Information processing system

Abstract

To provide proposal information utilizing a hypothesis.SOLUTION: An information processing system according to an aspect of the present disclosure is configured to: receive input of attribute data including attribute information related to an attribute of a customer; input the received attribute data and preference data storing preference information related to a preference of a market and / or a consumer to a large scale language model (LLM) to generate a persona of the customer reflecting the preference information for the attribute information; and generate proposal information to be proposed to the customer based on the persona generated by the large scale language model.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present disclosure relates to an information processing system. [Background technology]

[0002] Since the emergence of generative AI (Artificial Intelligence), sales support using large language models (LLMs) has also begun to be considered in the sales field. For example, an information processing device is known that receives an answer to a question corresponding to a query sentence from a generative AI device and acquires command information using the answer (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 7370118 Summary of the Invention [Problem to be solved by the invention]

[0004] However, because the sentences generated by LLM based on prompts are created while predicting the probability of the next word appearing, they tend to be general in nature. As a result, the generated sentences often lack sufficient evidence for their hypotheses, making it problematic to use proposal information based on the generated sentences in practical applications such as sales in business situations.

[0005] Therefore, one of the objects of the present disclosure is to provide an information processing system that can provide proposal information that utilizes hypotheses. [Means for solving the problem]

[0006] An information processing system according to one embodiment of the present disclosure accepts input of attribute data including attribute information relating to customer attributes, inputs the accepted attribute data and preference data storing preference information relating to market and / or consumer preferences into a large-scale language model (LLM), generates a persona of the customer that reflects the preference information for the attribute information, and generates proposal information to be proposed to the customer based on the persona generated by the large-scale language model. [Effects of the Invention]

[0007] According to one aspect of the present disclosure, it is possible to provide proposed information that utilizes hypotheses. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a diagram illustrating an example of a schematic configuration of an information processing system according to an embodiment. [Figure 2] FIG. 10 is a diagram illustrating an example of a flowchart of a suggested information output process according to an embodiment of the present invention. [Figure 3] FIG. 2 illustrates an example of an attribute database containing attribute data stored on a device. [Figure 4] FIG. 2 is a diagram illustrating an example of a preference database including preference data stored in an information processing server. [Figure 5] FIG. 10 is a diagram illustrating an example of a persona generated in this embodiment. [Figure 6] FIG. 10 is a diagram illustrating an example of a persona generated in this embodiment. [Figure 7] FIG. 10 is a diagram illustrating an example of generated proposal information. [Figure 8] FIG. 2 is a diagram illustrating an example of a functional configuration of an information processing server according to an embodiment. [Figure 9] FIG. 2 is a diagram illustrating an example of a hardware configuration of an information processing server according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In the following description, identical parts are designated by the same reference numerals. Since identical parts have the same names, functions, etc., detailed description thereof will not be repeated.

[0010] (Information Processing System) Fig. 1 is a diagram illustrating an example of a schematic configuration of an information processing system according to an embodiment. The information processing system 1 illustrated in Fig. 1 includes a device 10, an information processing server 20, and a customer terminal 30. The information processing system 1 does not necessarily include the customer terminal 30.

[0011] The device 10 may be a fixed communication terminal such as a personal computer (PC) or a server, or may be a portable terminal (mobile communication terminal) such as a mobile phone, a smartphone, or a tablet terminal. In other words, the device 10 in the present disclosure can be interpreted as a communication device.

[0012] The device 10 may be wired and / or wireless (e.g., Long Term Evolution (LTE), 5G, th The information processing server 20 and the customer terminal 30 may be communicated with through a network N via Generation New Radio (5G NR), Wi-Fi (registered trademark), etc. The network N is configured by the Internet or a dedicated line.

[0013] The information processing server 20 may be a fixed communication terminal such as a personal computer or a server. The information processing server 20 may be a server for acquiring customer attribute information input to the customer terminal 30. The information processing server 20 may also acquire customer attribute information from an external server without using the customer terminal 30. Note that the term "server" in the present disclosure may be interpreted as an apparatus, a device, a circuit, or the like.

[0014] The information processing server 20 may communicate with the device 10 and the customer terminal 30 through the network N via wired and / or wireless communication, such as Wi-Fi.

