Information processing device, information processing method, and information processing program

Abstract

To enable the distribution of an appropriate advertisement according to a user who is a recipient of a reply to an input made by another user.SOLUTION: An information processing device 100 includes: an estimation unit 121 configured to estimate, based on information about a user, a user image of another user to whom a reply to an input made by the user will be presented; a selection unit 122 configured to select an advertisement based on user information estimated by the estimation unit 121; a generation unit 123 configured to generate a sentence for a generative model so that the advertisement selected by the selection unit 122 is included in the reply; and an output unit 124 configured to output the sentence generated by the generation unit 123 to the generative model.SELECTED DRAWING: Figure 2

Description

[Technical Field]

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

[0002] BACKGROUND ART Conventionally, there are techniques for presenting answers to questions from users, and there are also techniques for delivering advertisements related to questions from users. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 6849982 [Patent Document 2] Japanese Patent Publication No. 2023-061663 Summary of the Invention [Problem to be solved by the invention]

[0004] In the prior art, it was sometimes impossible to deliver an appropriate advertisement to a user who was the recipient of the answer to an input made by the user. For example, in the prior art, an advertisement was delivered to the user who asked the question along with the answer to the question, but the delivery of an advertisement to a user who views the answer posted by the user who asked the question on a social networking service (SNS) or the like was not taken into consideration. [Means for solving the problem]

[0005] In order to solve the above-mentioned problems and achieve the objective, the information processing device is characterized by having an estimation unit that estimates a user image of another user who will provide an answer to an input made by the user based on information about the user, a selection unit that selects an advertisement based on the user image estimated by the estimation unit, a generation unit that generates a sentence for a generative model so that the advertisement selected by the selection unit is included in the answer, and an output unit that outputs the sentence generated by the generation unit to the generative model. [Effects of the Invention]

[0006] According to the present invention, it is possible to deliver an appropriate advertisement according to the user to whom the answer to the input made by the user is to be presented. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a diagram illustrating an outline of the processing performed by the information processing device according to the embodiment. [Figure 2] FIG. 2 is a block diagram illustrating an example of the configuration of the information processing device according to the embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of processing performed by the information processing apparatus according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of processing performed by the information processing device according to the embodiment. [Figure 5] FIG. 5 is a diagram illustrating an example of processing performed by the information processing device according to the embodiment. [Figure 6] FIG. 6 is a diagram illustrating an example of processing performed by the information processing device according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating an example of processing performed by the information processing device according to the embodiment. [Figure 8] FIG. 8 is a diagram illustrating an example of processing performed by the information processing device according to the embodiment. [Figure 9] FIG. 9 is a flowchart illustrating an example of processing by the information processing device according to the embodiment. [Figure 10]FIG. 10 is a diagram illustrating an example of a computer that executes an information processing program. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, with reference to the drawings, an information processing device, an information processing method, and an information processing program according to the present application will be described in detail. Note that the present invention is not limited to these embodiments. In addition, in the description of the drawings, the same parts are denoted by the same reference numerals, and duplicated explanations will be omitted.

[0009] [Introduction] First, an overview of the information processing device 100 will be described with reference to Fig. 1. Fig. 1 is a diagram illustrating an overview of the processing performed by the information processing device 100. Conventionally, generation models such as large-scale language models and image generation models have been used to generate answers to inputs such as questions and requests from users. However, with conventional technology, there have been cases where it has not been possible to deliver an appropriate advertisement to a user who is the recipient of the answer to the input made by the user.

[0010] For example, if an influencer or creator creates an image, they may publish the image to their followers on a social networking site. In such a situation, conventional technology delivers an advertisement to the user who asked the question along with the answer to the user's question, but does not sometimes take into consideration the delivery of advertisements to other users who view the answer posted by the user on a social networking site or the like.

