system

Abstract

The system according to the embodiment aims to analyze customer preferences and purchase history and propose optimal products and services. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects customer preferences and purchasing history. The analysis unit analyzes the data collected by the collection unit to identify customer preferences and purchasing patterns. The proposal unit proposes appropriate products and services based on the analysis results obtained 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 technologies do not adequately propose appropriate products and services based on customer preferences and purchasing history, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze customer preferences and purchase history and propose optimal products and services. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects customer preferences and purchase history. The analysis unit analyzes the data collected by the collection unit to identify customer preferences and purchasing patterns. The proposal unit proposes appropriate products and services based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze a customer's preferences and purchasing history and propose optimal products and services. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A sales support system according to an embodiment of the present invention utilizes a generative AI to support sales activities with customers. The sales support system collects customer preferences and purchase history, and the generative AI analyzes this data to propose optimal products and services. These proposals are customized to meet the customer's needs, improving customer satisfaction and increasing sales. For example, the sales support system collects data such as the customer's past purchases of products and services, browsing history, and ratings. The generative AI then analyzes the collected data to identify customer preferences and purchasing patterns. For example, the system can identify customers who frequently purchase products in a specific category or who prefer a specific brand. Furthermore, the generative AI in the sales support system proposes optimal products and services based on the analysis results. For example, for customers who frequently purchase products in a specific category, the system proposes products in that category, and for customers who prefer a specific brand, the system proposes products from that brand. This allows the sales support system to improve customer satisfaction and increase sales. The sales support system can propose optimal products and services based on the customer's preferences and purchase history, improving customer satisfaction and increasing sales. For example, by suggesting related products based on products previously purchased by the customer, the system can encourage additional purchases. Additionally, understanding customer preferences and purchasing patterns allows for a more personalized approach to customers, improving customer satisfaction.

[0029] A sales support system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects customer preferences and purchase histories. Customer preferences include, but are not limited to, past purchase histories, ratings, and browsing histories. For example, the collection unit collects data on which products customers have purchased and which products they have rated on an e-commerce site. The collection unit can also collect customer browsing histories. For example, the collection unit collects data on which products customers have viewed and which pages they have visited. The collection unit can also collect customer rating data. For example, the collection unit collects data on star ratings and comments given by customers to products. The analysis unit uses a generation AI to analyze the data collected by the collection unit and identify customer preferences and purchasing patterns. The analysis can be performed using, for example, data mining, statistical analysis, machine learning algorithms, or other methods. For example, the generation AI can identify customers who frequently purchase products in a specific category. The generation AI can also identify customers who prefer a specific brand. The generation AI can also analyze customer purchasing patterns to identify customer preferences. For example, the generation AI identifies customer preferences based on data on products purchased by the customer in the past. The suggestion unit uses the generation AI to suggest optimal products and services based on the analysis results obtained by the analysis unit. The suggestions are made, for example, by a recommendation algorithm based on customer preferences and purchase history, but are not limited to such examples. For example, the suggestion unit may suggest products in a specific category to a customer who frequently purchases products in that category. The suggestion unit may also suggest products from a specific brand to a customer who prefers that brand. The suggestion unit may also generate proposals customized to meet the customer's needs. For example, the suggestion unit may generate proposals tailored to the customer's needs based on the customer's past purchase history and evaluation data. As a result, the sales support system according to the embodiment can improve customer satisfaction and increase sales by suggesting optimal products and services based on the customer's preferences and purchase history.

[0030] The collection unit can collect data on products and services purchased by customers in the past, browsing history, and reviews. The collection unit, for example, collects data on products and services purchased by customers in the past. For example, the collection unit collects data on which products customers purchased and which services they used on an e-commerce site. The collection unit can also collect customer browsing history. For example, the collection unit collects data on which products customers viewed and which pages they visited. The collection unit can also collect customer evaluation data. For example, the collection unit collects data such as star ratings and comments given by customers to products. By collecting data on customers' past behavior, more accurate analysis and recommendations are possible. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input customers' past purchase data into the generation AI and cause the generation AI to collect data.

[0031] The analysis unit can analyze the collected data and identify customers who regularly purchase products in a specific category and customers who prefer a specific brand. The analysis unit, for example, analyzes the collected data and identifies customers who regularly purchase products in a specific category. For example, the analysis unit uses a generation AI to identify product categories that customers frequently purchase. The analysis unit can also identify customers who prefer a specific brand. For example, the analysis unit uses a generation AI to identify brands that customers prefer. The analysis unit can also analyze customer purchasing patterns and identify customer preferences. For example, the analysis unit uses a generation AI to identify customer preferences based on data on products that the customer has previously purchased. This enables more accurate suggestions by identifying customer purchasing patterns. 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 the collected data into a generation AI and have the generation AI identify customer preferences.

[0032] The suggestion unit can suggest products of a specific category to customers who regularly purchase products of that category, and can suggest products of that brand to customers who prefer a specific brand. For example, the suggestion unit can suggest products of that category to customers who regularly purchase products of that category. For example, the suggestion unit can use a generation AI to make suggestions based on the category of products frequently purchased by the customer. The suggestion unit can also suggest products of that brand to customers who prefer a specific brand. For example, the suggestion unit can use a generation AI to make suggestions based on the customer's preferred brand. The suggestion unit can also generate proposals customized to meet the customer's needs. For example, the suggestion unit can use a generation AI to generate proposals tailored to the customer's needs based on the customer's past purchase history and evaluation data. This improves customer satisfaction by providing proposals tailored to the customer's preferences. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input customer preference data into the generation AI and cause the generation AI to generate proposals.

