system

Abstract

The system according to the embodiment aims to efficiently and quickly propose optimal solutions based on customer requests. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, a feedback unit, and a support unit. The collection unit collects product information or customer requests. The analysis unit analyzes the information collected by the collection unit and proposes solutions. The proposal unit makes proposals to customers based on the solutions proposed by the analysis unit. The feedback unit collects customer feedback on the solutions proposed by the proposal unit. The support unit provides technical support based on the feedback collected by the feedback 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 had the problem of not being able to efficiently and quickly propose optimal solutions based on customer requests.

[0005] The system according to the embodiment aims to efficiently and quickly propose optimal solutions based on customer requests. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, a feedback unit, and a support unit. The collection unit collects product information or customer requests. The analysis unit analyzes the information collected by the collection unit and proposes solutions. The proposal unit makes proposals to customers based on the solutions proposed by the analysis unit. The feedback unit collects customer feedback on the solutions proposed by the proposal unit. The support unit provides technical support based on the feedback collected by the feedback unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently and quickly propose an optimal solution based on a customer's request. [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 utilizes a generative AI to efficiently and quickly propose services to corporate sales teams. This sales support system inputs product information and customer requests, and the generative AI analyzes the input information and automatically replies and proposes optimal solutions. Furthermore, sales and engineers collaborate to create more feasible solutions based on the output from the generative AI. This system improves productivity and enables rapid action. For example, product information and customer requests are input. If a customer is seeking information about a specific service, they input that request. This information is then input into the generative AI. The generative AI then analyzes the input information and automatically replies and proposes optimal solutions. The generative AI generates optimal solutions based on the product information and customer requests. For example, it proposes optimal plans and options for the service the customer is looking for. Furthermore, sales and engineers collaborate to create more feasible solutions based on the output from the generative AI. Sales staff make proposals to customers based on the proposals obtained from the generative AI, and engineers provide technical support to realize those proposals. This enables rapid response to customer needs. This system improves productivity and enables swift action. By collaborating with sales and engineers, it is possible to provide more feasible solutions based on the output obtained from generative AI. For example, by proposing the optimal plan and options for the service a customer is looking for and providing technical support to realize that proposal, customer satisfaction can be increased. This enables the sales support system to efficiently collect, analyze, propose, collect feedback, and support product information and customer requests.

[0029] A sales support system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, a feedback unit, and a support unit. The collection unit collects product information or customer requests. For example, the collection unit may conduct a questionnaire survey or website traffic analysis to collect the product information or customer requests. The collection unit may also use telephone or email to collect customer requests. The analysis unit analyzes the information collected by the collection unit and proposes an optimal solution. For example, the analysis unit may analyze the collected information using data mining or statistical analysis. The analysis unit may also analyze the collected information and propose an optimal solution using a generation AI. The proposal unit makes a proposal to the customer based on the solution proposed by the analysis unit. For example, the proposal unit may create a report or make a presentation. The proposal unit may also use the generation AI to make a proposal to the customer based on the analysis results. The feedback unit collects customer feedback on the proposed solution. For example, the feedback unit may conduct a questionnaire survey or interviews. The feedback unit may also use the generation AI to collect customer feedback. The support department provides technical support based on the feedback. For example, the support department can provide telephone support or online support. The support department can also provide technical support using generative AI. As a result, the sales support system according to the embodiment can efficiently collect, analyze, propose, collect feedback, and provide support for product information and customer requests.

[0030] The collection unit can analyze past collected data and select a collection method. For example, the collection unit can discover from past collected data that collection during a specific time period results in a high response rate and then collect data during that time period. The collection unit can also analyze past collected data and find that specific product information is effective for a specific customer segment and then collect data for that customer segment. The collection unit can also select the most effective collection channel (email, telephone, chat, etc.) based on past collected data and collect data using that channel. This improves collection efficiency by selecting the optimal collection method based on past data. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input past collected data into a generation AI and have the generation AI select the optimal collection method.

[0031] The collection unit can filter product information and requests based on the user's current business situation and areas of interest when collecting them. For example, the collection unit analyzes the user's current business situation and collects only relevant product information. The collection unit can also filter unnecessary information and collect only necessary information based on the user's areas of interest. The collection unit can also prioritize the information to be collected based on the user's business situation and areas of interest, and prioritize the collection of important information. This allows highly relevant information to be collected by filtering information based on the user's business situation and areas of interest. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's business situation data into a generation AI and have the generation AI perform filtering.

[0032] When collecting product information and requests, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting product information related to that area. The collection unit can also prioritize collecting region-specific requests based on the user's geographical location information. If the user is moving, the collection unit can also collect the most relevant information based on the user's current location. This allows region-specific information to be collected preferentially by taking the user's geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to collect highly relevant information.

