system

Abstract

The system according to the embodiment aims to meet the needs of sales staff and efficiently carry out proposal creation and service launch operations. [Solution] The system according to the embodiment comprises an acquisition unit, a generation unit, an evaluation unit, an automation unit, a reporting unit, a collection unit, and a utilization unit. The acquisition unit grasps the needs of sales staff. The generation unit generates a proposal based on the needs grasped by the acquisition unit. The evaluation unit evaluates the proposal generated by the generation unit. The automation unit automates service activation work. The reporting unit reports the progress of the work automated by the automation unit. The collection unit collects past data. The utilization unit utilizes the data collected by the collection unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has difficulty meeting the needs of sales staff and efficiently creating proposals and launching services, so there is room for improvement.

[0005] The system according to the embodiment aims to meet the needs of sales staff and efficiently carry out proposal creation and service launch operations. [Means for solving the problem]

[0006] The system according to the embodiment comprises an acquisition unit, a generation unit, an evaluation unit, an automation unit, a reporting unit, a collection unit, and a utilization unit. The acquisition unit grasps the needs of sales staff. The generation unit generates a proposal based on the needs grasped by the acquisition unit. The evaluation unit evaluates the proposal generated by the generation unit. The automation unit automates service activation work. The reporting unit reports the progress of the work automated by the automation unit. The collection unit collects past data. The utilization unit utilizes the data collected by the collection unit. [Effects of the Invention]

[0007] The system according to the embodiment meets the needs of sales staff and can efficiently create proposals and launch services. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A sales support system according to an embodiment of the present invention uses a generative AI to support salespeople in meeting their needs and developing information and knowledge. This system identifies the needs of salespeople, automatically generates proposals based on those needs, automates service launch operations, reports progress, and collects and utilizes past data. For example, the sales support system inputs customer requests and past transaction history, and the generative AI creates an optimal proposal. This allows salespeople to significantly reduce the time it takes to create proposals. Next, the sales support system automatically performs the procedures required to introduce new services and reports progress in real time. This shortens the time it takes to launch services and improves customer satisfaction. Furthermore, the sales support system creates a database of past successes and failures, allowing it to be used in new sales activities. This allows salespeople to develop effective sales strategies based on their past experience. This sales support system supports salespeople in meeting their needs and developing information and knowledge, thereby realizing a shift from personal sales to digital sales. This sales support system supports salespeople in meeting their needs and developing information and knowledge, thereby reducing labor hours and improving customer service. For example, if the target is the 6.5 million sales staff at Japanese companies, the reduction effect is expected to be 1.3 million man-months. The sales support system will also contribute to achieving the vision of making people happy through the information revolution.

[0029] A sales support system according to an embodiment includes an acquisition unit, a generation unit, an evaluation unit, an automation unit, a reporting unit, a collection unit, and an utilization unit. The acquisition unit identifies the needs of sales staff. Examples of the needs of sales staff include, but are not limited to, customer requests, business challenges, and goals. The acquisition unit can, for example, acquire customer requests from survey results or interview content. The acquisition unit can also identify the needs of sales staff based on past transaction history. The generation unit uses a generation AI to generate a proposal based on the needs identified by the acquisition unit. The generation unit generates a proposal based on, for example, customer requests and past transaction history. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates a proposal using these. For example, the generation AI receives a prompt such as, "Please create an optimal proposal based on the customer's requests," and generates a proposal. The evaluation unit evaluates the proposal generated by the generation unit. For example, the evaluation unit evaluates the accuracy of the proposal's content and the quality of its presentation. The evaluation unit can also use AI to evaluate the proposal. The automation unit automates service launch operations. The automation unit, for example, automatically performs procedures required for introducing a new service. The automation unit can also automate procedures using AI. The reporting unit reports the progress of operations automated by the automation unit. The reporting unit, for example, reports the progress of service launch operations in real time. The reporting unit can also report the progress using AI. The collection unit collects past data. The collection unit, for example, creates a database of past success stories and failure stories. The collection unit can also collect data using AI. The utilization unit utilizes the data collected by the collection unit. The utilization unit, for example, utilizes the collected data for new sales activities. The utilization unit can also utilize data using AI. As a result, the sales support system according to the embodiment can support sales staff in meeting their needs and turning information into knowledge, thereby reducing labor hours and improving customer service.

[0030] The generation unit can generate a proposal based on the customer's requests and past transaction history. For example, the generation unit obtains the customer's requests from survey results or interview content and generates a proposal based on those requests. The generation unit can also generate a proposal based on past transaction history. For example, the generation unit analyzes past transaction history and generates a proposal that meets the customer's needs. The generation unit can also generate a proposal based on the customer's requests and past transaction history using a generation AI. For example, the generation AI receives a prompt saying, "Please create the optimal proposal based on the customer's requests," and generates a proposal. This makes it possible to generate the optimal proposal based on the customer's requests and past transaction history.

[0031] The automation unit can automate the procedures required to introduce a new service. For example, the automation unit can automate the contract procedures required to introduce a new service. The automation unit can also automate configuration procedures. For example, the automation unit can automatically configure a new service and provide it to the customer. The automation unit can also automate confirmation procedures. For example, the automation unit can automatically perform confirmation procedures required to introduce a new service and report on the progress. This makes it possible to automate the procedures for introducing new services and improve efficiency.

[0032] The reporting unit can report the progress status of the service launch operation in real time. The reporting unit, for example, reports the progress status of the service launch operation in real time. The reporting unit can set the progress measurement criteria and the reporting frequency in order to report the progress status in real time. For example, the reporting unit can be set to report the progress status daily. The reporting unit can also set the report format in order to report the progress status in real time. For example, the reporting unit can display the progress status in a graph or chart. This makes it possible to report the progress status of the service launch operation in real time and improve customer satisfaction.

[0033] The collection unit can create a database of past success stories and failure stories. For example, the collection unit creates a database of past success stories. The collection unit can collect the criteria for success stories and details of the stories and store them in the database. The collection unit can also create a database of past failure stories. For example, the collection unit collects the criteria for failure stories and details of the stories and store them in the database. This allows the database of past success stories and failure stories to be used in new sales activities.

[0034] The utilization department can utilize the collected data for new sales activities. For example, the utilization department utilizes the collected data for new sales activities. The utilization department can analyze the collected data and create strategies for utilizing the data in new sales activities. For example, the utilization department can create a strategy for developing new customers based on the collected data. The utilization department can also create a follow-up strategy for existing customers based on the collected data. For example, the utilization department can understand the needs of existing customers based on the collected data and create a follow-up strategy. In this way, the collected data can be utilized for new sales activities and an effective sales strategy can be created.

[0035] The evaluation unit can evaluate the quality of the generated proposal. For example, the evaluation unit can evaluate the accuracy of the content of the generated proposal. The evaluation unit can evaluate whether the content of the proposal matches the customer's requirements. The evaluation unit can also evaluate the quality of the presentation of the generated proposal. For example, the evaluation unit can evaluate whether the presentation of the proposal is visually attractive. Furthermore, the evaluation unit can evaluate the customer's reaction to the generated proposal. For example, the evaluation unit collects customer feedback on the proposal and reflects it in the evaluation. This allows the quality of the generated proposal to be evaluated and the accuracy of the proposal to be improved.

[0036] The acquisition unit can analyze past sales activity data and select the optimal method for acquiring needs. For example, the acquisition unit selects the most effective question format based on the past sales activity data. The acquisition unit can also analyze the past sales activity data and select the optimal acquisition method for a specific customer segment. Furthermore, the acquisition unit can refer to the past sales activity data and select an acquisition method with a high success rate. This allows the optimal method for acquiring needs to be selected based on the past sales activity data, making it possible to achieve effective sales activities.

[0037] When acquiring sales needs, the acquisition unit can perform filtering based on the customer's current business situation and areas of interest. For example, the acquisition unit analyzes the customer's current business situation and acquires only relevant needs. The acquisition unit can also narrow down the needs to be acquired based on the customer's areas of interest. Furthermore, the acquisition unit can acquire the most appropriate needs by comprehensively considering the customer's business situation and areas of interest. This makes it possible to perform filtering based on the customer's current business situation and areas of interest and acquire only relevant needs.