[0033] The suggestion unit can generate proposals individually tailored to meet the customer's needs. The suggestion unit generates proposals individually tailored to meet the customer's needs, for example. For example, the suggestion unit uses a generation AI to generate proposals tailored to the customer's needs based on the customer's past purchase history and evaluation data. The suggestion unit can also generate proposals that will interest the customer based on the customer's browsing history. For example, the suggestion unit uses a generation AI to make proposals based on data on products frequently viewed by the customer. The suggestion unit can also make proposals for products highly rated by the customer based on customer evaluation data. For example, the suggestion unit uses a generation AI to make proposals based on data on products highly rated by the customer. This improves customer satisfaction by making proposals tailored to the customer's needs. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on customer needs into the generation AI and cause the generation AI to generate proposals.

[0034] The collection unit can analyze a customer's past purchase history and select an appropriate data collection method. For example, the collection unit analyzes a customer's past purchase history and selects an appropriate data collection method. For example, if a customer frequently purchases online, the collection unit can focus on collecting website browsing history. Furthermore, if a customer prefers in-store purchases, the collection unit can also focus on collecting store purchase history. Furthermore, if a customer prefers a particular brand, the collection unit can prioritize collecting data related to that brand. This enables efficient data collection by selecting the optimal data collection method based on the customer's purchase history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input customer purchase history data into a generation AI and have the generation AI select a data collection method.

[0035] The collection unit can filter data based on the customer's current living situation and areas of interest when collecting data. For example, the collection unit can filter data based on the customer's current living situation and areas of interest when collecting data. For example, if a customer has started a new hobby, the collection unit can prioritize collecting data related to that hobby. If a customer has moved, the collection unit can also collect data related to the customer's new address. If a customer plans to attend a specific event, the collection unit can also collect data related to the event. This allows for more relevant data to be collected by filtering data based on the customer's living situation and areas of interest. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input data on the customer's living situation and areas of interest into the generation AI and have the generation AI filter the data.

[0036] The collection unit can select an appropriate collection means depending on the customer's input method when collecting data. For example, the collection unit selects an appropriate collection means depending on the customer's input method (voice, text, image, etc.) when collecting data. For example, if the customer prefers voice input, the collection unit can prioritize collecting voice data. Also, if the customer prefers text input, the collection unit can prioritize collecting text data. Also, if the customer uses images frequently, the collection unit can prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the customer's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's input data into a generation AI and have the generation AI select the collection means.

[0037] The collection unit can prioritize collecting highly relevant data by taking into account the customer's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking into account the customer's geographical location information when collecting data. For example, if the customer is in a specific area, the collection unit collects data on products and services related to that area. Furthermore, if the customer is traveling, the collection unit can collect data related to the customer's travel destination. Furthermore, if the customer is at home, the collection unit can collect data on stores and services near the customer's home. This enables more appropriate suggestions by collecting highly relevant data based on the customer's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's geographical location data into the generation AI and cause the generation AI to collect data.

[0038] The collection unit can collect relevant data based on the customer's social media activities during data collection. For example, the collection unit collects relevant data based on the customer's social media activities during data collection. For example, the collection unit collects data related to products shared by the customer on social media. The collection unit can also collect data related to brands and influencers followed by the customer. The collection unit can also collect data related to online communities in which the customer participates. This enables more accurate suggestions by collecting relevant data based on the customer's social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the customer's social media data into a generation AI and cause the generation AI to collect data.

[0039] The collection unit can customize the collection method based on the customer's past feedback when collecting data. For example, the collection unit customizes the collection method based on the customer's past feedback when collecting data. For example, the collection unit adjusts the type of data to be collected based on feedback provided by the customer in the past. The collection unit can also prioritize collecting data related to products that the customer has previously rated. The collection unit can also avoid collection methods that the customer has previously expressed dissatisfaction with and collect data using methods that the customer prefers. This enables more appropriate data collection by customizing the collection method based on the customer's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input customer feedback data into a generation AI and cause the generation AI to customize the collection method.

