System

Abstract

To improve satisfaction of users by effectively analyzing end users' use states of sold products, providing regular reports and proposing after-sales services.SOLUTION: A system includes means for: causing AI to analyze end users' use states of sold products; providing regular reports to customers; and causing AI-dedicated staff to perform after-sales service such as proposing new products and new services.SELECTED DRAWING: Figure 1

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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] The present invention aims to solve the problem of improving user satisfaction by effectively analyzing the usage status of products sold to end users and providing regular reports and proposing after-sales services. [Means for solving the problem]

[0005] The system includes a means for AI to analyze the usage status of products sold to end users, a means for providing regular reports to customers, and a means for dedicated AI specialists to provide after-sales service such as proposing new products and services. [Brief description of the drawings]

[0006] [Figure 1]1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

[0007] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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).

[0010] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] 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.

[0012] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0014] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0015] 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.

[0016] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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 wide area network (WAN) and / or a local area network (LAN).

[0017] 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.

[0018] 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 (e.g., 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 indicative of 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 indicative of the user input.

[0019] The 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., voice and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs voice according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0020] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0021] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0022] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

[0023] 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.

[0024] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 60. The reception output program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads out the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0025] 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 ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, or a home appliance) owned by a user. Next, the identification process by the identification processing unit 290 of the data processing device 12 will be described.

[0026] (Example 1) The embodiment of the present invention is a system including a data collection means for collecting usage status of a product sold to an end user, an analysis means for analyzing the data collected by AI, a report provision means for periodically providing reports, and a proposal means for proposing new products and new services. Specifically, the data collection means is configured to incorporate a sensor into a product used by an end user and collect usage data. The analysis means can analyze the data collected by AI using a machine learning algorithm to grasp usage status and trends. The report provision means can periodically compile the collected data, generate a report including analysis results and proposals, and provide it to the end user. The proposal means can propose new products and new services tailored to the needs and preferences of the user based on the data collected by the AI ​​specialist and the analysis results. (Example 2) The embodiment of the present invention is a system for effectively analyzing the usage status of a product sold to an end user and providing after-sales service. Specifically, a sensor for collecting usage data is embedded in the product used by the end user. The collected data is analyzed by AI to grasp the usage status and trends. In addition, reports can be generated periodically and provided to the end user. Furthermore, an AI specialist can propose after-sales services tailored to the user's needs based on the collected data and analysis results. (Example 3) The embodiment of the present invention is a system in which AI analyzes the usage status of a product sold to an end user and aims to improve the user's satisfaction. Specifically, a sensor is built into the product used by the end user to collect usage data. The AI ​​analyzes the collected data using a machine learning algorithm to understand the usage status and trends. In addition, by providing regular reports, the end user can be informed of the usage status and improvements of the product. Furthermore, the AI ​​specialist can propose new products and services that meet the user's needs based on the collected data and analysis results.

[0027] The process flow of each embodiment will be described below.

[0028] (Example 1) Step 1: The product sold to the end user is embedded with sensors to collect usage data. The sensors collect data about the product’s usage and performance. Step 2: The collected data is analyzed by AI using machine learning algorithms. AI can process the data and understand usage and trends. Step 3: To provide regular reports, the AI ​​compiles the analysis results and generates reports, including product usage, improvements, and suggestions. The generated reports are provided to the end user. Step 4: Based on the collected data and analysis results, the AI ​​specialist will propose new products and services tailored to the user's needs. Proposals are made individually to improve end-user satisfaction. (Example 2) Step 1: The product sold to the end user is embedded with sensors to collect usage data. The sensors collect data about the product’s usage and performance. Step 2: The collected data is analyzed by AI, which uses machine learning algorithms to process the data and understand usage and trends. Step 3: To generate periodic reports, the AI ​​compiles the collected data and creates a report containing analysis results and recommendations. The generated report is provided to the end user. Step 4: The AI ​​specialist will propose after-sales services tailored to the user's needs based on the collected data and analysis results. Proposals are made individually to improve end-user satisfaction. (Example 3) Step 1: The product sold to the end user is embedded with sensors to collect usage data. The sensors collect data about the product’s usage and performance. Step 2: The collected data is analyzed by AI using machine learning algorithms. AI can process the data and understand usage and trends. Step 3: To provide regular reports, the AI ​​compiles the analysis results and generates reports, including product usage, improvements, and suggestions. The generated reports are provided to the end user. Step 4: Based on the collected data and analysis results, the AI ​​specialist will propose new products and services tailored to the user's needs. Proposals are made individually to improve end-user satisfaction.

