Chatbot control device, chatbot control method, and program

Abstract

To lighten an operation load for actualization of proposal for an article or service by a chat bot and also to improve the accuracy of the proposal.SOLUTION: A chat bot controller comprises: a first tag generation part which generates a plurality of first tags based upon information representing contents of an article or service and output data obtained by inputting a first prompt indicating the generation of the plurality of first tags related to the article or service to a first language model; a second tag generation part which generates a plurality of second tags based upon information representing sentences of a conversation between a chat bot and a user and output data obtained by inputting a second prompt indicative of the generation of the plurality of second tags related to the user to a second language model; and a proposal generation part which generates a proposal related to the article or service corresponding to the user based upon the plurality of generated first tags and the plurality of generated second tags.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a chatbot control device, a chatbot control method, and a 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] Conventional technologies employ a method for proposing products and the like suitable for each user by analyzing user information (such as hobbies, tastes, and purchasing objectives) obtained through conversations with the user on chat. To realize such user-appropriate product and other proposals, a designer (communication designer) must manually generate in advance a scenario with a conditional branching structure (tree-type structure) that defines a series of conversations with the user (see Patent Document 1). Because such scenarios tend to be complex, the workload for preparing the scenarios increases. Furthermore, if the scenario manually generated by the designer is inappropriate or does not comprehensively consider the user's information, there is a risk that products and the like unsuitable for the user will be proposed.

[0005] On the other hand, it is also possible to suggest products and other items based on user information by using a learning model based on machine learning techniques such as deep learning. However, preparing such a learning model requires associating (labeling, annotating) a huge amount of data to be analyzed (product data) with user information, which increases the workload. Furthermore, if the association is inappropriate or if the association with a wide variety of user information is not possible, there is a risk that products and other items that are not suitable for the user will be suggested.

[0006] The present invention has been made taking these circumstances into consideration, and one of its objectives is to provide a chatbot control device, a chatbot control method, and a program that can reduce the operational burden of realizing product or service suggestions by a chatbot and improve the accuracy of suggestions. [Means for solving the problem]

[0007] One aspect of the present invention is a chatbot control device that controls a chatbot that makes suggestions regarding products or services to a user, and includes: a first tag generation unit that generates multiple first tags based on output data obtained by inputting information indicating the content of the product or service and a first prompt that instructs the generation of multiple first tags associated with the product or service into a first language model; a second tag generation unit that generates multiple second tags based on output data obtained by inputting information indicating a conversation between the chatbot and a user and a second prompt that instructs the generation of multiple second tags associated with the user into a second language model; and a suggestion generation unit that generates suggestions regarding products or services tailored to the user based on the generated multiple first tags and multiple second tags. [Effects of the Invention]

[0008] According to one aspect of the present invention, it is possible to reduce the operational burden required to realize product or service suggestions by a chatbot and improve the accuracy of the suggestions. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating an example of a system configuration for realizing a chatbot service according to an embodiment. [Figure 2] 1 is a functional block diagram showing an example of the configuration of a chatbot server 1 according to an embodiment. [Figure 3] FIG. 2 is a diagram illustrating input and output data of a first language model (product tag generation) according to the embodiment. [Figure 4] FIG. 10 is a diagram illustrating input and output data of a second language model (user tag generation) according to the embodiment. [Figure 5] FIG. 10 is a diagram illustrating input and output data of a third language model (individual offer generation) according to the embodiment. [Figure 6] 1 is a functional block diagram showing an example of the configuration of a user terminal device 100 according to an embodiment. [Figure 7A] FIG. 2 is a diagram showing an example of a conversation screen P1 (initial conversation) displayed on the user terminal device 100 according to the embodiment. [Figure 7B] FIG. 10 is a diagram showing an example of a conversation screen P1 (individual offer) displayed on the user terminal device 100 according to the embodiment. [Figure 8] 10 is a flowchart showing an example of a product tag generation process by the chatbot server 1 according to the embodiment. [Figure 9] FIG. 2 is a diagram showing an example of product data PD according to the embodiment. [Figure 10] FIG. 10 is a sequence diagram illustrating an example of a chat control process in the chatbot service according to the embodiment. [Figure 11] FIG. 2 is a diagram showing an example of user data UD according to the embodiment. [Figure 12]FIG. 10 is a diagram illustrating the relationship between a user tag UT and a product tag PT when an individual offer is generated by the chatbot server 1 according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of a chatbot control device, a chatbot control method, and a program according to the present invention will be described with reference to the drawings. The chatbot generation device of the embodiment uses machine learning techniques such as PromptLearning, RetrievalAugmentedGeneration, and FineTuning using Large Language Models (LLM), thereby reducing the operational burden required for a chatbot to suggest products or services and improving the accuracy of the suggestions.

