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

Abstract

To distribute appropriate advertisements for users.SOLUTION: An information processing device 100 includes: an estimation unit 121 that estimates a user image of a user based on information of 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 generation model so that the advertisement selected by the selection unit 122 is included in an answer; and an output unit 124 that outputs a sentence generated by the generation unit 123, to the generation 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] Conventionally, there are techniques for presenting answers to questions from users, and techniques for delivering advertisements related to the questions from users. [Prior art documents] [Patent documents]

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

[0004] In the conventional technology, it may not be possible to deliver an appropriate advertisement according to a user. For example, in the conventional technology, advertisements related to keywords included in a sentence entered by a user or preset user attributes are delivered, and it may not be possible to deliver an advertisement to a user while taking into consideration the type of person the user is or the context. [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 a 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 sentences for a generative model so that the advertisement selected by the selection unit is included in an answer, and an output unit that outputs the sentences generated by the generation unit to the generative model. Effect of the Invention

[0006] According to the present invention, it is possible to deliver advertisements appropriate to the user. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a diagram illustrating an overview of the process performed by an information processing device according to an embodiment. [Diagram 2] FIG. 2 is a block diagram illustrating an example of the configuration of the information processing device according to the embodiment. [Diagram 3] FIG. 3 is a diagram illustrating an example of processing performed by the information processing device 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. [Diagram 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 flowchart illustrating an example of processing by the information processing device according to the embodiment. [Figure 9] FIG. 9 is a diagram illustrating an example of a computer that executes an information processing program. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, with reference to the drawings, an embodiment of 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 the embodiment. 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 for explaining an overview of the process performed by the information processing device 100. Conventionally, a generative model such as a large-scale language model has been utilized to generate answers to questions from users. In addition, there are technologies for delivering advertisements related to questions from users. However, in the conventional technologies, there are cases where an appropriate advertisement cannot be delivered according to the user.

[0010] For example, advertisement delivery using conventional technology only delivers advertisements related to keywords contained in a user's question or preset user attributes along with the answer to the user's question, and there is a problem in that it is not possible to select and deliver advertisements taking into account the type of person the user who asked the question is or the context in which the user asked the question.

[0011] Therefore, the information processing device 100 according to this embodiment performs the following process. First, the information processing device 100 estimates a user image of a user (FIG. 1(1)) who inputs a question (request) via an application, based on information about the user. Next, the information processing device 100 selects an advertisement (FIG. 1(2)) to be delivered 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 the questioner is ~~~. Please answer the questioner including an advertisement. The question is as follows: "~~~~~~"" so that the selected advertisement is included in the answer from the generative model.

[0012] Then, the information processing device 100 outputs the generated text to the generative model. As a result, the information processing device 100 generates and outputs text to the generative model so as to respond including an advertisement corresponding to the user's persona, thereby enabling delivery of an appropriate advertisement according to the user.

[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 question includes an advertisement that corresponds to the estimated user's persona, thereby making it possible to provide the information the user is looking for as an answer and deliver an advertisement that the user wants or that the user has little psychological resistance to.

[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 each of these units may be held in a distributed manner in a plurality of devices. The processing of each 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 an electric communication 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. The information stored in the storage unit 130 includes, for example, information about the user, a user profile, information about advertisements, an algorithm, learning data for machine learning, and a learned model. Here, the information about the user includes information such as a user ID, user attributes (age, sex, place of origin, place of residence, occupation, characteristic search keywords, etc.), personality (character, values, worries, etc.), lifestyle (daily way of spending time, consumption activity patterns, friendships, etc.), search history, browsing history, location information, conversation tendency, conversation content, and context.

[0017] Here, the user image includes user attributes and is information indicating the nature of the user estimated from information about the user, and is a so-called persona. The user image may also include a summary of conversations that the target user had during a predetermined period of time in the past. For example, the user image of user A is "gender: male, age: 20 years old, eats out with friends 3 to 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 image may be stored as vector information. The user image stored as vector information is updated as needed according to information about the user (for example, the contents of conversations that the target user had).

[0018] Characteristic search keywords are search keywords of a specific user that are narrowed down by information such as the number of searches, frequency, and date and time of searches, such as the number of searches, frequency of searches, and recent searches in the search history of the user. User attributes, personality, and lifestyle include not only those registered in advance, but also those estimated from the contents of conversations (text entered on the application, and contents recognized by voice recognition). Context is information estimated from the context of the flow of conversation, etc.

[0019] As an example of a context, in a chat application, if user A contacts the user with "Have you decided on a restaurant for the weekend party?" and user B contacts the user with "We had yakiniku for that weekend party," it is possible to know that "that" is the "restaurant for the weekend party" from the flow of the conversation before and after the conversation. Note that the subject of the context estimation is not limited to the conversation content, but includes all information about the user such as search history, browsing history, and location information. Furthermore, the text and context of the conversation content may be information that has been analyzed by natural language processing. Furthermore, the information about the advertisement includes information such as the advertisement ID, advertisement type, budget, period, and targeting information (user attributes, keywords, conversation tendency, context, etc.). Note that the information stored in the storage unit 130 is not limited to the above examples.

