Scenario generation device, scenario generation method, and scenario generation program

Abstract

To reduce the burden of generating a scenario to be executed by a chatbot.SOLUTION: A scenario generation device for generating a scenario that prescribes a series of conversational contents to be executed by a chatbot to provide a user with information about a product or service includes a scenario generation unit that inputs identification information identifying the product or service designated by a scenario designer into a first learning model prepared by learning previously generated scenarios to generate a scenario corresponding to the identification information.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a scenario generation device, a scenario generation method, and a scenario generation program. [Background technology]

[0002] Chatbots, which are communication robots that can automatically hold conversations with users over chat, have been known in the past. In recent years, efforts have been made to utilize chatbot technology to provide information useful for decision-making such as purchasing various products and services, and to carry out procedures required for purchases through automated conversations. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2022-77779 Summary of the Invention [Problem to be solved by the invention]

[0004] In conventional technology, in order to use a chatbot to have a conversation with a user, a designer (communication designer) had to manually create in advance a scenario that defined a series of conversations with the user (see Patent Document 1). In such a scenario, the designer had to define a conditional branching structure (tree-type structure) according to the expected content of the user's conversation, and define a series of conversation flows separately for each condition, so that the next conversation that the chatbot would respond to would be variable depending on the content of the user's conversation. Depending on the purpose of using the chatbot, scenarios created in this way could become extremely complex, which increased the workload of preparing the scenarios.

[0005] On the other hand, there are known methods for automating responses to user inquiries by utilizing machine learning techniques such as Large Language Models (LLMs). However, these conventional methods can only respond to one-off inquiries from users, and are unable to generate scenarios that define a series of conversations with users.

[0006] The present invention has been made in consideration of these circumstances, and one of its objectives is to provide a scenario generation device, a scenario generation method, and a program that can reduce the effort required to prepare a scenario to be executed by a chatbot. [Means for solving the problem]

[0007] One aspect of the present invention is a scenario generation device that generates a scenario that specifies a series of conversational content to be executed by a chatbot to provide a user with information about a product or service, and the scenario generation device includes a scenario generation unit that generates a scenario corresponding to specific information by inputting specific information that identifies a product or service specified by the scenario designer into a first learning model prepared by learning previously generated scenarios. [Effects of the Invention]

[0008] According to one aspect of the present invention, it is possible to reduce the effort required to prepare scenarios to be executed by a chatbot. Furthermore, by adopting a configuration in which scenarios are generated using a learning model generated by learning previously generated scenarios, the quality of the generated scenarios can be improved. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating an example of a configuration for realizing a chatbot service according to a first embodiment. [Figure 2]1 is a functional block diagram showing an example of the configuration of a chatbot server 1 according to a first embodiment. [Figure 3] FIG. 2 is a diagram illustrating input and output data of the first learning model (at the time of scenario generation) according to the first embodiment. [Figure 4] FIG. 10 is a diagram illustrating input and output data of the second learning model (during auto-chat) according to the first embodiment. [Figure 5] FIG. 10 is a diagram illustrating input and output data of the third learning model (individual offer generation) according to the first embodiment. [Figure 6] 1 is a functional block diagram showing an example of the configuration of a user terminal device 100 according to a first embodiment. [Figure 7] FIG. 2 is a diagram showing an example of a conversation screen P1 displayed on the user terminal device 100 according to the first embodiment. [Figure 8] 10 is a flowchart showing an example of a sequence of steps in a scenario generation process performed by the chatbot server 1 according to the first embodiment. [Figure 9] FIG. 10 is a diagram showing an example of a scenario condition setting screen P2 displayed on the operation terminal device 200 according to the first embodiment. [Figure 10] FIG. 10 is a diagram showing an example of a scenario confirmation screen (1) P3-1 displayed on the operation terminal device 200 according to the first embodiment. [Figure 11] FIG. 10 is a diagram showing an example of a scenario confirmation screen (2) P3-2 displayed on the operation terminal device 200 according to the first embodiment. [Figure 12] FIG. 10 is a diagram showing an example of a scenario confirmation screen (3) P3-3 displayed on the operation terminal device 200 according to the first embodiment. [Figure 13] FIG. 10 is a diagram showing an example of a scenario confirmation screen (4) P3-4 displayed on the operation terminal device 200 according to the first embodiment. [Figure 14] FIG. 4 is a sequence diagram showing an example of a scenario display process according to the first embodiment. [Figure 15] FIG. 2 is a diagram showing an example of response data RD according to the first embodiment. [Figure 16]FIG. 10 is a diagram showing an example of object type scenario data SD according to the second embodiment. [Figure 17] FIG. 10 is a diagram illustrating an example of an object structure according to the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of a scenario generation device, a scenario generation method, and a scenario generation program of the present invention will be described with reference to the drawings. The scenario generation device of the embodiment uses a machine learning-based technique such as LangChain that uses a large-scale language model (LLM), thereby reducing the effort required to prepare a scenario that defines a series of conversational contents to be executed by a chatbot to provide users with information about products or services.

