of game stages, the map shape, and the map size.

[0096] Regarding characters, setup parameters related to the content and quality of the content elements include the number of character types, the number of characters appearing, and the character's ability settings. Furthermore, focusing on a single character type, setup parameters include the polygon count, number of joints, and color scheme of the character's main model, and the polygon count and color scheme of the item model related to the items the character equips.

[0097] In this action RPG, which types of content elements are fixed and which types are variable are defined by the content initial setting data 510, as shown in Figure 4.

[0098] Content initial setup data 510 is pre-prepared for each type of content. One content initial setup data 510 includes a content ID 511, a content category 512, a content genre 513, and a content type 514. It also includes fixed content element initial setup data 520, variable content element initial setup data 524, and primary generation instruction information definition data 526. Of course, other information may be included as appropriate.

[0099] The fixed content element initial setting data 520 is provided for each type of fixed content element. The type of content element that is set as fixed determines the core structure and style of the game as intended by the game developer.

[0100] Variable content element initial setup data 524 is provided for each content element that may be replaced by a content element generated by the generation AI system 1200. Each variable content element initial setup data 524 includes a content element ID, an element classification, a generation priority, and initial setup data.

[0101] The generation priority may also represent the order of priority among multiple variable content elements. For example, the main character might have a high importance and therefore a high generation priority, while sub-characters might have a lower generation priority. Alternatively, the generation priority may represent the order of processing load assigned to the generation AI system 1200.

[0102] By deciding which types of content elements to make variable, game developers can limit how much the core structure and style of the game can change.

[0103] The primary generation instruction information definition data 526 is prepared for each variable content element and contains the basic generation instruction information for causing the generation AI system 1200 to generate the variable content element, and stores the initial generation instruction information. This is referred to as "primary generation instruction information." The generation instruction information or primary generation instruction information may also be referred to as "prompts."

[0104] The primary generation instruction information is information used to generate content elements that are the same as, or nearly the same as, the initial fixed content elements. Even if primary generation instruction information is provided to the generation AI system 1200, the same content elements may not necessarily be generated depending on the time, that is, the learning state of the generation AI system 1200.

[0105] Figure 5 is a diagram illustrating the overview of the process by which content elements are generated by the AI ​​system 1200. When the user selects the category, genre, and type of content to be played, the server system 1100 selects content elements from the variable content elements to be generated by the generation AI system 1200, according to the selection result. The server system 1100 then sets variable generation conditions (indicating requirements, generation limitations, etc.) for each content element to be generated. It then determines generation instruction information (prompt) that describes the set content and writes it in a format appropriate to the generation AI system 1200 to which it is instructed to generate. The server system 1100 sends this to the generation AI system 1200 corresponding to the type of content element to be generated.

[0106] As mentioned above, the generation AI system 1200 automatically performs dynamic load balancing. That is, the generation AI system 1200 estimates the computational load cost required for generation from the content of the generation instruction information and allocates resources to minimize the output timing according to the load.

[0107] From the perspective of content delivery system 1000, the goal is to reduce generation costs. Specifically, the goal is to ensure that the "generation waiting time" remains within a predetermined acceptable timeframe, while also maintaining user satisfaction regarding the time required from the user's content selection operation to the start of content play.

[0108] However, the generation AI system 1200 is shared by the server system 1100 (i.e., by multiple users) to generate multiple content elements. Furthermore, in the case of a commercial system, the generation AI system 1200 is not necessarily exclusive to the server system 1100, and may also generate content in response to requests from sources other than the server system 1100.

[0109] Therefore, the "generation waiting time" required for the server system 1100 to acquire generated data from the generation AI system 1200 will vary depending on the status of the generation AI system 1200 at any given time. Specifically, it will vary depending on the number of generation instructions (number of assignments / contracts) input to the generation AI system 1200, the computational processing load related to the generation of each generation instruction, and the communication environment between the server system 1100 and the generation AI system 1200. In practice, the computational processing load related to the generation of each generation instruction increases as the quality of the content elements required by the generation instruction information increases and the content becomes richer.

[0110] Therefore, as shown in Figure 6, the server system 1100 variably sets the content of the generation instructions according to the operating status of the generation AI system 1200 that issues the generation instructions. This prevents the "generation waiting time" from becoming too long and compromising user satisfaction.

[0111] Specifically, the server system 1100 acquires generation processing status information related to the generation AI system 1200.

[0112] "Generation processing status information" is information indicating the processing load status of the generation AI system 1200 and the congestion status of requests. "Processing load status information" includes, for example, the CPU load rate and the memory consumption rate (usage rate). "Information indicating congestion status" includes, for example, the number of pending executions. The generation processing status information is information that the generation AI system 1200 updates in real time as it executes its dynamic load balancing function, and the server system 1100 can obtain the generation processing status information from the generation AI system 1200 by using the API (Application Programming Interface) made public for the generation AI system 1200.

[0113] Furthermore, the generation processing status information may also include communication quality information. Communication quality information includes, for example, communication speed, PING value, JITTER value, etc., and is information regarding the time required from when the server system 1100 provides generation instruction information to the generation AI system 1200 until the generated data is acquired. For example, the communication quality information may be determined by the server system 1100 by calculating the communication speed based on the data size of the generation instruction information previously sent from the server system 1100 to the generation AI system 1200 and the time required for that transmission.

[0114] The server system 1100 determines the "applied generation level" to be applied to the content elements to be generated, based on the acquired generation processing status information. The "applied generation level" indicates the level of quality and richness to which the generation AI system 1200 should generate the content elements.