[0040] The analysis unit can adjust the level of detail of the analysis during analysis, taking into account the importance of the data. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on important data and provides it to the customer. The analysis unit can also perform a brief analysis on less important data and provide it to the customer. The analysis unit can also determine the priority of the analysis according to the importance of the data and prioritize analysis of important data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms based on the category of data during analysis. For example, the analysis unit can apply different analysis algorithms based on the category of data during analysis. For example, the analysis unit can apply an algorithm that identifies purchasing patterns to purchase history data. The analysis unit can also apply an algorithm that measures customer satisfaction to rating data. The analysis unit can also apply an algorithm that identifies customer interests to browsing history data. This allows for more accurate analysis by applying different analysis algorithms depending on the category of data. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the category of data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0042] The analysis unit can improve the accuracy of the analysis based on the customer's past analysis results during the analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the customer's past analysis results during the analysis. For example, the analysis unit can refer to the customer's past purchasing patterns to more accurately identify current purchasing patterns. The analysis unit can also refer to the customer's past evaluation data to more accurately measure current satisfaction levels. The analysis unit can also refer to the customer's past browsing history to more accurately identify current interests. This improves the accuracy of the analysis by referring to the customer's past analysis results. 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 customer's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] The analysis unit can determine the analysis priority during analysis, taking into account the time of data submission. The analysis unit, for example, determines the analysis priority based on the time of data submission during analysis. For example, the analysis unit prioritizes analyzing the most recent data and provides it to the customer. The analysis unit can also perform analysis by focusing on the most recent data while referring to past data. The analysis unit can also adjust the analysis priority according to the time of data submission and prioritize analyzing important data. In this way, by determining the analysis priority based on the time of data submission, the most recent data can be analyzed preferentially. 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 the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0044] The analysis unit can adjust the order of analysis taking into account the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analyzing highly relevant data and provides it to the customer. The analysis unit can also postpone analyzing less relevant data and prioritize analyzing important data. The analysis unit can also adjust the order of analysis according to the relevance of the data to perform the analysis efficiently. As a result, adjusting the order of analysis based on the relevance of the data enables efficient analysis. 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 the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0045] The analysis unit can adjust the use of technical terminology during analysis based on the customer's level of expertise. For example, the analysis unit can adjust the use of technical terminology during analysis according to the customer's level of expertise. For example, the analysis unit can provide analysis results using detailed technical terminology to customers with high levels of expertise. The analysis unit can also provide analysis results using simple, easy-to-understand language to customers with low levels of expertise. The analysis unit can also adjust the way the analysis results are expressed according to the customer's level of expertise and provide them in an easy-to-understand format. In this way, by adjusting the use of technical terminology according to the customer's level of expertise, it is possible to provide analysis results that are easy to understand. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the customer's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0046] The suggestion unit can adjust the level of detail of the suggestion taking into account the importance of the product when making a suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on the importance of the product when making a suggestion. For example, the suggestion unit makes detailed suggestions for important products and provides them to the customer. The suggestion unit can also make concise suggestions for less important products and provide them to the customer. The suggestion unit can also determine the priority of the suggestions according to the importance of the products and preferentially suggest important products. This enables efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the products. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the importance of the products to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0047] The suggestion unit can apply different suggestion algorithms based on the product category when making a suggestion. For example, the suggestion unit can make suggestions that emphasize technical features for electronic products. The suggestion unit can also make suggestions that emphasize trends and styles for fashion products. The suggestion unit can also make suggestions that emphasize nutritional value and taste for food products. This allows for more accurate suggestions by applying different suggestion algorithms depending on the product category. Some or all of the above-described processing in the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input the product category into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0048] The suggestion unit can improve the accuracy of the suggestion based on the customer's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the customer's past suggestion results when making a suggestion. For example, the suggestion unit can make the current suggestion more accurately by referring to products purchased by the customer in the past. The suggestion unit can also make the current suggestion more accurately by referring to the customer's past evaluation data. The suggestion unit can also make the current suggestion more accurately by referring to the customer's past browsing history. In this way, the accuracy of the suggestion is improved by referring to the customer's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the customer's past suggestion results into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0049] The suggestion unit can determine the priority of suggestions taking into account the time of product submission when making suggestions. For example, the suggestion unit determines the priority of suggestions based on the time of product submission when making suggestions. For example, the suggestion unit prioritizes the latest products and provides them to the customer. The suggestion unit can also make suggestions with an emphasis on the latest products while referring to past products. The suggestion unit can also adjust the priority of suggestions according to the time of product submission and prioritize important products. In this way, by determining the priority of suggestions based on the time of product submission, the latest products can be prioritized. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the time of product submission to the generation AI and cause the generation AI to determine the priority of suggestions.

[0050] The suggestion unit can adjust the order of suggestions taking into account the relevance of products when making suggestions. The suggestion unit, for example, adjusts the order of suggestions based on the relevance of products when making suggestions. For example, the suggestion unit prioritizes suggesting and providing highly relevant products to the customer. The suggestion unit can also postpone less relevant products and prioritize suggesting important products. The suggestion unit can also adjust the order of suggestions according to the relevance of products to make suggestions efficiently. As a result, adjusting the order of suggestions based on the relevance of products enables efficient suggestions. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the relevance of products to a generation AI and cause the generation AI to adjust the order of suggestions.

[0051] The suggestion unit can adjust the use of technical terminology in the proposal based on the customer's level of expertise when making a proposal. For example, the suggestion unit can adjust the use of technical terminology in the proposal according to the customer's level of expertise when making a proposal. For example, the suggestion unit can provide a proposal using detailed technical terminology for a customer with high level of expertise. The suggestion unit can also provide a proposal using simple and easy-to-understand language for a customer with low level of expertise. The suggestion unit can also adjust the way the proposal is expressed according to the customer's level of expertise and provide the proposal in an easy-to-understand format. In this way, by adjusting the use of technical terminology according to the customer's level of expertise, it is possible to provide an easy-to-understand proposal. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the customer's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The collection unit may monitor customers' social media activities and collect data on products and services shared by customers. For example, the collection unit may collect data on products shared by customers on Facebook® or Instagram® and provide the data to the analysis unit. The collection unit may also collect data on brands and influencers followed by customers. Furthermore, the collection unit may monitor activities in online communities and forums in which customers participate and collect related data. This may enable more personalized suggestions based on customers' social media activities.

[0054] The analysis department can analyze not only a customer's purchase history but also their lifestyle data. For example, if a customer is health-conscious, health-related products can be suggested. If a customer likes outdoor activities, outdoor equipment can be suggested. Furthermore, if a customer has a pet, pet supplies can be suggested. This makes it possible to make suggestions that are tailored to the customer's lifestyle, which is expected to improve customer satisfaction.

[0055] The collection unit can use the customer's geographic location information to collect information on local promotions and events. For example, if the customer is in a specific area, it can collect information on events and sales being held in that area and provide it to the proposal unit. In addition, if the customer is traveling, the collection unit can collect tourist information and data on recommended spots in the travel destination. Furthermore, if the customer is at home, the collection unit can collect data on stores and services around the customer's home. This makes it possible to make proposals based on the customer's geographic location information.