[0011] Therefore, the information processing device 100 according to this embodiment performs the following processing. First, the information processing device 100 estimates the user image of another user to whom an answer from a user who inputs a question, request, or the like via an application (FIG. 1(1)) will be presented, based on information about the user who made the input. Next, the information processing device 100 selects an advertisement to be delivered (FIG. 1(2)) based on the estimated user image. Next, as shown in FIG. 1(3), the information processing device 100 generates a sentence for the generative model: "The user image of another user to whom the user who made the input will present an answer is ~~~. Please reply to the user who made the input, including the advertisement. The input content is as follows: '~~~~~~'" so that the selected advertisement will be included in the answer from the generative model.

[0012] Then, the information processing device 100 outputs the generated sentence to the generative model. As a result, the information processing device 100 generates and outputs sentences to the generative model so as to provide an answer including an advertisement corresponding to the user persona to which the answer to the input made by the user is to be presented, thereby enabling delivery of an appropriate advertisement according to the user to which the answer to the input made by the user is to be presented.

[0013] In other words, the information processing device 100 generates and outputs text for a generative model so that the answer to a user's input includes an advertisement that corresponds to the persona of the other user to whom the user's answer is presented, thereby making it possible to provide information that the user who made the input, such as an influencer or creator, desires as an answer, and to deliver advertisements that are desired by the other users to whom the answer to the user's input is presented, or that have little psychological resistance.

[0014] [Configuration of information processing device] Next, the configuration of the information processing device 100 will be described with reference to Fig. 2. Fig. 2 is a diagram showing an example of the configuration of the information processing device 100 according to the embodiment. As shown in Fig. 2, the information processing device 100 includes a communication unit 110, a control unit 120, and a storage unit 130. Note that these units may be held in a distributed manner in multiple devices. The processing of these units will be described below.

[0015] The communication unit 110 is realized by a NIC (Network Interface Card) or the like, and enables communication between an external device and the control unit 120 via a telecommunication line such as a LAN (Local Area Network) or the Internet. For example, the communication unit 110 enables communication between the external device and the control unit 120.

[0016] The storage unit 130 is realized by a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. Examples of information stored in the storage unit 130 include information about users, user profiles, information about advertisements, algorithms, learning data for machine learning, and learned models. Here, the information about users includes information such as a user ID, user attributes (age, gender, birthplace, place of residence, occupation, characteristic search keywords, etc.), personality (characteristics, values, worries, etc.), lifestyle (daily habits, consumption patterns, friendships, etc.), search history, browsing history, location information, conversational tendencies, conversation content, sentences recognized by voice recognition, social media posts, context, and follower information.

[0017] Here, the user profile is information that includes user attributes and indicates the characteristics of the user estimated from information about the user, and is a so-called persona. The user profile may also include a summary of conversations that occurred over a predetermined period of time in the past, including the target user. For example, the user profile of user A is "gender: male, age: 20, eats out with friends 3-5 times a month, is dissatisfied with the hassle of finding restaurants and making reservations, currently looking for a place to eat with user B." Here, the user profile may be stored in the form of a vector. The user profile stored in the form of a vector is updated as needed according to information about the user (e.g., the content of the conversation, etc.).

[0018] Characteristic search keywords are search keywords of a specific user that are narrowed down based on information such as the number of searches, frequency of searches, and date and time of searches, such as the number of searches, frequency of searches, and recent search dates and times, in the search history of that user. In addition, user attributes, personality, and lifestyle may be registered in advance, or may be estimated from sentences entered on the application in the past.

[0019] Conversation content includes information entered on applications with communication functions. For example, the conversation content of User A includes information entered in a chat room where User A and users who follow User A on SNS "Y" are members. SNS posts include images (videos and still images), audio, text, date and time, location information, etc. posted by users to the SNS.

[0020] Context is information that can be inferred from the context of a conversation, such as the flow of a conversation. For example, in a chat application, if User A asks, "Have you decided on a restaurant for the weekend celebration?" and the next day User B asks, "We're having yakiniku for the weekend," it can be determined from the context of the conversation that "that" refers to the restaurant for the celebration. Note that the context that can be inferred is not limited to the content of the conversation; it can also include all user information, such as search history, browsing history, and location information. Furthermore, the text and context of the conversation may be information that has already been analyzed using natural language processing. Information about advertising includes information such as advertising ID, advertising type, budget, period, and targeting information (e.g., social networking site, user attributes, keywords, conversation trends, and context).

