system

Abstract

The system according to the embodiment aims to enable salespeople to efficiently recommend optimal products. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, and a recommendation unit. The reception unit receives requests in natural language. The analysis unit analyzes the requests received by the reception unit and analyzes past proposals or customer data. The recommendation unit recommends products based on the data analyzed by the analysis 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 the drawback of requiring salespeople to spend a lot of time and effort recommending optimal products, resulting in low efficiency.

[0005] The system according to the embodiment aims to enable salespeople to efficiently recommend optimal products. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a recommendation unit. The reception unit receives requests in natural language. The analysis unit analyzes the requests received by the reception unit and analyzes past proposals or customer data. The recommendation unit recommends products based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows salespeople to efficiently recommend optimal products. [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 product recommendation system according to an embodiment of the present invention allows a salesperson to input a product request in natural language, and an AI analyzes past proposals and customer data to recommend the most suitable product. In this product recommendation system, the salesperson simply inputs the product request in natural language, and the AI ​​recommends the most suitable product, and the system can also refer to success stories of other salespeople. For example, if a salesperson inputs "I would like to suggest a new smartphone," the AI ​​analyzes past successful smartphone proposals and customer data to recommend the most suitable smartphone. The AI ​​can also refer to the proposals made by other salespeople and the results they achieved. This improves the quality and efficiency of proposals. Furthermore, the AI ​​can optimize proposals based on the salesperson's past proposal history and customer feedback. For example, if a similar request has been made in the past, the AI ​​can recommend the most suitable product based on the proposal and customer data. This allows the salesperson to simply input the product request in natural language, and the AI ​​can recommend the most suitable product, and the system can also refer to success stories of other salespeople, thereby improving the quality and efficiency of proposals.

[0029] A product recommendation system according to an embodiment includes a reception unit, an analysis unit, and a recommendation unit. The reception unit receives a product request input in natural language by a sales representative. The request input by the sales representative may be, for example, a text-based request or a voice-input request, but is not limited to these examples. The reception unit analyzes the natural language request using, for example, text analysis technology. The reception unit can also convert the voice input into text data using voice recognition technology. For example, the reception unit converts the voice input into text data in real time and performs analysis. The analysis unit analyzes the request received by the reception unit and analyzes past proposals and customer data. The analysis unit analyzes past proposals using, for example, data mining technology. The analysis unit can also refer to a customer database and analyze customer purchase history and attribute information. For example, the analysis unit extracts data for recommending optimal products based on the customer's past purchase history. The recommendation unit recommends optimal products based on the data analyzed by the analysis unit. The recommendation unit selects optimal products using, for example, a recommendation algorithm. The recommendation unit can also refer to success stories of other sales representatives. For example, if a similar request has been made in the past, the recommendation unit recommends the most suitable product based on the proposal content and success stories. As a result, the product recommendation system according to the embodiment allows a sales representative to simply input a product request in natural language, and the AI ​​can recommend the most suitable product, improving the quality and efficiency of proposals.

[0030] The recommendation unit includes a reference unit that references success stories of other sales representatives. The reference unit improves the quality of proposals based on the success stories of other sales representatives. The reference unit, for example, references a database of past success stories to extract success stories. The reference unit can also select the most relevant case from among the success stories. For example, the reference unit prioritizes reference to success stories that have achieved a certain level of sales or have high customer satisfaction. This improves the quality of proposals by referring to the success stories of other sales representatives. Some or all of the above-described processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can input the success story database into AI and have the AI ​​select the optimal success story.

[0031] The reference unit can improve the quality of proposals based on success stories of other sales representatives. The reference unit, for example, selects the most relevant case from among the success stories. For example, the reference unit prioritizes reference to success stories with sales above a certain level or high customer satisfaction. The reference unit can also extract particularly effective proposal content from the success stories to improve the quality of proposals. For example, the reference unit improves the quality of proposals based on particularly effective proposal content from the success stories. This enables more effective proposals by improving the quality of proposals based on success stories of other sales representatives. Some or all of the above-mentioned processing in the reference unit may be performed using, for example, AI, or may be performed without AI. For example, the reference unit can input a database of success stories into AI and have the AI ​​select the optimal success story.

[0032] The reception unit can analyze past request history and select the optimal reception method. For example, the reception unit preferentially suggests reception methods that the user has frequently used in the past. The reception unit can also select the most efficient reception method from the user's past request history. Furthermore, the reception unit can also suggest the optimal reception method for a specific time period based on the user's past request history. In this way, the optimal reception method can be selected by analyzing the past request history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past request history data into a generation AI and have the generation AI select the optimal reception method.