[0038] When acquiring sales needs, the acquisition unit can select the optimal acquisition means depending on the user's input method. For example, when the user uses voice input, the acquisition unit acquires the sales needs using voice recognition technology. Furthermore, when the user uses text input, the acquisition unit can also acquire the sales needs using natural language processing technology. Furthermore, when the user uses image input, the acquisition unit can also acquire the sales needs using image recognition technology. This allows the optimal acquisition means to be selected depending on the user's input method, making it possible to efficiently acquire sales needs.

[0039] When acquiring sales needs, the acquisition unit can prioritize acquiring highly relevant needs by taking into account the customer's geographical location information. For example, the acquisition unit prioritizes acquiring region-specific needs based on the customer's geographical location information. The acquisition unit can also acquire needs based on nearby competitor information by taking into account the customer's geographical location information. Furthermore, the acquisition unit can also acquire needs based on regional market trends by referring to the customer's geographical location information. This makes it possible to prioritize acquiring highly relevant needs by taking into account the customer's geographical location information and respond to region-specific needs.

[0040] When acquiring sales needs, the acquisition unit can analyze the customer's social media activity and acquire related needs. For example, the acquisition unit analyzes the content of the customer's posts on social media to acquire related needs. The acquisition unit can also acquire needs that the customer is likely to be interested in based on the customer's social media activity history. Furthermore, the acquisition unit can also acquire related needs by referring to the activities of the customer's friends on social media. In this way, by analyzing the customer's social media activity and acquiring related needs, sales activities can be carried out based on the customer's interests.

[0041] When acquiring sales needs, the acquisition unit can customize the acquisition method by reflecting the customer's past feedback. The acquisition unit, for example, selects the optimal question format based on the customer's past feedback. The acquisition unit can also customize the acquisition method by referring to the customer's past feedback. Furthermore, the acquisition unit can also optimize the acquisition means by reflecting the customer's past feedback. This allows the acquisition method to be customized by reflecting the customer's past feedback, making it possible to acquire the customer's needs optimally.

[0042] When generating a proposal, the generation unit can adjust the level of detail of the proposal based on the importance of the customer. For example, the generation unit generates a detailed proposal for an important customer. The generation unit can also generate a standard proposal for an average customer. Furthermore, the generation unit can also generate a concise proposal for a new customer. In this way, the level of detail of the proposal can be adjusted based on the importance of the customer, and the optimal proposal can be provided to the customer.

[0043] When generating a proposal, the generation unit can apply different generation algorithms depending on the industry or business category of the customer. For example, for a manufacturing customer, the generation unit generates a proposal specialized for the manufacturing industry. Furthermore, for a service customer, the generation unit can generate a proposal specialized for the service industry. Furthermore, for a retail customer, the generation unit can generate a proposal specialized for the retail industry. This makes it possible to apply different generation algorithms depending on the industry or business category of the customer and generate an optimal proposal.

[0044] When generating a proposal, the generation unit can improve the accuracy of generation by referring to the success rate of past proposals. For example, the generation unit generates a similar proposal based on a past successful proposal. The generation unit can also analyze a past unsuccessful proposal and generate a proposal that reflects improvements. Furthermore, the generation unit can also generate an optimal proposal by referring to the success rate of past proposals. In this way, by referring to the success rate of past proposals, the accuracy of proposal generation can be improved.

[0045] When generating proposals, the generation unit can determine the priority of proposals based on the submission times of customers. For example, the generation unit generates proposals with priority for customers whose submission deadlines are approaching. The generation unit can also postpone the generation of proposals for customers whose submission deadlines are far away. Furthermore, the generation unit can generate proposals with standard priority for customers whose submission deadlines are unknown. This allows the priority of proposals to be determined based on the submission times of customers, and proposals to be generated efficiently.

[0046] The generation unit can adjust the order of proposals based on the relevance of the customers when generating proposals. For example, the generation unit generates proposals for important customers first. The generation unit can also generate proposals in a standard order for general customers. Furthermore, the generation unit can also generate proposals for new customers last. In this way, the order of proposals can be adjusted based on the relevance of the customers, and proposals can be generated preferentially for important customers.

[0047] When generating a proposal, the generation unit can adjust the use of technical terms in the proposal according to the customer's level of expertise. For example, the generation unit generates a proposal that uses a lot of technical terms for a customer with a high level of expertise. The generation unit can also generate a concise and easy-to-understand proposal for a customer with a low level of expertise. Furthermore, the generation unit can also generate a proposal that uses standard technical terms for a general customer. This makes it possible to adjust the use of technical terms in the proposal according to the customer's level of expertise and provide the customer with an optimal proposal.

[0048] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data when evaluating a proposal. For example, the evaluation unit selects an optimal evaluation algorithm based on the past evaluation data. The evaluation unit can also analyze the past evaluation data and optimize the evaluation criteria. Furthermore, the evaluation unit can improve the evaluation algorithm by referring to the past evaluation data. This makes it possible to optimize the evaluation algorithm by referring to the past evaluation data and improve the accuracy of the evaluation.

[0049] The evaluation unit can update the evaluation criteria by reflecting customer feedback when evaluating a proposal. The evaluation unit updates the evaluation criteria based on, for example, customer feedback. The evaluation unit can also improve the evaluation algorithm by referring to customer feedback. Furthermore, the evaluation unit can also optimize the evaluation criteria by reflecting customer feedback. This allows the evaluation criteria to be updated by reflecting customer feedback, thereby improving the accuracy of the evaluation.

[0050] When evaluating a proposal, the evaluation unit can analyze the degree of match between the contents of the proposal and the customer's needs and make an evaluation. For example, the evaluation unit analyzes the degree of match between the contents of the proposal and the customer's needs and makes an evaluation. The evaluation unit can also adjust the evaluation criteria based on the degree of match between the contents of the proposal and the customer's needs. Furthermore, the evaluation unit can also optimize the evaluation algorithm by referring to the degree of match between the contents of the proposal and the customer's needs. This allows the degree of match between the contents of the proposal and the customer's needs to be analyzed and a more appropriate evaluation to be made.

[0051] The evaluation department can take into account the geographical location information of the customer when evaluating the proposal. For example, the evaluation department can make an evaluation based on the geographical location information of the customer, taking into account region-specific needs. The evaluation department can also refer to the geographical location information of the customer and make an evaluation that reflects regional market trends. Furthermore, the evaluation department can also adjust the evaluation criteria by taking into account the geographical location information of the customer. This makes it possible to make an evaluation that takes into account the geographical location information of the customer and respond to region-specific needs.

[0052] When evaluating proposals, the evaluation unit can customize the evaluation criteria based on the client's industry or business category. For example, the evaluation unit applies evaluation criteria specialized for the manufacturing industry to a client in the manufacturing industry. The evaluation unit can also apply evaluation criteria specialized for the service industry to a client in the service industry. Furthermore, the evaluation unit can also apply evaluation criteria specialized for the retail industry to a client in the retail industry. This allows the evaluation criteria to be customized based on the client's industry or business category, making it possible to perform a more appropriate evaluation.

[0053] When evaluating a proposal, the evaluation unit can make an evaluation by referring to the customer's past transaction history. The evaluation unit can, for example, adjust the evaluation criteria based on the customer's past transaction history. The evaluation unit can also optimize the evaluation algorithm by referring to the customer's past transaction history. Furthermore, the evaluation unit can also make an evaluation by reflecting the customer's past transaction history. This allows for a more appropriate evaluation by making an evaluation by referring to the customer's past transaction history.

[0054] During automation, the automation unit can optimize the automation algorithm by referring to past automation data. For example, the automation unit selects an optimal automation algorithm based on past automation data. The automation unit can also analyze past automation data and optimize the automation procedure. Furthermore, the automation unit can improve the automation algorithm by referring to past automation data. This makes it possible to optimize the automation algorithm by referring to past automation data, thereby achieving efficient automation.

[0055] During automation, the automation unit can customize the automation procedure based on the customer's business process. For example, the automation unit analyzes the customer's business process and provides an optimal automation procedure. The automation unit can also customize the automation procedure based on the customer's business process. Furthermore, the automation unit can also optimize the automation procedure by referring to the customer's business process. This makes it possible to customize the automation procedure based on the customer's business process and provide a more appropriate procedure.

[0056] The automation unit can improve the automation procedure by reflecting customer feedback during automation. For example, the automation unit improves the automation procedure based on customer feedback. The automation unit can also refer to customer feedback and optimize the automation algorithm. Furthermore, the automation unit can also optimize the automation procedure by reflecting customer feedback. This makes it possible to improve the automation procedure by reflecting customer feedback and provide a more appropriate procedure.