[0033] The collection unit can analyze the user's social media activity and collect related information when collecting product information and requests. For example, the collection unit can analyze the user's social media activity and prioritize collecting product information of interest to the user. The collection unit can also collect related information based on requests posted by the user on social media. The collection unit can also identify trends and interests from the user's social media activity and collect information based on those trends. In this way, information of high interest can be collected by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0034] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on information of high importance. The analysis unit can also perform a concise analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to the importance of the information and prioritize analysis of important information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0035] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a specific analysis algorithm to product information. The analysis unit can also apply a different analysis algorithm to customer requests. The analysis unit can also select and apply the optimal analysis algorithm depending on the category of information. This improves analysis accuracy by applying the optimal analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data to the generation AI and have the generation AI select and apply an analysis algorithm.

[0036] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit prioritizes analysis of recently submitted information. The analysis unit can also postpone analysis of information submitted earlier. The analysis unit can also determine the priority of analysis based on the time of submission and respond quickly. This enables a quick response by determining the priority of analysis based on the time of submission of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of information to the generation AI and have the generation AI determine the priority of analysis.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also determine the order of analysis based on the relevance of the information and perform analysis efficiently. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0038] The proposal unit can adjust the level of detail of the proposal based on the importance of the solution when making a proposal. For example, the proposal unit makes a detailed proposal for a solution with a high importance. The proposal unit can also make a concise proposal for a solution with a low importance. The proposal unit can also determine the priority of the proposal according to the importance of the solution and preferentially propose important solutions. This enables efficient proposals by adjusting the level of detail of the proposal according to the importance of the solution. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI, for example. For example, the proposal unit can input solution importance data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0039] When making a proposal, the proposal unit can apply different proposal algorithms depending on the solution category. For example, the proposal unit can apply a specific proposal algorithm to product information. The proposal unit can also apply a different proposal algorithm to a customer request. The proposal unit can also select and apply an optimal proposal algorithm depending on the solution category. This improves proposal accuracy by applying the optimal proposal algorithm depending on the solution category. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input solution category data to a generation AI and cause the generation AI to select and apply a proposal algorithm.

[0040] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the solution. For example, the proposal unit can prioritize the most recently submitted solution. The proposal unit can also postpone proposing solutions that were submitted earlier. The proposal unit can also determine the priority of the proposal based on the time of submission and respond quickly. This enables a quick response by determining the priority of the proposal based on the time of submission of the solution. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the time of submission of the solution to the generation AI and have the generation AI determine the priority of the proposal.

[0041] The suggestion unit can adjust the order of proposals based on the relevance of the solutions when making a proposal. For example, the suggestion unit prioritizes proposing highly relevant solutions. The suggestion unit can also postpone proposing less relevant solutions. The suggestion unit can also determine the order of proposals based on the relevance of the solutions and make proposals efficiently. This enables efficient proposals by adjusting the order of proposals based on the relevance of the solutions. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input relevance data of the solutions to a generation AI and cause the generation AI to adjust the order of proposals.

[0042] When collecting feedback, the feedback unit can select a collection method by referring to past feedback data. For example, the feedback unit can discover from past feedback data that a particular collection method (email, telephone, questionnaire, etc.) is effective and collect feedback using that method. The feedback unit can also analyze past feedback data and find that collecting feedback during a specific time period results in a high response rate, and collect feedback during that time period. The feedback unit can also select the most effective collection channel based on past feedback data and collect feedback using that channel. This improves collection efficiency by selecting the optimal collection method based on past feedback data. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input past feedback data into a generation AI and have the generation AI select a collection method.

[0043] When collecting feedback, the feedback unit can apply different collection algorithms depending on the category of feedback. For example, the feedback unit can apply a specific collection algorithm to feedback related to a product. The feedback unit can also apply a different collection algorithm to feedback related to customer service. The feedback unit can also select and apply an optimal collection algorithm depending on the category of feedback. This improves collection accuracy by applying an optimal collection algorithm depending on the feedback category. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input feedback category data to a generation AI and cause the generation AI to select and apply a collection algorithm.

[0044] When collecting feedback, the feedback unit can determine the priority of collection based on the time of submission of the feedback. For example, the feedback unit prioritizes collection of recently submitted feedback. The feedback unit can also collect feedback that has been submitted earlier at a later date. The feedback unit can also determine the priority of collection based on the time of submission and respond quickly. This enables a quick response by determining the priority of collection based on the time of submission of feedback. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input feedback submission time data into a generation AI and have the generation AI determine the priority of collection.