[0033] The reception unit can filter requests based on the user's current work situation and areas of interest when receiving the request. For example, the reception unit preferentially receives requests related to a project the user is currently working on. The reception unit can also filter highly relevant requests based on the user's areas of interest. Furthermore, the reception unit can also accept requests at the optimal timing, taking the user's work situation into consideration. In this way, by filtering requests based on the user's work situation and areas of interest, highly relevant requests can be preferentially received. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's work situation data into a generation AI and cause the generation AI to perform optimal request filtering.

[0034] When receiving a request, the reception unit can prioritize receiving highly relevant requests based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving requests related to that area. The reception unit can also prioritize receiving requests related to locations close to the user's current location. Furthermore, the reception unit can filter optimal requests based on the user's geographical location information. This makes it possible to prioritize receiving highly relevant requests by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to a generation AI and cause the generation AI to filter optimal requests.

[0035] When receiving a request, the reception unit can analyze the user's social media activity and receive relevant requests. For example, the reception unit can analyze the content of the user's social media posts and prioritize receiving relevant requests. The reception unit can also filter out optimal requests based on the user's social media activity history. Furthermore, the reception unit can also accept relevant requests taking into account the user's areas of interest on social media. In this way, by analyzing the user's social media activity, it is possible to prioritize receiving highly relevant requests. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI perform filtering to find optimal requests.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the request. For example, the analysis unit performs a detailed analysis for requests with high importance. The analysis unit can also perform a brief analysis for requests with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the request. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the request. 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 requirement importance data to the generation AI and cause the generation AI to perform the optimal level of detail of the analysis.

[0037] During analysis, the analysis unit can apply multiple analysis algorithms depending on the category of the request. The analysis unit selects the optimal analysis algorithm depending on, for example, the product category. The analysis unit can also apply different analysis algorithms depending on the category of customer data. Furthermore, the analysis unit can select the optimal analysis method based on the category of the request. This enables highly accurate analysis by applying the optimal analysis algorithm depending on the category of the request. 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 request category data to the generation AI and have the generation AI select the optimal analysis algorithm.

[0038] During analysis, the analysis unit can determine the priority of analysis based on the time of request submission. For example, the analysis unit prioritizes analysis of recently submitted requests. The analysis unit can also postpone analysis of requests submitted earlier. Furthermore, the analysis unit can adjust the order of analysis based on the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of request submission. 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 submission time data into the generation AI and have the generation AI execute the optimal analysis priority.

[0039] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the requirements. For example, the analysis unit prioritizes analysis of highly relevant requirements. The analysis unit can also postpone analysis of less relevant requirements. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the requirements. This enables efficient analysis by adjusting the order of analysis based on the relevance of the requirements. 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. For example, the analysis unit can input requirement relevance data into the generation AI and cause the generation AI to execute the optimal analysis order.

[0040] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the product when making a recommendation. For example, the recommendation unit provides detailed recommendation content for products with high importance. The recommendation unit can also provide concise recommendation content for products with low importance. Furthermore, the recommendation unit can adjust the depth of the recommendation according to the importance of the product. This enables efficient recommendation by adjusting the level of detail of the recommendation according to the importance of the product. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input product importance data to the generation AI and cause the generation AI to perform the optimal level of detail of the recommendation.

[0041] The recommendation unit can apply multiple recommendation algorithms depending on the product category when making a recommendation. The recommendation unit selects the optimal recommendation algorithm depending on, for example, the product category. The recommendation unit can also apply different recommendation algorithms depending on the category of customer data. Furthermore, the recommendation unit can select the optimal recommendation method based on the product category. This enables highly accurate recommendations by applying the optimal recommendation algorithm depending on the product category. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input product category data into the generation AI and cause the generation AI to select the optimal recommendation algorithm.

[0042] When making a recommendation, the recommendation unit can determine the priority of recommendations based on the submission date of the product. For example, the recommendation unit prioritizes the most recently submitted product. The recommendation unit can also recommend older submitted products later. Furthermore, the recommendation unit can adjust the order of recommendations based on the submission date. This enables efficient recommendations by determining the priority of recommendations based on the submission date of the product. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI, for example. For example, the recommendation unit can input product submission date data into the generation AI and have the generation AI execute the optimal recommendation priority.