[0057] The automation unit can select automation procedures taking into consideration the geographic location information of the customer during automation. For example, the automation unit prioritizes automation of procedures specific to a region based on the geographic location information of the customer. The automation unit can also refer to the geographic location information of the customer and automate procedures that reflect regional market trends. Furthermore, the automation unit can also optimize automation procedures taking into consideration the geographic location information of the customer. This makes it possible to select automation procedures taking into consideration the geographic location information of the customer and prioritize automation of procedures specific to a region.

[0058] During automation, the automation unit can analyze the customer's social media activity and suggest automation procedures. For example, the automation unit analyzes the content of the customer's social media posts and automates related procedures. The automation unit can also suggest optimal automation procedures based on the customer's social media activity history. Furthermore, the automation unit can also optimize automation procedures by referring to the activities of the customer's friends on social media. This makes it possible to analyze the customer's social media activity to suggest automation procedures and provide more appropriate procedures.

[0059] The automation unit can customize the automation procedure by reflecting past customer feedback during automation. The automation unit customizes the automation procedure based on, for example, past customer feedback. The automation unit can also optimize the automation algorithm by referring to past customer feedback. Furthermore, the automation unit can also optimize the automation procedure by reflecting past customer feedback. This allows the automation procedure to be customized by reflecting past customer feedback, making it possible to provide a more appropriate procedure.

[0060] The reporting unit can optimize the reporting algorithm by referring to past reporting data when reporting. The reporting unit, for example, selects an optimal reporting algorithm based on the past reporting data. The reporting unit can also analyze the past reporting data and optimize the reporting procedure. Furthermore, the reporting unit can improve the reporting algorithm by referring to the past reporting data. This makes it possible to optimize the reporting algorithm by referring to the past reporting data and achieve efficient reporting.

[0061] When reporting, the reporting department can customize the report content based on the customer's business process. For example, the reporting department analyzes the customer's business process and provides optimal report content. The reporting department can also customize the report content based on the customer's business process. Furthermore, the reporting department can also refer to the customer's business process and optimize the report content. This allows the report content to be customized based on the customer's business process, making it possible to provide more appropriate reports.

[0062] The reporting unit can improve the report content by reflecting customer feedback when reporting. The reporting unit improves the report content based on, for example, customer feedback. The reporting unit can also refer to customer feedback and optimize the reporting algorithm. Furthermore, the reporting unit can also optimize the report content by reflecting customer feedback. This allows the report content to be improved by reflecting customer feedback, making it possible to provide more appropriate reports.

[0063] The reporting department can select report content taking into consideration the geographical location information of the client when providing a report. For example, the reporting department can prioritize displaying region-specific report content based on the geographical location information of the client. The reporting department can also refer to the geographical location information of the client to provide report content that reflects regional market trends. Furthermore, the reporting department can also optimize the report content taking into consideration the geographical location information of the client. This allows the report content to be selected taking into consideration the geographical location information of the client, and region-specific reports to be provided.

[0064] When making a report, the reporting department can analyze the customer's social media activity and suggest report content. For example, the reporting department analyzes the customer's social media posts and provides relevant report content. The reporting department can also suggest optimal report content based on the customer's social media activity history. Furthermore, the reporting department can also optimize the report content by taking into account the activities of the customer's friends on social media. This allows the customer's social media activity to be analyzed and report content to be suggested, making it possible to make more appropriate reports.

[0065] The reporting unit can customize the report content by reflecting the customer's past feedback when making a report. The reporting unit customizes the report content based on, for example, the customer's past feedback. The reporting unit can also optimize the reporting algorithm by referring to the customer's past feedback. Furthermore, the reporting unit can also optimize the report content by reflecting the customer's past feedback. This allows the report content to be customized by reflecting the customer's past feedback, making it possible to make more appropriate reports.

[0066] The collection unit can optimize the collection algorithm by referring to previously collected data when collecting data. For example, the collection unit selects an optimal collection algorithm based on previously collected data. The collection unit can also analyze previously collected data and optimize the collection procedure. Furthermore, the collection unit can improve the collection algorithm by referring to previously collected data. This makes it possible to optimize the collection algorithm by referring to previously collected data and achieve efficient data collection.

[0067] The collection unit can customize the collection means based on the customer's business process when collecting data. For example, the collection unit analyzes the customer's business process and provides the optimal collection means. The collection unit can also customize the collection means based on the customer's business process. Furthermore, the collection unit can also optimize the collection means by referencing the customer's business process. This allows the collection means to be customized based on the customer's business process, making it possible to collect more appropriate data.

[0068] The collection unit can improve the collection means by reflecting customer feedback when collecting data. For example, the collection unit improves the collection means based on customer feedback. The collection unit can also refer to customer feedback and optimize the collection algorithm. Furthermore, the collection unit can also optimize the collection means by reflecting customer feedback. This allows the collection means to be improved by reflecting customer feedback, and more appropriate data collection to be performed.

[0069] When collecting data, the collection unit can select a collection method taking into consideration the geographical location information of the customer. For example, the collection unit preferentially collects data specific to a region based on the geographical location information of the customer. The collection unit can also refer to the geographical location information of the customer and collect data that reflects regional market trends. Furthermore, the collection unit can also optimize the collection method taking into consideration the geographical location information of the customer. This makes it possible to select a collection method taking into consideration the geographical location information of the customer and preferentially collect data specific to a region.

[0070] When collecting data, the collection unit can analyze the customer's social media activity and suggest collection methods. For example, the collection unit analyzes the content posted by the customer on social media and collects related data. The collection unit can also suggest optimal collection methods based on the customer's social media activity history. Furthermore, the collection unit can also optimize collection methods by referring to the activities of the customer's friends on social media. This allows the collection unit to analyze the customer's social media activity and suggest collection methods, thereby enabling more appropriate data collection.

[0071] The collection unit can customize the collection means by reflecting the customer's past feedback when collecting data. For example, the collection unit customizes the collection means based on the customer's past feedback. The collection unit can also optimize the collection algorithm by referring to the customer's past feedback. Furthermore, the collection unit can also optimize the collection means by reflecting the customer's past feedback. This allows the collection means to be customized by reflecting the customer's past feedback, making it possible to collect more appropriate data.

[0072] When utilizing data, the utilization unit can optimize the utilization algorithm by referring to past utilization data. For example, the utilization unit selects the optimal utilization algorithm based on the past utilization data. The utilization unit can also analyze the past utilization data and optimize the utilization procedure. Furthermore, the utilization unit can also improve the utilization algorithm by referring to the past utilization data. This makes it possible to optimize the utilization algorithm by referring to the past utilization data and achieve efficient data utilization.

[0073] When utilizing data, the utilization department can customize the utilization method based on the customer's business process. For example, the utilization department analyzes the customer's business process and provides the optimal utilization method. The utilization department can also customize the utilization method based on the customer's business process. Furthermore, the utilization department can also refer to the customer's business process and optimize the utilization method. This allows the utilization method to be customized based on the customer's business process, enabling more appropriate data utilization.

[0074] When utilizing data, the utilization department can improve the utilization method by reflecting customer feedback. For example, the utilization department improves the utilization method based on customer feedback. The utilization department can also refer to customer feedback and optimize the utilization algorithm. Furthermore, the utilization department can also optimize the utilization method by reflecting customer feedback. This allows the utilization method to be improved by reflecting customer feedback, and more appropriate data utilization can be achieved.

[0075] When utilizing data, the utilization department can select utilization methods taking into consideration the geographical location information of the customer. For example, the utilization department prioritizes utilization of region-specific data based on the geographical location information of the customer. The utilization department can also refer to the geographical location information of the customer and utilize data that reflects regional market trends. Furthermore, the utilization department can also optimize utilization methods taking into consideration the geographical location information of the customer. This makes it possible to select utilization methods taking into consideration the geographical location information of the customer and prioritize utilization of region-specific data.

[0076] When utilizing data, the utilization department can analyze the customer's social media activity and suggest utilization methods. For example, the utilization department analyzes the content of the customer's posts on social media and utilizes related data. The utilization department can also suggest the optimal utilization method based on the customer's social media activity history. Furthermore, the utilization department can also optimize utilization methods by referring to the activities of the customer's friends on social media. This allows the customer's social media activity to be analyzed and utilization methods to be suggested, enabling more appropriate data utilization.