[0021] Follower information is information about other users who follow a certain user. For example, follower information is attribute information about other users who follow a certain user. Here, follower information may be information obtained by aggregating and analyzing attribute information about followers. For example, follower information for user A may be "gender: 'Male: 40%', 'Female: 60%', age: 'Teens: 60%', 'Twenties: 30%'," etc. In the above example, follower information includes gender and age, but follower information also includes information necessary to estimate the follower's user profile, such as the follower's hobbies and preferences and information on the date and time when the follower accessed the SNS. Note that the information stored in the storage unit 130 is not limited to the above example.

[0022] The control unit 120 is realized using a CPU (Central Processing Unit), an NP (Network Processor), an FPGA (Field Programmable Gate Array), or the like, and executes a processing program stored in a memory. As shown in Fig. 2, the control unit 120 has an estimation unit 121, a selection unit 122, a generation unit 123, and an output unit 124. Each unit of the control unit 120 will be described below.

[0023] The estimation unit 121 estimates an image of another user who will provide an answer to an input made by the user, based on information about the user. For example, the estimation unit 121 estimates the user image using, as information about the user, one or more of user attributes, search history, browsing history, location information, conversation content, conversation tendency, SNS posts, context, and follower information.

[0024] More specifically, the estimation unit 121 estimates that the user image of the other user to whom user A submits an answer is "interested in music" from the attributes of user A, "gender: male, age: 20s, occupation: singer," which are information about user A. Note that the user image estimated by the estimation unit 121 is updated as needed according to information about the user.

[0025] Furthermore, since the estimation unit 121 has information about user A, such as that many of the posts made by user A on social media are related to outdoor activities and camping, it estimates that the user profile of other users to whom user A provides answers is "interested in outdoor activities and camping."

[0026] Furthermore, the estimation unit 121 estimates that the user image of the other users to whom user A provides answers is "interested in interior design and cooking" based on the fact that the conversations held in the chat room to which user A belongs in an application with communication functions contain many topics related to interior design and cooking as information about user A.

[0027] Furthermore, the estimation unit 121 estimates, as information about user A, that the user profile of other users to whom user A will provide answers is "gender: male, age: 40s, hobby: cooking" based on the fact that, in an application with communication functions, many of the users participating in the chat room to which user A belongs have the attributes "gender: male, age: 40s, hobby: cooking."

[0028] Furthermore, the estimation unit 121 can analyze the aggregated attribute information of the followers of user A and estimate, for example, attribute information with a high percentage as the user image of the followers. For example, based on the follower attributes of "gender 'male: 30%', 'female: 70%', age 'teens: 30%', 'twenties: 40%', 'twenties: 20%'" as information about user A, the estimation unit 121 estimates the user image of other users to whom user A will provide answers as "gender: female, age: 20s".

[0029] Furthermore, the estimation unit 121 estimates a user image from information about the user using a model that has learned the relationship between information about the user and the user image. For example, the estimation unit 121 inputs information about the user into a learning model that inputs information about the user and outputs user images of other users to whom the user submits answers, and estimates the output result as the user image of other users to whom the user submits answers. For example, this model is stored in a server that processes information and is independently created by a business operator (Yahoo). Note that it is desirable to conceal input information, such as personal information, by performing learning so that the input information is not used as a new answer.

[0030] The selection unit 122 selects an advertisement based on the user image estimated by the estimation unit 121. For example, the selection unit 122 selects an advertisement that targets the user image estimated by the estimation unit 121 for advertisement delivery. For example, the selection unit 122 compares the user image estimated by the estimation unit 121 with targeting information of the advertisement and selects an advertisement with a high degree of match between the user image and the targeting information of the advertisement. At this time, the advertisement with the highest degree of match may be selected, or a predetermined number of advertisements with the highest degree of match may be selected.

