Information processing device and information processing method

Abstract

To easily and accurately estimate a user's level of interest in mutually different pieces of provided content.SOLUTION: An information processing device 10 comprises: a setting unit 12 which sets a plurality of mutually different pieces of provided content and provision conditions for the plurality of provided content in a virtual space where provided content including at least products or services is offered; a behavior estimation unit 14 which estimates behavior of a virtual user in the virtual space on the basis of a plurality of pieces of provided content and provision conditions set by the setting unit; and an interest level estimation unit 17 which estimates a real-world user's level of interest in each of the plurality of pieces of provided content on the basis of behavior of the virtual user predicted by the behavior estimation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device and an information processing method. [Background technology]

[0002] The following Patent Document 1 describes a method for supporting the formulation of new product launch plans that estimates the sales volume of a new product and the operating profits, etc. of the new product based on the evaluation values ​​that consumers have for the level of each attribute of the product, the level of each attribute of the new product, and the level of each attribute of competing products. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2004-206321 Summary of the Invention [Problem to be solved by the invention]

[0004] In the technology described in Patent Document 1, when the number of provided contents is estimated using the evaluation values ​​held by users, the evaluation values ​​depend on the number and trends of users surveyed for the evaluation values, and therefore it may not be possible to appropriately estimate the level of interest of users in the provided contents. Furthermore, in determining whether to provide the provided contents, in order to consider the number of provided contents, it has been necessary to understand how users will behave when different provided contents are provided under the same conditions.

[0005] Therefore, an object of the present disclosure is to easily and appropriately estimate a user's level of interest in different provided content. [Means for solving the problem]

[0006] The information processing device according to the present disclosure includes a setting unit that sets a plurality of different offerings and provision conditions for the plurality of offerings in a virtual space that provides offerings including at least one of goods and services, a behavior estimation unit that estimates the behavior of a virtual user in the virtual space based on the plurality of offerings and provision conditions set by the setting unit, and an interest level estimation unit that estimates the level of interest of a real-world user in each of the plurality of offerings based on the behavior of the virtual user predicted by the behavior estimation unit. [Effects of the Invention]

[0007] According to the present disclosure, it is possible to easily and appropriately estimate the user's level of interest in different provided content. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 2 is a functional block diagram of an information processing device and peripheral devices. [Figure 2] 10 is a diagram for explaining a process of estimating the behavior of a virtual user in response to target provided content in a virtual space. FIG. [Figure 3] FIG. 2 is a flowchart showing a process executed by the information processing device. [Figure 4] FIG. 2 is a diagram illustrating an example of a hardware configuration of an information processing device. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, an embodiment of an information processing device and an information processing method according to the present disclosure will be described with reference to the drawings.

[0010] FIG. 1 shows the configuration of an information processing system 1 including an information processing device 10 and its related devices. As shown in FIG. 1, the information processing system 1 includes the information processing device 10, a provision content database 3, a provision condition database 5, an attribute information database 7, and a virtual space control server 30. The information processing device 10 estimates the level of interest of real-world users in each of a plurality of provision contents based on information obtained from the provision content database 3, the provision condition database 5, the attribute information database 7, the virtual space control server 30, etc. Hereinafter, real-world users may be referred to as "real users."

[0011] The offerings include at least one of goods and services that can be provided to real users. A product is an item that has a physical form. A service is a right related to an experience or activity that does not have a physical form (for example, the right to participate in a specific event). The offerings may be paid or free of charge. The offerings may be virtual goods that can be used in the virtual space or services that the user enjoys in the virtual space, or real goods that are delivered to the user in the real world or services that the user enjoys in the real world. The offerings in this embodiment refer to paid goods that can be sold at convenience stores, including food, drinks, miscellaneous goods, etc.

[0012] The real user's interest in the content to be offered indicates the degree to which the real user wishes to receive the content to be offered. The interest is, for example, an index that quantifies the real user's willingness to purchase. Receiving the content to be offered includes a real user or a virtual user obtaining the content to be offered through an application for the content to be offered. In this embodiment, receiving the content to be offered refers to a real user or a virtual user paying money for a product and obtaining the product.

[0013] A virtual user is a user who is configured with a profile that has specific attribute information and is able to act in a virtual space. The virtual user can perform actions in the virtual space in response to the provided content. Details will be described later.

[0014] The provision locations in the virtual space include virtual stores and virtual facilities formed in the virtual space. The provision locations in the virtual space include locations where the provision content is placed, locations where the provision content can be received, etc. The locations where the provision content is placed include facilities such as stores and facilities where the provision content is provided, shelves or corners within the facilities, or websites.

[0015] The information processing device 10 is used to estimate the behavior of a virtual user in response to target content provided in a virtual space provided by the virtual space control server 30. Examples of the information processing device 10 include a desktop PC, a laptop PC, a smartphone, a tablet terminal, and a wearable terminal (e.g., a head-mounted display, smart glasses, etc.).

[0016] The information processing device 10 includes a setting unit 12 that sets a plurality of different provided contents and provision conditions for the plurality of provided contents in a virtual space where the provided contents are provided, a behavior estimation unit 14 that estimates the behavior of a virtual user in the virtual space based on the plurality of provided contents and provision conditions set by the setting unit 12, and an interest level estimation unit 17 that estimates the level of interest of a real-world user in each of the plurality of provided contents based on the behavior of the virtual user estimated by the behavior estimation unit 14.