[0011] <Chatbot service> FIG. 1 is a diagram illustrating an example of a system configuration for implementing a chatbot service according to an 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 (also referred to as "insights") and propose 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 that defines a series of conversations with a user 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 an 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 "chatbot control 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 product tag generation unit 134, a user tag generation unit 135, an individual offer generation unit 136, a learning unit 137, a scenario generation unit 138, and a display control unit 139. 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 a large-scale integration (LSI), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a graphics processing unit (GPU), 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 a hard disk drive (HDD) 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 types of information (data) from 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. Furthermore, for example, the acquisition unit 131 acquires instruction information related to tag generation and scenario generation input in response to an operation by a designer D from the operation terminal device 200.

[0015] The conversation generation unit 132 generates conversation text to be sent 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 business operator pre-stored in the storage unit 15. The conversation generation unit 132 generates conversation text including suggestions for products and the like tailored to the user. The conversation generation unit 132 is an example of a "conversation generation unit."

[0016] The transmission unit 133 transmits the conversation text 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 product tag generation unit 134 generates multiple product tags based on output data obtained by inputting information indicating the contents of a product or the like and a product tag generation prompt (command, first prompt) that instructs the generation of multiple product tags (first tags) associated with the product or the like into a first language model AI1 stored in advance in the storage unit 15. The product tag generation unit 134 plays a role in extending the product tag PT by generating multiple product tags PT based on information indicating the contents of a certain product or the like. The product tag generation unit 134 is an example of a "first tag generation unit."

[0018] FIG. 3 is a diagram illustrating input and output data of a first language model AI1 (product tag generation) according to an embodiment. The first language model AI1 is a large-scale language model (LLM) generated by training a large amount of text data, including product data indicating the contents of products, etc., included in catalogs provided by businesses, etc., as training data. The first language model AI1 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 first language model AI1 include ChatGPT (registered trademark), LaMDA, Llama, and Claude. As shown in FIG. 3, when the first language model AI1 is used for product tag generation, the input data for the first language model AI1 are product data PD and a product tag generation prompt PR1, and the output data are multiple product tags PT.

[0019] A product tag PT is a character string that represents the characteristics of a product or the like. The product tag PT is expressed by a part of speech, such as a noun, adjective, verb, or adverb. The product tag generation prompt PR1 includes an instruction to generate multiple product tags PTs. The product tag generation prompt PR1 includes, for example, at least one of a specification of the category of the multiple product tags PTs to be generated and a specification of the number of multiple product tags PTs to be generated (e.g., 10 for each category). Categories of product tags PT include, for example, features, occasions, user lifestyle, and user interests. If the product is "shoes," product tag PTs in the features category include, for example, retro, leather upper, 80s, black, and durability. Similarly, if the product is "shoes," product tag PTs in the occasions category include, for example, casual outdoor, everyday use, walking around town, shopping, leisure, and city life.

[0020] For example, the product tag generation prompt PR1 includes, in addition to a string specifying the product data PD, instructions for generating multiple product tags PT, such as "Please identify the product tags to be used to suggest products. Please describe the tags in words. Generate 10 product tags for each category."

[0021] Returning to FIG. 2, the user tag generation unit 135 generates multiple user tags based on output data obtained by inputting information indicating a conversation with user U conducted via the chat function and a user tag generation prompt (command, second prompt) instructing the generation of multiple user tags (second tags) associated with user U into a second language model AI2 stored in advance in the storage unit 15. The user tag generation unit 135 generates multiple user tags UT based on output data obtained by inputting a user tag generation prompt PR2 including information indicating an answer from user U to a question included in the conversation with user U (for example, information indicating one answer from user U to one question out of multiple questions) into the second language model AI2. The user tag generation unit 135 plays a role in expanding the user tags UT by generating multiple user tags UT based on information indicating the conversation between a certain user. The user tag generation unit 135 is an example of a "second tag generation unit."