[0015] The customer terminal 30 may be a fixed communication terminal such as a personal computer or a server, or may be a portable terminal (mobile communication terminal) such as a mobile phone, a smartphone, or a tablet terminal. In other words, the customer terminal 30 in the present disclosure can be interpreted as a communication device.

[0016] The customer terminal 30 may communicate with the device 10 and the information processing server 20 through the network N via wired and / or wireless communication, such as Wi-Fi.

[0017] An example of the functional configuration and hardware configuration of each device such as the device 10, the information processing server 20, and the customer terminal 30 will be described later.

[0018] Note that this system configuration is an example and is not limited to this. For example, although each device is configured to include one of each in FIG. 1, the number of each device is not limited to this and multiple devices may exist. The information processing system 1 may be configured to not include some devices, or may be configured such that the functions of one device are realized by multiple devices.

[0019] The functions of a plurality of devices may be realized by one device. For example, the device 10 and the information processing server 20 may be implemented on one server.

[0020] (Proposal information output method) A proposed information output method according to an embodiment of the present disclosure will be described below. The proposed information output method may be applied to the information processing system 1 described above. An example will be described below in which a customer visits a company's real estate information site and requests information about a property they are interested in. The information processing server 20 generates a persona based on attribute data including customer attribute information entered through a web form when the customer requests information, and preference data storing preference information related to market and / or consumer preferences, and generates proposed information based on the generated persona. A customer representative calls the customer based on the generated proposed information and guides them to a property tour. In this embodiment, the output proposed information can be used in the sales field in business situations.

[0021] <Proposal information output process> FIG. 2 is a diagram illustrating an example of a flowchart of a suggested information output process according to a suggested information output method according to an embodiment.

[0022] First, the device 10 determines whether or not input of customer attribute information has been accepted from a web form on the web via the customer terminal 30 based on an operation of the customer (individual customer) (step S101). The customer attribute information is information indicating the attributes of the customer. Examples of the customer attributes include customer identification information, name, age, gender, family structure, living environment, budget, and request information. In this embodiment, the age and gender of the customer are accepted as input of the customer attribute information.

[0023] In the above-described embodiment, the device 10 accepts input of customer attribute information from a web form on the web through the customer terminal 30 based on the customer's operation, but this is not limited to this. For example, the device 10 may accept input of customer attribute information that has been previously accepted through market research (questionnaire) without going through the customer terminal 30.

[0024] In the above-described embodiment, the device 10 determines whether or not input of customer attribute information has been accepted from a web form on the web through the customer terminal 30 based on the customer's operation, but this is not limiting. For example, the information processing server 20 may determine whether or not input of customer attribute information has been accepted from a web form on the web through the device 10 based on the customer's operation.

[0025] If the input of attribute information has not been accepted (step S101: NO), the process waits. If the input of attribute information has been accepted (step S101: YES), the device 10 creates attribute data based on the customer attributes included in the accepted attribute information (step S102). The device 10 stores the created attribute data in the device 10 (step S103).

[0026] In the above-described embodiment, the device 10 stores the created attribute data in the device 10, but this is not limited to this. For example, the device 10 may store the created attribute data in the information processing server 20.

[0027] FIG. 3 is a diagram showing an example of an attribute database including attribute data stored in the device 10. The attribute data is stored in the attribute database of the device 10. The attribute data may be zero party data or first party data. st Zero-party data is data that customers voluntarily share with a company, and includes information entered in chats on Network N. First-party data is data collected independently by a company, and includes demographics, customer purchase history, browsing history, and behavioral history. Demographics is data based on demographics, and includes attributes such as age, gender, residential area, income, occupation, and family structure.

[0028] An attribute database containing attribute data may be stored in the information processing server 20. While the attribute database is stored in the device 10, this is not a limitation and the database may be stored in another external server (not shown). In the embodiment of FIG. 3, the attribute information stored in the attribute database includes customer identification information, name, age, gender, family composition, and living environment information as customer attributes. An example of customer identification information includes a user ID. An example of a customer's living environment includes information about the customer's current residence. The attribute database may also store customer budgets and requests as customer attribute information. While age is listed as an example of customer identification information, this is not a limitation and customers may be classified by generation or specific age groups. The attribute database may store at least one or more pieces of attribute information. A customer's budget refers to, for example, the total amount needed to cover the costs associated with purchasing a home. For example, a customer's budget may be stored as attribute information, such as "70 million yen." A customer's requests refer to specific hopes and requirements for a product or service. For example, a customer request such as "I want a famous designer to design it" may be stored as attribute information. When the information processing server 20 generates proposed information, which will be described later, the device 10 stores the proposed information in association with the attribute information stored in the attribute database.