[0077] When utilizing data, the utilization unit can customize the utilization means by reflecting the customer's past feedback. For example, the utilization unit customizes the utilization means based on the customer's past feedback. The utilization unit can also refer to the customer's past feedback and optimize the utilization algorithm. Furthermore, the utilization unit can also optimize the utilization means by reflecting the customer's past feedback. This allows the utilization means to be customized by reflecting the customer's past feedback, making it possible to utilize data more appropriately.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The acquisition unit can analyze past sales activity data and select the optimal method for acquiring needs. For example, the most effective question format can be selected based on past sales activity data. The acquisition unit can also analyze past sales activity data and select the optimal acquisition method for a specific customer segment. Furthermore, the acquisition unit can refer to past sales activity data and select an acquisition method with a high success rate. This allows the optimal method for acquiring needs to be selected based on past sales activity data, enabling effective sales activities.

[0080] When generating a proposal, the generation unit can apply different generation algorithms depending on the industry or business category of the customer. For example, for a manufacturing customer, the generation unit can generate a proposal specialized for the manufacturing industry. The generation unit can also generate a proposal specialized for the service industry for a service customer. Furthermore, the generation unit can also generate a proposal specialized for the retail industry for a retail customer. This makes it possible to apply different generation algorithms depending on the industry or business category of the customer and generate an optimal proposal.

[0081] The evaluation department can update the evaluation criteria by reflecting customer feedback when evaluating proposals. For example, the evaluation criteria are updated based on customer feedback. The evaluation department can also refer to customer feedback to improve the evaluation algorithm. Furthermore, the evaluation department can also optimize the evaluation criteria by reflecting customer feedback. This allows the evaluation criteria to be updated by reflecting customer feedback, thereby improving the accuracy of the evaluation.

[0082] During automation, the automation department can customize the automation procedure based on the customer's business process. For example, the automation department analyzes the customer's business process and provides the optimal automation procedure. The automation department can also customize the automation procedure based on the customer's business process. Furthermore, the automation department can refer to the customer's business process and optimize the automation procedure. This makes it possible to customize the automation procedure based on the customer's business process and provide a more appropriate procedure.

[0083] The reporting department can improve the report content by reflecting customer feedback when reporting. For example, the report content is improved based on customer feedback. The reporting department can also refer to customer feedback and optimize the reporting algorithm. Furthermore, the reporting department can also optimize the report content by reflecting customer feedback. This allows the report content to be improved by reflecting customer feedback, and more appropriate reporting can be performed.

[0084] The processing flow of the first embodiment will be briefly explained below.

[0085] Step 1: The Acquisition Department understands the needs of sales staff. Sales staff needs include customer requests, work challenges, and goals. The Acquisition Department can obtain customer requests from survey results and interview content, and can also understand the needs of sales staff based on past transaction history. Step 2: The generation unit generates a proposal based on the needs identified by the acquisition unit. The generation unit uses generation AI to generate a proposal based on the customer's requests and past transaction history. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and uses these to generate the proposal. Step 3: The evaluation unit evaluates the proposals generated by the generation unit. The evaluation unit evaluates the accuracy of the proposal content and the quality of the presentation. The evaluation unit can also use AI to evaluate the proposals. Step 4: The automation department automates the service activation process. The automation department automates the procedures required to introduce a new service. The automation department can also automate procedures using AI. Step 5: The reporting department reports the progress of the work automated by the automation department. The reporting department reports the progress of the service launch work in real time. The reporting department can also use AI to report the progress. Step 6: The collection department collects past data. The collection department creates a database of past successes and failures. The collection department can also use AI to collect data. Step 7: The Utilization Department utilizes the data collected by the Collection Department. The Utilization Department utilizes the collected data for new sales activities. The Utilization Department can also utilize data using AI.

[0086] (Example 2) A sales support system according to an embodiment of the present invention uses a generative AI to support salespeople in meeting their needs and developing information and knowledge. This system identifies the needs of salespeople, automatically generates proposals based on those needs, automates service launch operations, reports progress, and collects and utilizes past data. For example, the sales support system inputs customer requests and past transaction history, and the generative AI creates an optimal proposal. This allows salespeople to significantly reduce the time it takes to create proposals. Next, the sales support system automatically performs the procedures required to introduce new services and reports progress in real time. This shortens the time it takes to launch services and improves customer satisfaction. Furthermore, the sales support system creates a database of past successes and failures, allowing it to be used in new sales activities. This allows salespeople to develop effective sales strategies based on their past experience. This sales support system supports salespeople in meeting their needs and developing information and knowledge, thereby realizing a shift from personal sales to digital sales. This sales support system supports salespeople in meeting their needs and developing information and knowledge, thereby reducing labor hours and improving customer service. For example, if the target is the 6.5 million sales staff at Japanese companies, the reduction effect is expected to be 1.3 million man-months. The sales support system will also contribute to achieving the vision of making people happy through the information revolution.

[0087] A sales support system according to an embodiment includes an acquisition unit, a generation unit, an evaluation unit, an automation unit, a reporting unit, a collection unit, and an utilization unit. The acquisition unit identifies the needs of sales staff. Examples of the needs of sales staff include, but are not limited to, customer requests, business challenges, and goals. The acquisition unit can, for example, acquire customer requests from survey results or interview content. The acquisition unit can also identify the needs of sales staff based on past transaction history. The generation unit uses a generation AI to generate a proposal based on the needs identified by the acquisition unit. The generation unit generates a proposal based on, for example, customer requests and past transaction history. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates a proposal using these. For example, the generation AI receives a prompt such as, "Please create an optimal proposal based on the customer's requests," and generates a proposal. The evaluation unit evaluates the proposal generated by the generation unit. For example, the evaluation unit evaluates the accuracy of the proposal's content and the quality of its presentation. The evaluation unit can also use AI to evaluate the proposal. The automation unit automates service launch operations. The automation unit, for example, automatically performs procedures required for introducing a new service. The automation unit can also automate procedures using AI. The reporting unit reports the progress of operations automated by the automation unit. The reporting unit, for example, reports the progress of service launch operations in real time. The reporting unit can also report the progress using AI. The collection unit collects past data. The collection unit, for example, creates a database of past success stories and failure stories. The collection unit can also collect data using AI. The utilization unit utilizes the data collected by the collection unit. The utilization unit, for example, utilizes the collected data for new sales activities. The utilization unit can also utilize data using AI. As a result, the sales support system according to the embodiment can support sales staff in meeting their needs and turning information into knowledge, thereby reducing labor hours and improving customer service.

[0088] The generation unit can generate a proposal based on the customer's requests and past transaction history. For example, the generation unit obtains the customer's requests from survey results or interview content and generates a proposal based on those requests. The generation unit can also generate a proposal based on past transaction history. For example, the generation unit analyzes past transaction history and generates a proposal that meets the customer's needs. The generation unit can also generate a proposal based on the customer's requests and past transaction history using a generation AI. For example, the generation AI receives a prompt saying, "Please create the optimal proposal based on the customer's requests," and generates a proposal. This makes it possible to generate the optimal proposal based on the customer's requests and past transaction history.

[0089] The automation unit can automate the procedures required to introduce a new service. For example, the automation unit can automate the contract procedures required to introduce a new service. The automation unit can also automate configuration procedures. For example, the automation unit can automatically configure a new service and provide it to the customer. The automation unit can also automate confirmation procedures. For example, the automation unit can automatically perform confirmation procedures required to introduce a new service and report on the progress. This makes it possible to automate the procedures for introducing new services and improve efficiency.

[0090] The reporting unit can report the progress status of the service launch operation in real time. The reporting unit, for example, reports the progress status of the service launch operation in real time. The reporting unit can set the progress measurement criteria and the reporting frequency in order to report the progress status in real time. For example, the reporting unit can be set to report the progress status daily. The reporting unit can also set the report format in order to report the progress status in real time. For example, the reporting unit can display the progress status in a graph or chart. This makes it possible to report the progress status of the service launch operation in real time and improve customer satisfaction.

[0091] The collection unit can create a database of past success stories and failure stories. For example, the collection unit creates a database of past success stories. The collection unit can collect the criteria for success stories and details of the stories and store them in the database. The collection unit can also create a database of past failure stories. For example, the collection unit collects the criteria for failure stories and details of the stories and store them in the database. This allows the database of past success stories and failure stories to be used in new sales activities.