[0045] When collecting feedback, the feedback unit can adjust the collection order based on the relevance of the feedback. For example, the feedback unit prioritizes collection of highly relevant feedback. The feedback unit can also collect less relevant feedback later. The feedback unit can also determine the collection order based on the relevance of the feedback and collect the feedback efficiently. This enables efficient collection by adjusting the collection order based on the relevance of the feedback. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input feedback relevance data to a generation AI and cause the generation AI to adjust the collection order.

[0046] When providing support, the support department can select a support method by referring to past support data. For example, the support department can discover from past support data that a particular support method (email, telephone, chat, etc.) is effective and provide support using that method. The support department can also analyze past support data and find that providing support during a specific time period results in a high response rate, and provide support during that time period. The support department can also select the most effective support channel based on past support data and provide support using that channel. This improves support efficiency by selecting the optimal support method based on past support data. Some or all of the above-mentioned processing in the support department can be performed using, for example, AI, or can be performed without using AI. For example, the support department can input past support data into a generation AI and have the generation AI select a support method.

[0047] When providing support, the support unit can customize support measures based on the user's current business situation. For example, the support unit analyzes the user's current business situation and provides highly relevant support measures. The support unit can also customize the necessary support measures based on the user's business situation. The support unit can also select and provide the optimal support measures according to the user's business situation. This allows for more appropriate support to be provided by customizing the support measures based on the user's business situation. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the user's business situation data into a generation AI and have the generation AI customize the support measures.

[0048] When providing support, the support unit can select a support method based on the user's geographical location information. For example, if the user is in a specific area, the support unit provides support means related to that area. The support unit can also provide support means specific to the area based on the user's geographical location information. If the user is moving, the support unit can also provide the most relevant support means based on the user's current location. This makes it possible to provide support means specific to the area by taking the user's geographical location information into consideration. Some or all of the above-described processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input the user's geographical location data into the generation AI and have the generation AI select a support method.

[0049] When providing support, the support unit can analyze the user's social media activity and suggest support methods. For example, the support unit can analyze the user's social media activity and prioritize providing support methods that the user is interested in. The support unit can also provide relevant support methods based on requests posted by the user on social media. The support unit can also identify trends and interests from the user's social media activity and suggest support methods based on that. In this way, by analyzing the user's social media activity, it is possible to provide support methods that are of high interest. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the user's social media data into a generation AI and have the generation AI suggest support methods.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The sales support system can further include a prediction unit. The prediction unit can predict future trends and demand based on past product information and customer request data. For example, the prediction unit can analyze past data and predict fluctuations in demand during specific seasons or events. The prediction unit can also predict which products a customer is likely to purchase next based on their purchasing history. Furthermore, the prediction unit can analyze trends across the industry and predict future market trends. This allows the sales support system to predict future demand and propose products at the appropriate time.

[0052] The collection unit can predict the optimal collection timing based on the user's past behavioral data. For example, it can discover from past data that collecting data during a specific time period results in a high response rate, and collect data during that time period. The collection unit can also analyze past behavioral data and find that collecting data on a specific day of the week or during an event will produce effective results, and collect data at that time. This improves collection efficiency by predicting the optimal collection timing based on past behavioral data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0053] The collection unit can determine the priority of information to be collected based on the user's current business situation and areas of interest. For example, if the user has started a new project, information related to that project can be collected preferentially. Also, if the user is interested in a particular industry, information related to that industry can be collected preferentially. Furthermore, the importance of the information to be collected can be evaluated based on the user's business situation and areas of interest, and important information can be collected preferentially. In this way, by determining the priority of information based on the user's business situation and areas of interest, highly relevant information can be collected.

[0054] The collection unit can adjust the format of the information to be collected based on the user's geographical location information. For example, if the user is in a specific area, information tailored to the language and culture of that area can be provided. Also, if the user is traveling, concise and to-the-point information can be provided. Furthermore, if the user is staying in a specific area for an extended period of time, detailed information related to that area can be provided. This allows for more effective information collection by adjusting the format of information based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0055] The collection unit can analyze the user's social media activity and determine the priority of information to be collected. For example, if the user frequently posts about a specific topic on social media, information related to that topic can be collected preferentially. Also, if the user uses a specific hashtag on social media, information related to that hashtag can be collected preferentially. Furthermore, the collection unit can identify the user's current interests from the user's social media activity and determine the priority of information based on that. In this way, by analyzing the user's social media activity, information of high interest can be collected preferentially.