[0115] The generation level has three stages: "STD (Standard)," which applies the high-quality initial settings of content initial setting data 510; "LOW1," which reduces the processing load by one level; and "LOW2," which reduces it by another level. Three ranges are set for the generation processing status information (see graph in Figure 6), and "STD," "LOW1," and "LOW2" are associated with the generation status, in order from the range closest to "idle (low load / quiet)."

[0116] The server system 1100 determines the types and number of content elements to be generated from among the variable content elements of the selected content according to the applied generation level. Specifically, it may select them in order from the highest generation priority, or it may select them randomly. The server system 1100 then changes the settings of the setup parameters of the content elements to be generated.

[0117] Specifically, if the AI ​​generation system 1200 is judged to be operating under low load and not congested (closer to idle on the horizontal axis of the graph in Figure 6), the applied generation level will be set to "STD". The number and types of content elements to be generated will be the initial values ​​for that content. Specifically, all variable content elements may be included as content elements to be generated. The setup parameter values ​​will also be the initial values ​​that represent the highest quality state initially desired by the game creator.

[0118] The more the generation AI system 1200 is under heavy load and congested (the closer the horizontal axis of the graph in Figure 6 is to "busy"), the more the server system 1100 sets the applied generation level to progressively lower levels, from "LOW1" to "LOW2". The lower the applied generation level, the fewer types of content elements are generated from the variable content, and the more content elements with smaller data sizes are selected. In this process, the generation priority may also be referenced.

[0119] In other words, the server system 1100 reduces the number of items to be generated in the first place before issuing generation instructions, thereby reducing the load on the generation AI system 1200 and shortening the "generation waiting time."

[0120] Furthermore, the lower the application level of the generation AI system 1200, the more the server system 1100 determines and creates generation instruction information with modified setup parameter values ​​so that the generation cost is lower than that of the primary generation instruction information.

[0121] Specifically, the server system 1100 copies the primary generation instruction information of the primary generation instruction information definition data 526 for the content elements to be generated. Then, it modifies the setup parameter values ​​so that the quality and richness of the generated content elements are lower quality and less abundant than the content elements obtained by providing the primary generation instruction information. In other words, the server system 1100 modifies the generation instruction information to reduce the generation load before issuing the generation instruction, thereby shortening the "generation waiting time." This is called the "modification process."

[0122] Figure 7 is a table showing an example of the contents of the change process. The content element type indicates "what" to generate in the generation instruction information. Setup parameters are items that indicate the generation conditions and constraints during generation in the generation instruction information.

[0123] The numerical values ​​and content corresponding to each generation level for each setup parameter indicate how the setup parameter values ​​are modified from the primary generation instruction information. In other words, those for generation levels "LOW1" and "LOW2" correspond to "poor content conditions," which indicate how much the quality and richness of the generated content elements will be reduced compared to when they are generated based on the primary generation instruction information.

[0124] For example, the content element "Game Scenario" and the setup parameter "Number of Game Stages" indicate how many game stages make up one scenario. In the case of generation level "STD," for example, the scenario will be composed of "4" stages, as per the initial setting of the setup parameter. In other words, the generation instruction information given to the generation AI system 1200 in this case will be the same as the primary generation instruction information.

[0125] For the applied generation level "LOW1," it consists of "3" stages, and for the applied generation level "LOW2," it consists of "2" stages. Therefore, in these cases, the generation instruction information given to the generation AI system 1200 will be the result after subtracting the specified number of stages included in the primary generation instruction information. The fewer stages that make up a single scenario, the lower the generation cost (processing load, required time), and the more it becomes possible to suppress the "generation waiting time."

[0126] For example, the content element "Humanoid Character" and the setup parameter "Design of the character's body model" will remain as default "Create New" when the applied generation level is "STD". In this case, the generation instruction information given to the generation AI system 1200 will remain as the primary generation instruction information, which specifies that, for example, a humanoid character should have all of the "head," "torso," "arms," ​​"legs," and "equipment" newly created.

[0127] When the applied generation level is "LOW1," the generation instruction information given to the generation AI system 1200 is determined by modifying the primary generation instruction information to inherit the base character's "head" and create the "torso," "arms," ​​"legs," and "equipment." By reducing the number of parts to be generated, it becomes possible to suppress the generation cost and reduce the "generation waiting time."

[0128] When the applied generation level is "LOW2," the generation instruction information given to the generation AI system 1200 is determined by modifying the primary generation instruction information to inherit the base character's "head," "torso," and "equipment" as they are, and create only the "arms" and "legs." Furthermore, by reducing the number of parts to be generated, the generation cost is further reduced, and the "generation waiting time" can be further reduced.

[0129] Furthermore, if the setup parameter value indicates generation conditions based on the base content element, the data of the base content element is added as supplementary information to the generation AI system 1200. In the example in Figure 7, this corresponds to the mark with the white text "supplementary" on a black background. The base content element is, for example, a content element that has been generated in the past, and by having the generation AI system 1200 reuse some of the data of the base content element, it becomes possible to reduce generation costs.

[0130] As shown in Figure 8, the server system 1100 excludes change processing from certain users. These users 2(2a) are called "special users," and the other users 2(2b) are called "non-special users."

[0131] A "special user" is any of the following users (1) through (4): (1) A user with a low estimated satisfaction with the applied generation level that is applied to the content elements that are generated. (2) A high-spending user. (3) A user who owns and / or has consumed a large number of paid items. (4) A user with a high level of proficiency and / or enthusiasm for the game.

[0132] This section explains "users with low estimated satisfaction." There are two types of estimated satisfaction levels: Level 1 and Level 2.

[0133] The "first estimated satisfaction level" is estimated statistically from the results of a satisfaction survey conducted at the end of play for content that includes the generated content elements. Specifically, as shown in Figure 9, the server system 1100 displays a survey screen W8 on the player's user terminal 1500 at the end of play. The survey screen W8 asks the player to input their overall satisfaction level with the content and their satisfaction level with each content element.