[0020] The control unit 120 is realized using a CPU (Central Processing Unit), an NP (Network Processor), an FPGA (Field Programmable Gate Array), etc., 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.

[0021] The estimation unit 121 estimates a user image of a user based on information about the user. For example, the estimation unit 121 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 tendency, and context.

[0022] More specifically, the estimation unit 121 estimates the user profile of user A as "gender: male, age: 20s, occupation: college student, looking for a place to hold a party, likes store A" from the user attributes "gender: male, age: 20s, occupation: college student" and the context "looking for a place to hold a party, likes store A" as information about the user A. Note that the user profile estimated by the estimation unit 121 is updated as needed according to information about the user (for example, the contents of the conversation the target user had).

[0023] In addition, the estimation unit 121 estimates the user profile of user A as "gender: male, age: 30s, residence: Kanagawa" based on the user attributes "gender: male, age: 30s, residence: Kanagawa Prefecture, interested in tourist spots in Asakusa that can be visited by family" and the search history "Asakusa sightseeing family".

[0024] In addition, the estimation unit 121 estimates the user profile of user A as "gender: female, age: 20s, residence: Tokyo", based on the user attributes "gender: female, age: 20s, residence: Tokyo", location information "Shibuya", browsing history "○○ popular cafes in Shibuya", and context "planning to go cafe hopping with friends", as information about user A. The estimation unit 121 estimates the user profile of user A as "gender: female, age: 20s, residence: Tokyo, looking for popular cafes in Shibuya".

[0025] 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 takes information about the user as input and outputs a user image, and estimates the output result as the user image. For example, this model is in a server that processes information, and is independently created by a business entity (Yahoo). It is preferable that the input information is learned so that it is not used as a new answer, thereby concealing the input information, such as personal information.

[0026] 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 selects an advertisement that is posted targeting a keyword corresponding to a characteristic search keyword included in the user attribute of the user image estimated by the estimation unit 121. At this time, as an element for narrowing down the characteristic search keywords included in the user attribute of the user image, information such as the number of times, frequency, date and time searched by a certain user, such as a search keyword searched for three or more times in the past week by user A, can be appropriately used.

[0027] More specifically, the selection unit 122 selects store A as an advertisement that targets the keyword "Store A drinking party Shinjuku coupon" corresponding to the information of the characteristic search keywords "Shinjuku izakaya 5 or more people" and "Store A coupon" that represent the user profile of user A estimated by the estimation unit 121.

[0028] As another example, the selection unit 122 compares the user image estimated by the estimation unit 121 with the targeting information of the advertisement, and selects an advertisement having a high degree of match between the user image and the targeting information of the advertisement.

[0029] More specifically, the selection unit 122 compares the information on the user profile of user A estimated by the estimation unit 121, "gender: male, age: 20s, occupation: college student, context: looking for a place for a party, likes store A," with the targeting information of each advertisement, and selects store A with the highest matching rate (targeting information "college student, party, store A") as the advertisement. Note that the selection unit 122 may select the advertisement with the highest matching rate between the user profile and the targeting information of the advertisement, or may select a predetermined number of advertisements with the highest matching rate.

[0030] The generation unit 123 generates a sentence for the generative model such 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, the user image estimated by the estimation unit 121, and the advertisement selected by the selection unit 122, as the sentence for the generative model.

[0031] More specifically, the generation unit 123 generates a sentence such as "The user image of user A is '~~'. Please answer user A including the selected advertisement A. The question is as follows: '~~~~~'" as a sentence for the generation model.

[0032] As another example, the generation unit 123 generates a sentence such as "Please answer the following questions based on the user image of user A. Also, please include the following advertisement text in your answer. The input from user A is as follows: "~~~~~" The user image of user A is as follows: "~~~~~" The advertisement is as follows: "~~~~~"" as a sentence for the generation model.

[0033] At this time, the generation unit 123 can use any combination of advertisement information and user profile (including user attributes) information according to the purpose, such as, "Please include in your response the advertisement keywords for the selected advertisement as well as information on search keywords that are characteristic of the target user profile."

[0034] The output unit 124 outputs the sentences generated by the generation unit 123 to the generation model. For example, the output unit 124 outputs the sentences generated by the generation unit 123 to a trained large-scale language model that is uniquely created by a business (Yahoo). Note that it is preferable to train the input information so that it is not used as a new answer, thereby concealing the input information such as personal information.

[0035] [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 7. Figures 3 to 7 are diagrams for explaining an example of processing performed by the information processing device 100 according to the embodiment.