[0031] More specifically, the selection unit 122 compares the information on the user profile of other users to whom user A, estimated by the estimation unit 121, presents an answer, such as "gender: male, age: in their 20s, context: likes store A," with the targeting information of each advertisement, and selects store A with the highest matching rate (targeting information "male, in their 20s, store A") as the advertisement.

[0032] The generation unit 123 generates a sentence for the generative model so that the answer includes the advertisement selected by the selection unit 122. For example, the generation unit 123 generates a sentence including a sentence by the user, a user image, and an advertisement as the sentence for the generative model.

[0033] More specifically, the generation unit 123 generates a sentence such as "The user image of other users to whom user A will provide an answer is '~~~~~'. Please reply to user A including the selected advertisement A. The input content is as follows: '~~~~~'" as a sentence for the generation model.

[0034] As another example, the generation unit 123 generates a sentence such as, as a sentence for the generation model, "Please answer the following question based on the user image of the other user to whom user A will provide the answer. Also, please include the following advertisement sentence in your answer. The input from user A is as follows: "~~~~~" The user image of the other user to whom user A will provide the answer is as follows: "~~~~~" The advertisement is as follows: "~~~~~"."

[0035] The output unit 124 outputs the sentence generated by the generation unit 123 to a generative large-scale language model. For example, the output unit 124 outputs the sentence generated by the generation unit 123 to a trained large-scale language model that has been independently created by a business (Yahoo). Note that it is desirable to conceal input information such as personal information by training the input information so that it will not be used as a new answer.

[0036] [Processing performed by information processing device] Next, an example of processing performed by the information processing device 100 according to the embodiment will be described with reference to Figures 3 to 8. Figures 3 to 8 are diagrams illustrating an example of processing performed by the information processing device 100 according to the embodiment.

[0037] First, the information processing device 100 receives an input from user A on an application, such as "Show me a map of the area around Tokyo Station," as shown in Fig. 3. Note that although this example shows an example in which input from the user is received on an application used for communication, the information processing device 100 can receive input from the user in any format. For example, the information processing device 100 may receive input from the user in the form of voice recognition.

[0038] Next, the information processing device 100 estimates the user image of the other user to whom user A, who has received the input, will submit an answer. FIG. 4(1) shows posts made by user A to an SNS for sharing images, many of which are related to fashion and camping. From the content of the posts by user A on the SNS shown in FIG. 4(1) as user information about user A, the estimation unit 121 estimates the user image of the other user to whom user A will submit an answer as "outdoorsy, hobbies: fashion, camping," as shown in FIG. 4(2).

[0039] Here, the estimation unit 121 can estimate the user image of other users to whom a user submits posts for each SNS. For example, FIG. 5(1) shows posts made by user A to an SNS for sharing text, many of which are related to movies and novels. In this example, the estimation unit 121 estimates the user image of other users to whom user A submits replies as "indoor type, hobbies: movies, reading" as shown in FIG. 5(2) from the content of the SNS post by user A shown in FIG. 5(1) as user information about user A.

[0040] As another example, the estimation unit 121 estimates the user profile of other users who participate in a talk group to which a certain user belongs in an application with a communication function. Fig. 6(1) shows the content of a conversation that took place in a talk room to which users A, B, and C belong in an application with a communication function, and the conversations include "User B: My hobby is cooking. Do you have any favorite foods, A-san?", "User C: I'm curious," and "User A: I've been hooked on chicken cutlet lately. It's delicious, so please try it if you have the chance."

[0041] In such an example, the estimation unit 121 estimates the user profile of the other user to whom user A provides an answer as “Hobby: Cooking” as shown in FIG. 6(2) from the content of the conversation in the chat room between user A, user B, and user C shown in FIG. 6(1) as information about user A.