[0056] During analysis, the analysis unit can adjust the level of detail of the analysis based on the reliability of the information. For example, a detailed analysis can be performed for highly reliable information. A brief analysis can also be performed for less reliable information. Furthermore, the analysis priority can be determined based on the reliability of the information, and more reliable information can be analyzed preferentially. This allows for efficient analysis by adjusting the level of detail of the analysis based on the reliability of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0057] During analysis, the analysis unit can apply different analysis algorithms based on the source of the information. For example, a specific analysis algorithm can be applied to information from an official data source. A different analysis algorithm can be applied to information from an unofficial data source. Furthermore, the analysis unit can select and apply the most appropriate analysis algorithm depending on the source of the information. This improves the accuracy of the analysis by applying the most appropriate analysis algorithm depending on the source of the information. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit collects product information or customer requests. For example, the collection unit can conduct a questionnaire survey or analyze website traffic. Customer requests can also be collected by telephone or email. Step 2: The analysis unit analyzes the information collected by the collection unit and proposes optimal solutions. The analysis unit can analyze the information using data mining, statistical analysis, and generative AI. Step 3: The proposal department makes a proposal to the customer based on the solution proposed by the analysis department. The proposal department can create reports, make presentations, and make proposals using generative AI. Step 4: The feedback department collects customer feedback on the proposed solution. The feedback department can collect feedback using surveys, interviews, or generative AI. Step 5: The support department provides technical support based on the feedback. The support department can provide technical support via phone, online, or using generative AI.

[0060] (Example 2) A sales support system according to an embodiment of the present invention utilizes a generative AI to efficiently and quickly propose services to corporate sales teams. This sales support system inputs product information and customer requests, and the generative AI analyzes the input information and automatically replies and proposes optimal solutions. Furthermore, sales and engineers collaborate to create more feasible solutions based on the output from the generative AI. This system improves productivity and enables rapid action. For example, product information and customer requests are input. If a customer is seeking information about a specific service, they input that request. This information is then input into the generative AI. The generative AI then analyzes the input information and automatically replies and proposes optimal solutions. The generative AI generates optimal solutions based on the product information and customer requests. For example, it proposes optimal plans and options for the service the customer is looking for. Furthermore, sales and engineers collaborate to create more feasible solutions based on the output from the generative AI. Sales staff make proposals to customers based on the proposals obtained from the generative AI, and engineers provide technical support to realize those proposals. This enables rapid response to customer needs. This system improves productivity and enables swift action. By collaborating with sales and engineers, it is possible to provide more feasible solutions based on the output obtained from generative AI. For example, by proposing the optimal plan and options for the service a customer is looking for and providing technical support to realize that proposal, customer satisfaction can be increased. This enables the sales support system to efficiently collect, analyze, propose, collect feedback, and support product information and customer requests.

[0061] A sales support system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, a feedback unit, and a support unit. The collection unit collects product information or customer requests. For example, the collection unit may conduct a questionnaire survey or website traffic analysis to collect the product information or customer requests. The collection unit may also use telephone or email to collect customer requests. The analysis unit analyzes the information collected by the collection unit and proposes an optimal solution. For example, the analysis unit may analyze the collected information using data mining or statistical analysis. The analysis unit may also analyze the collected information and propose an optimal solution using a generation AI. The proposal unit makes a proposal to the customer based on the solution proposed by the analysis unit. For example, the proposal unit may create a report or make a presentation. The proposal unit may also use the generation AI to make a proposal to the customer based on the analysis results. The feedback unit collects customer feedback on the proposed solution. For example, the feedback unit may conduct a questionnaire survey or interviews. The feedback unit may also use the generation AI to collect customer feedback. The support department provides technical support based on the feedback. For example, the support department can provide telephone support or online support. The support department can also provide technical support using generative AI. As a result, the sales support system according to the embodiment can efficiently collect, analyze, propose, collect feedback, and provide support for product information and customer requests.

[0062] The collection unit estimates the user's emotions and adjusts the timing of collecting product information and requests based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the collection timing so that information can be provided to the user in a relaxed state. Furthermore, if the user is excited, the collection unit can immediately collect information and respond quickly. Furthermore, if the user is tired, the collection unit can adjust the collection timing to collect information after the user has rested. By adjusting the collection timing according to the user's emotions, information can be collected at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0063] The collection unit can analyze past collected data and select a collection method. For example, the collection unit can discover from past collected data that collection during a specific time period results in a high response rate and then collect data during that time period. The collection unit can also analyze past collected data and find that specific product information is effective for a specific customer segment and then collect data for that customer segment. The collection unit can also select the most effective collection channel (email, telephone, chat, etc.) based on past collected data and collect data using that channel. This improves collection efficiency by selecting the optimal collection method based on past data. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input past collected data into a generation AI and have the generation AI select the optimal collection method.