[0036] First, the information processing device 100 accepts input of a question such as "Teach me how to write an XX report" from user A on an application, as shown in Fig. 3. Note that, although this example shows an example in which input of a question from a user is accepted on an application used for communication, the information processing device 100 can accept input of a question or request from a user in any format. For example, the information processing device 100 may accept input of a question or request from a user in the form of voice recognition.

[0037] Next, the information processing device 100 estimates a user image of the user A who has accepted the input of the question. Fig. 4(1) is an example showing an exchange between the user A and the user B on an application used for communication. As shown in Fig. 4(1), for example, an exchange such as "User A: Have you finished the report for the XX seminar?", "User B: I still need it to pass the grade so I need to put in some effort to finish it, let's have a party when I'm done" and "User A: That's great! By the way, the store A I went to the other day was really good" is taking place between the users A and B on the application used for communication.

[0038] The estimation unit 121 of the information processing device 100 estimates the user profile of user A as “college student, looking for a place to have a party, likes store A” as shown in FIG. 4(2) from the context of the interaction on the application between user A and user B shown in FIG. 4(1) as information about the user.

[0039] Next, the selection unit 122 selects an advertisement that targets the user image estimated by the estimation unit 121 for advertisement delivery. More specifically, the selection unit 122 compares the information on the user image of user A estimated by the estimation unit 121, "college student, looking for a place to have a party, likes store A," with the targeting information of each advertisement, and selects an advertisement for store A that has a high matching rate between the user image and the targeting information of the advertisement (for example, store A's "targeting information: college student, party, store A").

[0040] Next, as shown in FIG. 5(1), the generation unit 123 generates a sentence including the question "Please teach me how to write a XX report" from the user A whose input has been accepted, the information on the user profile of the user A "college student, looking for a place to have a party, likes store A" estimated by the estimation unit 121, and the targeting information of the advertisement "Store A" selected by the selection unit 122, as shown in FIG. 5(2). The sentence includes "The user profile of the user A is 'college student, looking for a place to have a party, likes store A'. Please answer the question for user A including the advertisement for store A. The question content is as follows. 'Please teach me how to write a XX report'".

[0041] At this time, the generation unit 123 may generate a sentence including the content such as "Please reply with the advertised keyword and the user's characteristic search keyword" as the advertisement information. Furthermore, the generation unit 123 may distribute coupon information targeted at the user profile of the questioner together with the reply. For example, the generation unit 123 distributes a coupon targeted at the user profile of the questioner, user A, "student", together with the reply.

[0042] 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 generated by the generation unit 123 to the large-scale language model, and the large-scale language model provides a response including the selected advertisement, such as "The way to write a ____ report is as follows. Organize the sections in the following order: introduction, main body, conclusion, and references. ~~~~~. ~~~~~. ~~~~~. By the way, I recommend Restaurant A for the after-party! Shall we make a reservation?", as shown in Fig. 6.

[0043] Furthermore, the question received by the information processing device 100 may be related to an image. For example, a case where a question such as "Show me a map of Tokyo Station" is input from user A on an application will be described. The estimation unit 121 estimates the user profile of user A to be "hungry, frequently uses store A" from user A's search history "Tokyo Station, lunch", current location information "XX, Marunouchi, Chiyoda-ku, Tokyo", past location information "△△, Marunouchi, Chiyoda-ku, Tokyo (address of store A)", and context "hungry".

[0044] Next, the selection unit 122 compares the user profile of user A estimated by the estimation unit 121, "Looks hungry, frequents store A", with the targeting information of each advertisement, and selects an advertisement having a high degree of match between the user profile and the targeting information of the advertisement (for example, store A's "targeting information: frequent user of store A").

[0045] Next, the generation unit 123 generates a sentence including the question from user A whose input was accepted, "Show me a map of Tokyo Station," the user image of user A estimated by the estimation unit 121, "He looks hungry, and frequently visits store A," and the targeting information of the advertisement "Store A" selected by the selection unit 122, such as "The user image of user A is, 'He looks hungry, and frequently visits store A.' Please answer the question for user A, including the advertisement for store A. The question content is as follows: 'Show me a map of Tokyo Station.'"

[0046] 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 makes a response including the selected advertisement A, such as "The map around Tokyo Station is as follows. [Image] By the way, Store A is currently distributing a coupon for XX. https: / / www.xxxxxxxx", as shown in FIG. 7.

[0047] In this way, the information processing device 100 generates and outputs text for the generative model to include an advertisement corresponding to the user's persona in the response, thereby enabling delivery of an appropriate advertisement according to the user. In other words, it is possible to deliver an advertisement that the user desires or that has little psychological resistance to the user while providing information that the user desires as a response.