[0134] The survey results are stored in association with the application generation level of the content elements (or other information that can be used to determine the application generation level) and statistically processed by generation level. As a result, the statistical values ​​for generation levels "LOW1" and "LOW2" (for example, the average satisfaction level or the median satisfaction level at that generation level) are used as the first estimated satisfaction level. Users whose first estimated satisfaction level does not meet the predetermined high satisfaction condition (dashed line in the graph in Figure 9) are then designated as special users with low satisfaction levels.

[0135] Returning to Figure 8, the "second estimated satisfaction level" is estimated from the user's usage of previously generated content elements. The usage of content elements may include, for example, the progress of content containing previously generated content elements, play time, number of uses, usage time, etc.

[0136] Specifically, the generated content element data is stored in association with information indicating which user it was generated for and information on the applied generation level. In addition, for each user, information on the content they played and the content elements contained in that content is stored. Then, for each generation level, a value indicating the usage status of content elements contained in content that the user has played in the past (a value that has a positive correlation with the number of uses and usage time) is statistically calculated, and a second estimated satisfaction level is determined to have a positive correlation with that statistical value.

[0137] Since users are more likely to abandon the game prematurely if their satisfaction with the content is low, users whose second estimated satisfaction level does not meet the predetermined high satisfaction criteria are designated as special users with low satisfaction.

[0138] Applying change processing to users with low estimated satisfaction levels relative to the content elements being generated is likely to decrease their satisfaction. Therefore, excluding users with low estimated satisfaction from change processing makes it possible to achieve both reduced "generation waiting time" and the maintenance of high satisfaction levels.

[0139] Next, I will explain "high-spending users." If the content provision system 1000 offers a subscription as its billing system, users who have a subscription contract will be considered high-spending users. Furthermore, if the content provision system 1000 offers a per-play billing system, users whose cumulative billing amount within a specified period (e.g., one month) meets a specified high-spending criterion will be considered high-spending users. If the content provision system 1000 has a function to sell paid items (in-game currency) usable in relation to content gameplay online, the purchase price of these items may be included in the cumulative billing amount.

[0140] By treating high-spending users as special users and exempting them from the change process, it is possible to maintain a high level of customer satisfaction.

[0141] Similarly, users who possess / consume a large number of paid items can be considered high-spending users and exempted from the change process, thereby maintaining high customer satisfaction. Alternatively, regardless of whether they are paying or not, users who possess or consume more than a certain number of in-game items could be designated as high-spending users.

[0142] Next, we will explain "users with a high level of proficiency and / or enthusiasm." "Proficiency" can be determined based on player level (or character level, depending on the game) which is automatically assigned according to gameplay performance, and information such as titles which are assigned and updated as players play the game. Users whose player level is above a given level value, users who meet high proficiency conditions such as obtaining specific titles, and top-ranked players are designated as special users.

[0143] "Loyalty" can be determined based on information that is updated as the game progresses, such as player level, title, number of plays, total play time, and play frequency. Users who meet high loyalty conditions, such as having a certain number of plays or a certain total number of plays, will be designated as special users.

[0144] By designating users with a high level of proficiency and / or loyalty as special users, it is possible to maintain a high level of satisfaction among valuable customers who consistently use the content delivery system 1000.

[0145] Special users represent a small fraction of the total user base. Non-special users, on the other hand, make up the majority of logged-in users. Since special users are exempt from modification processing, their generation cost is standard. Non-special users are subject to modification processing, so their generation cost is low, as mentioned above.

[0146] Even if the generation AI system 1200 is under heavy load and busy, a small number of standard loads will be mixed in with a large number of light loads, so the overall "generation waiting time" is likely to be reduced. In other words, the objective of reducing "generation waiting time" without lowering user satisfaction is likely to be achieved.

[0147] Figure 10 shows an example of programs and data stored by the server system 1100. The server system 1100 stores the server program 501, the distribution client program 502, the content initial setting data 510 (see Figure 4), and the applicable generation level determination criterion data 530 in the IC memory 1152. It also stores the change pattern definition data 540, the estimated satisfaction determination criterion data 580, the liking determination criterion data 582, and the user registration data 600. Furthermore, it stores the generation processing status monitoring data 680, play data 700, past generated content element data 760, and the current date and time 900. Of course, other data may be stored as appropriate.

[0148] The server system 1100 performs the server program 501 and calculates the results on the CPU 1151, thereby realizing the function of a server processing unit 200s, as shown in Figure 11.

[0149] The server processing unit 200s performs various controls related to content provision. Specifically, the server processing unit 200s includes a user information management unit 202, a payment processing unit 204, an online shopping control unit 206, a generation processing status information acquisition control unit 208, a content selection acceptance control unit 210, and a generation instruction control unit 220. It also includes a content provision control unit 230, a usage status storage control unit 232, a questionnaire control unit 234, and a timing unit 280.

[0150] The user information management unit 202 executes the prescribed player registration procedure (user registration procedure) and controls the registration and management of user registration data 600 for each user 2.

[0151] The payment processing unit 204 performs the payment processing related to the price of playing the game.

[0152] The online shopping control unit 206 processes online sales of items usable in playing content, such as charging fees and granting purchased items to users.

[0153] The generation processing status information acquisition control unit 208 performs processing related to acquiring generation processing status information (see Figure 6) of the generation AI system 1200.

[0154] The content selection reception control unit 210 performs processing related to the reception of the selection of content to be played (see Figure 2).