[0022] FIG. 4 is a diagram illustrating input and output data of a second language model AI2 (user tag generation) according to an embodiment. The second language model AI2 is a large-scale language model (LLM) generated by training a large amount of text data, including data indicating conversations with a user, as training data. The second language 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 language model AI2 include ChatGPT (registered trademark), LaMDA, Llama, and Claude. As shown in FIG. 4, when the second language model AI2 is used for user tag generation, the input data is conversation sentence data TD and a user tag generation prompt PR2, and the output data is a plurality of user tags UT.

[0023] A user tag UT is a character string that represents a characteristic of a user. The user tag UT is represented by a part of speech, such as a noun, adjective, verb, or adverb. The user tag generation prompt PR2 includes an instruction to generate a plurality of user tags UT associated with the user U. The user tag generation prompt PR2 includes, for example, a specification of categories of the plurality of user tags UT to be generated and a specification of the number of the plurality of user tags UT to be generated (e.g., 10 for each category). The categories of the user tag UT include, for example, features, user interests, user lifestyle, and occasions. If the product is "shoes," the user tag UT in the features category includes, for example, stylish, comfortable, formal, and durable. Similarly, if the product is "shoes," the user tag UT in the user interests category includes, for example, fashion, brand, and functionality.

[0024] For example, the user tag generation prompt PR2 includes a string specifying the content of the user's response included in the conversation with the user, as well as instructions for generating multiple user tags UT, such as "Based on the user's responses, identify the user tags to be used to suggest products. Describe the tags in words. Generate five user tags for each category. Try to minimize overlap in the tags generated for each option as much as possible."

[0025] The individual offer generation unit 136 generates a proposal for a product or service personalized for the user (hereinafter referred to as an "individual offer"). The individual offer generation unit 136 generates a proposal for a product or service tailored to the user based on the generated multiple product tags PT (first tags) and multiple user tags UT (second tags). For example, the individual offer generation unit 136 generates a proposal tailored to the user U based on output data obtained by inputting the generated multiple product tags (first tags) and multiple user tags (second tags) and an offer generation prompt (command, third prompt) that instructs the generation of a proposal tailored to the user U into a third language model AI3 stored in advance in the storage unit 15. The individual offer generation unit 136 is an example of a "proposal generation unit." The individual offer generation unit 136 may generate a proposal tailored to the user U by comparing the generated multiple product tags PT (first tags) with the multiple user tags UT (second tags).

[0026] FIG. 5 is a diagram illustrating input and output data of a third language model AI3 (individual offer generation) according to an embodiment. The third language model AI3 is a large-scale language model (LLM) generated by training a large amount of text data, including data on product tags and user tags, as training data. The third language model AI3 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 third language model AI3 include ChatGPT (registered trademark) and LaMDA. As shown in FIG. 5, when the third language model AI3 is used for individual offer generation, the input data of the third language model AI3 are multiple product tags, multiple user tags, and an offer generation prompt PR3, and the output data is an individual offer OF. The offer generation prompt PR3 includes an instruction to generate a proposal tailored to the user U. The offer generation prompt PR3 may include a specification of the number of products or services to be included in the proposal tailored to the user U (e.g., three products). The offer generation prompt PR3 also includes an instruction to generate a proposal tailored to the user based on the degree of association between the multiple product tags PT and the multiple user tags UT.

[0027] For example, the offer generation prompt PR3 includes, in addition to a character string specifying multiple product tags and multiple user tags, an instruction for generating an individual offer, such as "Based on the user tags and product tags, please identify three products to be used in proposing to the user." Note that instead of the third language model AI3, a machine learning model such as a neural network trained to output an individual offer OF when multiple product tags and multiple user tags are input may be used.

[0028] The first language model AI1, the second language model AI2, and the third language model AI3 may be configured as separate models, or may be configured as a model in which their functions are integrated. For example, the first language model AI1 and the second language model AI2 may be integrated into a single model. Furthermore, for example, the first language model AI1, the second language model AI2, and the third language model AI3 may be integrated into a single model. It has been confirmed that the performance of each LLM model varies depending on the collected data and its storage format, and the optimal model may be selected and implemented from that perspective.

[0029] 2, the learning unit 137 performs learning using a large amount of data as learning data, generates a first language model AI1, a second language model AI2, and a third language model AI3, and stores them in the storage unit 15. The learning unit 137 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 language model AI1, the second language model AI2, and the third language model AI3.

[0030] The scenario generation unit 138 generates a scenario based on instructions from the designer D input via the operation terminal device 200, and stores the generated scenario as scenario data SD in the storage unit 15. The scenario includes an initial conversation such as a greeting message or questions to the user U, and an offer to propose products or the like based on the user information obtained through the conversation with the user.