[0011] [First embodiment] <Chatbot service> FIG. 1 is a diagram illustrating an example of a configuration for implementing a chatbot service according to the first embodiment. The chatbot service uses a chatbot to automatically converse with users, such as businesses providing products or services (hereinafter referred to as "products, etc."), to learn about the user's hobbies, preferences, and personal background, and to suggest information on products, etc., tailored to the user. Examples of businesses include cosmetics retailers, food retailers, hospitals, human resources service providers, financial institutions, insurance companies, real estate agents, and telecommunications companies. The chatbot service is implemented primarily around a chatbot server 1. The chatbot server 1 communicates with, for example, one or more service servers 3, one or more message servers 5, one or more user terminal devices 100, and one or more operation terminal devices 200 via a communication network NW. The communication network NW includes, for example, the Internet, a local area network (LAN), a wireless base station, a provider device, and the like.

[0012] <Device configuration> <Chatbot Server 1> The chatbot server 1 provides a function for generating a scenario to a designer D who operates an operation terminal device 200. The chatbot server 1 also provides a chatbot service to a user U who operates a user terminal device 100. Figure 2 is a functional block diagram showing an example of the configuration of the chatbot server 1 according to the first embodiment. The chatbot server 1 includes, for example, a communication unit 11, a control unit 13, and a storage unit 15. The chatbot server 1 is an example of a "scenario generation device." The communication unit 11 is a communication interface for connecting to a communication network NW. The communication unit 11 is, for example, a network interface card.

[0013] The control unit 13 includes, for example, an acquisition unit 131, a conversation generation unit 132, a transmission unit 133, a scenario generation unit 134, an auto-chat unit 135, an individual offer generation unit 136, a display control unit 137, and a learning unit 138. The components of the control unit 13 are realized, for example, by a hardware processor such as a CPU executing a program (software). Some or all of these components may be realized by hardware such as an LSI (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a GPU (Graphics Processing Unit), or may be realized by a combination of software and hardware. The program may be stored in advance in a storage device (non-transitory computer-readable storage medium) such as an HDD (Hard Disk Drive) or flash memory, or may be stored in a removable storage medium (non-transitory computer-readable storage medium) such as a DVD or CD-ROM, and installed in the storage device by inserting the storage medium into a drive device.

[0014] The acquisition unit 131 acquires various information (data) from each of the service server 3, the message server 5, the user terminal device 100, and the operation terminal device 200 via the communication network NW. For example, the acquisition unit 131 acquires conversation information indicating the content of a conversation input in response to an operation by a user U from the user terminal device 100. Also, for example, the acquisition unit 131 acquires instruction information regarding scenario generation input in response to an operation by a designer D from the operation terminal device 200. The acquisition unit 131 is an example of an "acquisition unit."

[0015] The conversation generation unit 132 generates conversation text to be transmitted to the user terminal device 100 (conversation text to be presented to the user) based on conversation information acquired from the user terminal device 100 and scenario data SD for each provider pre-stored in the storage unit 15. The conversation generation unit 132 is an example of a "conversation generation unit."

[0016] The transmission unit 133 transmits conversation sentence data indicating the conversation sentence generated by the conversation generation unit 132 to the user terminal device 100. The transmission unit 133 is an example of a "transmission unit."

[0017] The scenario generation unit 134 generates a scenario using a first learning model AI1 pre-stored in the storage unit 15 based on instruction information related to scenario generation acquired from the operation terminal device 200, and stores the generated scenario as scenario data SD in the storage unit 15. The first learning model AI1 is a large-scale language model (LLM) generated by learning a large amount of data, such as past scenario data generated manually or by the scenario generation unit 134 in the past and conversation history data with past users (past chat data), as learning data. The first learning model AI1 is a model that predicts and arranges words that are likely to follow a specific word based on input text data, and outputs text in which these words are arranged. Examples of the first learning model AI1 include ChatGPT (registered trademark), LaMDA, etc. Each conversation sentence included in the scenario may include emoticons.

[0018] FIG. 3 is a diagram illustrating input and output data of the first learning model AI1 (at the time of scenario generation) according to the first embodiment. As shown in FIG. 3, when the first learning model AI1 is used for scenario generation, input data is specific information identifying a product (e.g., the website address of the business providing the product, product information indicating the details of the product, etc.), and output data is the generated scenario data. By using the website address as input data, information on companies, brands, and products provided on the web is read by web scraping, and products that can be suggested in chat are selected as product information, and their prices and features are read. The first learning model AI1 may also include, as input data, purpose information indicating the purpose of providing information on products to users (e.g., whether the purpose is to induce purchases of products (sales, conversion), or advertising of products, etc.), image information related to the products and businesses, target information indicating the type of users to whom the generated scenario is to be provided (e.g., all members, men only, women only, etc.), the type of conversation application platform to be used for distribution (e.g., conversation application service A, conversation application service B, etc.), and the user U's entry route (e.g., pop-up, trigger word, etc.). The purpose information is used to determine whether the overall purpose of the conversation is sales or something else. Image information is stored in advance, allowing appropriate images to be selected from a scenario and reflected in the scenario data. The input data for the first learning model AI1 may be obtained from information published on the web (e.g., information from an e-commerce store) via an API (Application Programming Interface). The scenario generation unit 134 is an example of a "scenario generation unit." That is, the scenario generation unit 134 inputs specific information identifying a product or service specified by a scenario designer D into the first learning model AI1, which has been prepared by learning a previously generated scenario, to generate a scenario corresponding to the specific information. The scenario generation unit 134 also inputs purpose information specified by an operator into the first learning model AI1, thereby generating a scenario corresponding to the purpose information.Furthermore, the scenario generation unit 134 generates a scenario in which images are incorporated into a series of conversational content by inputting images related to a product or service specified by the designer D into the first learning model AI1. The scenario generation unit 134 also edits the generated scenario in accordance with editing instructions from the designer D on the scenario confirmation screen displayed on the display unit.