[0064] The collection unit can filter product information and requests based on the user's current business situation and areas of interest when collecting them. For example, the collection unit analyzes the user's current business situation and collects only relevant product information. The collection unit can also filter unnecessary information and collect only necessary information based on the user's areas of interest. The collection unit can also prioritize the information to be collected based on the user's business situation and areas of interest, and prioritize the collection of important information. This allows highly relevant information to be collected by filtering information based on the user's business situation and areas of interest. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's business situation data into a generation AI and have the generation AI perform filtering.

[0065] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit postpones less important information and prioritizes collecting more important information. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed information and providing it to the user. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting the most important information in order to respond quickly. Thus, by determining the priority of information according to the user's emotions, important information can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI. For example, the collection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0066] When collecting product information and requests, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting product information related to that area. The collection unit can also prioritize collecting region-specific requests based on the user's geographical location information. If the user is moving, the collection unit can also collect the most relevant information based on the user's current location. This allows region-specific information to be collected preferentially by taking the user's geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to collect highly relevant information.

[0067] The collection unit can analyze the user's social media activity and collect related information when collecting product information and requests. For example, the collection unit can analyze the user's social media activity and prioritize collecting product information of interest to the user. The collection unit can also collect related information based on requests posted by the user on social media. The collection unit can also identify trends and interests from the user's social media activity and collect information based on those trends. In this way, information of high interest can be collected by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0068] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. If the user is excited, the analysis unit can also provide analysis results with visually stimulating effects. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0069] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on information of high importance. The analysis unit can also perform a concise analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to the importance of the information and prioritize analysis of important information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0070] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a specific analysis algorithm to product information. The analysis unit can also apply a different analysis algorithm to customer requests. The analysis unit can also select and apply the optimal analysis algorithm depending on the category of information. This improves analysis accuracy by applying the optimal analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data to the generation AI and have the generation AI select and apply an analysis algorithm.

[0071] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide an analysis result with a visually stimulating effect. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, 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 such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI adjust the length of the analysis.

[0072] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit prioritizes analysis of recently submitted information. The analysis unit can also postpone analysis of information submitted earlier. The analysis unit can also determine the priority of analysis based on the time of submission and respond quickly. This enables a quick response by determining the priority of analysis based on the time of submission of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of information to the generation AI and have the generation AI determine the priority of analysis.

[0073] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also determine the order of analysis based on the relevance of the information and perform analysis efficiently. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0074] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions that focus on the main points. If the user is excited, the suggestion unit can provide suggestions that add visually stimulating effects. This enables more appropriate suggestions by adjusting the way the suggestions are expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0075] The proposal unit can adjust the level of detail of the proposal based on the importance of the solution when making a proposal. For example, the proposal unit makes a detailed proposal for a solution with a high importance. The proposal unit can also make a concise proposal for a solution with a low importance. The proposal unit can also determine the priority of the proposal according to the importance of the solution and preferentially propose important solutions. This enables efficient proposals by adjusting the level of detail of the proposal according to the importance of the solution. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI, for example. For example, the proposal unit can input solution importance data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0076] When making a proposal, the proposal unit can apply different proposal algorithms depending on the solution category. For example, the proposal unit can apply a specific proposal algorithm to product information. The proposal unit can also apply a different proposal algorithm to a customer request. The proposal unit can also select and apply an optimal proposal algorithm depending on the solution category. This improves proposal accuracy by applying the optimal proposal algorithm depending on the solution category. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input solution category data to a generation AI and cause the generation AI to select and apply a proposal algorithm.

[0077] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows for more appropriate suggestions by adjusting the length of the suggestions according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0078] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the solution. For example, the proposal unit can prioritize the most recently submitted solution. The proposal unit can also postpone proposing solutions that were submitted earlier. The proposal unit can also determine the priority of the proposal based on the time of submission and respond quickly. This enables a quick response by determining the priority of the proposal based on the time of submission of the solution. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the time of submission of the solution to the generation AI and have the generation AI determine the priority of the proposal.

[0079] The suggestion unit can adjust the order of proposals based on the relevance of the solutions when making a proposal. For example, the suggestion unit prioritizes proposing highly relevant solutions. The suggestion unit can also postpone proposing less relevant solutions. The suggestion unit can also determine the order of proposals based on the relevance of the solutions and make proposals efficiently. This enables efficient proposals by adjusting the order of proposals based on the relevance of the solutions. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input relevance data of the solutions to a generation AI and cause the generation AI to adjust the order of proposals.