[0029] The device 10 transmits the attribute data stored in the attribute database to the information processing server 20 (step S104). The attribute data transmitted by the device 10 may include information other than the attribute information input by the device 10 in step S101.

[0030] The information processing server 20 receives the attribute data transmitted from the device 10 (step S105). The received attribute data includes the attribute information input by the device 10 accepted in step S101. The received attribute data is not limited to the attribute information input accepted in step S101, and may be attribute information acquired by an external server (not shown).

[0031] The information processing server 20 inputs the received attribute data and preference data into a large-scale language model (LLM) (step S106). The information processing server 20 is equipped with a function for accessing the API of the LLM. ChatGPT (registered trademark) (Generative Pretrained Transformer) is used as the large-scale language model. That is, in this embodiment, an internal or external LLM such as ChatGPT is used to generate the suggested information. The large-scale language model is not limited to ChatGPT, and any LLM that performs natural language processing can be used.

[0032] FIG. 4 is a diagram showing an example of a preference database including preference data stored in the information processing server 20. The preference data is stored in the preference database of the information processing server 20. The preference data is also called insight data. The preference database is stored in the information processing server 20, but is not limited to this, and may be stored in another external server (not shown). The preference data stored in the preference database is data acquired in advance through market research. For example, the preference data stored in the preference database is data collected in advance through market research such as questionnaire surveys, telephone surveys, or street interviews, and includes information on consumer preferences and trends in the market.

[0033] In the embodiment of FIG. 4, preference information such as priorities, image, decision factors, and awareness is stored as market and / or consumer preferences. Preference information is stored for each attribute information. In FIG. 4, among preference information corresponding to customer attributes, the difference between the percentage of preference information for a specific attribute, the percentage of all attributes, and the percentage of preference information for a specific attribute relative to the percentage of all attributes is stored. In FIG. 4, an example of a specific attribute is male, aged 35 to 39, but this is not limited thereto, and the percentage of preference information for all attributes may be stored. In FIG. 4, priorities for home purchases are an example of priorities for the market and / or consumers. Awareness and behavior regarding housing (real estate) is an example of the image of the market and / or consumers. Decision factors for making a final decision on a home are an example of decision factors for the market and / or consumers. Attitudes toward family are an example of the awareness of the market and / or consumers. FIG. 4 is an example of preference information, but is not limited thereto.

[0034] The LLM of the information processing server 20 weights the preference information corresponding to the input attribute information according to the proportion. That is, preference information with a high proportion can be weighted highly, and preference information with a low proportion can be weighted lightly.

[0035] The information processing server 20 generates a persona based on the attribute data and preference data input to the LLM in step S106 (step S107).

[0036] 5 and 6 are diagrams showing an example of a persona generated in this embodiment. The persona includes a virtual customer profile that embodies a customer as a hypothetical character by reflecting preference information against attribute information.

[0037] When creating a customer profile, the information processing server 20 uses a generation AI such as LLM to analyze the customer's preference information based on the preference data stored in the preference database, corresponding to the customer's attribute information, and summarizes the customer's attitudes regarding the preference information. The attribute information is also referred to as target information. In the example of Figure 5, the information processing server 20 summarizes the customer's attitudes regarding home purchases using the generation AI based on the attribute information and preference information. In this embodiment, summarizing the attitudes means understanding the customer's intentions, motivations, and values, and summarizing and expressing them. Summarizing the attitudes reveals the customer's deep-seated needs and desires, which can be used to improve sales pitches and customer service in business situations. In the embodiment of Figure 5, the summarized attitudes include priorities for home purchases, attitudes and behaviors regarding homes, information elements to consider when purchasing a home, information sensitivity, shopping attitudes, and attitudes toward careers, housework, and family.