[0029] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0030] (Example 1) The embodiment of the present invention is a system in which an emotion engine analyzes the usage status of a product sold to an end user and recognizes the user's emotions. Specifically, a sensor for collecting emotion data is built into the product used by the end user. The sensor detects changes in the user's facial expression and voice when using the product, and collects emotion data. The emotion engine analyzes the collected emotion data and recognizes the user's emotions. For example, it can recognize emotions such as "happiness" when the user is happy while using a product, or "dissatisfaction" when the user is dissatisfied or uncomfortable. In addition, in order to provide a periodic sentiment report, the sentiment engine compiles the collected sentiment data and generates a sentiment report, which includes the user's sentiment trends and changes, sentiment details regarding products, etc. The generated sentiment report is provided to the end user. Furthermore, to provide emotion-based after-sales service, the emotion engine can suggest personalized after-sales services based on the emotion data and analysis results, for example, if the user is dissatisfied, it can suggest improvement measures or alternative products. (Example 2) The embodiment of the present invention is a system in which an emotion engine analyzes the usage status of a product sold to an end user and recognizes the user's emotions. Specifically, a sensor for collecting emotion data is embedded in the product used by the end user. The sensor measures the user's heart rate and electrical activity of the skin when using the product, and collects emotion data. The emotion engine analyzes the collected emotion data and recognizes the user's emotion. For example, it can recognize emotions such as "excited" when the user is excited, or "relaxed" when the user is relaxed, based on heart rate fluctuations and skin electrical activity patterns. In addition, in order to provide a periodic sentiment report, the sentiment engine compiles the collected sentiment data and generates a sentiment report, which includes changes and trends in the user's sentiment, details of sentiment related to the product, etc. The generated sentiment report is provided to the end user. In addition, to provide emotion-based after-sales services, the emotion engine can suggest personalized after-sales services based on the user's emotion based on emotion data and analysis results. For example, if the user is excited, it can suggest related events and special offers. (Example 3) The embodiment of the present invention is a system in which an emotion engine analyzes the usage status of a product sold to an end user and recognizes the user's emotions. Specifically, a sensor for collecting emotion data is built into the product used by the end user. The sensor detects the user's voice, facial expression, etc. when using the product and collects emotion data. The emotion engine analyzes the collected emotion data and recognizes the user's emotions. For example, it can recognize emotions such as "happiness" when the user is happy, or "dissatisfaction" when the user is dissatisfied or uncomfortable, based on changes in voice tone and facial expression. In addition, in order to provide a periodic sentiment report, the sentiment engine compiles the collected sentiment data and generates a sentiment report, which includes changes and trends in the user's sentiment, details of sentiment related to the product, etc. The generated sentiment report is provided to the end user. Furthermore, to provide emotion-based after-sales service, the emotion engine can suggest personalized after-sales services based on the emotion data and analysis results, for example, if the user is dissatisfied, it can suggest improvement measures or alternative products.

[0031] The process flow of each embodiment will be described below.

[0032] (Example 1) Step 1: The product sold by the end user is equipped with a sensor to collect emotion data. The sensor detects changes in the user's facial expression and voice when the product is used, and collects emotion data. Step 2: The emotion engine analyzes the collected emotion data and recognizes the user's emotion. For example, it can recognize emotions such as smiling or anger from facial expression data. Step 3: To provide periodic sentiment reports, the sentiment engine consolidates the collected sentiment data and generates a sentiment report, which includes changes and trends in users' sentiment, details of sentiment related to products, etc. The generated sentiment report is provided to the end user. Step 4: To provide emotion-based after-sales service, the emotion engine will use emotion data and analysis results to suggest personalized after-sales services that match the user's emotions. For example, if the user is dissatisfied, it can suggest improvement measures or alternative products. (Example 2) Step 1: The products sold by end users are equipped with sensors to collect emotional data. The sensors measure the user's heart rate and electrical activity of the skin when the product is in use, and collect emotional data. Step 2: The emotion engine analyzes the collected emotion data and recognizes the user's emotion. For example, it can recognize emotions such as "excited" if the user is excited, or "relaxed" if the user is relaxed, based on heart rate fluctuations and skin electrical activity patterns. Step 3: To provide periodic sentiment reports, the sentiment engine consolidates the collected sentiment data and generates a sentiment report, which includes changes and trends in users' sentiment, details of sentiment related to products, etc. The generated sentiment report is provided to the end user. Step 4: To provide emotion-based after-sales service, the emotion engine will use emotion data and analysis results to suggest personalized after-sales services that match the user's emotions. For example, if the user is excited, it can suggest related events and special offers. (Example 3) Step 1: The product sold by the end user is equipped with a sensor to collect emotional data. The sensor detects the user's voice, facial expressions, etc. when using the product and collects emotional data. Step 2: The emotion engine analyzes the collected emotion data and recognizes the user's emotion. For example, from changes in voice tone and facial expression, it can recognize emotions such as "happiness" when the user is happy, or "dissatisfaction" when the user is dissatisfied or uncomfortable. Step 3: To provide periodic sentiment reports, the sentiment engine consolidates the collected sentiment data and generates a sentiment report, which includes changes and trends in users' sentiment, details of sentiment related to products, etc. The generated sentiment report is provided to the end user. Step 4: To provide emotion-based after-sales service, the emotion engine will suggest personalized after-sales services based on the emotion data and analysis results, for example, if the user is dissatisfied, it can suggest improvement measures or alternative products.

[0033] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0034] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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 while using the data generation model 58.

[0035] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0036] [Second embodiment]

[0037] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0038] 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.