[0017] The information processing device 10 further includes an acquisition unit 11 that acquires a plurality of content to be provided and conditions for providing the plurality of content to be provided, a profile generation unit 13 that generates a profile of a virtual user, an output unit 15 that outputs information on each functional unit, and a feature calculation unit 16 that calculates a feature for each of the plurality of content to be provided based on the behavior of the virtual user estimated by the behavior estimation unit 14. The output unit 15 includes, for example, at least one of a display and a storage device.

[0018] The acquisition unit 11 acquires, as the plurality of provision contents, a plurality of provision contents that differ from each other in at least one of type and quantity per set. Each of the plurality of provision contents may be a provision content that can be provided to a user in the real world, or may be a provision content that is different from existing provision content that is planned to be provided to a user in the real world in the future. The provision content that is the target of this embodiment is a provision content that has not been provided to a real user. At least one of the plurality of provision contents may include a provision content in the prototype stage, a provision content before release, etc.

[0019] The acquisition unit 11 acquires a plurality of content to be offered from the content to be offered database 3. The content to be offered database 3 stores content to be offered information regarding the content to be offered. The content to be offered information includes, for example, the name, type, and quantity of the content to be offered. The content to be offered information includes, for example, information input by a provider (store clerk) who provides the content to be offered or is considering providing the content to be offered. The content to be offered database 3 may store, for example, content to be offered information acquired by a POS (Point of Sale) system (point of sale information management system). The content to be offered database 3 may store, for example, content to be offered information output from a terminal owned by a content to be offered provider via a communication network (not shown).

[0020] The content information may also include property information. The property information includes at least one of detailed information about the content, information about the target of the content, and information about the environment in which the content is provided. The detailed information includes the name of the content, the quantity included in each content provided, the nutritional content of the content, the contents written on the package, the concept of the content, etc.

[0021] The target of the content to be provided is a user to whom the provider who is planning to provide the content to be provided intends to provide the content to. Information about the target of the content to be provided includes gender, age, affiliated organization, lifestyle, whether the target is accompanied or not, etc. Information about the environment in which the content to be provided includes the date, time period, weather, location (an example of the location where the content to be provided) when the content to be provided is provided, the congestion status of the location, and the manner in which the content to be provided is provided.

[0022] For example, a case will be described where the content to be offered is a single energy drink. The property information of the energy drink includes, for example, detailed information about the package and information about the concept. The information about the package, for example, lists several types of vitamin ingredients and includes content encouraging nutritional supplementation. The information about the concept includes, for example, content aiming to recover from fatigue. The property information of the energy drink includes, for example, information about the target of the content to be offered, including content that the target is an adult male who works in an office and has poor daytime concentration. The property information of the energy drink includes, for example, information about the environment in which the content to be offered is to be offered, including content that the day is a Wednesday morning, the humidity is 70%, the weather is sunny, the energy drink is sold in a relatively empty convenience store, and the energy drink is displayed with a pop-up by the store clerk.

[0023] The acquisition unit 11 also acquires the provision conditions for the plurality of content offerings from the provision condition database 5. The provision condition database 5 stores provision conditions that can be set for each of the plurality of content offerings. The acquisition unit 11 acquires the provision conditions that can be set for each of the plurality of content offerings from the provision condition database 5. The provision conditions include at least one of the date, time period, weather, location, congestion status of the location, provision mode of the content offering, and whether or not the virtual user is accompanied. The above-mentioned date includes the day of the week, the target sales period, etc. The above-mentioned time period includes morning, noon, evening, late night, etc. The above-mentioned location of the content offering includes the type of facility and store, the location in the store (corner location), the top, center, bottom of the shelf installed in the corner, etc.

[0024] Furthermore, the acquisition unit 11 acquires user information from the attribute information database 7. The attribute information database 7 stores attribute information that can be included in the profile of a virtual user in a virtual space. The attribute information includes information on at least one of the virtual user's gender, age, family structure, organization, lifestyle, personality, and preferences.

[0025] The setting unit 12 sets, in a virtual space where the content to be provided is provided, a plurality of different content to be provided acquired by the acquisition unit 11, and the provision conditions of the plurality of content to be provided. The setting unit 12 sets at least one provision condition of the plurality of content to be provided in cooperation with a virtual space control server 30 that controls the entire metaverse space. For example, the setting unit 12 sets a provision condition that can be set commonly to a plurality of content to be provided in the virtual space. The setting unit 12 determines that a plurality of provision conditions can be set according to the provision conditions acquired from the provision condition database 5. The setting unit 12 can set each provision condition in the virtual space.

[0026] When the provision conditions are set, for example, the provision contents are arranged in the virtual space under the set provision conditions. The setting unit 12 outputs the generated provision conditions to the output unit 15. The output unit 15 stores the provision conditions. Note that the setting unit 12 does not have to generate the provision conditions. In this case, the setting unit 12 may set the provision conditions acquired by the acquisition unit 11 from the provision condition database 5 as the provision conditions for the multiple provision contents in the virtual space.