[0155] The generation instruction control unit 220 determines generation instruction information, which is an instruction to the generation unit 1202 (see Figure 1) to generate content elements. At that time, if the given change conditions are met, the generation instruction control unit 220 performs a change process to change the generation instruction information (primary generation instruction information) that would be used if the change conditions were not met, using the user's user information, and then determines the generation instruction information.

[0156] The generation instruction control unit 220 includes an applicable generation level determination unit 222, an analysis unit 224, and a special user determination unit 226.

[0157] The application generation level determination unit 222 determines the application generation level based on the generation processing status information of the generation AI system 1200.

[0158] The analysis unit 224 estimates the user's estimated satisfaction with the newly generated content elements and analyzes the user's proficiency and level of preference.

[0159] The special user determination unit 226 determines whether a user who generates and provides new content elements is a special user, based on user information and the analysis results of the analysis unit 224.

[0160] The generation instruction control unit 220 determines that the modification conditions are met if the applicable generation level is a predetermined level ("LOW1" or "LOW2"; see Figure 6) and the user is a non-special user. If the modification conditions are met, the generation instruction control unit 220 determines generation instruction information that modifies the primary generation instruction information so that content elements that satisfy a given poor content condition are generated compared to when generated based on the primary generation instruction information.

[0161] The content provision control unit 230 acquires new content elements by providing the generation instruction information determined by the generation instruction control unit 220 to the generation unit 1202, and controls the provision of content including the acquired content elements to the user.

[0162] The usage status storage control unit 232 controls the recording of information regarding the usage status of each generated content element.

[0163] The survey control unit 234 controls the system to conduct a survey regarding user satisfaction with the content and its elements, in relation to the content's play (see Figure 9).

[0164] The timing unit 280 uses the system clock to perform various timings, such as determining the current date and time (900) and the time spent playing content.

[0165] Returning to Figure 10, the distribution client program 502 is the original client program provided to the user terminal 1500.

[0166] The applicable generation level determination criterion data 530 stores various data related to the criteria for determining the generation level to be applied to the generation of a new content element. Each applicable generation level determination criterion data 530 stores a generation level and requirement data indicating the conditions that must be met in order to apply that generation level.

[0167] The requirements data describes the requirements that the generation process status of the generation AI system 1200, which generates new content elements, must satisfy. For example, a sub-condition that is a threshold or range for the CPU load rate, a sub-condition that is a threshold or range for the number of pending executions, and a sub-condition that is a threshold or range for the communication speed may be described by combining them with AND or OR.

[0168] The change pattern definition data 540 stores various data that define how to modify the primary generation instruction information. One change pattern definition data 540 includes, for example, a change pattern ID 542, an applicable content element ID 544, a pattern application requirement 550, one or more change details data 560, and a reuse data ID list 562, as shown in Figure 12.

[0169] The applicable content element ID 544 indicates the identification information of the content element to which the change pattern applies.

[0170] Pattern application requirements 550 define the conditions that must be met in order to apply the modification pattern. Pattern application requirements 550 are written, for example, by combining one or more subconditions with AND or OR. Subconditions may include, for example, a generation level condition 552 indicating the type of generation level, a privacy condition 554, and a player level condition 556.

[0171] Privacy clause 554 outlines the conditions that must be met regarding personal information such as the user's age and gender. By including the player level requirement 556 in the pattern application requirement 550, the changes can be made more diverse, as the pattern application requirement 550 may be met or not met based on information that is updated as the game progresses.

[0172] The change data 560 defines what and how to change the primary generation instruction information, and is prepared for each setup parameter to be changed. Each change data 560 stores the type of setup parameter to be changed and the change value. The change value is a specified value that satisfies the poor content condition.

[0173] The change data 560 is configured to include content elements appropriate to the age condition if the privacy condition 554 of the pattern application requirement 550 includes an age condition. For example, if the applied content element ID 544 is a content element related to character design, then depending on the age condition, it will be set to a design for children, a design for teenagers, or a design for adults. It is preferable that the data is configured to change the expression to conform to a predetermined rating according to the age condition, such as PG (Parent Guidance).

[0174] Furthermore, if the privacy condition 554 of pattern application requirement 550 includes a gender condition, the content element will be configured to be appropriate for that gender condition. For example, if the applicable content element ID 544 is a content element related to character design, then depending on the gender condition, it will be set to a design for men, a design for women, or a unisex design.

[0175] The reuse data ID list 562 is set when data to be added due to changes is attached or added as supplementary information to the generation instruction information given to the generation AI system 1200, and stores the ID of the data in question.

[0176] Returning to Figure 10, the estimated satisfaction criterion data 580 is prepared for each type and level of estimated satisfaction and stores various data for determining that estimated satisfaction level. One estimated satisfaction criterion data 580 stores the estimated satisfaction level, the target generation level, survey result criterion data regarding the results of the satisfaction survey, and usage criterion data regarding the usage status of the content elements of the applied generation level. If the applied generation level matches the target generation level and the survey result criterion data and / or usage criterion data are met, the estimated satisfaction level indicated by the estimated satisfaction criterion data 580 is adopted.

[0177] The loyalty level determination criteria data 582 is prepared for each type and level of loyalty used to determine whether or not a user qualifies as a special user, and stores various data for determining that loyalty level.

[0178] One set of loyalty criteria data 582 stores, for example, loyalty levels and user information criteria data. User information criteria data is data that describes the conditions that must be met for various user information associated with each user. User information criteria data is described using, for example, billing conditions regarding the amount and frequency of billing, conditions regarding player level, conditions regarding cumulative play time, etc. When the criteria indicated by the user information criteria data are met, the loyalty level indicated by the loyalty criteria data 582 is adopted.

[0179] User registration data 600 is prepared for each user who has completed the prescribed user registration procedure and stores various information (user information) associated with that user.