[0043] The recommendation unit can adjust the order of recommendations based on the relevance of the products when making a recommendation. For example, the recommendation unit prioritizes recommending highly relevant products. The recommendation unit can also recommend less relevant products later. Furthermore, the recommendation unit can adjust the order of recommendations based on the relevance of the products. This enables efficient recommendations by adjusting the order of recommendations based on the relevance of the products. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input product relevance data into a generation AI and cause the generation AI to execute the optimal recommendation order.

[0044] The reference unit can improve the accuracy of the reference based on the interrelationships between success cases during reference. For example, the reference unit prioritizes reference of highly relevant success cases. The reference unit can also analyze the interrelationships between success cases and reference the most effective case. Furthermore, the reference unit can select the optimal case by taking into account the interrelationships between success cases. This enables more accurate reference by taking into account the interrelationships between success cases. Some or all of the above-described processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can input interrelationship data between success cases into the generation AI and have the generation AI select the optimal case.

[0045] When referencing, the reference unit can perform the reference based on the attribute information of the person who submitted the success story. The reference unit can reference relevant success stories based on, for example, the industry or occupation of the person who submitted the success story. The reference unit can also reference the most appropriate success story taking into account the person's years of experience and skill level. Furthermore, the reference unit can select the most effective success story based on the attribute information of the person who submitted the success story. This allows for more relevant cases to be referenced by taking into account the attribute information of the person who submitted the success story. Some or all of the above-mentioned processing in the reference unit can be performed using, for example, AI, or can be performed without using AI. For example, the reference unit can input the attribute information data of the person who submitted the success story into the generation AI and have the generation AI select the most appropriate success story.

[0046] When referencing, the reference unit can perform the reference based on the geographic distribution of success stories. For example, the reference unit can prioritize reference to geographically close success stories among the success stories. The reference unit can also reference the most relevant success story taking geographic distribution into consideration. Furthermore, the reference unit can select the most appropriate success story based on geographic distribution. This allows for more relevant cases to be referenced by taking the geographic distribution of success stories into consideration. Some or all of the above-described processing in the reference unit can be performed using, for example, AI, or can be performed without using AI. For example, the reference unit can input geographic distribution data of success stories into the generation AI and have the generation AI select the most appropriate success story.

[0047] The reference unit can improve the accuracy of the reference based on literature related to the success case during the reference. For example, the reference unit can refer to literature related to the success case and provide detailed information. The reference unit can also refer to the most effective case based on the literature related to the success case. Furthermore, the reference unit can select the optimal case by taking into account the literature related to the success case. This enables more accurate reference by referring to the literature related to the success case. Some or all of the above-mentioned processing in the reference unit can be performed using, for example, AI, or can be performed without using AI. For example, the reference unit can input literature data related to the success case into the generation AI and have the generation AI select the optimal case.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The reception unit can learn the user's past behavioral patterns and provide an optimal interface. For example, if the user has frequently used voice input in the past, the reception unit can preferentially display a voice input interface. Also, if the user tends to input requests during a specific time period, the reception unit can provide an interface optimal for that time period. Furthermore, if the user has frequently used a specific keyword in the past, the reception unit can provide an interface related to that keyword. This can improve the user experience by providing an optimal interface based on the user's past behavioral patterns.

[0050] When referencing success stories, the reference unit can select the most appropriate case based on the user's industry and occupation. For example, if the user belongs to the IT industry, success stories related to the IT industry can be referenced preferentially. Also, if the user works in sales, success stories related to sales can be referenced preferentially. Furthermore, the most relevant success story can be selected based on the user's work experience and skill level. This enables more effective proposals by referencing the most appropriate success story based on the user's industry and occupation.

[0051] The reception unit can analyze the user's past request history and select the optimal reception method. For example, it can preferentially suggest reception methods that the user has frequently used in the past. It can also select the most efficient reception method from the user's past request history. Furthermore, it can suggest the optimal reception method for a specific time period based on the user's past request history. In this way, the optimal reception method can be selected by analyzing the past request history. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input past request history data into a generation AI and have the generation AI select the optimal reception method.

[0052] The reception unit can perform filtering based on the user's current work situation and areas of interest. For example, it can preferentially accept requests related to a project the user is currently working on. It can also filter highly relevant requests based on the user's areas of interest. Furthermore, it can accept requests at the optimal timing, taking the user's work situation into consideration. In this way, by filtering requests based on the user's work situation and areas of interest, it is possible to preferentially accept highly relevant requests. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's work situation data into the generation AI and cause the generation AI to perform optimal request filtering.