[0056] The proposal unit can identify regular purchasing patterns based on a customer's purchasing history and make proposals for regular purchases. For example, if a customer purchases the same product every month, the proposal unit can propose that product as a regular purchase. Also, if a customer purchases specific products each season, the proposal unit can make proposals tailored to that season. Furthermore, if a customer purchases products before a specific event, the proposal unit can make proposals tailored to that event. This makes it possible to make efficient proposals based on the customer's purchasing patterns.

[0057] The analysis unit can analyze not only customer purchase history but also customer feedback data. For example, it can identify customer preferences and complaints based on feedback provided by the customer in the past. It can also identify products and services that customers have rated highly and make suggestions based on that. Furthermore, by avoiding products and services that customers have rated poorly, it is possible to make suggestions that will result in greater satisfaction. This enables highly accurate analysis based on customer feedback data.

[0058] The collection unit can collect reviews and evaluation data of related products based on the customer's purchase history. For example, it collects reviews and evaluations of other customers for products that the customer has purchased in the past and provides them to the analysis unit. The collection unit can also collect reviews and evaluations of products in which the customer has shown interest. Furthermore, the collection unit can also collect reviews and evaluations of products related to products that the customer has highly rated. This makes it possible to collect related data based on the customer's purchase history.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects customer preferences and purchasing history. Specifically, it collects data such as past purchase history, ratings, and browsing history. For example, on an e-commerce site, it collects data such as which products customers have purchased, which products they have rated, which products they have viewed, which pages they have visited, and the star ratings and comments they have given to products. Step 2: The analysis unit uses generative AI to analyze the data collected by the collection unit and identify customer preferences and purchasing patterns. The analysis is carried out using methods such as data mining, statistical analysis, and machine learning algorithms. For example, it identifies customers who frequently purchase products in a particular category or who prefer a particular brand. It also analyzes customer purchasing patterns and identifies customer preferences based on data on products purchased in the past. Step 3: The proposal unit uses generative AI to propose optimal products and services based on the analysis results obtained by the analysis unit. Suggestions are made using methods such as recommendation algorithms based on customer preferences and purchase history. For example, for customers who frequently purchase products in a particular category, products in that category will be proposed, and for customers who prefer a particular brand, products from that brand will be proposed. In addition, customized proposals tailored to customer needs will be generated based on the customer's past purchase history and evaluation data.

[0061] (Example 2) A sales support system according to an embodiment of the present invention utilizes a generative AI to support sales activities with customers. The sales support system collects customer preferences and purchase history, and the generative AI analyzes this data to propose optimal products and services. These proposals are customized to meet the customer's needs, improving customer satisfaction and increasing sales. For example, the sales support system collects data such as the customer's past purchases of products and services, browsing history, and ratings. The generative AI then analyzes the collected data to identify customer preferences and purchasing patterns. For example, the system can identify customers who frequently purchase products in a specific category or who prefer a specific brand. Furthermore, the generative AI in the sales support system proposes optimal products and services based on the analysis results. For example, for customers who frequently purchase products in a specific category, the system proposes products in that category, and for customers who prefer a specific brand, the system proposes products from that brand. This allows the sales support system to improve customer satisfaction and increase sales. The sales support system can propose optimal products and services based on the customer's preferences and purchase history, improving customer satisfaction and increasing sales. For example, by suggesting related products based on products previously purchased by the customer, the system can encourage additional purchases. Additionally, understanding customer preferences and purchasing patterns allows for a more personalized approach to customers, improving customer satisfaction.

[0062] A sales support system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects customer preferences and purchase histories. Customer preferences include, but are not limited to, past purchase histories, ratings, and browsing histories. For example, the collection unit collects data on which products customers have purchased and which products they have rated on an e-commerce site. The collection unit can also collect customer browsing histories. For example, the collection unit collects data on which products customers have viewed and which pages they have visited. The collection unit can also collect customer rating data. For example, the collection unit collects data on star ratings and comments given by customers to products. The analysis unit uses a generation AI to analyze the data collected by the collection unit and identify customer preferences and purchasing patterns. The analysis can be performed using, for example, data mining, statistical analysis, machine learning algorithms, or other methods. For example, the generation AI can identify customers who frequently purchase products in a specific category. The generation AI can also identify customers who prefer a specific brand. The generation AI can also analyze customer purchasing patterns to identify customer preferences. For example, the generation AI identifies customer preferences based on data on products purchased by the customer in the past. The suggestion unit uses the generation AI to suggest optimal products and services based on the analysis results obtained by the analysis unit. The suggestions are made, for example, by a recommendation algorithm based on customer preferences and purchase history, but are not limited to such examples. For example, the suggestion unit may suggest products in a specific category to a customer who frequently purchases products in that category. The suggestion unit may also suggest products from a specific brand to a customer who prefers that brand. The suggestion unit may also generate proposals customized to meet the customer's needs. For example, the suggestion unit may generate proposals tailored to the customer's needs based on the customer's past purchase history and evaluation data. As a result, the sales support system according to the embodiment can improve customer satisfaction and increase sales by suggesting optimal products and services based on the customer's preferences and purchase history.

[0063] The collection unit can collect data on products and services purchased by customers in the past, browsing history, and reviews. The collection unit, for example, collects data on products and services purchased by customers in the past. For example, the collection unit collects data on which products customers purchased and which services they used on an e-commerce site. The collection unit can also collect customer browsing history. For example, the collection unit collects data on which products customers viewed and which pages they visited. The collection unit can also collect customer evaluation data. For example, the collection unit collects data such as star ratings and comments given by customers to products. By collecting data on customers' past behavior, more accurate analysis and recommendations are possible. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input customers' past purchase data into the generation AI and cause the generation AI to collect data.