[0027] The setting unit 12 may change the provision condition selected from the generated plurality of provision conditions in a plurality of ways. In this case, the behavior estimation unit 14 described below estimates the behavior of the virtual user for each provision condition. Changing the provision condition includes changing the environment of the virtual space (metaverse space).

[0028] The profile generation unit 13 generates a profile of the virtual user based on the attribute information. Specifically, the profile generation unit 13 has a built-in Large Language Model (hereinafter referred to as "LLM") that has learned a huge amount of linguistic information in advance and includes attribute information of real users. The profile generation unit 13 inputs a query statement including the attribute information into the LLM, thereby acquiring a profile of the virtual user output from the LLM. The amount and type of attribute information input in the query statement may vary as appropriate. Note that it is not essential for the profile generation unit 13 to have an LLM built in; the profile generation unit 13 may obtain the above-mentioned profile of the virtual user by, for example, making a query to an LLM in an external device (such as a cloud server).

[0029] The profile generation unit 13 generates a profile for at least one virtual user. The profile generation unit 13 may generate profiles for a plurality of virtual users, for example. In this case, for example, the profile generation unit 13 generates profiles for a plurality of mutually different virtual users based on a plurality of mutually different attribute information. The profile generation unit 13 outputs the generated profile to the output unit 15, and the output unit 15 stores the profile.

[0030] The behavior estimation unit 14 estimates the behavior of a virtual user having a profile generated by the profile generation unit 13 in the virtual space. The behavior estimation unit 14 estimates the behavior of the virtual user with respect to the multiple offerings set by the setting unit 12. For example, the behavior estimation unit 14 cooperates with a virtual space control server 30 that controls the entire metaverse space, and causes the output unit 15 to display the metaverse space as shown in FIG. 2. The behavior estimation unit 14 establishes a virtual facility (in one example, a virtual store 31) that provides the offerings in the metaverse space, and acquires information regarding the behavior of a virtual user having the profile with respect to multiple products, which are the multiple offerings set by the setting unit 12. The behavior estimation unit 14 places the offerings in the virtual space under the offering conditions set by the setting unit 12.

[0031] The virtual user's behavior with respect to multiple offerings refers to at least one of the following actions: passing in front of a location where at least one offering is provided; looking at at least one offering; considering whether to accept (purchase) at least one offering; picking up and checking the at least one offering if looking at it; returning the at least one offering to its original position if picking it up; and deciding to accept (purchase) the at least one offering if picking it up.

[0032] A virtual user receiving content includes a virtual user in a virtual space acquiring the content through a request for the provision of the content in the virtual space. In this embodiment, a virtual user receiving content refers to a virtual user paying money for a product in a virtual store and acquiring the product. Hereinafter, a virtual user in a virtual space may be referred to as an avatar.

[0033] An example of the behavior of an avatar in response to a target offering, as estimated by the behavior estimation unit 14, will be described below with reference to FIG. 2. The behavior estimation unit 14, for example, cooperates with the virtual space control server 30 to cause an avatar 60 having the above profile to enter a virtual store 31 in the metaverse space. The behavior estimation unit 14 estimates (simulates) the behavior of the avatar that has entered the virtual store 31 in response to a plurality of offerings. The virtual store 31 is an example of a facility in the virtual space that is assumed to provide a plurality of offerings in the metaverse space. The virtual store 31 is, for example, a convenience store.

[0034] The plurality of offering contents are acquired by the acquisition unit 11. The behavior estimation unit 14 is arranged in the virtual store 31 according to the offering conditions generated (acquired) for the plurality of offering contents.

[0035] The avatar 60 that enters the virtual store 31 behaves within the virtual store 31 based on the profile generated by the profile generation unit 13. The avatar 60 behaves in relation to a plurality of offerings within the virtual store 31 according to the attribute information included in the profile. The behavior estimation unit 14 outputs information related to the behavior of the avatar 60 to the output unit 15. The output unit 15 stores the information related to the behavior of the avatar 60.

[0036] The feature amount calculation unit 16 calculates a feature amount for each of the plurality of content offerings. Specifically, the feature amount calculation unit 16 calculates the feature amount based on information on the behavior of the avatar 60 estimated by the behavior estimation unit 14. The feature amount includes at least one value of a value related to the provision of the content offering and at least one value related to the purchase of the content offering. The value related to the provision of the content offering includes the quantity related to the provision of at least one of the plurality of content offerings and the time related to the provision of at least one of the content offerings.

[0037] The quantity of the provided content includes at least one of the number of types of provided content that the avatar 60 visually recognizes from among the multiple provided content, the number of types of provided content that the avatar 60 picks up from among the multiple provided content, and the quantity of the same provided content that the avatar 60 picks up.

[0038] The time related to the provision of the offered content includes at least one of the time periods: the time when the avatar 60 looks at the offered content, the time from when the avatar 60 looks at the offered content until when it picks it up, the time from when the avatar 60 picks up the offered content until when it returns the offered content to its original position, and the time from when the avatar 60 picks up the offered content until when it purchases the offered content.