[0019] Returning to FIG. 2, the auto-chat unit 135 inputs user input text entered as free text by the user U when presenting a scenario to the user U, and generates a reply to the user input text (smart reply function) into the second learning model AI2. The second learning model AI2 is a large-scale language model (LLM) generated by learning a large amount of data, such as past conversation history data with users (past chat data), past scenario data generated manually or by the scenario generation unit 134, and other text data, as learning data. The second learning model AI2 is a model that predicts and arranges words that are likely to follow a specific word based on the input text data. Examples of the second learning model AI2 include ChatGPT (registered trademark) and LaMDA. FIG. 4 is a diagram illustrating input and output data of the second learning model (during auto-chat) according to the first embodiment. As shown in FIG. 4, when the second learning model AI2 is used for auto-chat generation, the input data is the user input text entered by the user U, and the output data is a reply. To prevent hallucination, the auto-chat unit 135 has a check function that excludes answer sentences output from the second learning model AI2 if the answer sentences are not related to the buying and selling of goods, etc. The auto-chat unit 135 is an example of an "auto-chat unit." That is, the auto-chat unit 135 generates answers to the conversation sentences by inputting free-text conversation sentences specified by the user U when the generated scenario is presented to the user U into the second learning model AI2.

[0020] Returning to FIG. 2 , the individual offer generation unit 136 uses the third learning model AI3 to output personalized product and other offers (hereinafter, “individual offers”) for the user U. The third learning model AI3 is a machine learning model such as a neural network that is trained to output personalized products and other offers (products that the user U is likely to prefer and purchase) for the user U when user data for the user U is input. The user data includes, for example, gender, age, and past purchase history. The user data is, for example, purchase data (e.g., name, address, email address, telephone number, identification ID (social handle), etc. based on past purchase history, also referred to as “first-party data”) previously accumulated in a CRM (Customer Relationship Management) or CDP (Customer Data Platform) by a business operator managing the service server 3. The chatbot server 1 may import the purchase data accumulated in the service server 3 via, for example, an API. The user data may be linked to an identification ID (social handle) that identifies an individual issued in a conversation application. Instead of a neural network, the third learning model AI3 may be another machine learning model such as a support vector machine, a decision tree, a random forest, or a logistic regression. FIG. 5 is a diagram illustrating input and output data of the third learning model AI3 (individual offer generation) according to the first embodiment. As shown in FIG. 5, the input data of the third learning model AI3 is user data of a user U, and the output data is an individual offer. The input data may include a conversation history with the user U according to a scenario. The individual offer generation unit 136 is an example of an "individual offer generation unit." That is, the individual offer generation unit 136 generates a proposal for a product or service tailored to the user U by inputting the user data of the user U who presented the generated scenario to the third learning model AI3, which has been trained to output information about a product or service tailored to the specific user when the user data of the specific user is input.

[0021] The display control unit 137 generates information for displaying various operation screens for generating a scenario, transmits the information to the operation terminal device 200, and causes the operation terminal device 200 to display the various operation screens. The display control unit 137 is an example of a "display control unit." In other words, the display control unit 137 causes the display unit to display a confirmation screen for the scenario generated by the scenario generation unit 134.

[0022] The learning unit 138 performs learning using a large amount of data, such as past scenario data and past conversation history data generated in the past, as learning data, to generate a first learning model AI1, which is stored in the memory unit 15. The learning unit 138 also performs learning using a large amount of data, such as past conversation history data with users, past scenario data generated in the past manually or by the scenario generation unit 134, and other text data, as learning data, to generate a second learning model AI2, which is stored in the memory unit 15. The learning unit 138 separates and tokenizes the text data included in the learning data into the smallest units, learns the context and meaning of words from the obtained tokens, and generates the first learning model AI1 and the second learning model AI2.

[0023] Furthermore, the learning unit 138 generates a third learning model AI3 by learning learning data that is a combination of user data of users who have purchased products, etc. in the past and the products, etc. purchased by the users (purchase history data), and stores the third learning model AI3 in the memory unit 15. For example, the learning unit 138 learns purchasing data (first-party data) that the business operator managing the service server 3 has accumulated in CRM or CDP in the past as learning data.

[0024] The storage unit 15 is a hard disk drive (HDD), flash memory, RAM (Random Access Memory), etc. The storage unit 15 may be a network-attached storage (NAS) device accessible by the chatbot server 1 via a network. The storage unit 15 stores the first learning model AI1, the second learning model AI2, the third learning model AI3, scenario data SD, product data ID, brand data BD, user data UD, and answer data RD. The product data ID is acquired from the service server 3 or another sales management server via the communication network NW and stored in the storage unit 15. The brand data BD is acquired from the service server 3 and stored in the storage unit 15. The brand data BD may be acquired from information about the business's brand from the business's homepage or a CVS-format list provided by the business and stored in the storage unit 15. The user data UD is acquired from the service server 3 and stored in the storage unit 15. The answer data RD is acquired from the user during a conversation with the user according to the scenario and stored in the storage unit 15.