[0064] The analysis unit can analyze the collected data and identify customers who regularly purchase products in a specific category and customers who prefer a specific brand. The analysis unit, for example, analyzes the collected data and identifies customers who regularly purchase products in a specific category. For example, the analysis unit uses a generation AI to identify product categories that customers frequently purchase. The analysis unit can also identify customers who prefer a specific brand. For example, the analysis unit uses a generation AI to identify brands that customers prefer. The analysis unit can also analyze customer purchasing patterns and identify customer preferences. For example, the analysis unit uses a generation AI to identify customer preferences based on data on products that the customer has previously purchased. This enables more accurate suggestions by identifying customer purchasing patterns. 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 the collected data into a generation AI and have the generation AI identify customer preferences.

[0065] The suggestion unit can suggest products of a specific category to customers who regularly purchase products of that category, and can suggest products of that brand to customers who prefer a specific brand. For example, the suggestion unit can suggest products of that category to customers who regularly purchase products of that category. For example, the suggestion unit can use a generation AI to make suggestions based on the category of products frequently purchased by the customer. The suggestion unit can also suggest products of that brand to customers who prefer a specific brand. For example, the suggestion unit can use a generation AI to make suggestions based on the customer's preferred brand. The suggestion unit can also generate proposals customized to meet the customer's needs. For example, the suggestion unit can use a generation AI to generate proposals tailored to the customer's needs based on the customer's past purchase history and evaluation data. This improves customer satisfaction by providing proposals tailored to the customer's preferences. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input customer preference data into the generation AI and cause the generation AI to generate proposals.

[0066] The suggestion unit can generate proposals individually tailored to meet the customer's needs. The suggestion unit generates proposals individually tailored to meet the customer's needs, for example. For example, the suggestion unit uses a generation AI to generate proposals tailored to the customer's needs based on the customer's past purchase history and evaluation data. The suggestion unit can also generate proposals that will interest the customer based on the customer's browsing history. For example, the suggestion unit uses a generation AI to make proposals based on data on products frequently viewed by the customer. The suggestion unit can also make proposals for products highly rated by the customer based on customer evaluation data. For example, the suggestion unit uses a generation AI to make proposals based on data on products highly rated by the customer. This improves customer satisfaction by making proposals tailored to the customer's needs. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on customer needs into the generation AI and cause the generation AI to generate proposals.

[0067] The collection unit can estimate a customer's emotions and adjust the timing of data collection taking the estimated customer emotions into consideration. The collection unit, for example, estimates a customer's emotions and adjusts the timing of data collection based on the estimated customer emotions. For example, if a customer is feeling stressed, the collection unit reduces the frequency of data collection and collects data when the customer is relaxed. Furthermore, if a customer is excited, the collection unit can collect data in real time and immediately reflect the data. Furthermore, if a customer is tired, the collection unit can temporarily stop data collection and resume it after the customer has rested. This allows for more appropriate data collection by adjusting the timing of data collection according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or without AI. For example, the collection unit can input customer emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0068] The collection unit can analyze a customer's past purchase history and select an appropriate data collection method. For example, the collection unit analyzes a customer's past purchase history and selects an appropriate data collection method. For example, if a customer frequently purchases online, the collection unit can focus on collecting website browsing history. Furthermore, if a customer prefers in-store purchases, the collection unit can also focus on collecting store purchase history. Furthermore, if a customer prefers a particular brand, the collection unit can prioritize collecting data related to that brand. This enables efficient data collection by selecting the optimal data collection method based on the customer's purchase history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input customer purchase history data into a generation AI and have the generation AI select a data collection method.

[0069] The collection unit can filter data based on the customer's current living situation and areas of interest when collecting data. For example, the collection unit can filter data based on the customer's current living situation and areas of interest when collecting data. For example, if a customer has started a new hobby, the collection unit can prioritize collecting data related to that hobby. If a customer has moved, the collection unit can also collect data related to the customer's new address. If a customer plans to attend a specific event, the collection unit can also collect data related to the event. This allows for more relevant data to be collected by filtering data based on the customer's living situation and areas of interest. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input data on the customer's living situation and areas of interest into the generation AI and have the generation AI filter the data.

[0070] The collection unit can select an appropriate collection means depending on the customer's input method when collecting data. For example, the collection unit selects an appropriate collection means depending on the customer's input method (voice, text, image, etc.) when collecting data. For example, if the customer prefers voice input, the collection unit can prioritize collecting voice data. Also, if the customer prefers text input, the collection unit can prioritize collecting text data. Also, if the customer uses images frequently, the collection unit can prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the customer's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's input data into a generation AI and have the generation AI select the collection means.

[0071] The collection unit can estimate a customer's emotions and determine the priority of data to be collected taking the estimated customer emotions into consideration. The collection unit, for example, estimates a customer's emotions and determines the priority of data to be collected based on the estimated customer emotions. For example, if a customer is excited, the collection unit can prioritize collecting real-time purchase data. Also, if a customer is relaxed, the collection unit can focus on collecting past purchase history. Also, if a customer is stressed, the collection unit can prioritize collecting customer evaluation data. In this way, by determining the priority of data according to the customer's emotions, more important data can be collected preferentially. 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-mentioned processing in the collection unit can be performed using, for example, an AI. For example, the collection unit can input customer emotion data into a generation AI and have the generation AI determine the priority of the data.

[0072] The collection unit can prioritize collecting highly relevant data by taking into account the customer's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking into account the customer's geographical location information when collecting data. For example, if the customer is in a specific area, the collection unit collects data on products and services related to that area. Furthermore, if the customer is traveling, the collection unit can collect data related to the customer's travel destination. Furthermore, if the customer is at home, the collection unit can collect data on stores and services near the customer's home. This enables more appropriate suggestions by collecting highly relevant data based on the customer's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's geographical location data into the generation AI and cause the generation AI to collect data.