[0039] The value related to the purchase of the content provided includes the number of types of content provided to the avatar 60 from among the multiple content provided, and at least one value of the sales amount, profit, and quantity of each of the content provided to the avatar 60. The feature amount calculation unit 16 may obtain in advance values ​​such as the unit price and cost of the content provided, which are necessary for calculating the sales amount and profit, from the content provided database 3 via the acquisition unit 11. The feature amount calculation unit 16 outputs the calculated feature amount to the output unit 15. The output unit 15 stores the feature amount. The output unit 15 may display the feature amount.

[0040] The interest level estimation unit 17 estimates the real user's level of interest in each of the multiple pieces of provided content based on the behavior of the avatar 60 estimated by the behavior estimation unit 14. The avatar 60 having a profile generated by the profile generation unit 13 is considered to reflect attribute information that is the same as or similar to the profile of at least one real user. Therefore, a correlation may be established between the behavior of the avatar 60 with respect to the multiple pieces of provided content and the behavior of the real user with respect to the multiple pieces of provided content. Therefore, the interest level estimation unit 17 can estimate the real user's level of interest in each of the multiple pieces of provided content based on the behavior of the avatar 60 with respect to the multiple pieces of provided content.

[0041] The interest level estimation unit 17 may obtain an estimated result of the real user's level of interest in each of the multiple pieces of provided content output from the large-scale language model by inputting a query including multiple pieces of provided content and conditions for providing the multiple pieces of provided content into a large-scale language model including information on the real user's level of interest in the provided content. The information on the real user's level of interest in the provided content includes information on characteristics of behavior exhibited by the real user with respect to the provided content and a correspondence between the characteristics and the level of interest. The interest level estimation unit 17 may obtain an estimated result of the real user's level of interest in each of the multiple pieces of provided content output from the large-scale language model by inputting a query into a large-scale language model including information on the real user's level of interest in the provided content and information on the behavior of the avatar 60 estimated by the behavior estimation unit 14.

[0042] Furthermore, the interest level estimation unit 17 may estimate the real user's level of interest in each of the plurality of provided contents based on the feature amount for each provided content calculated by the feature amount calculation unit 16. The interest level estimation unit 17 may vary the level of interest depending on, for example, whether the provided content is viewed, whether the provided content is picked up, or whether the provided content is purchased. For example, if the provided content is purchased, the higher the sales, profit, and quantity of the provided content, the more the avatar 60 is interested in and has purchased it, and therefore the interest level estimation unit 17 estimates that the real user's level of interest is also high.

[0043] For example, if the offered content is not purchased and is picked up, the longer the time between when the avatar 60 picks up the offered content and when it returns the content to its original position, the more interested the avatar 60 is in, and therefore the interest level estimation unit 17 estimates that the real user's interest level is lower than when the avatar 60 purchases the offered content, but that the real user's interest level is also high.

[0044] For example, if the offered content is not purchased, is not picked up, and is not visually recognized, it indicates that the avatar 60 is not interested, and the interest level estimation unit 17 estimates that the real user's interest level is lower than when the avatar 60 purchases the offered content or when the avatar 60 considers purchasing the offered content.

[0045] The interest level estimation unit 17 may combine the feature and the LLM to obtain an estimation result of the real user's interest level. For example, the interest level estimation unit 17 may input a query including multiple offering contents, the provision conditions for the multiple offering contents, and the feature for each offering content calculated by the feature calculation unit 16 into a large-scale language model including information on the real user's interest level in the offering contents and an index of the feature, thereby obtaining an estimation result of the real user's interest level in each of the multiple offering contents output from the large-scale language model. The index of the feature may be an index predetermined by a setter of the offering contents or the provision conditions, such as the number of types, quantity, time, etc., or may be an index estimated from POS data, existing financial statements, etc.

[0046] The interest level estimation unit 17 may estimate the interest level of the avatar 60 based on information about the behavior of the avatar 60 toward the multiple offered products estimated by the behavior estimation unit 14. The interest level estimation unit 17 may estimate the real user's interest level in each of the multiple offered contents based on the avatar 60's interest level. The interest level of the avatar 60 may be estimated using the same method as described above. For example, the interest level estimation unit 17 may predict that the interest level of an avatar 60 having a profile including attribute information that is identical to or similar to at least a part of a profile of a target of the offered content set by a provider of the offered content indicates the interest level of the target (real user). The behavior estimation unit 14 may predict that the interest level of a real user having a profile that is identical to or similar to the profile set for the avatar 60 will match the interest level of the avatar 60.

[0047] The interest level estimation unit 17 may calculate the interest level of a real user based on at least one of the proportion (likelihood or distribution) of real users who have the profile (attribute information) of the avatar 60 and the proportion of attribute information that real users may have in the profile (attribute information) of the avatar 60. The above proportions may be stored in advance in the attribute information database 7.

[0048] An information processing method executed by the information processing device 10 will be described below with reference to the flowchart shown in Fig. 3. As a premise, at the start of the information processing method, a plurality of content to be provided has already been stored in the content to be provided database 3, provision conditions to be set for the plurality of content to be provided have been stored in the provision condition database 5, and attribute information for generating a profile of the avatar 60 has been stored in the attribute information database 7.