[0025] <Service Server 3> The service server 3 is managed by a business that provides products and the like. The service server 3 provides the user terminal device 100 with a site such as a homepage where the user can complete the purchase procedure for the product and the like. When the user U performs a preset trigger operation on this site, processing for providing the chatbot service is initiated. Examples of trigger operations include pressing a button that starts the chatbot service displayed in a pop-up on the site, pressing an advertising banner displayed on the conversation application, or pressing an advertising display placed by a company that uses the conversation application.

[0026] <Message Server 5> The message server 5 provides a message service (chat service) that realizes a conversation function by sending and receiving messages to the user terminal device 100, the chatbot server 1, and the service server 3. The message server 5 provides the user terminal device 100 with a conversation application that can be installed in the user terminal device 100, and the user terminal device 100 executes this conversation application to send and receive messages with other user terminal devices 100 and various servers. The message server 5 also provides an API that enables use of the message service from an external device (server), and the chatbot server 1 and the service server 3 send and receive messages with the user terminal device 100 by using this API.

[0027] <User Terminal Device 100> The user terminal device 100 is operated by a user U who purchases and uses products and the like provided by a business operator. The user terminal device 100 is, for example, a portable terminal device such as a smartphone or a tablet terminal. FIG. 6 is a functional block diagram showing an example of the configuration of the user terminal device 100 according to the first embodiment. The user terminal device 100 includes, for example, a communication unit 101, a display unit 103, an operation unit 105, a control unit 107, and a storage unit 109.

[0028] The communication unit 101 is a wireless communication module that performs wireless communication with a wireless base station connected to the communication network NW. The communication unit 101 communicates with the chatbot server 1, the service server 3, the message server 5, etc. via the wireless base station. The display unit 103 is a display device such as a liquid crystal display device. The operation unit 105 is an input interface that accepts operation instructions from the user U. The display unit 103 and the operation unit 105 may be configured as touch panel displays.

[0029] The control unit 107 controls the overall operation of the user terminal device 100. The functions of the control unit 13 are realized, for example, by a hardware processor such as a CPU executing a program (software). The control unit 107 activates a conversation application (program) AP stored in the storage unit 109 to realize a conversation function via a network. The conversation application may be an application dedicated to conversation, or may also have other functions (such as a call function, an image upload function, and a message upload function). For example, the conversation application may be an application on the provider's side that is provided by a business providing products and installed on the user terminal device 100 and that includes a conversation function. Alternatively, the conversation application may be realized using a general-purpose application program such as a web browser. For example, the conversation application may be realized by a chat function on the provider's homepage provided by a service server 3 managed by the business.

[0030] Fig. 7 is a diagram showing an example of a conversation screen P1 displayed on the user terminal device 100 according to the first embodiment. The example in Fig. 7 shows a conversation screen P1 in which a chatbot service provided by a business operator A is started and a greeting message, question, etc. is sent to a user U as a scenario. By having a conversation with a chatbot according to such a scenario, the user U can receive information tailored to him or her, as well as suggestions for products, etc.

[0031] <Operation Terminal Device 200> The operation terminal device 200 is operated by a designer D who designs a scenario for a chatbot service. The operation terminal device 200 is, for example, a personal computer, a smartphone, a tablet terminal, etc. The operation terminal device 200 has at least a display function, an input function, a storage function, etc.

[0032] <Processing flow> <Scenario generation process> Next, various processes executed in the chatbot service will be described. First, the process on the operation side in which designer D generates a scenario will be described. FIG. 8 is a flowchart showing an example of a series of scenario generation processes by the chatbot server 1 according to the first embodiment. The process shown in FIG. 8 is started, for example, when designer D operates the operation terminal device 200 to input an instruction to start scenario generation, and the chatbot server 1 receives this start instruction.

[0033] (Step S101) First, the display control unit 137 of the chatbot server 1 displays a scenario condition setting screen on the operation terminal device 200. Then, the acquisition unit 131 acquires from the operation terminal device 200 the setting information entered by designer D into the scenario condition setting screen displayed on the operation terminal device 200.

[0034] FIG. 9 illustrates an example of a scenario condition setting screen P2 displayed on the operation terminal device 200 according to the first embodiment. The example of the scenario condition setting screen P2 shown in FIG. 9 includes a first column AR1 for accepting the URL of the business for which the scenario is to be generated, a second column AR2 for accepting the objective information of the scenario, a third column AR3 for accepting the target for which the scenario is to be provided, a fourth column AR4 for accepting whether or not to accept free text input from the user, a fifth column AR5 for accepting the product or service proposal to the user in the scenario, and a sixth column AR6 for accepting images related to the product or service (images of the business, product, etc.). Designer D can input scenario conditions into these columns by operating the input interface (keyboard, mouse, etc.) of the operation terminal device 200. Other scenario conditions may include the number of characters in the conversation included in the scenario and the conversion rate. When the scenario generation button BT1 is pressed, the setting information entered by designer D is transmitted from the operation terminal device 200 to the chatbot server 1.