[0073] The collection unit can collect relevant data based on the customer's social media activities during data collection. For example, the collection unit collects relevant data based on the customer's social media activities during data collection. For example, the collection unit collects data related to products shared by the customer on social media. The collection unit can also collect data related to brands and influencers followed by the customer. The collection unit can also collect data related to online communities in which the customer participates. This enables more accurate suggestions by collecting relevant data based on the customer's social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the customer's social media data into a generation AI and cause the generation AI to collect data.

[0074] The collection unit can customize the collection method based on the customer's past feedback when collecting data. For example, the collection unit customizes the collection method based on the customer's past feedback when collecting data. For example, the collection unit adjusts the type of data to be collected based on feedback provided by the customer in the past. The collection unit can also prioritize collecting data related to products that the customer has previously rated. The collection unit can also avoid collection methods that the customer has previously expressed dissatisfaction with and collect data using methods that the customer prefers. This enables more appropriate data collection by customizing the collection method based on the customer's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input customer feedback data into a generation AI and cause the generation AI to customize the collection method.

[0075] The analysis unit can estimate a customer's emotions and adjust the presentation of the analysis based on the estimated customer emotions. For example, the analysis unit estimates a customer's emotions and adjusts the presentation of the analysis based on the estimated customer emotions. For example, the analysis unit provides detailed analysis results when the customer is relaxed. For example, the analysis unit can provide concise analysis results that focus on the main points when the customer is in a hurry. For example, the analysis unit can provide analysis results using visually appealing graphs or charts when the customer is excited. This allows for more appropriate analysis results to be provided by adjusting the presentation of the analysis according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input customer emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0076] The analysis unit can adjust the level of detail of the analysis during analysis, taking into account the importance of the data. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on important data and provides it to the customer. The analysis unit can also perform a brief analysis on less important data and provide it to the customer. The analysis unit can also determine the priority of the analysis according to the importance of the data and prioritize analysis of important data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0077] The analysis unit can apply different analysis algorithms based on the category of data during analysis. For example, the analysis unit can apply different analysis algorithms based on the category of data during analysis. For example, the analysis unit can apply an algorithm that identifies purchasing patterns to purchase history data. The analysis unit can also apply an algorithm that measures customer satisfaction to rating data. The analysis unit can also apply an algorithm that identifies customer interests to browsing history data. This allows for more accurate analysis by applying different analysis algorithms depending on the category of data. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the category of data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0078] The analysis unit can improve the accuracy of the analysis based on the customer's past analysis results during the analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the customer's past analysis results during the analysis. For example, the analysis unit can refer to the customer's past purchasing patterns to more accurately identify current purchasing patterns. The analysis unit can also refer to the customer's past evaluation data to more accurately measure current satisfaction levels. The analysis unit can also refer to the customer's past browsing history to more accurately identify current interests. This improves the accuracy of the analysis by referring to the customer's past analysis results. 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 customer's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0079] The analysis unit can estimate the customer's emotions and adjust the length of the analysis based on the estimated customer emotions. For example, the analysis unit estimates the customer's emotions and adjusts the length of the analysis based on the estimated customer emotions. For example, if the customer is in a hurry, the analysis unit can provide a short, concise analysis result. If the customer is relaxed, the analysis unit can provide a longer analysis result with detailed explanations. If the customer is excited, the analysis unit can provide an analysis result with visually stimulating effects. This allows for adjusting the length of the analysis based on the customer's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative 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 analysis unit can be performed using AI, or without AI. For example, the analysis unit can input customer emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0080] The analysis unit can determine the analysis priority during analysis, taking into account the time of data submission. The analysis unit, for example, determines the analysis priority based on the time of data submission during analysis. For example, the analysis unit prioritizes analyzing the most recent data and provides it to the customer. The analysis unit can also perform analysis by focusing on the most recent data while referring to past data. The analysis unit can also adjust the analysis priority according to the time of data submission and prioritize analyzing important data. In this way, by determining the analysis priority based on the time of data submission, the most recent data can be analyzed preferentially. 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 the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0081] The analysis unit can adjust the order of analysis taking into account the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analyzing highly relevant data and provides it to the customer. The analysis unit can also postpone analyzing less relevant data and prioritize analyzing important data. The analysis unit can also adjust the order of analysis according to the relevance of the data to perform the analysis efficiently. As a result, adjusting the order of analysis based on the relevance of the data enables efficient analysis. 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 the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0082] The analysis unit can adjust the use of technical terminology during analysis based on the customer's level of expertise. For example, the analysis unit can adjust the use of technical terminology during analysis according to the customer's level of expertise. For example, the analysis unit can provide analysis results using detailed technical terminology to customers with high levels of expertise. The analysis unit can also provide analysis results using simple, easy-to-understand language to customers with low levels of expertise. The analysis unit can also adjust the way the analysis results are expressed according to the customer's level of expertise and provide them in an easy-to-understand format. In this way, by adjusting the use of technical terminology according to the customer's level of expertise, it is possible to provide analysis results that are easy to understand. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the customer's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0083] The suggestion unit can estimate a customer's emotions and adjust the way the suggestion is presented based on the estimated customer emotions. For example, the suggestion unit can estimate a customer's emotions and adjust the way the suggestion is presented based on the estimated customer emotions. For example, if the customer is relaxed, the suggestion unit can provide a detailed suggestion. If the customer is in a hurry, the suggestion unit can provide a concise suggestion that focuses on the main points. If the customer is excited, the suggestion unit can provide a suggestion using visually appealing graphs or charts. This allows for more appropriate suggestions by adjusting the way the suggestion is presented based on the customer's emotions. The suggestion unit can estimate emotions using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input customer emotion data into the generation AI and cause the generation AI to adjust the way the suggestion is presented.