[0053] The reception unit can prioritize receiving highly relevant requests based on the user's geographical location information. For example, if the user is in a specific area, requests related to that area can be prioritized. Also, requests related to locations close to the user's current location can be prioritized. Furthermore, optimal requests can be filtered based on the user's geographical location information. This allows highly relevant requests to be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information data into a generation AI and cause the generation AI to filter optimal requests.

[0054] The reception unit can analyze the user's social media activity and receive relevant requests. For example, the reception unit can analyze the content of the user's social media posts and prioritize receiving relevant requests. The reception unit can also filter out optimal requests based on the user's social media activity history. Furthermore, the reception unit can also receive relevant requests taking into account the user's areas of interest on social media. In this way, by analyzing the user's social media activity, highly relevant requests can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI filter out optimal requests.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The reception unit receives product requests input in natural language by the sales representative. The requests input by the sales representative include text-based requests and voice-input requests. The reception unit can also analyze the natural language requests using text analysis technology and convert the voice input into text data using voice recognition technology. Step 2: The analysis unit analyzes the request received by the reception unit and analyzes past proposals and customer data. The analysis unit uses data mining technology to analyze past proposals and analyzes customer purchase history and attribute information by referring to the customer database. Step 3: The recommendation department recommends the most suitable product based on the data analyzed by the analysis department. The recommendation department uses a recommendation algorithm to select the most suitable product and can also refer to the success stories of other sales representatives.

[0057] (Example 2) A product recommendation system according to an embodiment of the present invention allows a salesperson to input a product request in natural language, and an AI analyzes past proposals and customer data to recommend the most suitable product. In this product recommendation system, the salesperson simply inputs the product request in natural language, and the AI ​​recommends the most suitable product, and the system can also refer to success stories of other salespeople. For example, if a salesperson inputs "I would like to suggest a new smartphone," the AI ​​analyzes past successful smartphone proposals and customer data to recommend the most suitable smartphone. The AI ​​can also refer to the proposals made by other salespeople and the results they achieved. This improves the quality and efficiency of proposals. Furthermore, the AI ​​can optimize proposals based on the salesperson's past proposal history and customer feedback. For example, if a similar request has been made in the past, the AI ​​can recommend the most suitable product based on the proposal and customer data. This allows the salesperson to simply input the product request in natural language, and the AI ​​can recommend the most suitable product, and the system can also refer to success stories of other salespeople, thereby improving the quality and efficiency of proposals.

[0058] A product recommendation system according to an embodiment includes a reception unit, an analysis unit, and a recommendation unit. The reception unit receives a product request input in natural language by a sales representative. The request input by the sales representative may be, for example, a text-based request or a voice-input request, but is not limited to these examples. The reception unit analyzes the natural language request using, for example, text analysis technology. The reception unit can also convert the voice input into text data using voice recognition technology. For example, the reception unit converts the voice input into text data in real time and performs analysis. The analysis unit analyzes the request received by the reception unit and analyzes past proposals and customer data. The analysis unit analyzes past proposals using, for example, data mining technology. The analysis unit can also refer to a customer database and analyze customer purchase history and attribute information. For example, the analysis unit extracts data for recommending optimal products based on the customer's past purchase history. The recommendation unit recommends optimal products based on the data analyzed by the analysis unit. The recommendation unit selects optimal products using, for example, a recommendation algorithm. The recommendation unit can also refer to success stories of other sales representatives. For example, if a similar request has been made in the past, the recommendation unit recommends the most suitable product based on the proposal content and success stories. As a result, the product recommendation system according to the embodiment allows a sales representative to simply input a product request in natural language, and the AI ​​can recommend the most suitable product, improving the quality and efficiency of proposals.

[0059] The recommendation unit includes a reference unit that references success stories of other sales representatives. The reference unit improves the quality of proposals based on the success stories of other sales representatives. The reference unit, for example, references a database of past success stories to extract success stories. The reference unit can also select the most relevant case from among the success stories. For example, the reference unit prioritizes reference to success stories that have achieved a certain level of sales or have high customer satisfaction. This improves the quality of proposals by referring to the success stories of other sales representatives. Some or all of the above-described processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can input the success story database into AI and have the AI ​​select the optimal success story.