[0035] (Step S103) Next, the scenario generation unit 134 generates a scenario using the first learning model AI1 pre-stored in the storage unit 15 based on the setting information specified by the designer D transmitted from the operation terminal device 200. The scenario generation unit 134 generates a scenario by inputting the business's homepage address, product information, purpose information, image information, and target information included in the setting information into the first learning model AI1. There are, for example, two types of scenarios generated here. The first scenario, also referred to as an initial conversation, is a conversation scenario that is displayed only once when a user U enters the chatbot. This conversation scenario collects basic information about the user U (the most important information for developing a subsequent sales conversation), followed by product suggestions and advice. The second scenario, also referred to as a push conversation, is used to deliver additional information to the user U that has already accumulated in the chatbot's account. For example, the first day after starting to use the conversation application, a first distribution scenario is used, the second day a second distribution scenario, and the fifth day a further distribution scenario, with distribution settings being made with different time axes for each user U (also called "track push"). Alternatively, all users linked to a specific personal ID in the conversation application (e.g., registered as friends) are classified into some sort of segment, and a specific segment within that segment is distributed to everyone at the same time (also called "shot push").

[0036] (Step S105) Next, the display control unit 137 causes the operation terminal device 200 to display a scenario confirmation screen that displays the scenario generated by the scenario generation unit 134. FIGS. 10 to 12 are diagrams showing a scenario confirmation screen P3 displayed on the operation terminal device 200 according to the first embodiment. The scenario confirmation screen (1) P3-1 in FIG. 10 shows a conversation that specifies an initial conversation with the user among a series of conversations included in the scenario. The scenario confirmation screen (2) P3-2 in FIG. 11 shows a conversation that specifies a question requesting an answer from the user among a series of conversations included in the scenario. The scenario confirmation screen (3) P3-3 in FIG. 12 shows a conversation that specifies a proposal for a product or the like to the user among a series of conversations included in the scenario. The scenario confirmation screen (1) P3-1, the scenario confirmation screen (2) P3-2, and the scenario confirmation screen (3) P3-3 are actually a single screen, and are displayed consecutively by operating a scroll bar provided within the screen. In a scenario specified in this way, the conversation progresses from top to bottom. The scenario confirmation screen (1) P3-1, the scenario confirmation screen (2) P3-2, and the scenario confirmation screen (3) P3-3 may be displayed as different screens that can be switched between.

[0037] The scenario confirmation screen (1) P3-1 in FIG. 10 shows an example in which actions AC1-1 to AC1-5 are set in order as initial conversation sentences with the user. Action AC1-1 is an action that indicates the settings of the business site that is the transition source. Action AC1-2 is an action that displays a greeting, and action AC1-3 is an action that displays image 1. Action AC1-4 is an action that displays a question. Action AC1-5 is an action that displays answer options for the question sentence of action AC1-4.

[0038] The scenario confirmation screen (2) P3-2 in FIG. 11 shows an example in which actions AC2-1 to AC2-5 are set in order as questions requesting an answer from the user. Action AC2-1 is an action that displays the first question. Action AC2-2 is an action that displays answer options for the question of action AC2-1. Action AC2-3 is an action that displays the second question. Action AC2-4 is an action that displays answer options for the question of action AC2-3. Action AC2-5 is an action that displays the third question.

[0039] The scenario confirmation screen (3) P3-3 in FIG. 12 shows an example in which actions AC3-1 to AC3-2 are set in order as suggested sentences for products, etc. to the user. Action AC3-1 is an action that displays a message indicating that a product, etc. is being suggested. Action AC3-2 is an action that displays suggested products A to D corresponding to the message of action AC3-1. These suggested sentences are specified to be variable depending on the answer from user U to the question sentence included in scenario confirmation screen (2) P3-2.

[0040] Designer D can change the settings of each action displayed on the scenario confirmation screen P3 (correcting wording, adding or deleting options, adding or changing images, etc.) by operating the input interface of the operation terminal device 200. Designer D can also set a new action at any position by pressing the add action button BT2. Designer D can also decide on a scenario by pressing the decide scenario BT3.

[0041] 10 and 11, a case has been described in which an action is set to display answer options for a question, but a free input field may also be displayed that allows the user U to enter free text as a response to the question. FIG. 13 is a diagram showing an example of a scenario confirmation screen (4) P3-4 displayed on the operation terminal device 200 according to the first embodiment. Compared to the scenario confirmation screen (2) P3-2 in FIG. 11, in the scenario confirmation screen (4) P3-4 in FIG. 13, action AC2-6 in the free input field is set as an action that accepts a response to the question of action AC2-1.

[0042] (Step S107) Returning to FIG. 8, the scenario generation unit 134 determines whether or not an editing instruction has been received from the operation terminal device 200. For example, if designer D performs some editing operation on the scenario confirmation screen P3 displayed on the operation terminal device 200 and receives an editing instruction from the operation terminal device 200, the scenario generation unit 134 determines that an editing instruction has been received (step S107: YES). On the other hand, if designer D presses the scenario decision BT3 without performing any editing operation on the scenario confirmation screen P3 displayed on the operation terminal device 200 and receives a scenario decision instruction from the operation terminal device 200, the scenario generation unit 134 determines that an editing instruction has not been received (step S107: NO).