[0180] One user registration data 600 includes, for example, a user account 602, privacy information 604, avatar setting data 606, billing history data 608, play history data 610, and item information 612, as shown in Figure 13. It also includes satisfaction survey result data 614, satisfaction statistics data 616, and usage information 618. Of course, other information may be included as appropriate.

[0181] The billing history data 608 is data that is automatically updated each time a charge is made, and stores information such as whether or not there is a subscription contract, the total amount charged, and the frequency of charges.

[0182] Play history data 610 is created for each play of content and stores the play date and time, content ID of the played content, a list of content element IDs included in the played content, play progress, player level, cumulative play time, etc. The content element ID list in play history data 610 consists of content elements previously generated for that user and serves as the basis for tracing the content elements that the user used to play the content.

[0183] Item information 612 is created for each item acquired and used by the user, and stores the item ID, a purchase flag (set if the item is purchased), the number of items owned, and the number of items consumed.

[0184] The satisfaction survey results data 614 is created for each satisfaction survey and stores the date and time of implementation, the ID of the content element being evaluated, the application generation level at which the content element was generated, and the survey results in an associated manner.

[0185] Usage information 618 is created and updated for each content element ID generated for the user, and for each content ID used by the user, and stores the content element ID, the number of uses, and the cumulative usage time.

[0186] Returning to Figure 10, the generation processing status monitoring data 680 is created separately for each generation AI system 1200 and stores processing load information, communication quality information, compensation information, etc., obtained from the respective generation AI system 1200. The processing load information and communication quality information may be the latest values, or they may be the average or median values ​​over the most recent period. The communication quality information may be obtained using the communication used to acquire the processing load information.

[0187] Play Data 700 is prepared for each content provision, and from the user's perspective, for each play session, and stores various data related to that play session. One play data set 700 includes, for example, a player account 702, a play target content ID 704, content setting data 710, play performance data 716, and generation instruction control data 720, as shown in Figure 14. Of course, other information may be included as appropriate.

[0188] The playable content ID 704 stores the ID of one of the content items determined according to the input on the category selection screen W2, genre selection screen W4, and type selection screen W6 (see Figure 2).

[0189] The content setting data 710 is data on the specific content of the content to be provided, and includes fixed content element data 712 and variable content element data 714. The content setting data 710 is copied and initialized from the content initial setting data 510 (see Figure 4) of the content indicated by the play target content ID 704. Then, among the variable content element data 714, the content elements selected according to the applied generation level are updated with newly generated data by the generation AI system 1200 (see Figure 5).

[0190] The generation instruction control data 720 stores various data related to the determination of generation instruction information for newly generated content elements. For example, the generation instruction control data 720 includes content element ID 721, applicable generation level 722, estimated satisfaction level 724, likability level 726, and applicable generation instruction information 728. The applicable generation instruction information 728 is the generation instruction information ultimately given to the generation AI system 1200, and is modified by a modification process after the primary generation instruction information is copied and initialized.

[0191] Returning to Figure 10, the past generated content element data 760 stores various information related to the products previously generated by the generation AI system 1200. The past generated content element data 760 can be said to be, in substance, the past generated information 761.

[0192] The past generated content element data 760 is created for each type of generated content element. The past generated content element data 760 includes, for example, a content element ID 762, a classification tag 763, a player account at the time of generation 764, content element body data 765, system status information at the time of generation 766, applied generation level 767 at the time of generation, and applied generation instruction information 768 at the time of generation, as shown in Figure 15.

[0193] The generation-time system status information 766 is information indicating various statuses of the content provision system 1000 at the time of generation of the content element. The generation-time system status information 766 includes, for example, generation processing status information (generation processing status information of the generation AI system 1200, communication quality information, etc.) and the number of users logged into the content provision system 1000.

[0194] The content element body data 765, the generation application generation level 767, and the generation application generation instruction information 768 correspond to information indicating the content of the generated content element, particularly information indicating its quality and richness 769. Since the generation system status information 766 is the basis for determining the generation application generation level 767, the generation system status information 766 can also be said to indirectly correspond to information indicating the content of the generated content element 769.

[0195] Figures 16 and 17 are flowcharts illustrating the processing flow related to the provision of user-facing content executed by the server system 1100. It is assumed that the user, who will be the player, is registered and logged in. Furthermore, the server system 1100 has begun acquiring generation processing status information regarding the connectable generation AI system 1200.

[0196] The server system 1100 accepts the selection of content to be played (step S10; see Figure 2). This determines the content ID 704 to be played (see Figure 14).

[0197] Next, the server system 1100 refers to the latest generation processing status monitoring data 680 and the applicable generation level determination criterion data 530 (see Figure 10) and determines the applicable generation level 722 (see Figure 14) according to the generation processing status of the generation AI system 1200 (step S12).

[0198] Next, the server system 1100 performs user information analysis to determine the estimated satisfaction level and level of loyalty (step S14). Specifically, the server system 1100 estimates the estimated satisfaction level 724 based on the estimated satisfaction level determination criteria data 580 (see Figure 10) (see Figure 14), and determines the level of loyalty 726 (see Figure 14) based on the level of loyalty determination criteria data 582 (see Figure 10).

[0199] Next, the server system 1100 determines whether the user is a special user (see Figure 8) (step S20).

[0200] If the user is not a special user (NO in step S20), the server system 1100 sets the previously configured application generation level 722 to "STD" (step S22).

[0201] Next, the server system 1100 refers to the initial content data 510 (see Figure 4) of the content indicated by the playable content ID 704. Then, from among the variable content elements, it determines the number and types of content elements to be generated according to the applied generation level 722 (step S24). Specifically, if the applied generation level 722 is "STD", all variable content will be included in the generation target. If it is "LOW1", a predetermined first percentage (e.g., 80%) of the total number of variable content elements will be randomly selected for generation. If it is "LOW2", a predetermined second percentage (e.g., 50%) of the total number of variable content elements will be randomly selected for generation.