[0084] The suggestion unit can adjust the level of detail of the suggestion taking into account the importance of the product when making a suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on the importance of the product when making a suggestion. For example, the suggestion unit makes detailed suggestions for important products and provides them to the customer. The suggestion unit can also make concise suggestions for less important products and provide them to the customer. The suggestion unit can also determine the priority of the suggestions according to the importance of the products and preferentially suggest important products. This enables efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the products. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the importance of the products to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0085] The suggestion unit can apply different suggestion algorithms based on the product category when making a suggestion. For example, the suggestion unit can make suggestions that emphasize technical features for electronic products. The suggestion unit can also make suggestions that emphasize trends and styles for fashion products. The suggestion unit can also make suggestions that emphasize nutritional value and taste for food products. This allows for more accurate suggestions by applying different suggestion algorithms depending on the product category. Some or all of the above-described processing in the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input the product category into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0086] The suggestion unit can improve the accuracy of the suggestion based on the customer's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the customer's past suggestion results when making a suggestion. For example, the suggestion unit can make the current suggestion more accurately by referring to products purchased by the customer in the past. The suggestion unit can also make the current suggestion more accurately by referring to the customer's past evaluation data. The suggestion unit can also make the current suggestion more accurately by referring to the customer's past browsing history. In this way, the accuracy of the suggestion is improved by referring to the customer's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the customer's past suggestion results into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0087] The suggestion unit can estimate a customer's emotions and adjust the length of the suggestion taking the estimated customer emotions into consideration. The suggestion unit, for example, estimates a customer's emotions and adjusts the length of the suggestion based on the estimated customer emotions. For example, if the customer is in a hurry, the suggestion unit can provide a short, to-the-point suggestion. If the customer is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. If the customer is excited, the suggestion unit can provide a suggestion with visually stimulating effects. This allows for more appropriate suggestions by adjusting the length of the suggestion according to the customer's emotions. The suggestion unit can estimate emotions using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input customer emotion data into the generation AI and cause the generation AI to adjust the length of the suggestion.

[0088] The suggestion unit can determine the priority of suggestions taking into account the time of product submission when making suggestions. For example, the suggestion unit determines the priority of suggestions based on the time of product submission when making suggestions. For example, the suggestion unit prioritizes the latest products and provides them to the customer. The suggestion unit can also make suggestions with an emphasis on the latest products while referring to past products. The suggestion unit can also adjust the priority of suggestions according to the time of product submission and prioritize important products. In this way, by determining the priority of suggestions based on the time of product submission, the latest products can be prioritized. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the time of product submission to the generation AI and cause the generation AI to determine the priority of suggestions.

[0089] The suggestion unit can adjust the order of suggestions taking into account the relevance of products when making suggestions. The suggestion unit, for example, adjusts the order of suggestions based on the relevance of products when making suggestions. For example, the suggestion unit prioritizes suggesting and providing highly relevant products to the customer. The suggestion unit can also postpone less relevant products and prioritize suggesting important products. The suggestion unit can also adjust the order of suggestions according to the relevance of products to make suggestions efficiently. As a result, adjusting the order of suggestions based on the relevance of products enables efficient suggestions. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the relevance of products to a generation AI and cause the generation AI to adjust the order of suggestions.

[0090] The suggestion unit can adjust the use of technical terminology in the proposal based on the customer's level of expertise when making a proposal. For example, the suggestion unit can adjust the use of technical terminology in the proposal according to the customer's level of expertise when making a proposal. For example, the suggestion unit can provide a proposal using detailed technical terminology for a customer with high level of expertise. The suggestion unit can also provide a proposal using simple and easy-to-understand language for a customer with low level of expertise. The suggestion unit can also adjust the way the proposal is expressed according to the customer's level of expertise and provide the proposal in an easy-to-understand format. In this way, by adjusting the use of technical terminology according to the customer's level of expertise, it is possible to provide an easy-to-understand proposal. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the customer's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects customer preferences and purchase history using the camera 42 and microphone 38B of the smart device 14 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI to identify customer preferences and purchasing patterns. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal products and services based on the analysis results. The suggestion unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects customer preferences and purchase history using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generative AI to identify customer preferences and purchasing patterns. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal products and services based on the analysis results. The suggestion unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects customer preferences and purchase history using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a generative AI to identify customer preferences and purchasing patterns. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests optimal products and services based on the analysis results. The suggestion unit may be realized, for example, by the control unit 46A of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects customer preferences and purchase history using the camera 42 and microphone 238 of the robot 414 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a generative AI to identify customer preferences and purchasing patterns. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests optimal products and services based on the analysis results. The suggestion unit may be realized, for example, by the control unit 46A of the robot 414.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The collection unit may monitor customers' social media activities and collect data on products and services shared by customers. For example, it may collect data on products shared by customers on Facebook or Instagram and provide that data to the analysis unit. The collection unit may also collect data on brands and influencers followed by customers. Furthermore, the collection unit may monitor activities in online communities and forums in which customers participate and collect related data. This may enable more personalized suggestions based on customers' social media activities.

[0093] The analysis department can analyze not only a customer's purchase history but also their lifestyle data. For example, if a customer is health-conscious, health-related products can be suggested. If a customer likes outdoor activities, outdoor equipment can be suggested. Furthermore, if a customer has a pet, pet supplies can be suggested. This makes it possible to make suggestions that are tailored to the customer's lifestyle, which is expected to improve customer satisfaction.