[0080] The feedback unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user's emotions. For example, the feedback unit can request detailed feedback when the user is relaxed. The feedback unit can also request brief feedback when the user is in a hurry. The feedback unit can also request feedback with visually stimulating effects when the user is excited. This allows for more appropriate feedback to be collected by adjusting the feedback collection method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above-described processing in the feedback unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the feedback unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the feedback collection method.

[0081] When collecting feedback, the feedback unit can select a collection method by referring to past feedback data. For example, the feedback unit can discover from past feedback data that a particular collection method (email, telephone, questionnaire, etc.) is effective and collect feedback using that method. The feedback unit can also analyze past feedback data and find that collecting feedback during a specific time period results in a high response rate, and collect feedback during that time period. The feedback unit can also select the most effective collection channel based on past feedback data and collect feedback using that channel. This improves collection efficiency by selecting the optimal collection method based on past feedback data. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input past feedback data into a generation AI and have the generation AI select a collection method.

[0082] When collecting feedback, the feedback unit can apply different collection algorithms depending on the category of feedback. For example, the feedback unit can apply a specific collection algorithm to feedback related to a product. The feedback unit can also apply a different collection algorithm to feedback related to customer service. The feedback unit can also select and apply an optimal collection algorithm depending on the category of feedback. This improves collection accuracy by applying an optimal collection algorithm depending on the feedback category. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input feedback category data to a generation AI and cause the generation AI to select and apply a collection algorithm.

[0083] The feedback unit can estimate the user's emotions and prioritize feedback based on the estimated user emotions. For example, when the user is stressed, the feedback unit postpones less important feedback and prioritizes collecting more important feedback. Furthermore, when the user is relaxed, the feedback unit can prioritize collecting detailed feedback. Furthermore, when the user is in a hurry, the feedback unit can prioritize collecting the most important feedback in order to respond quickly. Thus, by prioritizing feedback according to the user's emotions, important feedback can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-described processing in the feedback unit can be performed using, for example, an AI. For example, the feedback unit can input the user's facial expression data into the generation AI and have the generation AI determine the feedback priorities.

[0084] When collecting feedback, the feedback unit can determine the priority of collection based on the time of submission of the feedback. For example, the feedback unit prioritizes collection of recently submitted feedback. The feedback unit can also collect feedback that has been submitted earlier at a later date. The feedback unit can also determine the priority of collection based on the time of submission and respond quickly. This enables a quick response by determining the priority of collection based on the time of submission of feedback. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input feedback submission time data into a generation AI and have the generation AI determine the priority of collection.

[0085] When collecting feedback, the feedback unit can adjust the collection order based on the relevance of the feedback. For example, the feedback unit prioritizes collection of highly relevant feedback. The feedback unit can also collect less relevant feedback later. The feedback unit can also determine the collection order based on the relevance of the feedback and collect the feedback efficiently. This enables efficient collection by adjusting the collection order based on the relevance of the feedback. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input feedback relevance data to a generation AI and cause the generation AI to adjust the collection order.

[0086] The support unit can estimate the user's emotions and adjust the support method based on the estimated user's emotions. For example, the support unit can provide detailed support when the user is relaxed. Furthermore, the support unit can provide concise support that focuses on the main points when the user is in a hurry. Furthermore, the support unit can provide support with visually stimulating effects when the user is excited. This allows for more appropriate support by adjusting the support method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-described processing in the support unit can be performed using, for example, an AI, or without an AI. For example, the support unit can input the user's facial expression data into the generation AI and have the generation AI adjust the support method.

[0087] When providing support, the support department can select a support method by referring to past support data. For example, the support department can discover from past support data that a particular support method (email, telephone, chat, etc.) is effective and provide support using that method. The support department can also analyze past support data and find that providing support during a specific time period results in a high response rate, and provide support during that time period. The support department can also select the most effective support channel based on past support data and provide support using that channel. This improves support efficiency by selecting the optimal support method based on past support data. Some or all of the above-mentioned processing in the support department can be performed using, for example, AI, or can be performed without using AI. For example, the support department can input past support data into a generation AI and have the generation AI select a support method.

[0088] When providing support, the support unit can customize support measures based on the user's current business situation. For example, the support unit analyzes the user's current business situation and provides highly relevant support measures. The support unit can also customize the necessary support measures based on the user's business situation. The support unit can also select and provide the optimal support measures according to the user's business situation. This allows for more appropriate support to be provided by customizing the support measures based on the user's business situation. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the user's business situation data into a generation AI and have the generation AI customize the support measures.