[0038] The information processing server 20 generates a persona using a generation AI such as LLM based on the summarized consciousness. FIG. 6 is a diagram showing an example of a persona generated based on the summarized consciousness. The persona includes a virtual customer profile. The persona may include information other than the virtual customer profile. The customer profile includes one or at least two of a persona hypothesis, a psychographic hypothesis, a needs hypothesis, a problem hypothesis, and an insight hypothesis. In FIG. 6, a persona hypothesis, a psychographic hypothesis, and a needs hypothesis are generated as the customer profile of the persona. A problem hypothesis and an insight hypothesis may also be generated as the customer profile of the persona. Preference information reflects market and / or consumer preferences, and therefore is well-founded information for hypotheses. Similarly, a persona generated based on preference information is well-founded information for hypotheses. Furthermore, since a persona is well-founded information for hypotheses that reflect market and / or consumer preferences, the customer profile included in the persona is also well-founded information for hypotheses.

[0039] In this embodiment, the persona hypothesis is information that indicates the characteristics of the customer desired by the customer. The psychographic hypothesis is information that indicates the psychological background of the customer, such as their values ​​and hobbies. The needs hypothesis is information that identifies the specific requests and desires that the customer has. The problem hypothesis is information that identifies the specific problems and difficulties that the customer faces. The insight hypothesis is information that attempts to reveal the deep motivations and psychological factors behind the customer's behavior, decisions, and needs.

[0040] The information processing server 20 generates proposed information based on the generated persona using a generating AI such as LLM (step S108). Fig. 7 is a diagram showing an example of the generated proposed information.

[0041] The proposal information is information shown when proposing a product and / or service to a customer. The proposal information is generated as a text message composed of sentences. The proposal information may also be information composed of audio or pictures. For example, the proposal information is generated as sentences when a customer service representative proposes a product and / or service to the customer. As the proposal information, for example, a sales script (talk script), a customer service scenario, a sales strategy, a content message, etc., for a field sales or inside sales representative to make a proposal can be generated. In the embodiment shown in FIG. 7, information on the approach, presentation, closing, etc. is generated as the proposal information. The proposal information generated based on a persona that reflects the market and / or consumer preferences is well-founded information, similar to preference information.

[0042] In this embodiment, the information processing system 1 generates a persona, thereby creating a virtual customer model that mimics specific characteristics and behavioral patterns. Then, proposal information can be generated based on the generated persona. This allows sales pitches and approaches based on proposal information that utilizes well-founded hypotheses based on the personalized persona, even for customers with limited attribute information, leading to the acquisition of sales qualified leads (SQL).

[0043] In the above-described embodiment, the proposal information is generated as text when a customer service representative proposes a product and / or service to the customer, but this is not limited to this. For example, the text may be used by a chatbot that answers customer questions to propose a product and / or service to the customer. This allows responses to customers for whom sufficient attribute information is unavailable to be provided using sales pitches and approaches based on proposal information that utilizes well-founded hypotheses based on personalized personas, leading to the acquisition of SQL.

[0044] The information processing server 20 transmits the generated proposal information to the device 10 (step S109). The device 10 stores the proposal information transmitted from the information processing server 20 (step S111). Specifically, the device 10 stores the proposal information in association with each piece of attribute information in the attribute database shown in FIG. 3. For example, as shown in FIG. 3, the first to third proposal information are stored in association with the identification information "001" to "003", respectively.

[0045] The device 10 determines whether or not the output of the proposed information has been accepted (step S112). The output of the proposed information is accepted based on, for example, an input operation by a person in charge of operating the device 10 or an input operation by a customer operating the customer terminal 30. If the output of the proposed information has not been accepted (step S112: NO), the process waits. If the output of the proposed information has been accepted (step S112: YES), the device 10 outputs the proposed information shown in FIG. 7 generated by the information processing server 20 in step S108 (step S113). The proposed information is output by being displayed on a display or the like of the device 10. When this process ends, the proposed information output process ends.

[0046] According to the embodiment described above, for example, the information processing server 20 inputs attribute information related to customer attributes and preference data storing preference information related to market and / or consumer preferences into the LLM, thereby generating a customer persona that reflects the preference information for the attribute information.The information processing server 20 then inputs the generated persona into the LLM, thereby generating proposal information to be proposed to the customer.Because the persona is information based on hypotheses that reflect market and / or consumer preferences, even for customers with limited attribute information, it is possible to make sales pitches and approaches based on proposal information that utilizes well-founded hypotheses based on the personalized persona, leading to the acquisition of SQL.