[0031] The display control unit 139 generates information for displaying various operation screens for generating product tags PT and scenarios, transmits the information to the operation terminal device 200, and causes the operation terminal device 200 to display the various operation screens.

[0032] The storage unit 15 is a hard disk drive (HDD), flash memory, RAM (Random Access Memory), etc. The storage unit 15 may be a NAS (Network Attached Storage) device that the chatbot server 1 can access via a network. The storage unit 15 stores a first language model AI1, a second language model AI2, a third language model AI3, scenario data SD, product data PD, user data UD, etc. The product data PD is acquired from the service server 3 or other sales management servers via the communication network NW and stored in the storage unit 15.

[0033] <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.

[0034] <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.

[0035] <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 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.

[0036] 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.

[0037] The control unit 107 controls the overall operation of the user terminal device 100. The functions of the control unit 107 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 provider that provides products and installed on the user terminal device 100 and that incorporates the 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 provider.

[0038] 7A is a diagram showing an example of a conversation screen P1 (initial conversation) displayed on the user terminal device 100 according to the embodiment. Fig. 7A shows a conversation screen P1 of an initial conversation in which a chatbot service provided by a business A is started and a greeting message, question, etc. is sent to a user U according to a scenario.

[0039] Fig. 7B is a diagram showing an example of a conversation screen P1 (individual offer) displayed on the user terminal device 100 according to the embodiment. As a result of acquiring information about the user U (interests, tastes, purchase purpose, etc.) in the initial conversation as shown in Fig. 7A, products etc. according to the information about the user U (in this example, three individual offers OF for product A, product B, and product C) are presented as shown in Fig. 7B.

[0040] <Operation Terminal Device 200> The operation terminal device 200 is operated by a designer D who sets product tags PT in the chatbot service and designs scenarios. 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.

[0041] <Processing flow> <Product tag generation process> Next, various processes executed in the chatbot service will be described. First, the process on the operation side, which is performed based on the operation of designer D, will be described. FIG. 8 is a flowchart showing an example of a product tag generation process by the chatbot server 1 according to the embodiment. The process shown in FIG. 8 is started, for example, in the chat control process described below, when a program is automatically triggered in response to an operation by user U on the user terminal device 100 within a chat. Alternatively, the process shown in FIG. 8 may be started, for example, when designer D operates the operation terminal device 200 to input an instruction to start product tag generation, and the chatbot server 1 receives this instruction to start.

[0042] (Step S101) First, the acquisition unit 131 of the chatbot server 1 acquires the product data PD, for example, from the memory unit 15. The acquisition unit 131 may acquire the product data PD from the service server 3, a website such as a homepage provided by the service server 3, another sales management server, etc., via the communication network NW.

[0043] (Step S103) Next, the product tag generation unit 134 generates multiple product tags based on output data obtained by inputting the product data PD and the product tag generation prompt PR1 into the first language model AI1. Note that the designer D can also change the instructions included in the product tag generation prompt by operating the operation terminal device 200. For example, the designer D can specify the category of the multiple product tags PT to be generated and the number of multiple product tags PT to be generated (e.g., 10 for each category).

[0044] (Step S105) Next, the product tag generation unit 134 stores the generated multiple product tags PT in association with the product data PD in the storage unit 15. FIG. 9 is a diagram illustrating an example of product data PD according to an embodiment. In the example illustrated in FIG. 9, various information is registered in association with a product ID that identifies a product. The product name, price, classification, category, and feature items included in the product data PD are acquired from the service server 3, websites such as homepages provided by the service server 3, other sales management servers, etc. Meanwhile, the product tags (first tag, second tag, ..., Xth tag) included in the product data PD are generated by the product tag generation unit 134. The first tag, second tag, ..., Xth tag are classified according to the category specified in the product tag generation prompt PR1. For example, the product ID "1001" includes tags such as "retro," "leather," "black," "durable," and "low-cut" as first product tags (features). This completes the processing of this flowchart.

[0045] <Chat control processing> Next, the processing on the service side when a user U experiences a chatbot service will be described. Fig. 10 is a sequence diagram showing an example of chat control processing in a chatbot service according to an embodiment. Here, the description will be given taking as an example a situation in which a user U operates a user terminal device 100 and browses a 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 the initial conversation 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. (Step S209) 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.