[0060] The reference unit can improve the quality of proposals based on success stories of other sales representatives. The reference unit, for example, selects the most relevant case from among the success stories. For example, the reference unit prioritizes reference to success stories with sales above a certain level or high customer satisfaction. The reference unit can also extract particularly effective proposal content from the success stories to improve the quality of proposals. For example, the reference unit improves the quality of proposals based on particularly effective proposal content from the success stories. This enables more effective proposals by improving the quality of proposals based on success stories of other sales representatives. Some or all of the above-mentioned processing in the reference unit may be performed using, for example, AI, or may be performed without AI. For example, the reference unit can input a database of success stories into AI and have the AI ​​select the optimal success story.

[0061] The reception unit can estimate the user's emotions and adjust the timing of request acceptance based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit allows the AI ​​to immediately accept the request and quickly start processing. Furthermore, if the user is relaxed, the reception unit can allow the AI ​​to accept the request at an appropriate time and perform detailed confirmation. Furthermore, if the user is in a hurry, the reception unit can allow the AI ​​to prioritize accepting the request and quickly proceed to the next step. This allows the request to be accepted at a more appropriate time by adjusting the timing of request acceptance 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 may 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 reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI execute emotion estimation.

[0062] The reception unit can analyze past request history and select the optimal reception method. For example, the reception unit preferentially suggests reception methods that the user has frequently used in the past. The reception unit can also select the most efficient reception method from the user's past request history. Furthermore, the reception unit can also suggest the optimal reception method for a specific time period based on the user's past request history. In this way, the optimal reception method can be selected by analyzing the past request history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past request history data into a generation AI and have the generation AI select the optimal reception method.

[0063] The reception unit can filter requests based on the user's current work situation and areas of interest when receiving the request. For example, the reception unit preferentially receives requests related to a project the user is currently working on. The reception unit can also filter highly relevant requests based on the user's areas of interest. Furthermore, the reception unit can also accept requests at the optimal timing, taking the user's work situation into consideration. In this way, by filtering requests based on the user's work situation and areas of interest, highly relevant requests can be preferentially received. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's work situation data into a generation AI and cause the generation AI to perform optimal request filtering.

[0064] The reception unit can estimate the user's emotions and determine the priority of received requests based on the estimated user emotions. For example, when the user is stressed, the reception unit can prioritize requests with high importance. Furthermore, when the user is relaxed, the reception unit can also prioritize requests with normal priority. Furthermore, when the user is in a hurry, the reception unit can prioritize requests with high urgency. This allows important requests to be processed preferentially by determining the priority of requests 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 reception unit can be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0065] When receiving a request, the reception unit can prioritize receiving highly relevant requests based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving requests related to that area. The reception unit can also prioritize receiving requests related to locations close to the user's current location. Furthermore, the reception unit can filter optimal requests based on the user's geographical location information. This makes it possible to prioritize receiving highly relevant requests by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to a generation AI and cause the generation AI to filter optimal requests.

[0066] When receiving a request, the reception unit can analyze the user's social media activity and receive relevant requests. For example, the reception unit can analyze the content of the user's social media posts and prioritize receiving relevant requests. The reception unit can also filter out optimal requests based on the user's social media activity history. Furthermore, the reception unit can also accept relevant requests taking into account the user's areas of interest on social media. In this way, by analyzing the user's social media activity, it is possible to prioritize receiving highly relevant requests. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI perform filtering to find optimal requests.

[0067] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is stressed, the analysis unit uses a concise and easy-to-understand presentation. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. By adjusting the way the analysis is presented based on 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 perform emotion estimation.

[0068] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the request. For example, the analysis unit performs a detailed analysis for requests with high importance. The analysis unit can also perform a brief analysis for requests with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the request. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the request. 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 requirement importance data to the generation AI and cause the generation AI to perform the optimal level of detail of the analysis.

[0069] During analysis, the analysis unit can apply multiple analysis algorithms depending on the category of the request. The analysis unit selects the optimal analysis algorithm depending on, for example, the product category. The analysis unit can also apply different analysis algorithms depending on the category of customer data. Furthermore, the analysis unit can select the optimal analysis method based on the category of the request. This enables highly accurate analysis by applying the optimal analysis algorithm depending on the category of the request. 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 request category data to the generation AI and have the generation AI select the optimal analysis algorithm.

[0070] 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. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is stressed, the analysis unit can provide a concise and easy-to-understand analysis result. 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 perform emotion estimation.

[0071] During analysis, the analysis unit can determine the priority of analysis based on the time of request submission. For example, the analysis unit prioritizes analysis of recently submitted requests. The analysis unit can also postpone analysis of requests submitted earlier. Furthermore, the analysis unit can adjust the order of analysis based on the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of request submission. 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 submission time data into the generation AI and have the generation AI execute the optimal analysis priority.