[0047] Furthermore, the proposal information can be used by a chatbot that answers customer questions as text to propose products and / or services to customers. Because personas are hypothetical, well-founded information that reflects the market and / or consumer preferences, even for customers for whom sufficient attribute information is unavailable, responses can be made using sales talks and approaches based on proposal information that utilizes well-founded hypotheses based on personalized personas, which can lead to the acquisition of SQL.

[0048] (Device configuration) 8 is a diagram showing an example of the functional configuration of an information processing server according to an embodiment. As shown in this example, the information processing server 20 has a control unit 110, a storage unit 120, a communication unit 130, an input unit 140, and an output unit 150. Note that this example mainly shows functional blocks characteristic of this embodiment, and the device 10 may also have other functional blocks necessary for other processing. Also, the configuration may not include some of the functional blocks.

[0049] The control unit 110 controls the device 10. The control unit 110 can be configured by a controller, a control circuit, or a control device that is described based on common understanding in the technical field to which the present disclosure relates.

[0050] The storage unit 120 stores (holds) information used in the device 10. The storage unit 120 can be configured, for example, by a memory, storage, storage device, or the like that is described based on common understanding in the technical field to which the present disclosure relates.

[0051] The communication unit 130 communicates with other communication devices (apparatus, servers, etc.) via a network. The communication unit 130 may output various received information to the control unit 110.

[0052] The communication unit 130 may be configured by a transmitter / receiver, a transmission / reception circuit, or a transmission / reception device that is described based on common understanding in the technical field to which the present disclosure relates. Note that the communication unit 130 may be configured by a transmission unit and a communication unit.

[0053] The input unit 140 accepts input through operation by a customer. The input unit 140 may also be connected to a predetermined device, storage medium, etc., and may accept data input. The input unit 140 may output the input result to the control unit 110, for example.

[0054] The input unit 140 can be configured with input devices such as a keyboard, a mouse, and buttons, input / output terminals, input / output circuits, etc., which are explained based on common understanding in the technical field to which the present disclosure relates. The input unit 140 may also be configured as an integrated unit with a display unit (for example, a touch panel).

[0055] The output unit 150 outputs data, content, etc. in a format that can be perceived by the customer. For example, the output unit 150 may be configured to include a display unit that displays images, an audio output unit that outputs audio, etc.

[0056] The display unit may be configured with a display device such as a monitor or the like that is described based on common understanding in the technical field to which the present disclosure relates, and the audio output unit may be configured with an output device such as a speaker that is described based on common understanding in the technical field to which the present disclosure relates.

[0057] The output unit 150 may be configured to include, for example, an arithmetic unit, an arithmetic circuit, an arithmetic device, a player, an image / video / audio processing circuit, an image / video / audio processing device, an amplifier, etc., which are described based on common understanding in the technical field to which the present disclosure relates.

[0058] Note that any one or a combination of the communication unit 130, the input unit 140, and the control unit 110 may be referred to as a reception unit. The reception unit may receive input of attribute data including attribute information relating to attributes of a customer.

[0059] The control unit 110 may perform processing based on the steps shown in FIG.

[0060] The device 10 may also have the same configuration as that shown in Fig. 8. Those skilled in the art can understand the description related to the information processing server 20 in the description of Fig. 8 by appropriately replacing it.

[0061] Some units will be described below as examples. The symbols of the devices corresponding to the functional blocks in Fig. 8 are expressed by applying the first digit of the symbol indicating each device (for example, the first digit "2" of "20" for the information processing server 20) to the first digit of the symbol in the diagram.

[0062] The communication unit 230 of the information processing server 20 may receive attribute data including attribute information relating to attributes of the customer. The communication unit 230 of the information processing server 20 may also transmit proposal information to be proposed to the customer.

[0063] The control unit 210 of the information processing server 20 may input attribute data including attribute information regarding customer attributes and preference data storing preference information regarding market and / or consumer preferences into a large-scale language model (LLM) to generate a customer persona that reflects the preference information for the attribute information.

[0064] The control unit 210 of the information processing server 20 may generate proposal information to be proposed to the customer based on the persona generated by the large-scale language model.

[0065] The control unit 210 of the information processing server 20 may generate a persona as a virtual customer profile in which a customer is embodied as a hypothetical character.

[0066] The control unit 210 of the information processing server 20 may generate a persona including any one or at least two or more of a persona hypothesis, a psychographic hypothesis, a needs hypothesis, a problem hypothesis, and an insight hypothesis.