[0092] The utilization department can utilize the collected data for new sales activities. For example, the utilization department utilizes the collected data for new sales activities. The utilization department can analyze the collected data and create strategies for utilizing the data in new sales activities. For example, the utilization department can create a strategy for developing new customers based on the collected data. The utilization department can also create a follow-up strategy for existing customers based on the collected data. For example, the utilization department can understand the needs of existing customers based on the collected data and create a follow-up strategy. In this way, the collected data can be utilized for new sales activities and an effective sales strategy can be created.

[0093] The evaluation unit can evaluate the quality of the generated proposal. For example, the evaluation unit can evaluate the accuracy of the content of the generated proposal. The evaluation unit can evaluate whether the content of the proposal matches the customer's requirements. The evaluation unit can also evaluate the quality of the presentation of the generated proposal. For example, the evaluation unit can evaluate whether the presentation of the proposal is visually attractive. Furthermore, the evaluation unit can evaluate the customer's reaction to the generated proposal. For example, the evaluation unit collects customer feedback on the proposal and reflects it in the evaluation. This allows the quality of the generated proposal to be evaluated and the accuracy of the proposal to be improved.

[0094] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring sales needs based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit postpones acquiring the sales needs until a time when the user is relaxed. Furthermore, if the user is relaxed, the acquisition unit can immediately acquire the sales needs and respond quickly. Furthermore, if the user is in a hurry, the acquisition unit can quickly acquire the sales needs by asking simple questions. This allows the timing of acquiring sales needs to be adjusted according to the user's emotions and the needs to be acquired at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0095] The acquisition unit can analyze past sales activity data and select the optimal method for acquiring needs. For example, the acquisition unit selects the most effective question format based on the past sales activity data. The acquisition unit can also analyze the past sales activity data and select the optimal acquisition method for a specific customer segment. Furthermore, the acquisition unit can refer to the past sales activity data and select an acquisition method with a high success rate. This allows the optimal method for acquiring needs to be selected based on the past sales activity data, making it possible to achieve effective sales activities.

[0096] When acquiring sales needs, the acquisition unit can perform filtering based on the customer's current business situation and areas of interest. For example, the acquisition unit analyzes the customer's current business situation and acquires only relevant needs. The acquisition unit can also narrow down the needs to be acquired based on the customer's areas of interest. Furthermore, the acquisition unit can acquire the most appropriate needs by comprehensively considering the customer's business situation and areas of interest. This makes it possible to perform filtering based on the customer's current business situation and areas of interest and acquire only relevant needs.

[0097] When acquiring sales needs, the acquisition unit can select the optimal acquisition means depending on the user's input method. For example, when the user uses voice input, the acquisition unit acquires the sales needs using voice recognition technology. Furthermore, when the user uses text input, the acquisition unit can also acquire the sales needs using natural language processing technology. Furthermore, when the user uses image input, the acquisition unit can also acquire the sales needs using image recognition technology. This allows the optimal acquisition means to be selected depending on the user's input method, making it possible to efficiently acquire sales needs.

[0098] The acquisition unit can estimate the user's emotions and determine the priority of sales needs to be acquired based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit postpones less important needs. Furthermore, if the user is relaxed, the acquisition unit can also prioritize acquiring more important needs. Furthermore, if the user is in a hurry, the acquisition unit can prioritize acquiring needs that require a quick response. This allows the prioritization of sales needs to be determined according to the user's emotions and important needs to be acquired preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0099] When acquiring sales needs, the acquisition unit can prioritize acquiring highly relevant needs by taking into account the customer's geographical location information. For example, the acquisition unit prioritizes acquiring region-specific needs based on the customer's geographical location information. The acquisition unit can also acquire needs based on nearby competitor information by taking into account the customer's geographical location information. Furthermore, the acquisition unit can also acquire needs based on regional market trends by referring to the customer's geographical location information. This makes it possible to prioritize acquiring highly relevant needs by taking into account the customer's geographical location information and respond to region-specific needs.

[0100] When acquiring sales needs, the acquisition unit can analyze the customer's social media activity and acquire related needs. For example, the acquisition unit analyzes the content of the customer's posts on social media to acquire related needs. The acquisition unit can also acquire needs that the customer is likely to be interested in based on the customer's social media activity history. Furthermore, the acquisition unit can also acquire related needs by referring to the activities of the customer's friends on social media. In this way, by analyzing the customer's social media activity and acquiring related needs, sales activities can be carried out based on the customer's interests.

[0101] When acquiring sales needs, the acquisition unit can customize the acquisition method by reflecting the customer's past feedback. The acquisition unit, for example, selects the optimal question format based on the customer's past feedback. The acquisition unit can also customize the acquisition method by referring to the customer's past feedback. Furthermore, the acquisition unit can also optimize the acquisition means by reflecting the customer's past feedback. This allows the acquisition method to be customized by reflecting the customer's past feedback, making it possible to acquire the customer's needs optimally.

[0102] The generation unit can estimate the user's emotions and adjust the presentation style of the proposal based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a proposal that includes detailed explanations. If the user is in a hurry, the generation unit can also generate a concise proposal that focuses on the main points. Furthermore, if the user is excited, the generation unit can also generate a visually appealing proposal. This allows the presentation style of the proposal to be adjusted according to the user's emotions, resulting in a more effective proposal. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0103] When generating a proposal, the generation unit can adjust the level of detail of the proposal based on the importance of the customer. For example, the generation unit generates a detailed proposal for an important customer. The generation unit can also generate a standard proposal for an average customer. Furthermore, the generation unit can also generate a concise proposal for a new customer. In this way, the level of detail of the proposal can be adjusted based on the importance of the customer, and the optimal proposal can be provided to the customer.

[0104] When generating a proposal, the generation unit can apply different generation algorithms depending on the industry or business category of the customer. For example, for a manufacturing customer, the generation unit generates a proposal specialized for the manufacturing industry. Furthermore, for a service customer, the generation unit can generate a proposal specialized for the service industry. Furthermore, for a retail customer, the generation unit can generate a proposal specialized for the retail industry. This makes it possible to apply different generation algorithms depending on the industry or business category of the customer and generate an optimal proposal.

[0105] When generating a proposal, the generation unit can improve the accuracy of generation by referring to the success rate of past proposals. For example, the generation unit generates a similar proposal based on a past successful proposal. The generation unit can also analyze a past unsuccessful proposal and generate a proposal that reflects improvements. Furthermore, the generation unit can also generate an optimal proposal by referring to the success rate of past proposals. In this way, by referring to the success rate of past proposals, the accuracy of proposal generation can be improved.

[0106] The generation unit can estimate the user's emotions and adjust the length of the proposal based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point proposal. If the user is relaxed, the generation unit can also generate a longer proposal with detailed explanations. Furthermore, if the user is excited, the generation unit can also generate a visually appealing proposal. This allows the length of the proposal to be adjusted according to the user's emotions, resulting in a more effective proposal. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0107] When generating proposals, the generation unit can determine the priority of proposals based on the submission times of customers. For example, the generation unit generates proposals with priority for customers whose submission deadlines are approaching. The generation unit can also postpone the generation of proposals for customers whose submission deadlines are far away. Furthermore, the generation unit can generate proposals with standard priority for customers whose submission deadlines are unknown. This allows the priority of proposals to be determined based on the submission times of customers, and proposals to be generated efficiently.

[0108] The generation unit can adjust the order of proposals based on the relevance of the customers when generating proposals. For example, the generation unit generates proposals for important customers first. The generation unit can also generate proposals in a standard order for general customers. Furthermore, the generation unit can also generate proposals for new customers last. In this way, the order of proposals can be adjusted based on the relevance of the customers, and proposals can be generated preferentially for important customers.

[0109] When generating a proposal, the generation unit can adjust the use of technical terms in the proposal according to the customer's level of expertise. For example, the generation unit generates a proposal that uses a lot of technical terms for a customer with a high level of expertise. The generation unit can also generate a concise and easy-to-understand proposal for a customer with a low level of expertise. Furthermore, the generation unit can also generate a proposal that uses standard technical terms for a general customer. This makes it possible to adjust the use of technical terms in the proposal according to the customer's level of expertise and provide the customer with an optimal proposal.