[0042] Next, the selection unit 122 selects an advertisement that targets the advertisement delivery to the user image estimated by the estimation unit 121. More specifically, the selection unit 122 compares the information on the user image of user A estimated by the estimation unit 121, "outdoorsy, hobbies: fashion, camping," with the targeting information of each advertisement, and selects an advertisement with a high degree of match between the user image and the targeting information of the advertisement (for example, outdoor brand store A, "targeting information: fashion, camping").

[0043] Next, as shown in FIG. 7, the generation unit 123 generates a sentence including the question "Show me a map of the area around Tokyo Station" from user A whose input has been accepted, information on the user image of user A "outdoorsy, hobbies: fashion, camping" estimated by the estimation unit 121, and targeting information for the advertisement "Store A" selected by the selection unit 122, as shown in FIG. 7(2), such as "The user image of other users to whom user A will provide an answer is 'outdoorsy, hobbies: fashion, camping'. Please answer to user A including an advertisement for Store A. The question content is as follows: 'Show me a map of the area around Tokyo Station'".

[0044] Then, the output unit 124 outputs the sentence generated by the generation unit 123 to the generation model. For example, the output unit 124 outputs the sentence to the large-scale language model. As a result, the large-scale language model responds by including an advertisement for the selected store A, such as "The map of the area around Tokyo Station is as follows. [Image of the map showing store A]", as shown in FIG. 8. Note that when multiple user images are estimated, the information processing device 100 can perform the above-described processing for each user image and output multiple answers from a generation model such as a large-scale language model.

[0045] As a result, the information processing device 100 generates and outputs sentences for the generative model so that the user's answer includes an advertisement corresponding to the persona of the other user who submits the answer, thereby enabling delivery of an appropriate advertisement according to the user who is the recipient of the answer to the input made by the user. In other words, it is possible to provide information desired by the user as an answer, while delivering an advertisement that is desired by the other user who submits the answer or that has little psychological resistance.

[0046] 〔flowchart〕 Next, the flow of processing by the information processing device 100 will be described with reference to Fig. 9. Fig. 9 is a flowchart showing an example of the flow of processing according to this embodiment.

[0047] First, the information processing device 100 receives advertisement information (step S101). For example, the information processing device 100 receives information such as an advertisement ID, an advertisement type, a budget, a period, and targeting information as the advertisement information, and stores the information in the storage unit 130.

[0048] Next, the information processing device 100 receives an input such as a question or a request from the user (step S102). For example, the information processing device 100 receives a question input by the user on an application and stores it in the storage unit .

[0049] Next, the estimation unit 121 estimates a user image of a user to whom an answer to an input made by the user will be presented, based on the information about the user (step S103). For example, the estimation unit 121 estimates a user image of a user to whom an answer to an input made by the user will be presented, using one or more pieces of information about the user stored in the storage unit 130 (user attributes, search history, browsing history, location information, conversation content, conversation tendency, SNS posts, context, and follower information).

[0050] Next, the selection unit 122 selects an advertisement based on the estimated user image (step S14). For example, the selection unit 122 selects an advertisement that targets the estimated user image for advertisement delivery.

[0051] Next, the generation unit 123 generates a sentence for the generative model so that the advertisement selected by the selection unit 122 is included in the answer (step S105). For example, the generation unit 123 generates a sentence including a sentence by the user, a user image, and an advertisement as the sentence for the generative model.

[0052] Then, the output unit 124 outputs the sentence generated by the generation unit 123 to the generative model (step S106). For example, the output unit 124 outputs the sentence generated by the generation unit 123 to a trained large-scale language model independently created by a business (Yahoo).

[0053] 〔effect〕 As described above, the information processing device 100 according to the embodiment includes an estimation unit 121 that estimates the user image of another user who will provide an answer to an input made by the user based on information about the user, a selection unit 122 that selects an advertisement based on the user image estimated by the estimation unit 121, a generation unit 123 that generates a sentence for a generative model so that the advertisement selected by the selection unit 122 is included in the answer, and an output unit 124 that outputs the sentence generated by the generation unit 123 to the generative model.