[0089] The support unit can estimate the user's emotions and determine the priority of support based on the estimated user emotions. For example, when the user is feeling stressed, the support unit postpones less important support and prioritizes more important support. Furthermore, when the user is relaxed, the support unit can prioritize providing detailed support. Furthermore, when the user is in a hurry, the support unit can prioritize providing the most important support in order to respond quickly. Thus, by determining the priority of support according to the user's emotions, important support can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the support unit can input the user's facial expression data into the generation AI and have the generation AI determine the priority of support.

[0090] When providing support, the support unit can select a support method based on the user's geographical location information. For example, if the user is in a specific area, the support unit provides support means related to that area. The support unit can also provide support means specific to the area based on the user's geographical location information. If the user is moving, the support unit can also provide the most relevant support means based on the user's current location. This makes it possible to provide support means specific to the area by taking the user's geographical location information into consideration. Some or all of the above-described processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input the user's geographical location data into the generation AI and have the generation AI select a support method.

[0091] When providing support, the support unit can analyze the user's social media activity and suggest support methods. For example, the support unit can analyze the user's social media activity and prioritize providing support methods that the user is interested in. The support unit can also provide relevant support methods based on requests posted by the user on social media. The support unit can also identify trends and interests from the user's social media activity and suggest support methods based on that. In this way, by analyzing the user's social media activity, it is possible to provide support methods that are of high interest. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the user's social media data into a generation AI and have the generation AI suggest support methods. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, feedback unit, and support unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects product information and customer requests using the camera 42 and microphone 38B of the smart device 14, and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and proposes optimal solutions. The proposal unit, realized, for example, by the control unit 46A of the smart device 14, makes proposals to customers based on the analysis results. The feedback unit, realized, for example, by the control unit 46A of the smart device 14, collects customer feedback and transmits it to the data processing device 12. The support unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides technical support based on the feedback. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, feedback unit, and support unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects product information and customer requests using the camera 42 and microphone 238 of the smart glasses 214, and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected information and proposes optimal solutions. The proposal unit, for example, is realized by the control unit 46A of the smart glasses 214, and makes proposals to customers based on the analysis results. The feedback unit, for example, is realized by the control unit 46A of the smart glasses 214, and collects customer feedback and transmits it to the data processing device 12. The support unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and provides technical support based on the feedback. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, feedback unit, and support unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects product information and customer requests using the camera 42 and microphone 238 of the headset terminal 314, and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information and proposes optimal solutions. The proposal unit is realized, for example, by the control unit 46A of the headset terminal 314, and makes proposals to customers based on the analysis results. The feedback unit is realized, for example, by the control unit 46A of the headset terminal 314, and collects customer feedback and transmits it to the data processing device 12. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides technical support based on the feedback. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, feedback unit, and support unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects product information and customer requests using the camera 42 and microphone 238 of the robot 414, and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information and proposes optimal solutions. The proposal unit is realized, for example, by the control unit 46A of the robot 414, and makes proposals to customers based on the analysis results. The feedback unit is realized, for example, by the control unit 46A of the robot 414, and collects customer feedback and transmits it to the data processing device 12. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides technical support based on the feedback.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The sales support system can further include a prediction unit. The prediction unit can predict future trends and demand based on past product information and customer request data. For example, the prediction unit can analyze past data and predict fluctuations in demand during specific seasons or events. The prediction unit can also predict which products a customer is likely to purchase next based on their purchasing history. Furthermore, the prediction unit can analyze trends across the industry and predict future market trends. This allows the sales support system to predict future demand and propose products at the appropriate time.

[0094] The collection unit can estimate the user's emotions and adjust the format of the information to be collected based on the estimated user emotions. For example, if the user is feeling stressed, concise and to the point information can be provided. If the user is relaxed, detailed information can be provided. Furthermore, if the user is excited, information can be provided in a visually appealing format. This allows for more effective information collection by adjusting the format of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI.

[0095] The collection unit can predict the optimal collection timing based on the user's past behavioral data. For example, it can discover from past data that collecting data during a specific time period results in a high response rate, and collect data during that time period. The collection unit can also analyze past behavioral data and find that collecting data on a specific day of the week or during an event will produce effective results, and collect data at that time. This improves collection efficiency by predicting the optimal collection timing based on past behavioral data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0096] The collection unit can determine the priority of information to be collected based on the user's current business situation and areas of interest. For example, if the user has started a new project, information related to that project can be collected preferentially. Also, if the user is interested in a particular industry, information related to that industry can be collected preferentially. Furthermore, the importance of the information to be collected can be evaluated based on the user's business situation and areas of interest, and important information can be collected preferentially. In this way, by determining the priority of information based on the user's business situation and areas of interest, highly relevant information can be collected.