[0110] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria for the proposal based on the estimated user emotions. For example, the evaluation unit can apply detailed evaluation criteria when the user is relaxed. The evaluation unit can also apply concise evaluation criteria when the user is in a hurry. Furthermore, the evaluation unit can apply visually appealing evaluation criteria when the user is excited. This allows the evaluation criteria for the proposal to be adjusted according to the user's emotions, resulting in a more appropriate evaluation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0111] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data when evaluating a proposal. For example, the evaluation unit selects an optimal evaluation algorithm based on the past evaluation data. The evaluation unit can also analyze the past evaluation data and optimize the evaluation criteria. Furthermore, the evaluation unit can improve the evaluation algorithm by referring to the past evaluation data. This makes it possible to optimize the evaluation algorithm by referring to the past evaluation data and improve the accuracy of the evaluation.

[0112] The evaluation unit can update the evaluation criteria by reflecting customer feedback when evaluating a proposal. The evaluation unit updates the evaluation criteria based on, for example, customer feedback. The evaluation unit can also improve the evaluation algorithm by referring to customer feedback. Furthermore, the evaluation unit can also optimize the evaluation criteria by reflecting customer feedback. This allows the evaluation criteria to be updated by reflecting customer feedback, thereby improving the accuracy of the evaluation.

[0113] When evaluating a proposal, the evaluation unit can analyze the degree of match between the contents of the proposal and the customer's needs and make an evaluation. For example, the evaluation unit analyzes the degree of match between the contents of the proposal and the customer's needs and makes an evaluation. The evaluation unit can also adjust the evaluation criteria based on the degree of match between the contents of the proposal and the customer's needs. Furthermore, the evaluation unit can also optimize the evaluation algorithm by referring to the degree of match between the contents of the proposal and the customer's needs. This allows the degree of match between the contents of the proposal and the customer's needs to be analyzed and a more appropriate evaluation to be made.

[0114] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. For example, if the user is nervous, the evaluation unit provides a simple, highly visible display method. If the user is relaxed, the evaluation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the evaluation unit can also provide a display method that focuses on the main points. This allows the display method of the evaluation results to be adjusted according to the user's emotions, resulting in a more appropriate display. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0115] The evaluation department can take into account the geographical location information of the customer when evaluating the proposal. For example, the evaluation department can make an evaluation based on the geographical location information of the customer, taking into account region-specific needs. The evaluation department can also refer to the geographical location information of the customer and make an evaluation that reflects regional market trends. Furthermore, the evaluation department can also adjust the evaluation criteria by taking into account the geographical location information of the customer. This makes it possible to make an evaluation that takes into account the geographical location information of the customer and respond to region-specific needs.

[0116] When evaluating proposals, the evaluation unit can customize the evaluation criteria based on the client's industry or business category. For example, the evaluation unit applies evaluation criteria specialized for the manufacturing industry to a client in the manufacturing industry. The evaluation unit can also apply evaluation criteria specialized for the service industry to a client in the service industry. Furthermore, the evaluation unit can also apply evaluation criteria specialized for the retail industry to a client in the retail industry. This allows the evaluation criteria to be customized based on the client's industry or business category, making it possible to perform a more appropriate evaluation.

[0117] When evaluating a proposal, the evaluation unit can make an evaluation by referring to the customer's past transaction history. The evaluation unit can, for example, adjust the evaluation criteria based on the customer's past transaction history. The evaluation unit can also optimize the evaluation algorithm by referring to the customer's past transaction history. Furthermore, the evaluation unit can also make an evaluation by reflecting the customer's past transaction history. This allows for a more appropriate evaluation by making an evaluation by referring to the customer's past transaction history.

[0118] The automation unit can estimate the user's emotions and adjust the automation procedure based on the estimated user's emotions. For example, the automation unit can provide detailed instructions when the user is relaxed. The automation unit can also provide concise instructions when the user is in a hurry. Furthermore, the automation unit can provide visually appealing instructions when the user is excited. This allows the automation procedure to be adjusted according to the user's emotions and provide more appropriate instructions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0119] During automation, the automation unit can optimize the automation algorithm by referring to past automation data. For example, the automation unit selects an optimal automation algorithm based on past automation data. The automation unit can also analyze past automation data and optimize the automation procedure. Furthermore, the automation unit can improve the automation algorithm by referring to past automation data. This makes it possible to optimize the automation algorithm by referring to past automation data, thereby achieving efficient automation.

[0120] During automation, the automation unit can customize the automation procedure based on the customer's business process. For example, the automation unit analyzes the customer's business process and provides an optimal automation procedure. The automation unit can also customize the automation procedure based on the customer's business process. Furthermore, the automation unit can also optimize the automation procedure by referring to the customer's business process. This makes it possible to customize the automation procedure based on the customer's business process and provide a more appropriate procedure.

[0121] The automation unit can improve the automation procedure by reflecting customer feedback during automation. For example, the automation unit improves the automation procedure based on customer feedback. The automation unit can also refer to customer feedback and optimize the automation algorithm. Furthermore, the automation unit can also optimize the automation procedure by reflecting customer feedback. This makes it possible to improve the automation procedure by reflecting customer feedback and provide a more appropriate procedure.

[0122] The automation unit can estimate the user's emotions and determine automation priorities based on the estimated user emotions. For example, when the user is relaxed, the automation unit prioritizes automation of procedures with high importance. Furthermore, when the user is in a hurry, the automation unit can also prioritize automation of procedures that require a quick response. Furthermore, when the user is excited, the automation unit can also prioritize automation of procedures that are visually appealing. In this way, automation priorities can be determined according to the user's emotions and important procedures can be prioritized for automation. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0123] The automation unit can select automation procedures taking into consideration the geographic location information of the customer during automation. For example, the automation unit prioritizes automation of procedures specific to a region based on the geographic location information of the customer. The automation unit can also refer to the geographic location information of the customer and automate procedures that reflect regional market trends. Furthermore, the automation unit can also optimize automation procedures taking into consideration the geographic location information of the customer. This makes it possible to select automation procedures taking into consideration the geographic location information of the customer and prioritize automation of procedures specific to a region.

[0124] During automation, the automation unit can analyze the customer's social media activity and suggest automation procedures. For example, the automation unit analyzes the content of the customer's social media posts and automates related procedures. The automation unit can also suggest optimal automation procedures based on the customer's social media activity history. Furthermore, the automation unit can also optimize automation procedures by referring to the activities of the customer's friends on social media. This makes it possible to analyze the customer's social media activity to suggest automation procedures and provide more appropriate procedures.

[0125] The automation unit can customize the automation procedure by reflecting past customer feedback during automation. The automation unit customizes the automation procedure based on, for example, past customer feedback. The automation unit can also optimize the automation algorithm by referring to past customer feedback. Furthermore, the automation unit can also optimize the automation procedure by reflecting past customer feedback. This allows the automation procedure to be customized by reflecting past customer feedback, making it possible to provide a more appropriate procedure.

[0126] The reporting unit can estimate the user's emotions and adjust the display method of the report based on the estimated user's emotions. For example, if the user is nervous, the reporting unit can provide a simple, highly visible display method. If the user is relaxed, the reporting unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the reporting unit can also provide a display method that focuses on the main points. This allows the display method of the report to be adjusted according to the user's emotions, resulting in a more appropriate display. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0127] The reporting unit can optimize the reporting algorithm by referring to past reporting data when reporting. The reporting unit, for example, selects an optimal reporting algorithm based on the past reporting data. The reporting unit can also analyze the past reporting data and optimize the reporting procedure. Furthermore, the reporting unit can improve the reporting algorithm by referring to the past reporting data. This makes it possible to optimize the reporting algorithm by referring to the past reporting data and achieve efficient reporting.

[0128] When reporting, the reporting department can customize the report content based on the customer's business process. For example, the reporting department analyzes the customer's business process and provides optimal report content. The reporting department can also customize the report content based on the customer's business process. Furthermore, the reporting department can also refer to the customer's business process and optimize the report content. This allows the report content to be customized based on the customer's business process, making it possible to provide more appropriate reports.

[0129] The reporting unit can improve the report content by reflecting customer feedback when reporting. The reporting unit improves the report content based on, for example, customer feedback. The reporting unit can also refer to customer feedback and optimize the reporting algorithm. Furthermore, the reporting unit can also optimize the report content by reflecting customer feedback. This allows the report content to be improved by reflecting customer feedback, making it possible to provide more appropriate reports.