[0072] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the requirements. For example, the analysis unit prioritizes analysis of highly relevant requirements. The analysis unit can also postpone analysis of less relevant requirements. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the requirements. This enables efficient analysis by adjusting the order of analysis based on the relevance of the requirements. 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. For example, the analysis unit can input requirement relevance data into the generation AI and cause the generation AI to execute the optimal analysis order.

[0073] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on the estimated user emotions. For example, if the user is feeling stressed, the recommendation unit can use a concise and easy-to-understand presentation. Furthermore, if the user is relaxed, the recommendation unit can provide detailed recommendations. Furthermore, if the user is in a hurry, the recommendation unit can provide recommendations that focus on the main points. By adjusting the way recommendations are presented according to the user's emotions, more appropriate recommendations can be provided. The emotion estimation is realized 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 these examples. Some or all of the above-described processing in the recommendation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the recommendation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0074] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the product when making a recommendation. For example, the recommendation unit provides detailed recommendation content for products with high importance. The recommendation unit can also provide concise recommendation content for products with low importance. Furthermore, the recommendation unit can adjust the depth of the recommendation according to the importance of the product. This enables efficient recommendation by adjusting the level of detail of the recommendation according to the importance of the product. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input product importance data to the generation AI and cause the generation AI to perform the optimal level of detail of the recommendation.

[0075] The recommendation unit can apply multiple recommendation algorithms depending on the product category when making a recommendation. The recommendation unit selects the optimal recommendation algorithm depending on, for example, the product category. The recommendation unit can also apply different recommendation algorithms depending on the category of customer data. Furthermore, the recommendation unit can select the optimal recommendation method based on the product category. This enables highly accurate recommendations by applying the optimal recommendation algorithm depending on the product category. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input product category data into the generation AI and cause the generation AI to select the optimal recommendation algorithm.

[0076] The recommendation unit can estimate the user's emotions and adjust the length of the recommendation based on the estimated user emotions. For example, if the user is in a hurry, the recommendation unit can provide short and to-the-point recommendations. Furthermore, if the user is relaxed, the recommendation unit can provide detailed recommendations. Furthermore, if the user is stressed, the recommendation unit can provide concise and easy-to-understand recommendations. By adjusting the length of the recommendation according to the user's emotions, more appropriate recommendations can be provided. 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 recommendation unit can be performed using, for example, an AI, or without an AI. For example, the recommendation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0077] When making a recommendation, the recommendation unit can determine the priority of recommendations based on the submission date of the product. For example, the recommendation unit prioritizes the most recently submitted product. The recommendation unit can also recommend older submitted products later. Furthermore, the recommendation unit can adjust the order of recommendations based on the submission date. This enables efficient recommendations by determining the priority of recommendations based on the submission date of the product. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI, for example. For example, the recommendation unit can input product submission date data into the generation AI and have the generation AI execute the optimal recommendation priority.

[0078] The recommendation unit can adjust the order of recommendations based on the relevance of the products when making a recommendation. For example, the recommendation unit prioritizes recommending highly relevant products. The recommendation unit can also recommend less relevant products later. Furthermore, the recommendation unit can adjust the order of recommendations based on the relevance of the products. This enables efficient recommendations by adjusting the order of recommendations based on the relevance of the products. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input product relevance data into a generation AI and cause the generation AI to execute the optimal recommendation order.

[0079] The reference unit can estimate the user's emotions and determine the priority of success stories to be referenced based on the estimated user emotions. For example, when the user is stressed, the reference unit can prioritize particularly effective success stories. Furthermore, when the user is relaxed, the reference unit can also reference detailed success stories. Furthermore, when the user is in a hurry, the reference unit can prioritize success stories that focus on the key points. This allows the user to prioritize success stories according to the user's emotions, thereby referencing more effective success stories. Emotion estimation is achieved 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 reference unit can be performed using AI, for example, or without AI. For example, the reference unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0080] The reference unit can improve the accuracy of the reference based on the interrelationships between success cases during reference. For example, the reference unit prioritizes reference of highly relevant success cases. The reference unit can also analyze the interrelationships between success cases and reference the most effective case. Furthermore, the reference unit can select the optimal case by taking into account the interrelationships between success cases. This enables more accurate reference by taking into account the interrelationships between success cases. Some or all of the above-described processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can input interrelationship data between success cases into the generation AI and have the generation AI select the optimal case.