[0043] (Step S111) If it is determined that an editing instruction has been received (step S107: YES), the scenario generation unit 134 edits the scenario in accordance with the editing instruction. After that, the process returns to step S105, and the display control unit 137 causes the operation terminal device 200 to display a scenario confirmation screen that displays the edited scenario, and repeats the subsequent processes.

[0044] (Step S109) If it is determined that an edit instruction has not been received (step S107: NO), the scenario generation unit 134 decides (confirms) the scenario and stores it in the storage unit 15. This ends the processing of this flowchart.

[0045] <Scenario display process> Next, the processing on the service side where the user U experiences the chatbot service will be described. Fig. 14 is a sequence diagram showing an example of the scenario display processing according to the first embodiment. Here, the description will be given taking as an example a situation where the user U operates the user terminal device 100 and browses the provider's website using a web browser.

[0046] (Step S201) First, when a trigger operation is performed on a site in response to an operation by a user U, the user terminal device 100 transmits a trigger operation signal indicating that a trigger operation has been performed to the service server 3. The trigger operation is, for example, pressing a button that starts a chatbot service displayed in a pop-up on the site.

[0047] (Step S203) Next, in response to the trigger operation signal received from the user terminal device 100, the service server 3 transmits to the chatbot server 1 a conversation start instruction to start a series of conversations according to a scenario in the chatbot service.

[0048] (Step S205) Next, when the conversation generation unit 132 of the chatbot server 1 receives a conversation start instruction from the service server 3, it generates a conversation based on the scenario data SD stored in the memory unit 15. Here, for example, the conversation generation unit 132 generates the first conversation (first conversation) set as an initial conversation sentence with the user in the scenario associated with the business operator.

[0049] (Step S207) Next, the sending unit 133 of the chatbot server 1 sends a request to send conversation data indicating the generated conversation to the message server 5. The chatbot server 1 sends the request to send conversation data to the message server 5, for example, by using an API provided by the message server 5. Having received the request to send conversation data from the chatbot server 1, the message server 5 sends this conversation data to the user terminal device 100 (step S209).

[0050] (Step S211) Next, the user terminal device 100 starts the conversation application AP based on the conversation data received from the chatbot server 1 via the message server 5, and displays the conversation text based on the conversation data on the display unit 103. This allows the user U to check the conversation text automatically sent from the chatbot server 1.

[0051] (Step S213) Next, if the conversation data received from the chatbot server 1 via the message server 5 is a question requesting an answer, the user terminal device 100 transmits answer data indicating the answer determined in response to the user U's operation of the operation unit 105 to the message server 5. Note that if the conversation data received from the chatbot server 1 via the message server 5 is a simple message or image that does not request an answer, the user terminal device 100 may transmit receipt confirmation data indicating receipt to the message server 5. (Step S215) Having received the answer data from the user terminal device 100, the message server 5 transmits this answer data to the chatbot server 1.

[0052] (Step S217) Next, the acquisition unit 131 of the chatbot server 1 stores the answer data received from the user terminal device 100 via the message server 5 in the answer data RD of the storage unit 15. FIG. 15 is a diagram showing an example of the answer data RD according to the first embodiment. The answer data RD stores answer data acquired from users, linked to a user ID that identifies each user.

[0053] If the answer data received from the user terminal device 100 via the message server 5 is input as free text, the received answer data is stored in the memory unit 15. The auto-chat unit 135 generates an answer sentence for the user input text by inputting the answer data (user input text) to the second learning model AI2, and the transmission unit 133 transmits the generated answer sentence to the user terminal device 100 via the message server 5. Additionally, if the user U does not find an appropriate answer option for a question from the chatbot server 1 to the user U and inputs a different answer as free text, the conversation generation unit 132 stores the answer content in the memory unit 15 and proceeds to the next scenario step. In this case, the answer of the user U input as free text is ultimately used to determine the product to be proposed by the individual offer generation unit 136.

[0054] The series of processing QA from steps S205 to S217 above is repeatedly executed according to the scenario. That is, the transmission of conversations and reception of responses from objects set in order in the scenario are repeatedly executed (step S219). Finally, when the acquisition unit 131 of the chatbot server 1 receives response data from the user U in response to the conversation proposing a product or the like, the transmission unit 133 transmits purchase request data indicating a desire to purchase to the service server 3.

[0055] When the target scenario is set to be set to make an individual offer based on instructions from the designer D or the like, the individual offer generation unit 136 acquires user data of the user U from the user data UD pre-stored in the storage unit 15, and inputs the acquired user data into the third learning model AI3 to determine products, etc. personalized for the user U (products, etc. that the user U is likely to prefer and purchase).The conversation generation unit 132 then generates a conversation proposing products, etc. personalized for the user U, and the transmission unit 133 transmits conversation data indicating the generated conversation to the user terminal device 100 via the message server 5.

[0056] (Step S221) When the service server 3 receives the purchase request data from the chatbot server 1, it generates a purchase screen for performing payment processing, etc. (Step S223) Next, the service server 3 transmits purchase screen data for displaying the generated purchase screen to the user terminal device 100.

[0057] (Step S225) Next, the user terminal device 100 displays the purchase screen on the display unit 103 based on the purchase screen data received from the service server 3. (Step S227) Next, the user terminal device 100 transmits the purchase data input in response to the operation of the operation unit 105 by the user U to the service server 3.