[0054] As a result, the information processing device 100 estimates the personas of other users to whom the user will submit answers from the attributes of the user who accepted the input and posts on SNS, etc., selects an advertisement corresponding to the estimated persona, and generates and outputs text for the generative model so that the advertisement is included in the input answer, thereby enabling the user who accepted the input to deliver appropriate advertisements according to the other users to whom the user will submit answers.

[0055] Furthermore, the information processing device 100 generates and outputs text for a generative model so that the answer to a user's input includes an advertisement that matches the persona of the other user to whom the user submits the answer, thereby making it possible to provide information that the user who provided the input, such as an influencer or creator, desires as an answer, while also delivering an advertisement that the other user to whom the user submitted the answer desires or that the user who provided the input will have little psychological resistance to.

[0056] The estimation unit 121 of the information processing device 100 according to the embodiment estimates a user image using one or more of the following information about the user: user attributes, search history, browsing history, location information, conversation content, conversation trends, SNS posts, context, and follower information.

[0057] As a result, the information processing device 100 uses information about the user who accepted the input, such as user attributes, search history, browsing history, location information, conversation content, conversation trends, SNS posts, context, and follower information, to estimate the personas of other users to whom the user who made the input will submit answers, selects advertisements corresponding to the personas, and generates and outputs text for the generative model so that the advertisements are included in the answers to the input, thereby enabling the user who accepted the input to deliver appropriate advertisements according to the other users to whom the answers will be submitted.

[0058] The estimation unit 121 of the information processing device 100 according to the embodiment estimates a user image from information about the user using a model that has learned the relationship between information about the user and the user image. As a result, the information processing device 100 uses the model to estimate the persona of another user to whom an answer will be presented based on the attributes of the user who has accepted a question, selects an advertisement corresponding to the persona, and generates and outputs a sentence for the generative model so that the advertisement is included in the answer to the input, thereby enabling the user who has accepted the input to deliver an appropriate advertisement according to the other user who will present an answer.

[0059] The selection unit 122 of the information processing device 100 according to the embodiment selects an advertisement that targets the user image estimated by the estimation unit 121 for advertisement delivery. As a result, the information processing device 100 selects an advertisement that targets the persona of another user to whom an answer is to be presented, estimated from the attributes of the user who has received the input, and generates and outputs a sentence for the generative model so that the advertisement is included in the answer to the input, thereby enabling the user who has received the input to deliver an appropriate advertisement according to the other user who will present an answer.

[0060] The generation unit 123 of the information processing device 100 according to the embodiment generates a sentence including a user's sentence, a user image, and an advertisement as a sentence for the generation model. As a result, the information processing device 100 generates and outputs a sentence including the user's sentence, a user image, and an advertisement for the generation model so that the advertisement is included in the answer to the question, thereby enabling the user who has accepted the input to deliver an appropriate advertisement according to another user who submits an answer.

[0061] 〔program〕 It is also possible to create a program written in a computer-executable language that executes the processes executed by the information processing device 100 described in the above embodiment. In this case, the same effects as those of the above embodiment can be achieved by having a computer execute the program. Furthermore, such a program may be recorded on a computer-readable recording medium, and the program recorded on the recording medium may be read and executed by a computer to achieve the same processes as those of the above embodiment.

[0062] Fig. 10 is a diagram showing an example of a computer that executes an information processing program. As shown in Fig. 10, the controller 200 includes a computer 1000, which includes, for example, a memory 1010, a CPU 1020, a hard disk drive interface 1030, a disk drive interface 1040, a serial port interface 1050, a video adapter 1060, and a network interface 1070. These components are connected by a bus 1080.

[0063] The memory 1010 includes a ROM (Read Only Memory) 1011 and a RAM 1012. The ROM 1011 stores, for example, a boot program such as a BIOS (Basic Input Output System). The hard disk drive interface 1030 is connected to a hard disk drive 1090. The disk drive interface 1040 is connected to a disk drive 1041. A removable storage medium such as a magnetic disk or optical disk is inserted into the disk drive 1041. The serial port interface 1050 is connected to, for example, a mouse 1110 and a keyboard 1120. The video adapter 1060 is connected to, for example, a display 1130.