[0081] When referencing, the reference unit can perform the reference based on the attribute information of the person who submitted the success story. The reference unit can reference relevant success stories based on, for example, the industry or occupation of the person who submitted the success story. The reference unit can also reference the most appropriate success story taking into account the person's years of experience and skill level. Furthermore, the reference unit can select the most effective success story based on the attribute information of the person who submitted the success story. This allows for more relevant cases to be referenced by taking into account the attribute information of the person who submitted the success story. Some or all of the above-mentioned processing in the reference unit can be performed using, for example, AI, or can be performed without using AI. For example, the reference unit can input the attribute information data of the person who submitted the success story into the generation AI and have the generation AI select the most appropriate success story.

[0082] The reference unit can estimate the user's emotions and adjust the display method of the referenced success stories based on the estimated user emotions. For example, if the user is stressed, the reference unit uses a concise and easy-to-understand display method. Furthermore, if the user is relaxed, the reference unit can display detailed success stories. Furthermore, if the user is in a hurry, the reference unit can display success stories that focus on the main points. This allows for more appropriate display by adjusting the display method of the success stories according to the user's emotions. The emotion estimation is achieved 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 reference unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reference unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0083] When referencing, the reference unit can perform the reference based on the geographic distribution of success stories. For example, the reference unit can prioritize reference to geographically close success stories among the success stories. The reference unit can also reference the most relevant success story taking geographic distribution into consideration. Furthermore, the reference unit can select the most appropriate success story based on geographic distribution. This allows for more relevant cases to be referenced by taking the geographic distribution of success stories into consideration. Some or all of the above-described processing in the reference unit can be performed using, for example, AI, or can be performed without using AI. For example, the reference unit can input geographic distribution data of success stories into the generation AI and have the generation AI select the most appropriate success story.

[0084] The reference unit can improve the accuracy of the reference based on literature related to the success case during the reference. For example, the reference unit can refer to literature related to the success case and provide detailed information. The reference unit can also refer to the most effective case based on the literature related to the success case. Furthermore, the reference unit can select the optimal case by taking into account the literature related to the success case. This enables more accurate reference by referring to the literature related to the success case. Some or all of the above-mentioned processing in the reference unit can be performed using, for example, AI, or can be performed without using AI. For example, the reference unit can input literature data related to the success case into the generation AI and have the generation AI select the optimal case. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, recommendation unit, and reference unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives product requests input in natural language by a sales representative. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes past proposal contents and customer data. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends optimal products. The reference unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and improves the quality of proposals based on success stories of other sales representatives. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, recommendation unit, and reference unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives product requests input by a sales representative in natural language. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes past proposal contents and customer data. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends optimal products. The reference unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and improves the quality of proposals based on success stories of other sales representatives. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, recommendation unit, and reference unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives product requests input in natural language by a sales representative. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes past proposal contents and customer data. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends optimal products. The reference unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and improves the quality of proposals based on success stories of other sales representatives. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, recommendation unit, and reference unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives product requests input in natural language by a sales representative. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes past proposal contents and customer data. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends optimal products. The reference unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and improves the quality of proposals based on success stories of other sales representatives.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The reception unit can learn the user's past behavioral patterns and provide an optimal interface. For example, if the user has frequently used voice input in the past, the reception unit can preferentially display a voice input interface. Also, if the user tends to input requests during a specific time period, the reception unit can provide an interface optimal for that time period. Furthermore, if the user has frequently used a specific keyword in the past, the reception unit can provide an interface related to that keyword. This can improve the user experience by providing an optimal interface based on the user's past behavioral patterns.

[0087] The reference unit can estimate the user's emotions and adjust the display method of the referenced success stories based on the estimated user emotions. For example, if the user is stressed, a concise and easy-to-understand display method can be used. Alternatively, if the user is relaxed, detailed success stories can be displayed. Furthermore, if the user is in a hurry, success stories that focus on the main points can be displayed. This allows for more appropriate display by adjusting the display method of the success stories 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, 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 reference unit can be performed using, for example, AI, or without AI. For example, the reference unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0088] When referencing success stories, the reference unit can select the most appropriate case based on the user's industry and occupation. For example, if the user belongs to the IT industry, success stories related to the IT industry can be referenced preferentially. Also, if the user works in sales, success stories related to sales can be referenced preferentially. Furthermore, the most relevant success story can be selected based on the user's work experience and skill level. This enables more effective proposals by referencing the most appropriate success story based on the user's industry and occupation.