[0039] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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 wide area network (WAN) and / or a local area network (LAN).

[0040] 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.

[0041] 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 the voice according to instructions from the processor 46.

[0042] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0043] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0044] Fig. 4 shows an example of 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.

[0045] 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 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.

[0046] 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.

[0047] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0048] 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 ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, or a home appliance) owned by a user. Next, the identification process by the identification processing unit 290 of the data processing device 12 will be described.

[0049] (Example 1) The embodiment of the present invention is a system including a data collection means for collecting usage status of a product sold to an end user, an analysis means for analyzing the data collected by AI, a report provision means for periodically providing reports, and a proposal means for proposing new products and new services. Specifically, the data collection means is configured to incorporate a sensor into a product used by an end user and collect usage data. The analysis means can analyze the data collected by AI using a machine learning algorithm to grasp usage status and trends. The report provision means can periodically compile the collected data, generate a report including analysis results and proposals, and provide it to the end user. The proposal means can propose new products and new services tailored to the needs and preferences of the user based on the data collected by the AI ​​specialist and the analysis results. (Example 2) The embodiment of the present invention is a system for effectively analyzing the usage status of a product sold to an end user and providing after-sales service. Specifically, a sensor for collecting usage data is embedded in the product used by the end user. The collected data is analyzed by AI to grasp the usage status and trends. In addition, reports can be generated periodically and provided to the end user. Furthermore, an AI specialist can propose after-sales services tailored to the user's needs based on the collected data and analysis results. (Example 3) The embodiment of the present invention is a system in which AI analyzes the usage status of a product sold to an end user and aims to improve the user's satisfaction. Specifically, a sensor is built into the product used by the end user to collect usage data. The AI ​​analyzes the collected data using a machine learning algorithm to understand the usage status and trends. In addition, by providing regular reports, the end user can be informed of the usage status and improvements of the product. Furthermore, the AI ​​specialist can propose new products and services that meet the user's needs based on the collected data and analysis results.

[0050] The process flow of each embodiment will be described below.

[0051] (Example 1) Step 1: The product sold to the end user is embedded with sensors to collect usage data. The sensors collect data about the product’s usage and performance. Step 2: The collected data is analyzed by AI using machine learning algorithms. AI can process the data and understand usage and trends. Step 3: To provide regular reports, the AI ​​compiles the analysis results and generates reports, including product usage, improvements, and suggestions. The generated reports are provided to the end user. Step 4: Based on the collected data and analysis results, the AI ​​specialist will propose new products and services tailored to the user's needs. Proposals are made individually to improve end-user satisfaction. (Example 2) Step 1: The product sold to the end user is embedded with sensors to collect usage data. The sensors collect data about the product’s usage and performance. Step 2: The collected data is analyzed by AI, which uses machine learning algorithms to process the data and understand usage and trends. Step 3: To generate periodic reports, the AI ​​compiles the collected data and creates a report containing analysis results and recommendations. The generated report is provided to the end user. Step 4: The AI ​​specialist will propose after-sales services tailored to the user's needs based on the collected data and analysis results. Proposals are made individually to improve end-user satisfaction. (Example 3) Step 1: The product sold to the end user is embedded with sensors to collect usage data. The sensors collect data about the product’s usage and performance. Step 2: The collected data is analyzed by AI using machine learning algorithms. AI can process the data and understand usage and trends. Step 3: To provide regular reports, the AI ​​compiles the analysis results and generates reports, including product usage, improvements, and suggestions. The generated reports are provided to the end user. Step 4: Based on the collected data and analysis results, the AI ​​specialist will propose new products and services tailored to the user's needs. Proposals are made individually to improve end-user satisfaction.