[0130] The reporting unit can estimate the user's emotions and determine the priority of reports based on the estimated user emotions. For example, when the user is relaxed, the reporting unit can prioritize displaying reports with high importance. Furthermore, when the user is in a hurry, the reporting unit can prioritize displaying reports that require a quick response. Furthermore, when the user is excited, the reporting unit can prioritize displaying visually appealing reports. In this way, the priority of reports can be determined according to the user's emotions and important reports can be prioritized and displayed. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0131] The reporting department can select report content taking into consideration the geographical location information of the client when providing a report. For example, the reporting department can prioritize displaying region-specific report content based on the geographical location information of the client. The reporting department can also refer to the geographical location information of the client to provide report content that reflects regional market trends. Furthermore, the reporting department can also optimize the report content taking into consideration the geographical location information of the client. This allows the report content to be selected taking into consideration the geographical location information of the client, and region-specific reports to be provided.

[0132] When making a report, the reporting department can analyze the customer's social media activity and suggest report content. For example, the reporting department analyzes the customer's social media posts and provides relevant report content. The reporting department can also suggest optimal report content based on the customer's social media activity history. Furthermore, the reporting department can also optimize the report content by taking into account the activities of the customer's friends on social media. This allows the customer's social media activity to be analyzed and report content to be suggested, making it possible to make more appropriate reports.

[0133] The reporting unit can customize the report content by reflecting the customer's past feedback when making a report. The reporting unit customizes the report content based on, for example, the customer's past feedback. The reporting unit can also optimize the reporting algorithm by referring to the customer's past feedback. Furthermore, the reporting unit can also optimize the report content by reflecting the customer's past feedback. This allows the report content to be customized by reflecting the customer's past feedback, making it possible to make more appropriate reports.

[0134] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit can collect detailed data when the user is relaxed. Furthermore, the collection unit can also collect concise data when the user is in a hurry. Furthermore, the collection unit can collect visually appealing data when the user is excited. This allows the timing of data collection to be adjusted according to the user's emotions, allowing data to be collected at a more appropriate time. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0135] The collection unit can optimize the collection algorithm by referring to previously collected data when collecting data. For example, the collection unit selects an optimal collection algorithm based on previously collected data. The collection unit can also analyze previously collected data and optimize the collection procedure. Furthermore, the collection unit can improve the collection algorithm by referring to previously collected data. This makes it possible to optimize the collection algorithm by referring to previously collected data and achieve efficient data collection.

[0136] The collection unit can customize the collection means based on the customer's business process when collecting data. For example, the collection unit analyzes the customer's business process and provides the optimal collection means. The collection unit can also customize the collection means based on the customer's business process. Furthermore, the collection unit can also optimize the collection means by referencing the customer's business process. This allows the collection means to be customized based on the customer's business process, making it possible to collect more appropriate data.

[0137] The collection unit can improve the collection means by reflecting customer feedback when collecting data. For example, the collection unit improves the collection means based on customer feedback. The collection unit can also refer to customer feedback and optimize the collection algorithm. Furthermore, the collection unit can also optimize the collection means by reflecting customer feedback. This allows the collection means to be improved by reflecting customer feedback, and more appropriate data collection to be performed.

[0138] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is relaxed, the collection unit prioritizes collecting data of high importance. Furthermore, when the user is in a hurry, the collection unit can also prioritize collecting data that requires a quick response. Furthermore, when the user is excited, the collection unit can also prioritize collecting visually appealing data. In this way, the priority of data to be collected can be determined according to the user's emotions, and important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0139] When collecting data, the collection unit can select a collection method taking into consideration the geographical location information of the customer. For example, the collection unit preferentially collects data specific to a region based on the geographical location information of the customer. The collection unit can also refer to the geographical location information of the customer and collect data that reflects regional market trends. Furthermore, the collection unit can also optimize the collection method taking into consideration the geographical location information of the customer. This makes it possible to select a collection method taking into consideration the geographical location information of the customer and preferentially collect data specific to a region.

[0140] When collecting data, the collection unit can analyze the customer's social media activity and suggest collection methods. For example, the collection unit analyzes the content posted by the customer on social media and collects related data. The collection unit can also suggest optimal collection methods based on the customer's social media activity history. Furthermore, the collection unit can also optimize collection methods by referring to the activities of the customer's friends on social media. This allows the collection unit to analyze the customer's social media activity and suggest collection methods, thereby enabling more appropriate data collection.

[0141] The collection unit can customize the collection means by reflecting the customer's past feedback when collecting data. For example, the collection unit customizes the collection means based on the customer's past feedback. The collection unit can also optimize the collection algorithm by referring to the customer's past feedback. Furthermore, the collection unit can also optimize the collection means by reflecting the customer's past feedback. This allows the collection means to be customized by reflecting the customer's past feedback, making it possible to collect more appropriate data.

[0142] The utilization unit can estimate the user's emotions and adjust the data utilization method based on the estimated user emotions. For example, if the user is relaxed, the utilization unit can perform a detailed data analysis. If the user is in a hurry, the utilization unit can also perform a concise data analysis. Furthermore, if the user is excited, the utilization unit can also perform a visually appealing data analysis. This allows the data utilization method to be adjusted according to the user's emotions, enabling more effective data utilization. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0143] When utilizing data, the utilization unit can optimize the utilization algorithm by referring to past utilization data. For example, the utilization unit selects the optimal utilization algorithm based on the past utilization data. The utilization unit can also analyze the past utilization data and optimize the utilization procedure. Furthermore, the utilization unit can also improve the utilization algorithm by referring to the past utilization data. This makes it possible to optimize the utilization algorithm by referring to the past utilization data and achieve efficient data utilization.

[0144] When utilizing data, the utilization department can customize the utilization method based on the customer's business process. For example, the utilization department analyzes the customer's business process and provides the optimal utilization method. The utilization department can also customize the utilization method based on the customer's business process. Furthermore, the utilization department can also refer to the customer's business process and optimize the utilization method. This allows the utilization method to be customized based on the customer's business process, enabling more appropriate data utilization.

[0145] When utilizing data, the utilization department can improve the utilization method by reflecting customer feedback. For example, the utilization department improves the utilization method based on customer feedback. The utilization department can also refer to customer feedback and optimize the utilization algorithm. Furthermore, the utilization department can also optimize the utilization method by reflecting customer feedback. This allows the utilization method to be improved by reflecting customer feedback, and more appropriate data utilization can be achieved.

[0146] The utilization unit can estimate the user's emotions and determine the priority of data utilization based on the estimated user emotions. For example, when the user is relaxed, the utilization unit prioritizes utilization of data with high importance. Furthermore, when the user is in a hurry, the utilization unit can prioritize utilization of data that requires a quick response. Furthermore, when the user is excited, the utilization unit can prioritize utilization of visually appealing data. In this way, the priority of data utilization can be determined according to the user's emotions and important data can be prioritized for utilization. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0147] When utilizing data, the utilization department can select utilization methods taking into consideration the geographical location information of the customer. For example, the utilization department prioritizes utilization of region-specific data based on the geographical location information of the customer. The utilization department can also refer to the geographical location information of the customer and utilize data that reflects regional market trends. Furthermore, the utilization department can also optimize utilization methods taking into consideration the geographical location information of the customer. This makes it possible to select utilization methods taking into consideration the geographical location information of the customer and prioritize utilization of region-specific data.

[0148] When utilizing data, the utilization department can analyze the customer's social media activity and suggest utilization methods. For example, the utilization department analyzes the content of the customer's posts on social media and utilizes related data. The utilization department can also suggest the optimal utilization method based on the customer's social media activity history. Furthermore, the utilization department can also optimize utilization methods by referring to the activities of the customer's friends on social media. This allows the customer's social media activity to be analyzed and utilization methods to be suggested, enabling more appropriate data utilization.