[0058] (Step S229) Next, the service server 3 performs purchase processing, including payment processing and shipping processing, based on the purchase data received from the user terminal device 100. This completes the processing of this sequence diagram.

[0059] According to the first embodiment described above, it is possible to reduce the effort required to prepare a scenario using a machine learning technique. Furthermore, by adopting a configuration in which a scenario is generated using a learning model generated by learning previously generated scenarios, it is possible to improve the quality of the generated scenario. Furthermore, by making it possible to respond to conversational sentences entered by the user in free text using the auto-chat function, it is possible to improve user convenience. Furthermore, by making it possible to suggest products, etc. based on user data, it is possible to suggest products, etc. that are more suitable for the user. By repeating this series of processes, it is possible to accumulate data on user information and preferences and deepen understanding of the user.

[0060] [Second embodiment] Next, a second embodiment will be described. The second embodiment differs from the first embodiment in that a scenario is composed of multiple objects that divide a series of conversational content according to purpose. Below, the chatbot server 1 of the second embodiment will be described, focusing on the differences from the first embodiment.

[0061] In the second embodiment, a scenario is composed of a plurality of objects that are a series of conversational content divided (grouped) according to purpose (function, role). For example, a scenario has a structure in which a plurality of objects are related to each other and arranged in a tree structure. Each of the plurality of objects includes a plurality of conversational sentences in a specified order.

[0062] FIG. 16 is a diagram showing an example of object-type scenario data SD according to the second embodiment. The object-type scenario data SD includes one or more scenarios SC defined for each business operator. Each scenario SC is composed of multiple objects. The objects include, for example, an entry object OB1, a question object OB2, an offer object OB3, and a retention object OB4. The multiple objects are not limited to these four objects, and may include other objects set according to other functions or purposes. The multiple objects include at least an entry object that defines an initial conversation with user U, a question object that defines a question requesting an answer from user U, and an offer object that defines a proposal for a product or service to user U.

[0063] The entry object OB1 defines an initial conversation to be made when starting a conversation with a user. The entry object OB1 includes, for example, a greeting message or a question for determining the user's attributes. The question object OB2 defines a conversation (question) requesting an answer from the user to determine a product or the like to be proposed to the user. The offer object OB3 defines a conversation (proposal) content to propose a product or the like to the user based on the user's answer collected by the question object OB2. The retention object OB4 defines a conversation to bring a user who did not end up purchasing a product or the like back into the conversation. The retention object OB4 includes, for example, a conversation for push notification set for each user.

[0064] FIG. 17 is a diagram showing an example of an object structure according to the second embodiment. The object structure specifies the types of objects included in each scenario, the number of objects, how the objects are connected to each other, and so on. The example shown in FIG. 17 shows an object structure including an entry object OB1, a question object (male) OB2-1 and a question object (female) OB2-2 each connected to the entry object OB1, an offer object (male) OB3-1 connected to the question object (male) OB2-1, and an offer object (female) OB3-2 connected to the question object (female) OB2-2. In a scenario, a conversation progresses from top to bottom of the object structure set up in this way.

[0065] The scenario generation unit 134 generates an object-type scenario using a first learning model AI1 pre-stored in the storage unit 15 based on instruction information related to scenario generation acquired from the operation terminal device 200, and stores the generated object-type scenario as object-type scenario data SD in the storage unit 15. The first learning model AI1 is a large-scale language model (LLM) generated by learning using large amounts of data as learning data, such as past object-type scenario data generated in the past and conversation history data with past users (past chat data). In other words, the scenario generation unit 134 generates a scenario composed of multiple objects that divide a series of conversation contents according to purpose.

[0066] According to the second embodiment described above, the load of generating a scenario can be reduced by using a machine learning technique. Furthermore, by adopting a configuration in which a scenario is generated using a learning model generated by learning previously generated scenarios, the quality of the generated scenario can be improved. Furthermore, by using object-type scenario data, it is possible to learn and generate dialogue sentences for each object, thereby improving the accuracy of the scenario.

[0067] The above describes the form for carrying out the present invention using an embodiment, but the present invention is not limited to such an embodiment, and various modifications and substitutions can be made within the scope that does not deviate from the gist of the present invention. [Explanation of symbols]

[0068] 1...chatbot server, 11...communication unit, 13...control unit, 15...storage unit, 131...acquisition unit, 132...conversation generation unit, 133...transmission unit, 134...scenario generation unit, 135...auto chat unit, 136...individual offer generation unit, 137...display control unit, 138...learning unit, 100...user terminal device, 101...communication unit, 103...display unit, 105...operation unit, 107...control unit, 109...storage unit, 200...operation terminal device, 3...service server, 5...message server, NW...communication network

Claims

1. A scenario generation device that generates a scenario that defines a series of conversation contents to be executed by a chatbot to provide a user with information about a product or service, a display control unit that displays on a display unit a scenario condition setting screen that accepts specification of specific information that identifies a product or service specified by a scenario designer; a scenario generation unit that generates a scenario corresponding to the specific information by inputting the specific information specified on the scenario condition setting screen into a first learning model that is provided in the scenario generation device and that is prepared by learning a scenario that has been generated in the past; Scenario generation device.