[0052] In addition, an emotion engine that estimates the emotion of the user may be further combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0053] (Example 1) The embodiment of the present invention is a system in which an emotion engine analyzes the usage status of a product sold to an end user and recognizes the user's emotions. Specifically, a sensor for collecting emotion data is built into the product used by the end user. The sensor detects changes in the user's facial expression and voice when using the product, and collects emotion data. The emotion engine analyzes the collected emotion data and recognizes the user's emotions. For example, it can recognize emotions such as "happiness" when the user is happy while using a product, or "dissatisfaction" when the user is dissatisfied or uncomfortable. In addition, in order to provide a periodic sentiment report, the sentiment engine compiles the collected sentiment data and generates a sentiment report, which includes the user's sentiment trends and changes, sentiment details regarding products, etc. The generated sentiment report is provided to the end user. Furthermore, to provide emotion-based after-sales service, the emotion engine can suggest personalized after-sales services based on the emotion data and analysis results, for example, if the user is dissatisfied, it can suggest improvement measures or alternative products. (Example 2) The embodiment of the present invention is a system in which an emotion engine analyzes the usage status of a product sold to an end user and recognizes the user's emotions. Specifically, a sensor for collecting emotion data is embedded in the product used by the end user. The sensor measures the user's heart rate and electrical activity of the skin when using the product, and collects emotion data. The emotion engine analyzes the collected emotion data and recognizes the user's emotion. For example, it can recognize emotions such as "excited" when the user is excited, or "relaxed" when the user is relaxed, based on heart rate fluctuations and skin electrical activity patterns. In addition, in order to provide a periodic sentiment report, the sentiment engine compiles the collected sentiment data and generates a sentiment report, which includes changes and trends in the user's sentiment, details of sentiment related to the product, etc. The generated sentiment report is provided to the end user. In addition, to provide emotion-based after-sales services, the emotion engine can suggest personalized after-sales services based on the user's emotion based on emotion data and analysis results. For example, if the user is excited, it can suggest related events and special offers. (Example 3) The embodiment of the present invention is a system in which an emotion engine analyzes the usage status of a product sold to an end user and recognizes the user's emotions. Specifically, a sensor for collecting emotion data is built into the product used by the end user. The sensor detects the user's voice, facial expression, etc. when using the product and collects emotion data. The emotion engine analyzes the collected emotion data and recognizes the user's emotions. For example, it can recognize emotions such as "happiness" when the user is happy, or "dissatisfaction" when the user is dissatisfied or uncomfortable, based on changes in voice tone and facial expression. In addition, in order to provide a periodic sentiment report, the sentiment engine compiles the collected sentiment data and generates a sentiment report, which includes changes and trends in the user's sentiment, details of sentiment related to the product, etc. The generated sentiment report is provided to the end user. Furthermore, to provide emotion-based after-sales service, the emotion engine can suggest personalized after-sales services based on the emotion data and analysis results, for example, if the user is dissatisfied, it can suggest improvement measures or alternative products.

[0054] The process flow of each embodiment will be described below.

[0055] (Example 1) Step 1: The product sold by the end user is equipped with a sensor to collect emotion data. The sensor detects changes in the user's facial expression and voice when the product is used, and collects emotion data. Step 2: The emotion engine analyzes the collected emotion data and recognizes the user's emotion. For example, it can recognize emotions such as smiling or anger from facial expression data. Step 3: To provide periodic sentiment reports, the sentiment engine consolidates the collected sentiment data and generates a sentiment report, which includes changes and trends in users' sentiment, details of sentiment related to products, etc. The generated sentiment report is provided to the end user. Step 4: To provide emotion-based after-sales service, the emotion engine will use emotion data and analysis results to suggest personalized after-sales services that match the user's emotions. For example, if the user is dissatisfied, it can suggest improvement measures or alternative products. (Example 2) Step 1: The products sold by end users are equipped with sensors to collect emotional data. The sensors measure the user's heart rate and electrical activity of the skin when the product is in use, and collect emotional data. Step 2: The emotion engine analyzes the collected emotion data and recognizes the user's emotion. For example, it can recognize emotions such as "excited" if the user is excited, or "relaxed" if the user is relaxed, based on heart rate fluctuations and skin electrical activity patterns. Step 3: To provide periodic sentiment reports, the sentiment engine consolidates the collected sentiment data and generates a sentiment report, which includes changes and trends in users' sentiment, details of sentiment related to products, etc. The generated sentiment report is provided to the end user. Step 4: To provide emotion-based after-sales service, the emotion engine will use emotion data and analysis results to suggest personalized after-sales services that match the user's emotions. For example, if the user is excited, it can suggest related events and special offers. (Example 3) Step 1: The product sold by the end user is equipped with a sensor to collect emotional data. The sensor detects the user's voice, facial expressions, etc. when using the product and collects emotional data. Step 2: The emotion engine analyzes the collected emotion data and recognizes the user's emotion. For example, from changes in voice tone and facial expression, it can recognize emotions such as "happiness" when the user is happy, or "dissatisfaction" when the user is dissatisfied or uncomfortable. Step 3: To provide periodic sentiment reports, the sentiment engine consolidates the collected sentiment data and generates a sentiment report, which includes changes and trends in users' sentiment, details of sentiment related to products, etc. The generated sentiment report is provided to the end user. Step 4: To provide emotion-based after-sales service, the emotion engine will suggest personalized after-sales services based on the emotion data and analysis results, for example, if the user is dissatisfied, it can suggest improvement measures or alternative products.

[0056] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0057] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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 while using the data generation model 58.

[0058] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0059] [Third embodiment]

[0060] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0061] 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.

[0062] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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 wide area network (WAN) and / or a local area network (LAN).

[0063] 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.

[0064] 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 the voice according to instructions from the processor 46.

[0065] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0066] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0067] Fig. 6 shows an example of 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.

[0068] 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 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.

[0069] 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.

[0070] In the headset type terminal 314, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0071] 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 ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, or a home appliance) owned by a user. Next, the identification process by the identification processing unit 290 of the data processing device 12 will be described.