[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 (for example, a question requesting a selection from options showing answer candidates), 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 user data UD of the storage unit 15. FIG. 11 is a diagram showing an example of user data UD according to an embodiment. The user data UD stores answer data acquired from users, linked to a user ID that identifies each user. In the example of FIG. 11, based on the answer data from the user to the question about intended use (first question), "1 (office casual)" is stored as answer data to the first question (intended use), linked to the user ID "0001."

[0053] (Step S219) Next, the user tag generation unit 135 of the chatbot server 1 generates multiple user tags based on the output data obtained by inputting the response data to the first question (usage) and the user tag generation prompt PR2 into the second language model AI2, and stores them in the user data UD of the storage unit 15. In the example of Figure 11, user tags UT such as "stylish," "formal," "business," "office," and "casual event" are stored as first user tags (usage) linked to the user ID "0001" and linked to the response data "1 (office casual)" to the first question (usage).

[0054] The series of process QA from steps S205 to S219 above is repeatedly executed according to the scenario. That is, the transmission of conversations and the reception of responses of objects set in order in the scenario are repeatedly executed. For example, in step S219, the user tag generation unit 135 of the chatbot server 1 generates multiple user tags (second user tags (selection points)) based on the output data obtained by inputting the response data to the second question (selection points) and the user tag generation prompt PR2 into the second language model AI2, and stores them in the user data UD of the storage unit 15. Finally, when the acquisition unit 131 of the chatbot server 1 receives the response data from the user U to the last question, the process proceeds to the next step S221. In addition, in parallel with the series of process QA above, the product tag generation process is executed, and the generated multiple product tags PT are linked to the product data PD of the storage unit 15 and stored.

[0055] (Step S221) Next, the individual offer generation unit 136 generates an individual offer. The individual offer generation unit 136 generates an individual offer based on output data obtained by inputting the generated multiple product tags PT and multiple user tags UT, and an offer generation prompt PR3 that instructs the generation of a proposal based on the user U, into the third language model AI3.

[0056] FIG. 12 is a diagram illustrating the relationship between user tags UT and product tags PT when an individual offer is generated by the chatbot server 1 according to the embodiment. In the example shown in FIG. 12, the user tag UT set for the user ID "0001" and the product tag PT set for the product ID "1001" share common tags such as "leather," "black," "durable," "office," "business," and "men's." In this way, the individual offer generation unit 136 generates a proposal for a user U based on the degree of association between the generated multiple product tags PT and the multiple user tags UT. For example, the individual offer generation unit 136 generates, as an individual offer, a product that has a large number of product tags PT in common with the user tag UT of a certain user U (e.g., a threshold value or more). Note that the associated user tag UT and product tag PT do not necessarily have to be tags with the same wording; tags that are related in terms of the content indicated by the wording of the tags may be associated. The method of association can be specified in an instruction included in the offer generation prompt PR3.

[0057] (Step S223) Next, the sending unit 133 of the chatbot server 1 sends a request to send conversation data including the generated individual offer to the message server 5. (Step S225) Having received the request to send conversation data including the individual offer from the chatbot server 1, the message server 5 sends this conversation data to the user terminal device 100.

[0058] (Step S227) Next, the user terminal device 100 displays on the display unit 103 the conversational text based on the conversation data including the individual offer received from the chatbot server 1 via the message server 5. This allows the user U to check the conversational text including the individual offer automatically sent from the chatbot server 1.

[0059] (Step S229) Next, the user terminal device 100 transmits a purchase instruction determined from the individual offers in response to the user U's operation of the operation unit 105 to the message server 5. (Step S231) The message server 5, which has received the purchase instruction from the user terminal device 100, transmits this purchase instruction to the chatbot server 1. (Step S233) The transmitting unit 133 of the chatbot server 1, which has received the purchase instruction from the message server 5, transmits this purchase instruction to the service server 3.

[0060] (Step S235) When the service server 3 receives the purchase instruction from the chatbot server 1, it generates a purchase screen for performing payment processing, etc. (Step S237) Next, the service server 3 transmits purchase screen data for displaying the generated purchase screen to the user terminal device 100.

[0061] (Step S239) 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 S241) Next, the user terminal device 100 transmits to the service server 3 the purchase information input in response to the operation of the operation unit 105 by the user U.

[0062] (Step S243) Next, the service server 3 performs purchase processing including payment processing, shipping processing, etc. based on the purchase information received from the user terminal device 100. This completes the processing of this sequence diagram.