[0048] 〔flowchart〕 Next, a flow of processing by the information processing device 100 will be described with reference to Fig. 8. Fig. 8 is a flowchart showing an example of a flow of processing according to this embodiment.

[0049] 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.

[0050] Next, the information processing device 100 accepts a question (request) from the user (step S102). For example, the information processing device 100 accepts a question sentence input by the user on an application, and stores it in the storage unit 130.

[0051] Next, the estimation unit 121 estimates a user image of the user based on the information about the user (step S103). For example, the estimation unit 121 estimates the user image by 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, and context).

[0052] 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.

[0053] 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.

[0054] 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 that is independently created by a business (Yahoo).

[0055] 〔effect〕 Thus, the information processing device 100 according to the embodiment includes an estimation unit 121 that estimates a user image of a 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 sentences 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 sentences generated by the generation unit 123 to the generative model.

[0056] As a result, the information processing device 100 estimates a persona from the attributes of the user who accepted the question, selects an advertisement corresponding to the persona, and generates and outputs text for the generative model so that the advertisement is included in the answer to the question, thereby enabling the delivery of an appropriate advertisement according to the user.

[0057] In addition, the information processing device 100 generates and outputs text for a generative model so that the answer to a user's question includes an advertisement that corresponds to the estimated user's persona, thereby making it possible to provide the information the user is looking for as an answer and deliver an advertisement that the user is looking for or that has little psychological resistance to the user.

[0058] The estimation unit 121 of the information processing device 100 according to the embodiment estimates a user image using one or more of user attributes, search history, browsing history, location information, conversation content, conversation tendency, and context as information related to the user.

[0059] As a result, the information processing device 100 estimates a persona using information about the user who accepted the question, such as user attributes, search history, browsing history, location information, conversation content, conversation tendencies, and context information, selects an advertisement corresponding to the persona, and generates and outputs text for the generative model so that the answer to the question includes the advertisement, thereby enabling the delivery of an appropriate advertisement according to the user.

[0060] 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 estimates a persona from attributes of a user who has accepted a question using the model, selects an advertisement corresponding to the persona, and generates and outputs a sentence for the generative model so that the answer to the question includes an advertisement, thereby enabling delivery of an appropriate advertisement according to the user.

[0061] 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 a persona estimated from attributes of a user who has accepted a question, and generates and outputs a sentence for a generative model so that an advertisement is included in an answer to the question, thereby enabling the delivery of an appropriate advertisement according to the user.

[0062] 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 delivery of an appropriate advertisement according to the user.

[0063] 〔program〕 It is also possible to create a program in which the processes executed by the information processing device 100 described in the above embodiment are written in a language executable by a computer. In this case, the same effects as those of the above embodiment can be obtained by having the 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 realize the same processes as those of the above embodiment.

[0064] Fig. 9 is a diagram showing an example of a computer that executes an information processing program. As shown in Fig. 9, the controller 200, the computer 1000, for example, has 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. Each of these components is connected by a bus 1080.

[0065] The memory 1010 includes a ROM (Read Only Memory) 1011 and a RAM 1012. The ROM 1011 stores 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 an 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.

[0066] 9, 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 memory 1010, for example.

[0067] Furthermore, the control program is stored in the hard disk drive 1090, for example, as a program module in which instructions to be executed by the computer 1000 are written. Specifically, a program module 1093 in which each process to be executed by the controller 2000 described in the above embodiment is written is stored in the hard disk drive 1090.

[0068] 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 the program data 1094 stored in the hard disk drive 1090 into the RAM 1012 as necessary, and executes each of the above-mentioned procedures.

[0069] The program module 1093 and program data 1094 relating to the control program are not limited to being stored in the hard disk drive 1090, and may be stored in, for example, a removable storage medium and read by the CPU 1020 via the disk drive 1041 or the like. Alternatively, the program module 1093 and program data 1094 relating 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.

[0070] 〔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 is capable of learning, constructing, and updating various multi-value classification models and estimating a desired probability by changing the feature amount used as an input. The features described herein can be realized by various methods, including various modifications and improvements based on the knowledge of those skilled in the art.

[0071] Furthermore, the above-mentioned "module (-er suffix, -or suffix)" can be read as a unit, a means, a 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]

[0072] 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 a 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 an answer; an output unit that outputs the sentence generated by the generation unit to the generative model; 13. An information processing device comprising:

2. The information processing device according to claim 1 , wherein the estimation unit estimates the user image using one or more of user attributes, search history, browsing history, location information, conversation content, conversation tendency, and context as information about the user.

3. The information processing apparatus according to claim 1 , wherein the estimation unit estimates the user image from information about the user by 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 delivery.

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

6. An information processing method executed by an information processing device, an estimation step of estimating a user profile of 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 sentences 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 by the generation step to the generative model; 13. An information processing method comprising:

7. an estimation step of estimating a user profile of 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 sentences 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