[0089] The reception unit can estimate the user's emotions and adjust the timing of request acceptance based on the estimated user emotions. For example, if the user is feeling stressed, the AI ​​can immediately accept the request and quickly start processing. Alternatively, if the user is relaxed, the AI ​​can accept the request at an appropriate time and perform detailed confirmation. Furthermore, if the user is in a hurry, the AI ​​can prioritize the request and quickly proceed to the next step. By adjusting the timing of request acceptance according to the user's emotions, the request can be accepted at a more appropriate time. Emotion estimation is realized using an emotion estimation function, such as 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 reception unit may be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0090] The reception unit can analyze the user's past request history and select the optimal reception method. For example, it can preferentially suggest reception methods that the user has frequently used in the past. It can also select the most efficient reception method from the user's past request history. Furthermore, it can suggest the optimal reception method for a specific time period based on the user's past request history. In this way, the optimal reception method can be selected by analyzing the past request history. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input past request history data into a generation AI and have the generation AI select the optimal reception method.

[0091] The reception unit can perform filtering based on the user's current work situation and areas of interest. For example, it can preferentially accept requests related to a project the user is currently working on. It can also filter highly relevant requests based on the user's areas of interest. Furthermore, it can accept requests at the optimal timing, taking the user's work situation into consideration. In this way, by filtering requests based on the user's work situation and areas of interest, it is possible to preferentially accept highly relevant requests. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's work situation data into the generation AI and cause the generation AI to perform optimal request filtering.

[0092] The reception unit can estimate the user's emotions and determine the priority of requests to be received based on the estimated user emotions. For example, if the user is feeling stressed, requests with high importance can be received with priority. Furthermore, if the user is relaxed, requests with normal priority can be received. Furthermore, if the user is in a hurry, requests with high urgency can be received with top priority. By determining the priority of requests according to the user's emotions, important requests can be processed with priority. The emotion estimation is realized 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 reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0093] The reception unit can prioritize receiving highly relevant requests based on the user's geographical location information. For example, if the user is in a specific area, requests related to that area can be prioritized. Also, requests related to locations close to the user's current location can be prioritized. Furthermore, optimal requests can be filtered based on the user's geographical location information. This allows highly relevant requests to be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information data into a generation AI and cause the generation AI to filter optimal requests.

[0094] The reception unit can analyze the user's social media activity and receive relevant requests. For example, the reception unit can analyze the content of the user's social media posts and prioritize receiving relevant requests. The reception unit can also filter out optimal requests based on the user's social media activity history. Furthermore, the reception unit can also receive relevant requests taking into account the user's areas of interest on social media. In this way, by analyzing the user's social media activity, highly relevant requests can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI filter out optimal requests.

[0095] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is stressed, a concise and easy-to-understand presentation can be used. If the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, analysis results that focus on the main points can be provided. By adjusting the way the analysis is presented based on 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 perform emotion estimation.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The reception unit receives product requests input in natural language by the sales representative. The requests input by the sales representative include text-based requests and voice-input requests. The reception unit can also analyze the natural language requests using text analysis technology and convert the voice input into text data using voice recognition technology. Step 2: The analysis unit analyzes the request received by the reception unit and analyzes past proposals and customer data. The analysis unit uses data mining technology to analyze past proposals and analyzes customer purchase history and attribute information by referring to the customer database. Step 3: The recommendation department recommends the most suitable product based on the data analyzed by the analysis department. The recommendation department uses a recommendation algorithm to select the most suitable product and can also refer to the success stories of other sales representatives.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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, in order to avoid confusion and to 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.

[0168] 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.

[0169] [Explanation of symbols]

[0170] 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 reception unit that receives requests in natural language; an analysis unit that analyzes the request received by the reception unit and analyzes past proposal contents or customer data; a recommendation unit that recommends products based on the data analyzed by the analysis unit; Equipped with A system characterized by:

2. The recommendation unit Have a reference section where you can refer to other salespeople's success stories 2. The system of claim 1.

3. The reference portion is Improve your proposals based on the success stories of other salespeople 3. The system of claim 2.

4. The reception unit Estimate the user's emotions and adjust the timing of request acceptance according to the estimated user emotions.

2. The system of claim 1.

5. The reception unit Analyze past request history and select the reception method 2. The system of claim 1.

6. The reception unit Filter requests based on the user's current work situation or interests as they are received 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and prioritize requests based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit When accepting requests, prioritize relevant requests based on the user's geographic location.

2. The system of claim 1.

9. The reception unit When accepting requests, analyze your social media activity to accept relevant requests.

2. The system of claim 1.

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

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