[0049] First, in step S1, the acquisition unit 11 acquires a plurality of provided contents for which the degree of interest is to be estimated, provision conditions that can be set when estimating the degree of interest, and attribute information that can be used when generating a profile of the avatar 60. For example, the acquisition unit 11 acquires a plurality of provided contents from the provided content database 3, acquires provision conditions from the provision condition database 5, and acquires attribute information from the attribute information database 7. The acquisition unit 11 may acquire a plurality of provision conditions from the provision condition database 5. The acquisition unit 11 may also randomly extract attribute information from the attribute information database 7.

[0050] Subsequently, in Step S2, the setting unit 12 sets a plurality of different provision contents to be targets for estimating the degree of interest from among the plurality of provision contents acquired by the acquisition unit 11. In Step S2, the setting unit 12 sets one provision condition from among the plurality of provision conditions acquired by the acquisition unit 11 for estimating the degree of interest for the plurality of provision contents.

[0051] The setting by the setting unit 12 includes setting up a virtual store 31 in a virtual space in accordance with the supply conditions and arranging a plurality of supply contents in a predetermined corner within the virtual store 31. In the example shown in Fig. 2, the plurality of supply contents are a new nutritional drink product and an existing nutritional drink product. The setting unit 12 can generate, for example, a supply condition for arranging each supply content in beverage corners A and B, and a supply condition for arranging each supply content on the same shelf in the same beverage corner A.

[0052] The behavior estimation unit 14 cooperates with the virtual space control server 30 to arrange the offerings in the virtual store 31 based on the offering conditions. In step S2 of this embodiment, the new nutritional drink product and the existing nutritional drink product are arranged side by side in the beverage corners A and B, respectively, according to the offering conditions. The offering conditions may include the various conditions described above, such as the time period during which the multiple offerings are offered being in the morning.

[0053] Next, in step S3, the profile generation unit 13 generates a profile of the avatar 60 based on the attribute information acquired by the acquisition unit 11. The generated profile of the avatar 60 may include information such as "easy to keep up with trends" or "busy male working professional." Because the attribute information is randomly extracted by the acquisition unit 11, the profile of the avatar 60 generated by the profile generation unit 13 may be different each time the information processing method is executed. The profile generation unit 13 may output the generated profile to the output unit 15. The output unit 15 may store the generated profile.

[0054] Next, in step S4, the behavior estimation unit 14 places an avatar 60 having the generated profile in the virtual space. The behavior estimation unit 14 cooperates with the virtual space control server 30, which controls the entire metaverse space, to display a virtual store 31 as part of the metaverse space on the output unit 15, as shown in FIG. 2. The behavior estimation unit 14 cooperates with the virtual space control server 30 to control the output unit 15 to display the avatar 60 in the virtual store 31.

[0055] Next, in step S5, the behavior estimation unit 14 estimates (simulates) the behavior of the avatar 60. In cooperation with the virtual space control server 30, the behavior estimation unit 14 causes the avatar 60 to act within the virtual store 31 and estimates the behavior of the avatar 60 in response to the multiple offerings. The avatar 60 acts in response to the target offerings within the virtual store 31 according to the attribute information included in the profile. In the example of the energy drink mentioned above, an avatar 60 who is prone to following trends will not pick up a ready-made energy drink, but will pick up a new energy drink and complete the purchase procedure with a short consideration time. The behavior estimation unit 14 outputs information regarding the behavior of the avatar 60 in response to the multiple offerings to the output unit 15. The output unit 15 stores information regarding the behavior of the avatar 60 in response to the multiple offerings.

[0056] Next, in step S6, the feature calculation unit 16 calculates a feature for each of the plurality of provided contents. The feature calculation unit 16 calculates the feature based on information about the behavior of the avatar 60 estimated by the behavior estimation unit 14. The feature calculation unit 16 calculates, as feature amounts, for example, the time from when the avatar 60 sees the new energy drink product until it picks it up, the time from when the avatar 60 picks up the new energy drink product until it purchases it, the profit of the new energy drink product, etc. Note that when this information processing method is executed for a plurality of avatars 60, the calculated feature amounts may be added together. The feature calculation unit 16 outputs the calculated feature amounts to the output unit 15. The output unit 15 stores the calculated feature amounts.

[0057] Next, in step S7, the interest level estimation unit 17 estimates the real user's level of interest in each of the plurality of provided contents based on the behavior of the avatar 60 estimated by the behavior estimation unit 14. For example, because the time from when the avatar 60 picked up the new energy drink product, which is a feature, to when he or she purchased the new energy drink product is short, the interest level estimation unit 17 estimates that the new energy drink product has generated a higher level of interest for the avatar 60 than existing energy drinks. Note that the interest level estimation unit 17 may estimate the real user's level of interest using the LLM as described above.

[0058] Next, in step S8, the interest level estimation unit 17 determines whether or not the interest levels of real users in the plurality of provision contents have been estimated for all the patterns of provision conditions that have been set. If the interest level estimation unit 17 determines in step S8 that the interest levels of real users in the plurality of provision contents have not been estimated for all the patterns of provision conditions (NO in step S8), the interest level estimation unit 17 proceeds to step S9.