[0063] According to the above-described embodiment, it is possible to reduce the operational burden for realizing product or service suggestions by a chatbot and improve the accuracy of the suggestions. In particular, by generating product tags and user tags using a language model, it is possible to reduce the burden of manual tag generation. Furthermore, by generating product or service suggestions tailored to the user based on the product tags and user tags generated in this way, it is possible to improve the accuracy of the suggestions.

[0064] 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]

[0065] 1...chatbot server, 11...communication unit, 13...control unit, 15...storage unit, 131...acquisition unit, 132...conversation generation unit, 133...transmission unit, 134...product tag generation unit, 135...user tag generation unit, 136...individual offer generation unit, 137...learning unit, 138...scenario generation unit, 139...display control 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 chatbot control device that controls a chatbot that makes suggestions about products or services to a user, a first tag generation unit that generates the plurality of first tags based on output data obtained by inputting information indicating the content of the product or service and a first prompt that instructs generation of the plurality of first tags associated with the product or service into a first language model; a second tag generation unit that generates the plurality of second tags based on output data obtained by inputting information indicating a conversation between the chatbot and the user and a second prompt that instructs the generation of a plurality of second tags associated with the user into a second language model; a proposal generation unit that generates a proposal regarding a product or service according to the user based on the generated plurality of first tags and the generated plurality of second tags; Equipped with the proposal generation unit generates a proposal regarding a product or service suited to the user based on output data obtained by inputting the generated plurality of first tags and the plurality of second tags and a third prompt that instructs generation of a proposal regarding a product or service suited to the user based on the relevance between the plurality of first tags and the plurality of second tags into a third language model; the first prompt includes specifying a category of the first plurality of tags to be generated; the second prompt includes specifying a category of the plurality of second tags to be generated; Chatbot control device.

2. a conversation generation unit that generates a conversation sentence including a proposal for a product or service according to the generated user; a transmitting unit that transmits the generated conversation sentence to the user's terminal device; Further provided with The chatbot control device according to claim 1 .

3. the first prompt includes specifying the number of the plurality of first tags to be generated; The chatbot control device according to claim 1 .

4. the second prompt includes specifying the number of the plurality of second tags to be generated; The chatbot control device according to claim 1 .

5. the third prompt includes specifying the number of product or service suggestions to generate according to the user; The chatbot control device according to claim 1 .

6. the second tag generation unit generates the plurality of second tags based on output data obtained by inputting the second prompt, which includes an answer from the user to a question included in a conversation sentence with the user, into the second language model. The chatbot control device according to claim 1 .

7. A chatbot control method for controlling a chatbot that makes suggestions about products or services to a user, comprising: The computer generating the plurality of first tags based on output data obtained by inputting information indicating the content of the product or service and a first prompt instructing generation of the plurality of first tags associated with the product or service into a first language model; generating the plurality of second tags based on output data obtained by inputting information indicating a conversation between the chatbot and the user and a second prompt instructing generation of a plurality of second tags associated with the user into a second language model; generating a product or service proposal tailored to the user based on the generated plurality of first tags and the generated plurality of second tags; A chatbot control method, comprising: generating a proposal for a product or service tailored to the user based on output data obtained by inputting the generated plurality of first tags and the plurality of second tags and a third prompt that instructs the generation of a proposal for a product or service tailored to the user based on the relevance between the plurality of first tags and the plurality of second tags into a third language model; the first prompt includes specifying a category of the first plurality of tags to be generated; the second prompt includes specifying a category of the plurality of second tags to be generated; Chatbot control method.

8. A program for controlling a chatbot that makes product or service suggestions to a user, On the computer, generating the plurality of first tags based on output data obtained by inputting information indicating the content of the product or service and a first prompt instructing generation of the plurality of first tags associated with the product or service into a first language model; generating the plurality of second tags based on output data obtained by inputting information indicating a conversation between the chatbot and the user and a second prompt instructing generation of a plurality of second tags associated with the user into a second language model; generating a proposal for a product or service tailored to the user based on the generated plurality of first tags and the generated plurality of second tags; A program, generating a proposal for a product or service tailored to the user based on output data obtained by inputting the generated plurality of first tags and the plurality of second tags and a third prompt that instructs the generation of a proposal for a product or service tailored to the user based on the relevance between the plurality of first tags and the plurality of second tags into a third language model; the first prompt includes specifying a category of the first plurality of tags to be generated; the second prompt includes specifying a category of the plurality of second tags to be generated; program.

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