[0149] When utilizing data, the utilization unit can customize the utilization means by reflecting the customer's past feedback. For example, the utilization unit customizes the utilization means based on the customer's past feedback. The utilization unit can also refer to the customer's past feedback and optimize the utilization algorithm. Furthermore, the utilization unit can also optimize the utilization means by reflecting the customer's past feedback. This allows the utilization means to be customized by reflecting the customer's past feedback, making it possible to utilize data more appropriately. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, generation unit, evaluation unit, automation unit, reporting unit, collection unit, and utilization unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the smart device 14 and acquires customer requests from survey results and interview content. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a proposal using a generation AI. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the accuracy of the proposal content and the quality of the presentation. The automation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically performs procedures necessary for introducing a new service. The reporting unit is realized, for example, by the control unit 46A of the smart device 14 and reports the progress of service activation work in real time. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a database of past success stories and failure stories. The utilization unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and utilizes the collected data for new sales activities. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, generation unit, evaluation unit, automation unit, reporting unit, collection unit, and utilization unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the smart glasses 214 and acquires customer requests from questionnaire results and interview content. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a proposal using a generation AI. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the accuracy of the proposal content and the quality of the presentation. The automation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically performs procedures necessary for introducing a new service. The reporting unit is realized, for example, by the control unit 46A of the smart glasses 214 and reports the progress of service activation work in real time. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a database of past success stories and failure stories. The utilization unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and utilizes the collected data for new sales activities. === Hard Collateral 1-3 === Each of the multiple elements, including the acquisition unit, generation unit, evaluation unit, automation unit, reporting unit, collection unit, and utilization unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the acquisition unit is implemented by the control unit 46A of the headset terminal 314 and acquires customer requests from survey results and interview content. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates a proposal using a generation AI. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the accuracy of the proposal content and the quality of the presentation. The automation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and automatically performs procedures required for introducing a new service. The reporting unit is implemented, for example, by the control unit 46A of the headset terminal 314 and reports the progress of service activation work in real time. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and compiles a database of past success stories and failure stories. The utilization unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and utilizes the collected data for new sales activities. === Hard Collateral 1-4 === Each of the multiple elements, including the acquisition unit, generation unit, evaluation unit, automation unit, reporting unit, collection unit, and utilization unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the robot 414 and acquires customer requests from questionnaire results and interview content. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a proposal using a generation AI. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the accuracy of the proposal content and the quality of the presentation. The automation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically performs procedures necessary for introducing a new service. The reporting unit is realized, for example, by the control unit 46A of the robot 414 and reports the progress of service activation work in real time. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a database of past success stories and failure stories. The utilization unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and utilizes the collected data for new sales activities.

[0150] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0151] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring sales needs based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition of sales needs can be postponed until a more relaxed time. Also, if the user is relaxed, the sales needs can be acquired immediately and a quick response can be made. Furthermore, if the user is in a hurry, the sales needs can be acquired quickly by asking simple questions. This allows the timing of acquiring sales needs to be adjusted according to the user's emotions, and the needs can be acquired at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0152] The generation unit can estimate the user's emotions and adjust the presentation style of the proposal based on the estimated user emotions. For example, if the user is relaxed, a proposal with detailed explanations can be generated. If the user is in a hurry, a concise proposal that focuses on the main points can be generated. Furthermore, if the user is excited, a visually appealing proposal can be generated. This allows the presentation style of the proposal to be adjusted according to the user's emotions, and a more effective proposal can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0153] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria for the proposal based on the estimated user's emotions. For example, if the user is relaxed, detailed evaluation criteria can be applied. If the user is in a hurry, concise evaluation criteria can be applied. Furthermore, if the user is excited, visually appealing evaluation criteria can be applied. This allows the evaluation criteria for the proposal to be adjusted according to the user's emotions, resulting in a more appropriate evaluation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0154] The automation unit can estimate the user's emotions and adjust the automation procedure based on the estimated user's emotions. For example, if the user is relaxed, detailed instructions can be provided. If the user is in a hurry, concise instructions can be provided. Furthermore, if the user is excited, visually appealing instructions can be provided. This allows the automation procedure to be adjusted according to the user's emotions and more appropriate instructions to be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0155] The reporting unit can estimate the user's emotions and adjust the display method of the report based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows the display method of the report to be adjusted according to the user's emotions, resulting in a more appropriate display. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0156] The acquisition unit can analyze past sales activity data and select the optimal method for acquiring needs. For example, the most effective question format can be selected based on past sales activity data. The acquisition unit can also analyze past sales activity data and select the optimal acquisition method for a specific customer segment. Furthermore, the acquisition unit can refer to past sales activity data and select an acquisition method with a high success rate. This allows the optimal method for acquiring needs to be selected based on past sales activity data, enabling effective sales activities.

[0157] When generating a proposal, the generation unit can apply different generation algorithms depending on the industry or business category of the customer. For example, for a manufacturing customer, the generation unit can generate a proposal specialized for the manufacturing industry. The generation unit can also generate a proposal specialized for the service industry for a service customer. Furthermore, the generation unit can also generate a proposal specialized for the retail industry for a retail customer. This makes it possible to apply different generation algorithms depending on the industry or business category of the customer and generate an optimal proposal.

[0158] The evaluation department can update the evaluation criteria by reflecting customer feedback when evaluating proposals. For example, the evaluation criteria are updated based on customer feedback. The evaluation department can also refer to customer feedback to improve the evaluation algorithm. Furthermore, the evaluation department can also optimize the evaluation criteria by reflecting customer feedback. This allows the evaluation criteria to be updated by reflecting customer feedback, thereby improving the accuracy of the evaluation.

[0159] During automation, the automation department can customize the automation procedure based on the customer's business process. For example, the automation department analyzes the customer's business process and provides the optimal automation procedure. The automation department can also customize the automation procedure based on the customer's business process. Furthermore, the automation department can refer to the customer's business process and optimize the automation procedure. This makes it possible to customize the automation procedure based on the customer's business process and provide a more appropriate procedure.

[0160] The reporting department can improve the report content by reflecting customer feedback when reporting. For example, the report content is improved based on customer feedback. The reporting department can also refer to customer feedback and optimize the reporting algorithm. Furthermore, the reporting department can also optimize the report content by reflecting customer feedback. This allows the report content to be improved by reflecting customer feedback, and more appropriate reporting can be performed.

[0161] The processing flow of the second embodiment will be briefly explained below.

[0162] Step 1: The Acquisition Department understands the needs of sales staff. Sales staff needs include customer requests, work challenges, and goals. The Acquisition Department can obtain customer requests from survey results and interview content, and can also understand the needs of sales staff based on past transaction history. Step 2: The generation unit generates a proposal based on the needs identified by the acquisition unit. The generation unit uses generation AI to generate a proposal based on the customer's requests and past transaction history. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and uses these to generate the proposal. Step 3: The evaluation unit evaluates the proposals generated by the generation unit. The evaluation unit evaluates the accuracy of the proposal content and the quality of the presentation. The evaluation unit can also use AI to evaluate the proposals. Step 4: The automation department automates the service activation process. The automation department automates the procedures required to introduce a new service. The automation department can also automate procedures using AI. Step 5: The reporting department reports the progress of the work automated by the automation department. The reporting department reports the progress of the service launch work in real time. The reporting department can also use AI to report the progress. Step 6: The collection department collects past data. The collection department creates a database of past successes and failures. The collection department can also use AI to collect data. Step 7: The Utilization Department utilizes the data collected by the Collection Department. The Utilization Department utilizes the collected data for new sales activities. The Utilization Department can also utilize data using AI.

[0163] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0168] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0169] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0170] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0171] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0173] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0174] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0175] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0176] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0177] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0178] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0179] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0184] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0185] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0186] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0187] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0188] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0189] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0190] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0191] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0192] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0193] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0194] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0195] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0196] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0197] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0198] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0199] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0200] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0201] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0202] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0203] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0204] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0205] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0206] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0207] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0208] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0209] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0210] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0211] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0212] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0213] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0214] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0215] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0216] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0217] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0218] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0219] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0220] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0221] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0222] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0223] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0224] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0225] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0226] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0227] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0228] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0229] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0230] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0231] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0232] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0233] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0234] [Explanation of symbols]

[0235] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The Acquisition Department understands the needs of sales staff, a generation unit that generates a proposal based on the needs identified by the acquisition unit; an evaluation unit that evaluates the proposal generated by the generation unit; An automation department that automates service activation operations, a reporting unit that reports the progress of the work automated by the automation unit; a collection unit that collects past data; and an utilization unit that utilizes the data collected by the collection unit. A system characterized by:

2. The generation unit Generate proposals based on customer requests and past transaction history 2. The system of claim 1.

3. The automation unit Automate the procedures required to introduce new services 2. The system of claim 1.

4. The reporting unit Real-time progress reports on service launch operations 2. The system of claim 1.

5. The collecting unit Create a database of past successes and failures 2. The system of claim 1.

6. The utilization part is Use the collected data for new sales activities 2. The system of claim 1.

7. The evaluation unit Evaluate the quality of the generated proposals 2. The system of claim 1.

8. The acquisition unit Estimate user emotions and adjust the timing of acquiring sales needs based on the estimated user emotions 2. The system of claim 1.

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