[0072] (Example 1) The embodiment of the present invention is a system including a data collection means for collecting usage status of a product sold to an end user, an analysis means for analyzing the data collected by AI, a report provision means for periodically providing reports, and a proposal means for proposing new products and new services. Specifically, the data collection means is configured to incorporate a sensor into a product used by an end user and collect usage data. The analysis means can analyze the data collected by AI using a machine learning algorithm to grasp usage status and trends. The report provision means can periodically compile the collected data, generate a report including analysis results and proposals, and provide it to the end user. The proposal means can propose new products and new services tailored to the needs and preferences of the user based on the data collected by the AI ​​specialist and the analysis results. (Example 2) The embodiment of the present invention is a system for effectively analyzing the usage status of a product sold to an end user and providing after-sales service. Specifically, a sensor for collecting usage data is embedded in the product used by the end user. The collected data is analyzed by AI to grasp the usage status and trends. In addition, reports can be generated periodically and provided to the end user. Furthermore, an AI specialist can propose after-sales services tailored to the user's needs based on the collected data and analysis results. (Example 3) The embodiment of the present invention is a system in which AI analyzes the usage status of a product sold to an end user and aims to improve the user's satisfaction. Specifically, a sensor is built into the product used by the end user to collect usage data. The AI ​​analyzes the collected data using a machine learning algorithm to understand the usage status and trends. In addition, by providing regular reports, the end user can be informed of the usage status and improvements of the product. Furthermore, the AI ​​specialist can propose new products and services that meet the user's needs based on the collected data and analysis results.

[0073] The process flow of each embodiment will be described below.

[0074] (Example 1) Step 1: The product sold to the end user is embedded with sensors to collect usage data. The sensors collect data about the product’s usage and performance. Step 2: The collected data is analyzed by AI using machine learning algorithms. AI can process the data and understand usage and trends. Step 3: To provide regular reports, the AI ​​compiles the analysis results and generates reports, including product usage, improvements, and suggestions. The generated reports are provided to the end user. Step 4: Based on the collected data and analysis results, the AI ​​specialist will propose new products and services tailored to the user's needs. Proposals are made individually to improve end-user satisfaction. (Example 2) Step 1: The product sold to the end user is embedded with sensors to collect usage data. The sensors collect data about the product’s usage and performance. Step 2: The collected data is analyzed by AI, which uses machine learning algorithms to process the data and understand usage and trends. Step 3: To generate periodic reports, the AI ​​compiles the collected data and creates a report containing analysis results and recommendations. The generated report is provided to the end user. Step 4: The AI ​​specialist will propose after-sales services tailored to the user's needs based on the collected data and analysis results. Proposals are made individually to improve end-user satisfaction. (Example 3) Step 1: The product sold to the end user is embedded with sensors to collect usage data. The sensors collect data about the product’s usage and performance. Step 2: The collected data is analyzed by AI using machine learning algorithms. AI can process the data and understand usage and trends. Step 3: To provide regular reports, the AI ​​compiles the analysis results and generates reports, including product usage, improvements, and suggestions. The generated reports are provided to the end user. Step 4: Based on the collected data and analysis results, the AI ​​specialist will propose new products and services tailored to the user's needs. Proposals are made individually to improve end-user satisfaction.

[0075] In addition, an emotion engine that estimates the emotion of the user may be further combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0076] (Example 1) The embodiment of the present invention is a system in which an emotion engine analyzes the usage status of a product sold to an end user and recognizes the user's emotions. Specifically, a sensor for collecting emotion data is built into the product used by the end user. The sensor detects changes in the user's facial expression and voice when using the product, and collects emotion data. The emotion engine analyzes the collected emotion data and recognizes the user's emotions. For example, it can recognize emotions such as "happiness" when the user is happy while using a product, or "dissatisfaction" when the user is dissatisfied or uncomfortable. In addition, in order to provide a periodic sentiment report, the sentiment engine compiles the collected sentiment data and generates a sentiment report, which includes the user's sentiment trends and changes, sentiment details regarding products, etc. The generated sentiment report is provided to the end user. Furthermore, to provide emotion-based after-sales service, the emotion engine can suggest personalized after-sales services based on the emotion data and analysis results, for example, if the user is dissatisfied, it can suggest improvement measures or alternative products. (Example 2) The embodiment of the present invention is a system in which an emotion engine analyzes the usage status of a product sold to an end user and recognizes the user's emotions. Specifically, a sensor for collecting emotion data is embedded in the product used by the end user. The sensor measures the user's heart rate and electrical activity of the skin when using the product, and collects emotion data. The emotion engine analyzes the collected emotion data and recognizes the user's emotion. For example, it can recognize emotions such as "excited" when the user is excited, or "relaxed" when the user is relaxed, based on heart rate fluctuations and skin electrical activity patterns. In addition, in order to provide a periodic sentiment report, the sentiment engine compiles the collected sentiment data and generates a sentiment report, which includes changes and trends in the user's sentiment, details of sentiment related to the product, etc. The generated sentiment report is provided to the end user. In addition, to provide emotion-based after-sales services, the emotion engine can suggest personalized after-sales services based on the user's emotion based on emotion data and analysis results. For example, if the user is excited, it can suggest related events and special offers. (Example 3) The embodiment of the present invention is a system in which an emotion engine analyzes the usage status of a product sold to an end user and recognizes the user's emotions. Specifically, a sensor for collecting emotion data is built into the product used by the end user. The sensor detects the user's voice, facial expression, etc. when using the product and collects emotion data. The emotion engine analyzes the collected emotion data and recognizes the user's emotions. For example, it can recognize emotions such as "happiness" when the user is happy, or "dissatisfaction" when the user is dissatisfied or uncomfortable, based on changes in voice tone and facial expression. In addition, in order to provide a periodic sentiment report, the sentiment engine compiles the collected sentiment data and generates a sentiment report, which includes changes and trends in the user's sentiment, details of sentiment related to the product, etc. The generated sentiment report is provided to the end user. Furthermore, to provide emotion-based after-sales service, the emotion engine can suggest personalized after-sales services based on the emotion data and analysis results, for example, if the user is dissatisfied, it can suggest improvement measures or alternative products.