[0059] In step S9, the setting unit 12 switches at least one of the serving contents and the serving conditions to a different pattern. For example, in step S2 (or step S9), the setting unit 12 sets a combination of multiple serving contents that is different from the combination of multiple serving contents that has already been set. For example, in step S2 (or step S9), the setting unit 12 sets a serving condition that is different from the serving condition that has already been set. As an example of this embodiment, in step S9, the setting unit 12 switches the serving conditions of the multiple serving contents to a serving condition in which each serving content is placed on the same shelf in the same beverage corner A. The behavior estimation unit 14 executes step S5 after step S9.

[0060] If the interest level estimation unit 17 determines in step S8 that the interest levels of real users in a plurality of provided contents have been estimated for all patterns of providing conditions (YES in step S8), the information processing method ends.

[0061] When estimating the behavior of avatars 60 with different profiles for multiple pieces of provided content, the information processing method may start again from step S1. In this case, the feature amounts may be summed or updated using a predetermined formula in step S6, and the interest levels of real users may be summed or updated using a predetermined formula in step S7.

[0062] In the embodiment described above, the behavior estimation unit 14 of the information processing device 10 estimates the behavior of the virtual user (avatar 60) in the virtual space with respect to multiple pieces of provided content. Therefore, the behavior of the virtual user with respect to multiple pieces of provided content is estimated based on the behavior of the virtual user in the virtual space. Therefore, there is no need to conduct a survey of real users in the real world regarding multiple pieces of provided content, and information regarding their behavior with respect to multiple pieces of provided content is easily acquired. Furthermore, the interest level estimation unit 17 estimates the real-world user's level of interest in each of the multiple pieces of provided content based on the behavior of the virtual user predicted by the behavior estimation unit 14. Although it may be difficult to predict the quantity, etc., of the multiple pieces of provided content, the interest level is appropriately estimated based on the behavior of the virtual user. For example, it is possible to evaluate how a real-world user reacts to changes in the type, quantity, provision conditions, etc. of the multiple pieces of provided content through the interest level. Therefore, the information processing device 10 and the information providing method make it possible to easily and appropriately estimate the user's level of interest in different pieces of provided content.

[0063] The system further includes a feature calculation unit 16 that calculates feature quantities for each of the plurality of offerings based on the virtual user's behavior estimated by the behavior estimation unit 14. The feature quantities include at least one value related to the provision of the offerings and a value related to the purchase of the offerings. In this case, feature quantities for each of the plurality of offerings are calculated based on the virtual user's behavior. Therefore, feature quantities related to the offerings can be predicted based on the estimated results of the virtual user's behavior without incurring costs such as conducting a survey each time. For example, the feature quantities can be used to evaluate how real-world users react to changes in the type, quantity, and terms of provision of the plurality of offerings. Therefore, the interest level estimation unit 17 can easily predict the level of interest that real users will have in offerings, such as offerings in the prototype stage and offerings before release.

[0064] Furthermore, the interest level estimation unit 17 estimates the real user's level of interest in each of the plurality of provided contents based on the feature amount for each of the provided contents calculated by the feature amount calculation unit 16. In this case, since the feature amount is a value related to the provision or purchase of the provided content, the interest level estimation unit 17 can more appropriately estimate the real user's level of interest in the provided content.

[0065] Furthermore, the interest level estimation unit 17 inputs a query including a plurality of provided contents and the provision conditions of the plurality of provided contents into a large-scale language model including information on the real user's interest level in the provided contents, thereby obtaining an estimation result of the real user's interest level in each of the plurality of provided contents output from the large-scale language model. In this case, the interest level estimation unit 17 can more easily obtain an estimation result of the real user's interest level.

[0066] Furthermore, the interest level estimation unit 17 estimates the virtual user's level of interest in each of the plurality of provided contents based on the virtual user's behavior estimated by the behavior estimation unit 14, and estimates the real user's level of interest in each of the plurality of provided contents based on the virtual user's level of interest. In this case, the interest level estimation unit 17 estimates the virtual user's level of interest in each of the plurality of provided contents based on the estimated result of the virtual user's behavior, and can estimate the real user's level of interest in each of the plurality of provided contents. Therefore, it is possible to easily estimate the real user's level of interest in each of the plurality of provided contents.

[0067] The provision conditions include at least one of the following information: the date, time period, weather, location, congestion status of the location, the provision mode of the content, and whether the user is accompanied. This makes it easy to estimate how a real user will behave in various provided content under various conditions (environments) in the virtual space. The interest level estimation unit 17 can estimate the interest level of a real user based on the estimated results of the virtual user's behavior, without incurring costs such as conducting a survey each time.