[0067] The control unit 210 of the information processing server 20 may include, as attribute information, at least one of the customer's identification information, name, age, sex, family structure, living environment, budget, and requests.

[0068] The control unit 210 of the information processing server 20 may include, as preference information, at least one of the market and / or consumer priorities, image, decision-making factors, and awareness in the preference data.

[0069] The control unit 210 of the information processing server 20 may generate a persona and generate suggested information based on the generated persona.

[0070] The control unit 210 of the information processing server 20 may input attribute data created based on attribute information input through a web form on a website based on an input operation by a customer.

[0071] The control unit 210 of the information processing server 20 may input attribute data created based on attribute information previously acquired from customers through market research.

[0072] The control unit 210 of the information processing server 20 may generate proposal information as text that a person in charge of dealing with a customer uses to propose a product and / or a service to the customer.

[0073] The control unit 210 of the information processing server 20 may generate suggestion information as text for a chatbot that answers questions from customers to suggest products and / or services to customers.

[0074] The output unit 150 of the device 10 may output the proposal information generated by the information processing server 20.

[0075] (Hardware configuration) The block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of hardware and / or software. Furthermore, the means for realizing each functional block is not particularly limited. That is, each functional block may be realized by a single physically coupled device, or may be realized by two or more physically separate devices connected by wire or wirelessly.

[0076] For example, an apparatus (such as the information processing server 20) according to an embodiment of the present disclosure may function as a computer that performs processing of the schedule setting method of the present disclosure. Fig. 9 is a diagram illustrating an example of a hardware configuration of an information processing server according to an embodiment. The above-described device 10, information processing server 20, etc. may be physically configured as a computer including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, etc.

[0077] In this disclosure, terms such as apparatus, circuit, device, unit, and server can be used interchangeably. The hardware configuration of the device 10, the information processing server 20, and the like may be configured to include one or more of the devices shown in the drawings, or may be configured to exclude some of the devices.

[0078] For example, although only one processor 1001 is shown, there may be multiple processors. Furthermore, processing may be performed by one processor, or processing may be performed by two or more processors simultaneously, serially, or in other ways. Furthermore, processor 1001 may be implemented by one or more chips.

[0079] Each function in the device 10, the information processing server 20, etc. is realized by loading specified software (programs) onto hardware such as the processor 1001, memory 1002, etc., so that the processor 1001 performs calculations and controls communication via the communication device 1004, reading and / or writing of data in the memory 1002 and storage 1003, etc.

[0080] The processor 1001 controls the entire computer by running, for example, an operating system. The processor 1001 may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. Note that each unit such as the control unit 110 described above may be realized by the processor 1001.

[0081] The processor 1001 may also control the entire computer by a method of searching for the minimum value (global minimum) of an arbitrary objective function from an arbitrary set of solution candidates through a process using quantum fluctuations by quantum annealing, thereby making it possible to provide proposal information that utilizes hypotheses.

[0082] The processor 1001 also reads programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002, and executes various processes in accordance with these. The programs used are those that cause a computer to execute at least some of the operations described in the above-described embodiments. For example, the control unit 110 may be realized by a control program stored in the memory 1002 and running on the processor 1001, and similar implementations may be made for other functional blocks.

[0083] The memory 1002 is a computer-readable recording medium and may be configured by at least one of, for example, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), an electrically EEPROM (EEPROM), a random access memory (RAM), or other suitable storage medium. The memory 1002 may also be referred to as a register, a cache, a main memory, or the like. The memory 1002 may store executable programs (program codes), software modules, and the like for implementing a method according to one embodiment.

[0084] The storage 1003 is a computer-readable recording medium, and may be configured by at least one of a flexible disk, a floppy disk, a magneto-optical disk (e.g., a compact disk (CD-ROM (Compact Disc ROM)), a digital versatile disk, a Blu-ray (registered trademark) disk), a removable disk, a hard disk drive, a solid-state drive, a smart card, a flash memory device (e.g., a card, a stick, a key drive), a magnetic stripe, a database, a server, or other suitable storage medium. The storage 1003 may also be called an auxiliary storage device. Note that the above-mentioned storage unit 120 may be realized by the memory 1002 and / or the storage 1003.

[0085] The communication device 1004 is hardware (transmission / reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as a network device, a network controller, a network card, a communication module, etc. The communication device 1004 may include a SIM card (Subscriber Identity Module Card). Note that the above-mentioned communication unit 130 may be realized by the communication device 1004.