[0077] The process flow of each embodiment will be described below.

[0078] (Example 1) Step 1: The product sold by the end user is equipped with a sensor to collect emotion data. The sensor detects changes in the user's facial expression and voice when the product is used, and collects emotion data. Step 2: The emotion engine analyzes the collected emotion data and recognizes the user's emotion. For example, it can recognize emotions such as smiling or anger from facial expression data. Step 3: To provide periodic sentiment reports, the sentiment engine consolidates the collected sentiment data and generates a sentiment report, which includes changes and trends in users' sentiment, details of sentiment related to products, etc. The generated sentiment report is provided to the end user. Step 4: To provide emotion-based after-sales service, the emotion engine will suggest personalized after-sales services based on the emotion data and analysis results, for example, if the user is dissatisfied, it can suggest improvement measures or alternative products. (Example 2) Step 1: The products sold by end users are equipped with sensors to collect emotional data. The sensors measure the user's heart rate and electrical activity of the skin when the product is in use, and collect emotional data. Step 2: The emotion engine analyzes the collected emotion data and recognizes the user's emotion. For example, it can recognize emotions such as "excited" if the user is excited, or "relaxed" if the user is relaxed, based on heart rate fluctuations and skin electrical activity patterns. Step 3: To provide periodic sentiment reports, the sentiment engine consolidates the collected sentiment data and generates a sentiment report, which includes changes and trends in users' sentiment, details of sentiment related to products, etc. The generated sentiment report is provided to the end user. Step 4: To provide emotion-based after-sales service, the emotion engine will use emotion data and analysis results to suggest personalized after-sales services that match the user's emotions. For example, if the user is excited, it can suggest related events and special offers. (Example 3) Step 1: The product sold by the end user is equipped with a sensor to collect emotional data. The sensor detects the user's voice, facial expressions, etc. when using the product and collects emotional data. Step 2: The emotion engine analyzes the collected emotion data and recognizes the user's emotion. For example, from changes in voice tone and facial expression, it can recognize emotions such as "happiness" when the user is happy, or "dissatisfaction" when the user is dissatisfied or uncomfortable. Step 3: To provide periodic sentiment reports, the sentiment engine consolidates the collected sentiment data and generates a sentiment report, which includes changes and trends in users' sentiment, details of sentiment related to products, etc. The generated sentiment report is provided to the end user. Step 4: To provide emotion-based after-sales service, the emotion engine will use emotion data and analysis results to suggest personalized after-sales services that match the user's emotions. For example, if the user is dissatisfied, it can suggest improvement measures or alternative products.

[0079] 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 voice indicating a user input for 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.

[0080] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 infers the input inference data according to 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 while using the data generation model 58.

[0081] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314. [Fourth embodiment]

[0082] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0083] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0084] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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 wide area network (WAN) and / or a local area network (LAN).

[0085] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0086] 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 the voice according to instructions from the processor 46.

[0087] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0088] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0089] The control target 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, legs, etc. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, legs, etc. 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.

[0090] Fig. 8 shows an example of 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.

[0091] 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 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.

[0092] 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.

[0093] In the robot 414, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0094] 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 ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, or a home appliance) owned by a user. Next, the identification process by the identification processing unit 290 of the data processing device 12 will be described.

[0095] (Example 1) The embodiment of the present invention is a system including a data collection means for collecting usage status of a product sold to an end user, an analysis means for analyzing the data collected by AI, a report provision means for periodically providing reports, and a proposal means for proposing new products and new services. Specifically, the data collection means is configured to incorporate a sensor into a product used by an end user and collect usage data. The analysis means can analyze the data collected by AI using a machine learning algorithm to grasp usage status and trends. The report provision means can periodically compile the collected data, generate a report including analysis results and proposals, and provide it to the end user. The proposal means can propose new products and new services tailored to the needs and preferences of the user based on the data collected by the AI ​​specialist and the analysis results. (Example 2) The embodiment of the present invention is a system for effectively analyzing the usage status of a product sold to an end user and providing after-sales service. Specifically, a sensor for collecting usage data is embedded in the product used by the end user. The collected data is analyzed by AI to grasp the usage status and trends. In addition, reports can be generated periodically and provided to the end user. Furthermore, an AI specialist can propose after-sales services tailored to the user's needs based on the collected data and analysis results. (Example 3) The embodiment of the present invention is a system in which AI analyzes the usage status of a product sold to an end user and aims to improve the user's satisfaction. Specifically, a sensor is built into the product used by the end user to collect usage data. The AI ​​analyzes the collected data using a machine learning algorithm to understand the usage status and trends. In addition, by providing regular reports, the end user can be informed of the usage status and improvements of the product. Furthermore, the AI ​​specialist can propose new products and services that meet the user's needs based on the collected data and analysis results.