[0068] The gist of the present disclosure lies in the following [1] to [7]. [1] A setting unit that sets a plurality of different content items and provision conditions for the plurality of content items in a virtual space that provides content items including at least one of goods and services; a behavior estimation unit that estimates a behavior of the virtual user in the virtual space based on the plurality of provision contents and the provision conditions set by the setting unit; an interest level estimation unit that estimates a level of interest of a real-world user in each of the plurality of provided contents based on the behavior of the virtual user estimated by the behavior estimation unit; An information processing device comprising: [2] further comprising a feature amount calculation unit that calculates a feature amount for each of the plurality of provided contents based on the behavior of the virtual user estimated by the behavior estimation unit; the feature value includes at least one value of a value related to the provision of the content offered and a value related to the purchase of the content offered; [1] The information processing device according to [1]. [3] The interest level estimation unit estimating a real-world level of interest of the user in each of the plurality of pieces of provision content based on the feature amount for each piece of provision content calculated by the feature amount calculation unit; [2] The information processing device according to [2]. [4] The interest level estimation unit a query including the plurality of provision contents and provision conditions for the plurality of provision contents is input to a large-scale language model including information on the degree of interest of the user in the real world in the provision contents, thereby obtaining an estimation result of the degree of interest of the user in the real world in each of the plurality of provision contents output from the large-scale language model; The information processing device according to any one of [1] to [3]. [5] The interest level estimation unit estimating a degree of interest of the virtual user in each of the plurality of provided contents based on the behavior of the virtual user estimated by the behavior estimation unit; estimating a real-world user's level of interest in each of the plurality of provided contents based on the virtual user's level of interest; The information processing device according to any one of [1] to [4]. [6] The provision conditions include at least one of information regarding the date, time period, weather, location, congestion status of the location, provision mode of the provision content, and whether or not the user is accompanied by a companion. The information processing device according to any one of [1] to [5]. [7] In a virtual space that provides content to be provided, including at least one of goods and services, a step of setting a plurality of different content to be provided and provision conditions for the plurality of content to be provided; a step of estimating the behavior of the virtual user in the virtual space based on the plurality of provision contents and the provision conditions set in the setting step; a step of estimating a degree of interest of a real-world user in each of the plurality of provided contents based on the behavior of the virtual user estimated in the step of estimating; An information processing method comprising:

[0069] [Explanation of terms, explanation of hardware configuration (Figure 4), etc.] The block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wires, wirelessly, etc.) and these multiple devices. The functional block may also be realized by combining the single device or multiple devices with software.

[0070] Functions include, but are not limited to, judgment, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, election, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocation, mapping, and assignment. For example, a functional block (component) that performs transmission is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on how these functions are implemented.

[0071] For example, an information processing device according to an embodiment of the present disclosure may function as a computer that executes the processes of the present disclosure. Fig. 4 is a diagram illustrating an example of a hardware configuration of an information processing device 10 according to an embodiment of the present disclosure. The information processing device 10 described above may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, etc.

[0072] In the following description, the term "apparatus" can be interpreted as a circuit, a device, a unit, etc. The hardware configuration of the information processing device 10 may be configured to include one or more of the apparatuses shown in the drawings, or may be configured to exclude some of the apparatuses.

[0073] Each function of the information processing device 10 is realized by loading predetermined software (programs) onto hardware such as the processor 1001, memory 1002, etc., so that the processor 1001 performs calculations, controls communication via the communication device 1004, and controls at least one of reading and writing data in the memory 1002 and storage 1003.

[0074] The processor 1001 controls the entire computer by running, for example, an operating system, and may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc.

[0075] The processor 1001 also reads programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002, and executes various processes in accordance with these. The programs used are those that cause a computer to execute at least some of the operations described in the above-mentioned embodiments. Although the various processes have been described as being executed by one processor 1001, they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The programs may be transmitted from a network via a telecommunications line.

[0076] The memory 1002 is a computer-readable recording medium and may be configured, for example, by at least one of a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a random access memory (RAM), etc. The memory 1002 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 1002 can store executable programs (program codes), software modules, etc. for implementing a wireless communication method according to an embodiment of the present disclosure.

[0077] Storage 1003 is a computer-readable recording medium, and may be composed of at least one of, for example, an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray disc), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy disk, a magnetic strip, etc. Storage 1003 may also be referred to as an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, a server, or other appropriate medium including at least one of memory 1002 and storage 1003.

[0078] The communication device 1004 is hardware (transmission / reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as, for example, a network device, a network controller, a network card, a communication module, etc. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc., to realize at least one of, for example, Frequency Division Duplex (FDD) and Time Division Duplex (TDD).

[0079] The input device 1005 is an input device (for example, a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that receives input from the outside. The output device 1006 is an output device (for example, a display, a speaker, an LED lamp, etc.) that outputs to the outside. The input device 1005 and the output device 1006 may be integrated into one device (for example, a touch panel).

[0080] Furthermore, each device, such as the processor 1001 and the memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

[0081] Furthermore, the information processing device 10 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

[0082] The notification of information is not limited to the aspects / embodiments described in the present disclosure and may be performed using other methods. For example, the notification of information may be performed by physical layer signaling (e.g., Downlink Control Information (DCI) and Uplink Control Information (UCI)), higher layer signaling (e.g., Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, and broadcast information (Master Information Block (MIB) and System Information Block (SIB))), other signals, or a combination thereof. Furthermore, the RRC signaling may be referred to as an RRC message, and may be, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, or the like.

[0083] Each aspect / embodiment described in the present disclosure may be any of the following: LTE (Long Term Evolution), LTE-Advanced (LTE-A), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), 6th generation mobile communication system (6G), xth generation mobile communication system (xG) (xG (x is, for example, an integer or decimal number)), FRA (Future Radio Access), NR (new Radio), New radio access (NX), Future generation radio access (FX), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE The present invention may be applied to at least one of systems using 802.20, UWB (Ultra-Wideband), Bluetooth (registered trademark), or other appropriate systems, and next-generation systems that are extended, modified, created, or defined based on these systems. The present invention may also be applied to a combination of multiple systems (e.g., a combination of at least one of LTE and LTE-A with 5G).