2. the scenario generation unit further inputs, into the first learning model, purpose information indicating a purpose of providing information about a product or service specified by the designer, thereby generating the scenario corresponding to the purpose information. The scenario generation device according to claim 1 .

3. The purpose indicated by the purpose information includes guiding the user to purchase a product or service. The scenario generation device according to claim 2 .

4. the scenario generation unit further inputs an image relating to a product or service designated by the designer into the first learning model, thereby generating the scenario in which the image is incorporated into the scenario. The scenario generation device according to claim 1 .

5. a display control unit that displays a confirmation screen of the scenario generated by the scenario generation unit on a display unit, The scenario generation device according to any one of claims 1 to 4.

6. the scenario generation unit edits the generated scenario in response to an editing instruction from the designer on the scenario confirmation screen displayed on the display unit. The scenario generation device according to claim 5 .

7. the scenario generation unit generates the scenario composed of a plurality of objects in which the series of conversation contents are divided according to purpose. The scenario generation device according to any one of claims 1 to 4.

8. The plurality of objects include at least an entry object that defines an initial conversation with the user, a question object that defines a question to which the user is requested to respond, and an offer object that defines a proposal for the product or service to the user. The scenario generation device according to claim 7.

9. The scenario generation device further comprises an auto-chat unit that generates a reply to a conversation sentence in free text format specified by a user when the generated scenario is presented to the user, by inputting the conversation sentence in free text format specified by the user into a second learning model that is provided in the scenario generation device and that is trained to output a reply sentence corresponding to the text data when the text data is input using learning data including conversation history data with the user and past scenario data. The scenario generation device according to any one of claims 1 to 4.

10. The second learning model is provided in a device accessible via a network and is trained to output a reply sentence corresponding to text data when the text data is input using learning data including conversation history data with past users and past scenario data, and further comprises an auto-chat unit that generates a reply sentence to the conversation sentence by inputting a free text conversation sentence specified by the user when the generated scenario is presented to the user. The scenario generation device according to any one of claims 1 to 4.

11. The scenario generation device further comprises an individual offer generation unit that generates a proposal for a product or service that corresponds to a specific user by inputting the user data of the user who presented the generated scenario into a third learning model that is trained to output information about a product or service that corresponds to a specific user when the user data of the specific user is input, using learning data that is provided in the scenario generation device and is a combination of user data of a user who has previously purchased a product or service and the user's purchase history data. The scenario generation device according to any one of claims 1 to 4.

12. The third learning model is provided in a device accessible via a network and is trained to output information about products or services appropriate for a specific user when user data of the specific user is input using learning data that is a combination of user data of a user who has previously purchased a product or service and purchase history data of the user, and further comprises an individual offer generation unit that generates a proposal for a product or service appropriate for the user by inputting user data of the user who presented the generated scenario to the third learning model. The scenario generation device according to any one of claims 1 to 4.

13. A scenario generation device that generates a scenario that defines a series of conversational content to be executed by a chatbot to provide a user with information about a product or service, comprising: a display control unit that displays on a display unit a scenario condition setting screen that accepts specification of specific information that identifies a product or service specified by a scenario designer; a scenario generation unit that generates a scenario corresponding to the specific information by inputting the specific information specified on the scenario condition setting screen into a first learning model that is provided in a device accessible via a network and that is prepared by learning a scenario that has been generated in the past; Scenario generation device.

14. A scenario generation method for generating a scenario that defines a series of conversation contents to be executed by a chatbot to provide a user with information about a product or service, comprising: The computer of the scenario generation device displaying a scenario condition setting screen on the display unit for accepting specification of specific information for identifying a product or service specified by a scenario designer; generating a scenario corresponding to the specific information by inputting the specific information specified on the scenario condition setting screen into a first learning model that is provided in the scenario generation device and that is prepared by learning a scenario that has been generated in the past; Scenario generation method.

15. A scenario generation method for generating a scenario that defines a series of conversational content to be executed by a chatbot to provide a user with information about a product or service, comprising: The computer of the scenario generation device displaying a scenario condition setting screen on the display unit for accepting specification of specific information for identifying a product or service specified by a scenario designer; generating a scenario corresponding to the specific information by inputting the specific information specified on the scenario condition setting screen into a first learning model that is provided in a device accessible via a network and that is prepared by learning a previously generated scenario; Scenario generation method.

16. A scenario generation program that generates a scenario that defines a series of conversation contents to be executed by a chatbot to provide a user with information about a product or service, The computer of the scenario generation device displaying a scenario condition setting screen on the display unit for accepting specification of specific information for identifying a product or service specified by a scenario designer; generating a scenario corresponding to the specific information by inputting the specific information specified on the scenario condition setting screen into a first learning model that is provided in the scenario generation device and that is prepared by learning a scenario that has been generated in the past; Scenario generator.

17. A scenario generation program executed by a chatbot to generate a scenario defining a series of conversational content for providing a user with information about a product or service, comprising: The computer of the scenario generation device displaying a scenario condition setting screen on the display unit for accepting specification of specific information for identifying a product or service specified by a scenario designer; a first learning model that is provided in a device accessible via a network and that is prepared by learning a previously generated scenario, and that generates a scenario corresponding to the specific information by inputting the specific information that has been specified and accepted on the scenario condition setting screen; Scenario generator.

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