[0096] The process flow of each embodiment will be described below.

[0097] (Example 1) Step 1: The product sold to the end user is embedded with sensors to collect usage data. The sensors collect data about the product’s usage and performance. Step 2: The collected data is analyzed by AI using machine learning algorithms. AI can process the data and understand usage and trends. Step 3: To provide regular reports, the AI ​​compiles the analysis results and generates reports, including product usage, improvements, and suggestions. The generated reports are provided to the end user. Step 4: Based on the collected data and analysis results, the AI ​​specialist will propose new products and services tailored to the user's needs. Proposals are made individually to improve end-user satisfaction. (Example 2) Step 1: The product sold to the end user is embedded with sensors to collect usage data. The sensors collect data about the product’s usage and performance. Step 2: The collected data is analyzed by AI, which uses machine learning algorithms to process the data and understand usage and trends. Step 3: To generate periodic reports, the AI ​​compiles the collected data and creates a report containing analysis results and recommendations. The generated report is provided to the end user. Step 4: The AI ​​specialist will propose after-sales services tailored to the user's needs based on the collected data and analysis results. Proposals are made individually to improve end-user satisfaction. (Example 3) Step 1: The product sold to the end user is embedded with sensors to collect usage data. The sensors collect data about the product’s usage and performance. Step 2: The collected data is analyzed by AI using machine learning algorithms. AI can process the data and understand usage and trends. Step 3: To provide regular reports, the AI ​​compiles the analysis results and generates reports, including product usage, improvements, and suggestions. The generated reports are provided to the end user. Step 4: Based on the collected data and analysis results, the AI ​​specialist will propose new products and services tailored to the user's needs. Proposals are made individually to improve end-user satisfaction.

[0098] In addition, an emotion engine that estimates the emotion of the user may be further combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0099] (Example 1) The embodiment of the present invention is a system in which an emotion engine analyzes the usage status of a product sold to an end user and recognizes the user's emotions. Specifically, a sensor for collecting emotion data is built into the product used by the end user. The sensor detects changes in the user's facial expression and voice when using the product, and collects emotion data. The emotion engine analyzes the collected emotion data and recognizes the user's emotions. For example, it can recognize emotions such as "happiness" when the user is happy while using a product, or "dissatisfaction" when the user is dissatisfied or uncomfortable. In addition, in order to provide a periodic sentiment report, the sentiment engine compiles the collected sentiment data and generates a sentiment report, which includes the user's sentiment trends and changes, sentiment details regarding products, etc. The generated sentiment report is provided to the end user. Furthermore, to provide emotion-based after-sales service, the emotion engine can suggest personalized after-sales services based on the emotion data and analysis results, for example, if the user is dissatisfied, it can suggest improvement measures or alternative products. (Example 2) The embodiment of the present invention is a system in which an emotion engine analyzes the usage status of a product sold to an end user and recognizes the user's emotions. Specifically, a sensor for collecting emotion data is embedded in the product used by the end user. The sensor measures the user's heart rate and electrical activity of the skin when using the product, and collects emotion data. The emotion engine analyzes the collected emotion data and recognizes the user's emotion. For example, it can recognize emotions such as "excited" when the user is excited, or "relaxed" when the user is relaxed, based on heart rate fluctuations and skin electrical activity patterns. In addition, in order to provide a periodic sentiment report, the sentiment engine compiles the collected sentiment data and generates a sentiment report, which includes changes and trends in the user's sentiment, details of sentiment related to the product, etc. The generated sentiment report is provided to the end user. In addition, to provide emotion-based after-sales services, the emotion engine can suggest personalized after-sales services based on the user's emotion based on emotion data and analysis results. For example, if the user is excited, it can suggest related events and special offers. (Example 3) The embodiment of the present invention is a system in which an emotion engine analyzes the usage status of a product sold to an end user and recognizes the user's emotions. Specifically, a sensor for collecting emotion data is built into the product used by the end user. The sensor detects the user's voice, facial expression, etc. when using the product and collects emotion data. The emotion engine analyzes the collected emotion data and recognizes the user's emotions. For example, it can recognize emotions such as "happiness" when the user is happy, or "dissatisfaction" when the user is dissatisfied or uncomfortable, based on changes in voice tone and facial expression. In addition, in order to provide a periodic sentiment report, the sentiment engine compiles the collected sentiment data and generates a sentiment report, which includes changes and trends in the user's sentiment, details of sentiment related to the product, etc. The generated sentiment report is provided to the end user. Furthermore, to provide emotion-based after-sales service, the emotion engine can suggest personalized after-sales services based on the emotion data and analysis results, for example, if the user is dissatisfied, it can suggest improvement measures or alternative products.