[0084] The order of the procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed unless it is consistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0085] Input and output information may be stored in a specific location (for example, memory) or may be managed using a management table. Input and output information may be overwritten, updated, or added to. Output information may be deleted. Input information may be sent to another device.

[0086] The determination may be made based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0087] Each aspect / embodiment described in this disclosure may be used alone, in combination, or switched depending on the implementation. Furthermore, notification of predetermined information (e.g., notification that "X is true") is not limited to being done explicitly, but may be done implicitly (e.g., by not notifying the predetermined information).

[0088] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.

[0089] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0090] Software, instructions, information, etc. may also be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), then these wired and / or wireless technologies are included within the definition of transmission media.

[0091] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0092] Note that terms explained in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, at least one of a channel and a symbol may be a signal (signaling). Furthermore, a signal may be a message. Furthermore, a component carrier (CC) may be called a carrier frequency, a cell, a frequency carrier, etc.

[0093] As used in this disclosure, the terms "system" and "network" are used interchangeably.

[0094] Furthermore, the information, parameters, etc. described in the present disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using other corresponding information. For example, a radio resource may be indicated by an index.

[0095] The names used for the above-described parameters are not intended to be limiting in any way. Furthermore, the mathematical expressions using these parameters may differ from those explicitly disclosed in this disclosure. The various channels (e.g., PUCCH, PDCCH, etc.) and information elements may be identified by any suitable names, and therefore the various names assigned to these various channels and information elements are not intended to be limiting in any way.

[0096] As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (e.g., searching in a table, database, or other data structure), ascertaining, and the like. "Determining" and "determining" may also include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), and the like. Furthermore, "judgment" and "decision" can include regarding resolving, selecting, choosing, establishing, comparing, etc. as having been "judged" or "decided." In other words, "judgment" and "decision" can include regarding some action as having been "judged" or "decided." Furthermore, "judgment (decision)" can be interpreted as "assuming," "expecting," "considering," etc.

[0097] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0098] As used in this disclosure, any reference to an element using a designation such as "first," "second," etc. does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.

[0099] When used in this disclosure, the terms "include," "including," and variations thereof are intended to be inclusive, similar to the term "comprising." Furthermore, when used in this disclosure, the term "or" is not intended to be an exclusive or.

[0100] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0101] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different." [Explanation of symbols]

[0102] 10...information processing device, 11...acquisition unit, 12...setting unit, 13...profile generation unit, 14...behavior estimation unit, 15...output unit, 16...feature calculation unit, 17...interest level estimation unit, 30...virtual space control server, 1001...processor, 1002...memory, 1003...storage, 1004...communication device, 1005...input device, 1006...output device, 1007...bus.

Claims

1. a setting unit that sets a plurality of different content to be provided and provision conditions for the plurality of content to be provided in a virtual space that provides content to be provided, the content including at least one of a product and a service; a behavior estimation unit that estimates a behavior of the virtual user in the virtual space based on the plurality of provision contents and the provision conditions set by the setting unit; an interest level estimation unit that estimates a level of interest of a real-world user in each of the plurality of provided contents based on the behavior of the virtual user estimated by the behavior estimation unit; An information processing device comprising:

2. a feature amount calculation unit that calculates a feature amount for each of the plurality of pieces of provision content based on the behavior of the virtual user estimated by the behavior estimation unit; the feature amount includes at least one value of a value related to the provision of the content to be offered and a value related to the purchase of the content to be offered; The information processing device according to claim 1 .

3. The interest level estimation unit estimating a real-world level of interest of the user in each of the plurality of pieces of provision content based on the feature amount for each piece of provision content calculated by the feature amount calculation unit; The information processing device according to claim 2 .

4. The interest level estimation unit a query including the plurality of provision contents and provision conditions for the plurality of provision contents is input to a large-scale language model including information on the degree of interest of the user in the real world in the provision contents, thereby obtaining an estimation result of the degree of interest of the user in the real world in each of the plurality of provision contents output from the large-scale language model; 10. The apparatus of claim 1.

5. The interest level estimation unit estimating a degree of interest of the virtual user in each of the plurality of provided contents based on the behavior of the virtual user estimated by the behavior estimation unit; estimating a real-world user's level of interest in each of the plurality of provided contents based on the virtual user's level of interest; The information processing device according to claim 1 .

6. The information processing device according to claim 1 , wherein the provision conditions include at least one of the date, time period, weather, location, congestion status of the location, provision mode of the content, and whether the user is accompanied by someone.

7. a step of setting a plurality of different content offerings including at least one of goods and services in a virtual space where the content offerings are provided, and provision conditions for the plurality of content offerings; a step of estimating the behavior of the virtual user in the virtual space based on the plurality of provision contents and the provision conditions set in the setting step; a step of estimating a degree of interest of a real-world user in each of the plurality of provided contents based on the behavior of the virtual user estimated in the step of estimating; An information processing method comprising:

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