[0097] The collection unit can estimate the user's emotions and select a channel for collecting information based on the estimated user emotions. For example, if the user is stressed, non-face-to-face channels such as email or chat can be used. Alternatively, if the user is relaxed, information can be collected by phone or face-to-face. Furthermore, if the user is excited, visually appealing channels such as social media or video calls can be used. This allows for more effective information collection by selecting an information collection channel according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI.

[0098] The collection unit can adjust the format of the information to be collected based on the user's geographical location information. For example, if the user is in a specific area, information tailored to the language and culture of that area can be provided. Also, if the user is traveling, concise and to-the-point information can be provided. Furthermore, if the user is staying in a specific area for an extended period of time, detailed information related to that area can be provided. This allows for more effective information collection by adjusting the format of information based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0099] The collection unit can analyze the user's social media activity and determine the priority of information to be collected. For example, if the user frequently posts about a specific topic on social media, information related to that topic can be collected preferentially. Also, if the user uses a specific hashtag on social media, information related to that hashtag can be collected preferentially. Furthermore, the collection unit can identify the user's current interests from the user's social media activity and determine the priority of information based on that. In this way, by analyzing the user's social media activity, information of high interest can be collected preferentially.

[0100] The analysis unit can estimate the user's emotions and adjust the visual presentation of the analysis based on the estimated user emotions. For example, if the user is relaxed, detailed graphs and charts can be provided. If the user is in a hurry, simple icons and symbols can be used to display the analysis results. Furthermore, if the user is excited, analysis results can be provided with animations and interactive elements. This allows for more appropriate analysis results to be provided by adjusting the visual presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or without AI.

[0101] During analysis, the analysis unit can adjust the level of detail of the analysis based on the reliability of the information. For example, a detailed analysis can be performed for highly reliable information. A brief analysis can also be performed for less reliable information. Furthermore, the analysis priority can be determined based on the reliability of the information, and more reliable information can be analyzed preferentially. This allows for efficient analysis by adjusting the level of detail of the analysis based on the reliability of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0102] During analysis, the analysis unit can apply different analysis algorithms based on the source of the information. For example, a specific analysis algorithm can be applied to information from an official data source. A different analysis algorithm can be applied to information from an unofficial data source. Furthermore, the analysis unit can select and apply the most appropriate analysis algorithm depending on the source of the information. This improves the accuracy of the analysis by applying the most appropriate analysis algorithm depending on the source of the information. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection unit collects product information or customer requests. For example, the collection unit can conduct a questionnaire survey or analyze website traffic. Customer requests can also be collected by telephone or email. Step 2: The analysis unit analyzes the information collected by the collection unit and proposes optimal solutions. The analysis unit can analyze the information using data mining, statistical analysis, and generative AI. Step 3: The proposal department makes a proposal to the customer based on the solution proposed by the analysis department. The proposal department can create reports, make presentations, and make proposals using generative AI. Step 4: The feedback department collects customer feedback on the proposed solution. The feedback department can collect feedback using surveys, interviews, or generative AI. Step 5: The support department provides technical support based on the feedback. The support department can provide technical support via phone, online, or using generative AI.

[0105] 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.

[0106] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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. a collection unit for collecting product information or customer requests; an analysis unit that analyzes the information collected by the collection unit and proposes a solution; a proposal unit that makes a proposal to a customer based on the solution proposed by the analysis unit; a feedback unit that collects customer feedback on the solution proposed by the proposal unit; a support unit that provides technical support based on the feedback collected by the feedback unit. A system characterized by:

2. The collecting unit Estimate user emotions and adjust the timing of collecting product information and requests based on the estimated user emotions 2. The system of claim 1.

3. The collecting unit Analyze past collected data and select collection methods 2. The system of claim 1.

4. The collecting unit When collecting product information or requests, filter them based on the user's current business situation or areas of interest.

2. The system of claim 1.

5. The collecting unit Estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions.

2. The system of claim 1.

6. The collecting unit When collecting product information or requests, prioritize collection of relevant information based on the user's geographic location.

2. The system of claim 1.

7. The collecting unit When collecting product information or requests, analyze users' social media activity and collect related information.

2. The system of claim 1.

8. The analysis unit Inferring user emotions and adjusting the presentation of analysis based on the estimated user emotions 2. The system of claim 1.

9. The analysis unit During analysis, adjust the level of detail based on the importance of the information 2. The system of claim 1.

10. The analysis unit During analysis, analysis algorithms are applied depending on the category of information 2. The system of claim 1.

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