[0100] The process flow of each embodiment will be described below.

[0101] (Example 1) Step 1: The product sold by the end user is equipped with a sensor to collect emotion data. The sensor detects changes in the user's facial expression and voice when the product is used, and collects emotion data. Step 2: The emotion engine analyzes the collected emotion data and recognizes the user's emotion. For example, it can recognize emotions such as smiling or anger from facial expression data. Step 3: To provide periodic sentiment reports, the sentiment engine consolidates the collected sentiment data and generates a sentiment report, which includes changes and trends in users' sentiment, details of sentiment related to products, etc. The generated sentiment report is provided to the end user. Step 4: To provide emotion-based after-sales service, the emotion engine will suggest personalized after-sales services based on the emotion data and analysis results, for example, if the user is dissatisfied, it can suggest improvement measures or alternative products. (Example 2) Step 1: The products sold by end users are equipped with sensors to collect emotional data. The sensors measure the user's heart rate and electrical activity of the skin when the product is in use, and collect emotional data. Step 2: The emotion engine analyzes the collected emotion data and recognizes the user's emotion. For example, it can recognize emotions such as "excited" if the user is excited, or "relaxed" if the user is relaxed, based on heart rate fluctuations and skin electrical activity patterns. Step 3: To provide periodic sentiment reports, the sentiment engine consolidates the collected sentiment data and generates a sentiment report, which includes changes and trends in users' sentiment, details of sentiment related to products, etc. The generated sentiment report is provided to the end user. Step 4: To provide emotion-based after-sales service, the emotion engine will use emotion data and analysis results to suggest personalized after-sales services that match the user's emotions. For example, if the user is excited, it can suggest related events and special offers. (Example 3) Step 1: The product sold by the end user is equipped with a sensor to collect emotional data. The sensor detects the user's voice, facial expressions, etc. when using the product and collects emotional data. Step 2: The emotion engine analyzes the collected emotion data and recognizes the user's emotion. For example, from changes in voice tone and facial expression, it can recognize emotions such as "happiness" when the user is happy, or "dissatisfaction" when the user is dissatisfied or uncomfortable. Step 3: To provide periodic sentiment reports, the sentiment engine consolidates the collected sentiment data and generates a sentiment report, which includes changes and trends in users' sentiment, details of sentiment related to products, etc. The generated sentiment report is provided to the end user. Step 4: To provide emotion-based after-sales service, the emotion engine will use emotion data and analysis results to suggest personalized after-sales services that match the user's emotions. For example, if the user is dissatisfied, it can suggest improvement measures or alternative products.

[0102] 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 a voice indicating a user input for 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.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 infers the input inference data according to 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 while using the data generation model 58.

[0104] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the robot 414.

[0105] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0106] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0107] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0108] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0109] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. For robots, cars, motorcycles, etc., emotions can also be created based on various balances such as posture and battery level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated based on, for example, the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). In the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. In addition, in the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0110] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0111] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0112] 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.

[0113] 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 Universal Serial Bus (USB) 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.

[0114] In addition, 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 upon request from the data processing device 12.

[0115] 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.

[0116] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing the specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0117] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0118] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0119] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0120] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 description and illustrations, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above description and illustrations omit explanations of technical common sense that do not require explanation in order to enable the implementation of the technology of the present disclosure.

[0121] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or standard was specifically and individually indicated to be incorporated by reference.

[0122] The following is further disclosed regarding the above embodiment.

[0123] (Appendix 1) The system includes a means for AI to analyze the usage status of products sold to end users, a means for providing regular reports to customers, and a means for dedicated AI specialists to provide after-sales service such as proposing new products and services. (Appendix 2) In the system described in Appendix 1, when the AI ​​analyzes product usage, it collects and analyzes usage data of the products sold by end users. (Appendix 3) In the system described in Appendix 1, when an AI specialist provides after-sales service, the AI ​​specialist will take into account the user's needs and preferences and make individual suggestions.

[0124] (Appendix 1) The system includes a means for an emotion engine to analyze the usage status of a product sold to an end user and recognize the user's emotions, a means for periodically providing an emotion report, a means for proposing new products and new services, and a means for providing after-sales service based on the user's emotions. (Appendix 2) In the system described in Appendix 1, the emotion engine extracts and analyzes emotions from end-user usage data of sold products. (Appendix 3) In the system described in Appendix 1, the emotion report providing means periodically compiles the collected emotion data, generates an emotion report, and provides it to the end user. In addition, the emotion-based after-sales service providing means provides individual after-sales service tailored to the user's emotions based on the emotion data and analysis results. [Explanation of symbols]

[0125] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The system includes a means for AI to analyze the usage status of products sold to end users, a means for providing regular reports to customers, and a means for dedicated AI specialists to provide after-sales service such as proposing new products and services.

2. In the system described in claim 1, when the AI ​​analyzes product usage, it collects and analyzes usage data of the products sold by end users.

3. In the system described in claim 1, when an AI specialist provides after-sales service, the AI ​​specialist takes into account the user's needs and preferences and makes individual suggestions.

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