[0094] The suggestion unit can estimate the customer's emotions and adjust the timing of suggestions based on the estimated emotions. For example, if the customer is feeling stressed, the suggestion unit can refrain from making suggestions or suggest products that will help the customer relax. Also, if the customer is excited, the suggestion unit can make immediate suggestions to increase the customer's motivation to purchase. Furthermore, if the customer is relaxed, the suggestion unit can make detailed suggestions. This makes it possible to make suggestions at appropriate times according to the customer's emotions.

[0095] The collection unit can use the customer's geographic location information to collect information on local promotions and events. For example, if the customer is in a specific area, it can collect information on events and sales being held in that area and provide it to the proposal unit. In addition, if the customer is traveling, the collection unit can collect tourist information and data on recommended spots in the travel destination. Furthermore, if the customer is at home, the collection unit can collect data on stores and services around the customer's home. This makes it possible to make proposals based on the customer's geographic location information.

[0096] The analysis unit can estimate the customer's emotions and adjust the visual presentation of the analysis based on the estimated emotions. For example, if the customer is relaxed, the analysis results can be presented using detailed graphs and charts. If the customer is in a hurry, the analysis unit can provide a concise summary. Furthermore, if the customer is excited, the analysis results can be presented using visually appealing infographics. In this way, the analysis results can be presented in visual presentations according to the customer's emotions.

[0097] The proposal unit can identify regular purchasing patterns based on a customer's purchasing history and make proposals for regular purchases. For example, if a customer purchases the same product every month, the proposal unit can propose that product as a regular purchase. Also, if a customer purchases specific products each season, the proposal unit can make proposals tailored to that season. Furthermore, if a customer purchases products before a specific event, the proposal unit can make proposals tailored to that event. This makes it possible to make efficient proposals based on the customer's purchasing patterns.

[0098] The collection unit can estimate the customer's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the customer is feeling stressed, the frequency of data collection can be reduced and data collection can be performed when the customer is relaxed. Also, if the customer is excited, data can be collected in real time and reflected immediately. Furthermore, if the customer is tired, data collection can be temporarily stopped and resumed after the customer has rested. This makes it possible to collect appropriate data according to the customer's emotions.

[0099] The analysis unit can analyze not only customer purchase history but also customer feedback data. For example, it can identify customer preferences and complaints based on feedback provided by the customer in the past. It can also identify products and services that customers have rated highly and make suggestions based on that. Furthermore, by avoiding products and services that customers have rated poorly, it is possible to make suggestions that will result in greater satisfaction. This enables highly accurate analysis based on customer feedback data.

[0100] The suggestion unit can estimate the customer's emotions and adjust the content of the suggestion based on the estimated emotions. For example, if the customer is relaxed, the suggestion unit can provide a detailed product description and review. If the customer is in a hurry, the suggestion unit can provide a concise, to-the-point suggestion. Furthermore, if the customer is excited, the suggestion unit can provide a visually appealing image or video. This makes it possible to make appropriate suggestions according to the customer's emotions.

[0101] The collection unit can collect reviews and evaluation data of related products based on the customer's purchase history. For example, it collects reviews and evaluations of other customers for products that the customer has purchased in the past and provides them to the analysis unit. The collection unit can also collect reviews and evaluations of products in which the customer has shown interest. Furthermore, the collection unit can also collect reviews and evaluations of products related to products that the customer has highly rated. This makes it possible to collect related data based on the customer's purchase history.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit collects customer preferences and purchasing history. Specifically, it collects data such as past purchase history, ratings, and browsing history. For example, on an e-commerce site, it collects data such as which products customers have purchased, which products they have rated, which products they have viewed, which pages they have visited, and the star ratings and comments they have given to products. Step 2: The analysis unit uses generative AI to analyze the data collected by the collection unit and identify customer preferences and purchasing patterns. The analysis is carried out using methods such as data mining, statistical analysis, and machine learning algorithms. For example, it identifies customers who frequently purchase products in a particular category or who prefer a particular brand. It also analyzes customer purchasing patterns and identifies customer preferences based on data on products purchased in the past. Step 3: The proposal unit uses generative AI to propose optimal products and services based on the analysis results obtained by the analysis unit. Suggestions are made using methods such as recommendation algorithms based on customer preferences and purchase history. For example, for customers who frequently purchase products in a particular category, products in that category will be proposed, and for customers who prefer a particular brand, products from that brand will be proposed. In addition, customized proposals tailored to customer needs will be generated based on the customer's past purchase history and evaluation data.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, the 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects customer preferences and purchasing history; an analysis unit that analyzes the data collected by the collection unit and identifies customer preferences and purchasing patterns; a proposal unit that proposes appropriate products and services based on the analysis results obtained by the analysis unit. A system characterized by:

2. The collecting unit Collect data on customers' past purchases, browsing history, and ratings 2. The system of claim 1.

3. The analysis unit Analyze the collected data to identify customers who regularly purchase products in a particular category and who have a preference for a particular brand.

2. The system of claim 1.

4. The proposal unit For customers who regularly purchase products in a particular category, suggest products from that category; for customers who prefer a particular brand, suggest products from that brand.

2. The system of claim 1.

5. The proposal unit Generate personalized offers tailored to your customers' needs 2. The system of claim 1.

6. The collecting unit Estimate customer sentiment and adjust the timing of data collection based on the estimated sentiment 2. The system of claim 1.

7. The collecting unit Analyze customers' past purchase history and select the appropriate data collection method 2. The system of claim 1.

8. The collecting unit When collecting data, filter it based on the customer's current life situation and areas of interest 2. The system of claim 1.

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