[0202] Next, the server system 1100 executes loop A for each content element to be generated (steps S40 to S50). Specifically, in loop A, the primary generation instruction information definition data 526 (see Figure 4) of the content element to be processed is copied to the applied generation instruction information 728 (see Figure 14) (step S42). Then, if the applied generation level 722 is "LOW1" or "LOW2" (YES in step S44), the modified pattern definition data 540 (see Figure 12) that satisfies the pattern application requirement 550 is referenced. Then, according to the modified content data 560, the primary generation instruction information copied to the applied generation instruction information 728 is modified (step S46).

[0203] Next, the server system 1100 provides the application generation instruction information 728 to the generation AI system 1200 to obtain a new content element to be processed (step S48). The newly acquired content elements to be processed are overwritten in the variable content element data 714 of the content setting data 710 (see Figure 14), and the previously generated content element data 760 (see Figure 16) is generated.

[0204] When Loop A is executed for all content elements to be generated, content configuration data 710 will be prepared in which at least some of the initial settings have been replaced with newly generated content elements.

[0205] Moving to Figure 17, the server system 1100 begins controlling the provision of content (step S60). If the content is a game, game progress control will begin. From this point onward, various user information (information that can be traced from the user account of the player, and linked information) is updated as needed according to game play. For example, purchases and consumption of paid items during content provision, and other charges are reflected in the charge history data 608 and item information 612 in real time.

[0206] When the content being provided is finished (step S62), the server system 1100 updates the play history data 610 and usage information 618 (see Figure 13) (step S64), conducts a satisfaction survey (step S70), and then terminates the series of processes.

[0207] In summary, according to this embodiment, when constructing a system that provides content including content elements generated by the generation unit to multiple users, it becomes possible to reduce the costs associated with using the generation unit.

[0208] User information is updated in real time as the game progresses, so different content elements are generated in response to changes in user information, enabling the provision of diverse content.

[0209] [Variation] Although examples of embodiments to which the present invention is applied have been described above, the forms to which the present invention can be applied are not limited to the above forms, and components can be added, omitted, or modified as appropriate.

[0210] (Variation 1) The content provision system 1000 is not limited to a client-server system configuration. The content provision system 1000 may also be a standalone user terminal 1500. In this case, the user terminal 1500 executes a predetermined program to realize the functions corresponding to the server processing unit 200s (see Figure 11) in the above embodiment.

[0211] (Variation 2) Alternatively, for example, the content provision system 1000 may be implemented using a P2P (Peer to Peer) architecture with multiple user terminals 1500. In this case, programs and data corresponding to the functional division are stored in the user terminals 1500, and the functions corresponding to the server processing unit 200s in the above embodiment are distributed and implemented by the user terminals 1500, which act as P2P nodes. The same effects as in the above embodiment can be obtained with this configuration as well.

[0212] (Variation 3) As shown in Figure 18, the content provision system 1000B may be configured to include a generation AI system 1200B. The generation AI system 1200B corresponds to the generation AI system 1200 in the above embodiment. In this configuration, since the generation AI system 1200B is configured internally rather than as an external system, the creator of the content provision system 1000B can construct the generation AI system 1200B.

[0213] In a configuration where the server system 1100B has a generation AI system 1200B, the generation AI system 1200 may be configured to have multiple element-specific generation AIs 1204 for each type of content element. Furthermore, the generation instruction information may include instruction destination information that instructs which element-specific generation AI 1204 to perform the generation, so that the generation is performed by the element-specific generation AI 1204 indicated by the instruction destination information.

[0214] When the generation AI system 1200B is constructed in this manner, the server system 1100B may acquire generation processing status information for each element-specific generation AI 1204. Furthermore, the server system 1100B may include information about the target of the instruction to direct the generation AI with the lightest and least busy processing status among the element-specific generation AIs 1204 capable of generating the content elements to be generated, and determine this destination in the generation instruction information. In this case, it becomes possible to more efficiently suppress the "generation waiting time".

[0215] (Modification #4) Furthermore, based on the third modification, the generative AI system 1200B may be constructed as a deep learning-trained AI model having a function to self-determine its own generation level and a function to determine whether a user is a special user. Specifically, the generative AI system 1200B is trained on training data that correctly determines whether a user of various user information is a special user or not, using various combinations of user information.

[0216] Furthermore, a dedicated AI with a special user determination function may be provided. That is, the generation AI system 1200B may have a configuration comprising an element-specific generation AI 1204 and a special user determination AI.

[0217] When the generation AI system 1200B is constructed in this manner, the functions for acquiring generation processing status information, determining the generation level, and determining special users may be omitted from the server system 1100B.

[0218] In this case, the server system 1100B determines the generation instruction information as shown in Figure 19 and provides it to the generation AI system 1200B. That is, the generation instruction information is in a format that includes the generation target, generation conditions including the setting values ​​of the setup parameters, and supplementary information. In the change process, the server system 1100B sets the generation conditions to the setting values ​​(initial settings) of the setup parameters set in the primary generation instruction information definition data 526 (see Figure 4). Furthermore, the supplementary information may include various user information necessary for determining special users, and a reuse data ID list 562 (see Figure 12).

[0219] (Variation 5) Furthermore, natural language may be made available in the content element generation instruction information, allowing users to specify in their own words what kind of content elements should be generated.

[0220] Specifically, the generative AI system 1200 will be constructed as a deeply trained AI model capable of receiving generation instructions in natural language.