[0064] 10, the hard disk drive 1090 stores, for example, an OS 1091, an application program 1092, a program module 1093, and program data 1094. The tables described in the above embodiments are stored in the hard disk drive 1090 or the memory 1010, for example.

[0065] The control program is stored in the hard disk drive 1090 as a program module in which instructions to be executed by the computer 1000 are written. Specifically, the hard disk drive 1090 stores a program module 1093 in which each process executed by the controller 2000 described in the above embodiment is written.

[0066] Furthermore, data used for information processing by the control program is stored as program data, for example, in the hard disk drive 1090. Then, the CPU 1020 reads out the program module 1093 and program data 1094 stored in the hard disk drive 1090 into the RAM 1012 as necessary, and executes each of the above-described procedures.

[0067] The program module 1093 and program data 1094 related to the control program are not limited to being stored in the hard disk drive 1090, but may be stored in a removable storage medium and read by the CPU 1020 via the disk drive 1041, etc. Alternatively, the program module 1093 and program data 1094 related to the control program may be stored in another computer connected via a network such as a LAN (Local Area Network) or a WAN (Wide Area Network), and read by the CPU 1020 via the network interface 1070.

[0068] 〔others〕 Although various embodiments have been described in detail herein with reference to the drawings, these embodiments are merely examples and are not intended to limit the present invention. In other words, the present invention makes it possible to learn, build, and update various multi-class classification models and estimate desired probabilities by changing the features used as input. The features described herein can be realized in various ways, including various modifications and improvements based on the knowledge of those skilled in the art.

[0069] Furthermore, the above-mentioned "module (-er suffix, -or suffix)" can be read as a unit, means, circuit, etc. For example, a communication module, a control module, and a storage module can be read as a communication unit, a control unit, and a storage unit, respectively. [Explanation of symbols]

[0070] 100 Information processing device 110 Communications Department 120 control section 121 Estimation Department 122 Selection section 123 Generation part 124 Output section 130 Storage section

Claims

1. an estimation unit that estimates a user image of another user who will provide an answer to an input made by the user based on information about the user; a selection unit that selects an advertisement based on the user image estimated by the estimation unit; a generation unit that generates a sentence for a generative model so that the advertisement selected by the selection unit is included in the answer; an output unit that outputs the sentence generated by the generation unit to the generative model; An information processing device comprising:

2. The information processing device according to claim 1, characterized in that the estimation unit estimates the user image using one or more of the following information about the user: user attributes, search history, browsing history, location information, conversation content, conversation trends, SNS posts, context, and follower information.

3. The information processing apparatus according to claim 1 , wherein the estimation unit estimates the user image from the information about the user using a model that has learned a relationship between information about the user and the user image.

4. The information processing device according to claim 1 , wherein the selection unit selects an advertisement that targets the user image estimated by the estimation unit for advertisement distribution.

5. The information processing device according to claim 1 , wherein the generation unit generates a sentence including the user's sentence, the user image, and the advertisement as the sentence for the generation model.

6. An information processing method executed by an information processing device, an estimation step of estimating a user image of another user who will provide an answer to an input made by the user based on information about the user; a selection step of selecting an advertisement based on the user image estimated by the estimation step; a generation step of generating a sentence for a generative model so that the advertisement selected by the selection step is included in an answer; an output step of outputting the sentence generated in the generation step to the generative model; An information processing method comprising:

7. an estimation step of estimating a user image of another user who will provide an answer to an input made by the user based on information about the user; a selection step of selecting an advertisement based on the user image estimated by the estimation step; a generation step of generating a sentence for a generative model so that the advertisement selected by the selection step is included in an answer; an output step of outputting the sentence generated in the generation step to the generative model; An information processing program characterized by causing a computer to execute the above.

Images