[0086] The input device 1005 is an input device (for example, a keyboard, a mouse, etc.) that receives input from the outside. The output device 1006 is an output device (for example, a display, a speaker, etc.) that performs output to the outside. The input device 1005 and the output device 1006 may be integrated into one unit (for example, a touch panel). The above-mentioned input unit 140 and output unit 150 may be realized by the input device 1005 and the output device 1006, respectively.

[0087] Furthermore, each device such as the processor 1001 and the memory 1002 is connected by a bus 1008 for communicating information. The bus 1008 may be configured as a single bus, or may be configured as different buses between the devices.

[0088] Furthermore, the information processing server 20 and the like may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented by at least one of these pieces of hardware.

[0089] (Variation) In addition, terms explained in this disclosure and / or terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings.

[0090] The information, parameters, etc. described in this disclosure may be expressed using absolute values, relative values ​​from a predetermined value, or other corresponding information. Furthermore, the names used for parameters, etc. in this disclosure are not limiting in any way.

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

[0092] Information, signals, etc. may be input / output via multiple network nodes. The input / output information, signals, etc. may be stored in a specific location (e.g., memory) or may be managed using a table. The input / output information, signals, etc. may be overwritten, updated, or added. Output information, signals, etc. may be deleted. Input information, signals, etc. may be transmitted to another device.

[0093] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0094] Software, instructions, information, etc. may also be transmitted and received via a transmission medium and / or signal waveform. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), then these wired and / or wireless technologies are included within the definition of transmission media.

[0095] As used in this disclosure, the terms "system" and "network" may be used interchangeably.

[0096] Each aspect / embodiment described in this disclosure may be used alone, in combination, or switched depending on the implementation. Furthermore, the order of the processing procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed unless inconsistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the specific order presented.

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

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

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

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

[0101] Although the invention according to the present disclosure has been described in detail above, it is clear to those skilled in the art that the invention according to the present disclosure is not limited to the embodiments described in the present disclosure. The invention according to the present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the invention as defined by the description of the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not impose any limiting meaning on the invention according to the present disclosure. [Explanation of symbols]

[0102] 1. Information Processing Systems 10 devices 13 Communications Department 20 Information Processing Server 30 Customer terminals 110 control section 120 Storage section 130 Communications Department 140 Input section 150 Output section 210 Control Unit 230 Communications Department 1001 processor 1002 memory 1003 Storage 1004 Communication equipment 1005 Input Device 1006 Output Device 1007 Bus 1008 Bus N Network

Claims

1. Accepting input of attribute data including attribute information regarding customer attributes; inputting the received attribute data and preference data in which preference information relating to market and / or consumer preferences is stored into a large-scale language model (LLM) to generate a persona of the customer that reflects the preference information for the attribute information; generating proposal information to be proposed to the customer based on the persona generated by the large-scale language model; An information processing system comprising:

2. The persona is a virtual customer profile that embodies the customer as a hypothetical persona.

2. The information processing system according to claim 1, wherein:

3. The persona includes any one or at least two or more of a persona hypothesis, a psychographic hypothesis, a needs hypothesis, a problem hypothesis, and an insight hypothesis.

3. The information processing system according to claim 2.

4. The attribute data includes at least one of the customer's identification information, name, age, sex, family structure, living environment, budget, and requests.

4. The information processing system according to claim 3.

5. The preference data includes at least one of the following preference information: the importance of the market and / or the consumer; an image; factors for judgment; and consciousness.

5. The information processing system according to claim 4.

6. The information processing system includes a device and an information processing server. the information processing server generates the persona and generates the proposal information based on the generated persona; The device outputs the proposal information generated by the information processing server.

6. The information processing system according to claim 5.

7. The attribute data is created based on the attribute information input by the customer through a web form on a website.

7. The information processing system according to claim 6.

8. The attribute data is attribute information obtained in advance from the customer through market research.

7. The information processing system according to claim 6.

9. The proposal information is a sentence written by a person in charge of handling the customer when proposing a product and / or a service to the customer.

9. The information processing system according to claim 7 or 8.

10. The proposal information is a sentence that a chatbot that answers questions from the customer uses to propose products and / or services to the customer.

9. The information processing system according to claim 7 or 8.

Images