[0221] When the server system 1100 detects a generation instruction request operation entered in natural language on the user terminal 1500, it displays the generation instruction setting screen W10, which was entered in natural language, on the user terminal 1500, for example, as shown in Figure 20. The text entered on the setting screen may then be included in the generation instruction information and provided to the generation AI system 1200.

[0222] (Variation #6) As shown in Figure 21, based on the above embodiment, a configuration may be added to modify the process so that content elements that satisfy given rich content conditions are generated for preferred users. In the example in Figure 21, user 2a is a special user, user 2b is a non-special user, and user 2c is a preferred user.

[0223] "Preferred users" are the top-ranking users among the special users in the above embodiment. In other words, users who meet one or more of the following criteria are designated as preferred users: "users with an estimated satisfaction level even lower than special users," "users with a predetermined high ranking in the number of purchases or total purchase amount," "users with a predetermined high ranking in the number of items owned / consumed," and "users with a predetermined high ranking in usage." However, it is important to set the special user criteria and low satisfaction criteria so that the proportion of logged-in users is relatively small: a very small number of preferred users, a small number of special users, and a large majority of non-special users.

[0224] In the configuration change process, the server system 1100 modifies the primary generation instruction information so that the newly generated content elements are of higher quality and richer in content than the content generated by simply providing the primary generation instruction information as is. In other words, the server system 1100 modifies the primary generation instruction information so that the content elements satisfy the given rich content conditions.

[0225] Specifically, for example, if the content element is "game scenario," the setup parameter value determines the number of game stages that make up the scenario. As in the embodiment described above, when the application generation level is "LOW1," the setup parameter value is set to "4" for special users (as per the initial setting), "3" for non-special users, and "5" for preferred users.

[0226] (Variation 7) In the above embodiment, the content elements to be generated were determined in the process of the user, who will be the player, selecting the content to be played before starting to play the content, but this is not limited to this. For example, the present invention can also be applied when new content elements are generated during content play.

[0227] In that case, by including a sub-condition (specifically, a player level condition 556) regarding the information displayed according to gameplay in the sub-condition of the pattern application requirement 550 of the change pattern definition data 540 (see Figure 12), a variety of content gameplay (gameplay) can be realized.

[0228] (Variation #8) In the above embodiment, the change process was performed on non-special users, but it is also possible to configure the system to perform the change process on special users instead.

[0229] For example, as shown in Figure 22, the YES / NO branch in step S20 (see Figure 16) of the above embodiment is reversed (step S20B). In addition, in step S46B, which corresponds to step S46 of the above embodiment, the process is modified to reduce the generation cost but increase the rarity of the content elements to be generated. Note that the flowchart following Figure 22 is the same as in Figure 17 and is therefore omitted.

[0230] Specifically, let's assume, for example, that we use a generation AI to generate content elements such as still images of a character that will appear as cutscenes during gameplay. This character is a handsome man popular with women, and is designed to maintain a cool demeanor throughout the game.

[0231] For non-special users, step S20B is NO → step S22, so it proceeds to NO in step S44 and step S46B is skipped. In other words, the generation instruction information for generating a still image for a non-special user is the primary generation instruction information set to generate an image of a cool, normal demeanor.

[0232] On the other hand, for special users, step S44 is YES and step S46B is executed. In step S46B, the server system 1100 modifies the generation instruction information for generating still images for special users, reducing the generation cost by making the image size smaller. In other words, it modifies the information to generate content elements that satisfy the poor content conditions. However, it modifies the generation instruction information so that the generation AI draws rare scenes that are not shown in the game (for example, a carefree laugh). The rarity is set higher for the applied generation level "LOW2" than for "LOW1".

[0233] If the game employs realistic character designs, then in step S46B, the server system 1100 may be modified to draw simple character images with a limited color palette and a comic-book style of stylization.

[0234] In other words, this modification process is designed to generate rare content elements for specific users, although the generation cost is relatively low.

[0235] At first glance, this modification might seem to lower user satisfaction by providing low-cost content elements that meet the criteria for poor content to special users. However, special users (especially those with a high level of loyalty) prefer to collect high-rarity content elements (e.g., still images, inserted movies, events, items). Therefore, in reality, high rarity can actually increase satisfaction, thus achieving the same effect as the above embodiment.

[0236] Furthermore, such modifications may be added to the above embodiments. For example, a generation level of "LOW0.5" may be provided between "STD" and "LOW1". After step S14, if the applicable generation level is "LOW1" or "LOW2", the process proceeds to step S20 of the above embodiment (see Figure 16), and if it is "LOW0.5", the process proceeds to step S20B of the modified example, and then to step S46B. [Explanation of symbols]

[0237] 2…User 200s... Server Processing Unit 202...User Information Management Department 208...Generation Processing Status Information Acquisition Control Unit 210...Content Selection Reception Control Unit 220...Generation instruction control unit 222...Applicable generation level determination unit 224…Analysis Department 226...Special User Determination Unit 230...Content Provision Control Unit 232...Usage Status Memory and Control Unit 234... Questionnaire Control Unit 501…Server program 510...Content Initial Setup Data 524… Variable content element initial settings data 526... Primary generation instruction information definition data 530…Applicable generation level determination criteria data 562…Reusable Data ID List 580…Estimated satisfaction rating criteria data 582... Affection Level Judgment Criteria Data 600...User registration data 604...Privacy Information 608…Charging history data 610... Play history data 612... Item Information 614…Satisfaction survey results data 616…Satisfaction Statistics Data 618…Usage Information 680…Data monitoring the generation process status 700... Play data 704... Playable content ID 710…Content settings data 714… Variable content element data 720...Generation instruction control data 721... Content element ID 722...Applicable generation level 724…Estimated satisfaction level 726…Love level 728…Apply generation instruction information 760…Previously generated content element data 766... ​​System status information at the time of generation 767... Generation level applied at generation 768...Generation instruction information applied at generation time 1000... Content delivery system 1100... Server System 1200...Generating AI system 1202...Generation section 1204…Element-based generation AI 1500... User terminal

Claims

1. A content provision system that provides a user with a given content including the content elements generated by a generation unit that generates content elements, A generation instruction control means for determining generation instruction information which is an instruction for the generation unit to generate the content elements, wherein when a given change condition is met, the generation instruction control means performs a change process to change the generation instruction information for when the change condition is not met (hereinafter referred to as "primary generation instruction information") using the user information of the user to determine the generation instruction information, Content provision control means that acquires content elements by providing the generation instruction information determined by the generation instruction control means to the generation unit, and controls the provision of the content including the acquired content elements to the user, A content delivery system equipped with the following features.

2. The generation instruction control means performs the modification process to change at least one of the types, number of types, and quality of the content elements to be generated by the generation unit. The content provision system according to claim 1.

3. The user information includes privacy information such as age information and / or gender information. The generation instruction control means performs the modification process using the privacy information. The content provision system according to claim 1.

4. The generation instruction control means is Analysis means for analyzing the user information, The analysis means performs the modification process based on the analysis results. The content provision system according to claim 1.

5. The user information includes past generation information, which is information from when the content element was generated for that user. The analysis means analyzes the previously generated information. The content provision system according to claim 4.

6. The aforementioned past generation information includes information indicating the content of the generated content element, The aforementioned analysis means analyzes information indicating the aforementioned content. The content provision system according to claim 5.

7. The aforementioned past generation information includes system status information at the time the content element was generated. The analysis means analyzes the system status information. The content provision system according to claim 5.

8. The analysis means estimates the user's estimated satisfaction with the content elements to be generated based on the previously generated information. The generation instruction control means performs the modification process using the estimated satisfaction level. The content provision system according to claim 5.

9. The generation instruction control means performs the modification process to modify the primary generation instruction information so that, when the estimated satisfaction level satisfies a given high satisfaction condition, the content element becomes one that satisfies a given poor content condition compared to the content element that would be generated by providing the primary generation instruction information to the generation unit. The content provision system according to claim 8.

10. The generation instruction control means performs the modification process to modify the primary generation instruction information so that, when the estimated satisfaction level satisfies a given low satisfaction condition, the content element becomes one that satisfies a given rich content condition compared to the content element that would be generated by providing the primary generation instruction information to the generation unit. The content provision system according to claim 8.

11. The aforementioned content is a game, The analysis means analyzes the user's proficiency and / or fondness for the game based on the user information, The generation instruction control means performs the modification process based on the analysis results of the analysis means. The content provision system according to claim 4.

12. The user information includes one of the following: the user's billing information, the user's item information, and the usage information of the content elements generated for that user. The content provision system according to claim 1.

13. The generation instruction control means performs the modification process based on whether or not the special user conditions based on the user information are met. The content provision system according to claim 1.

14. The aforementioned user information includes the user's billing information, The generation instruction control means determines whether or not the special user conditions are met based on the billing information. The content provision system according to claim 13.

15. The aforementioned user information includes the user's item information, The generation instruction control means determines whether or not the special user conditions are met based on the item information. The content provision system according to claim 13.

16. The generation instruction control means performs the modification process to modify the primary generation instruction information if the special user conditions are not met, so that the content element becomes one that satisfies a given poor content condition compared to the content element that would be generated by providing the primary generation instruction information to the generation unit. The content provision system according to claim 13.

17. The generation instruction control means performs the modification process to modify the primary generation instruction information so that, when the special user conditions are met, the content element becomes one that satisfies a given rich content condition compared to the content element that is generated by providing the primary generation instruction information to the generation unit. The content provision system according to claim 13.

18. The generation instruction control means performs the modification process based on the generation processing status of the generation unit. A content provision system according to any one of claims 1 to 17.

19. The generation processing status includes information related to the processing load of the generation unit. The content provision system according to claim 18.

20. The generation processing status includes information related to the time required from the time the generation instruction information is given to the generation unit until the content element is acquired. The content provision system according to claim 18.

21. The generation unit is an external system that requires payment for the generation of the content elements. The aforementioned generation processing status includes the information of the consideration, The content provision system according to claim 18.

22. The generation unit has multiple types of generation units corresponding to the types and / or quality of the content elements that can be generated. The generation instruction control means determines which of the multiple types of generation units to instruct to generate the product by including instruction destination information in the generation instruction information. The content provision system according to claim 1.

23. The user information includes past generation information, which is information from when the content element was generated for that user. The generation instruction control means modifies the generation instruction information based on the past generation information to reuse content elements that have been generated in the past. The content provision system according to claim 1.

24. The aforementioned content is a game, The user information is information that is updated as needed in response to gameplay. The content provision system according to claim 1, wherein different content elements are generated in response to changes in the user information, and the content is provided to the user to realize diverse game progression.

25. A program for causing a computer system to execute control to provide a given content, including the content elements generated by a content generation unit, to a user, A generation instruction control means for determining generation instruction information, which is an instruction for the generation unit to generate the content elements, wherein, when a given change condition is met, the generation instruction control means performs a change process to change the generation instruction information for when the change condition is not met (hereinafter referred to as "primary generation instruction information") using the user information of the user to determine the generation instruction information, Content provision control means that acquires content elements by providing the generation instruction information determined by the generation instruction control means to the generation unit, and controls the provision of the content including the acquired content elements to the user. A program for causing the aforementioned computer system to function.

Images