System

Abstract

To provide a system for repairing commodities by using a robot by summarizing instruction manuals of the commodities.SOLUTION: A system includes an acquisition section, a summary section, and a repairing section. The acquisition section acquires contents of instruction manuals of commodities. The summary section summarizes contents of the instruction manuals acquires by the acquisition section by a generative AI. The repairing section repairs commodities by operating a robot by using a generative AI on the basis of contents summarized by the summary section.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] Conventional technology has had the problem that product instruction manuals are long and difficult to understand, making repair work difficult.

[0005] The system according to the embodiment aims to summarize the instruction manual of the product and repair the product using a robot. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a summarization unit, and a repair unit. The acquisition unit acquires the contents of an instruction manual for a product. The summarization unit summarizes the contents of the instruction manual acquired by the acquisition unit using a generation AI. The repair unit operates a robot using the generation AI based on the contents summarized by the summarization unit to repair the product. Effect of the Invention

[0007] An embodiment of the system can summarize product instruction manuals and repair products using robots. [Brief description 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. [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

[0009] 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.

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

[0011] 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).

[0012] 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 the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 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), or 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. 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."

[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, 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 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 (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 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] 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.

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

[0024] 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.

[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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The repair support system according to the embodiment of the present invention is a system that analyzes and summarizes the instruction manual of a product, and operates a robot to perform repairs. This repair support system analyzes the instruction manual of the product with a camera, and the generation AI summarizes the contents. Next, the robot is operated based on the contents summarized by the generation AI to repair the product. For example, when a user takes a picture of the instruction manual of a product with a camera, the system acquires the contents, and the generation AI summarizes them. Based on the summarized contents, the robot's hands are operated to repair the product. In addition, the user can also input symptoms of the product's malfunction, and that information is also reflected in the repair. This allows the repair support system to analyze and summarize the instruction manual of the product, and operate the robot to perform repairs.

[0029] The repair support system according to the embodiment includes an acquisition unit, a summarization unit, and a repair unit. The acquisition unit acquires the contents of the instruction manual of the product. For example, the acquisition unit photographs the instruction manual using a camera and acquires the image data. The acquisition unit can also directly acquire the instruction manual in a digital format. For example, the acquisition unit acquires the instruction manual in a PDF format or a text format. The summarization unit summarizes the contents of the instruction manual acquired by the acquisition unit using a generation AI. For example, the summarization unit generates a summary by having the generation AI analyze the contents of the instruction manual and extract important information. The generation AI performs the summarization using a text generation AI (for example, LLM). For example, the generation AI receives a prompt such as "Please summarize the main points of this instruction manual" and generates a summary. The repair unit repairs the product by operating a robot using the generation AI based on the contents summarized by the summarization unit. The repair unit repairs the product by operating, for example, the robot's hand. The robot's hand is equipped with, for example, an arm capable of precise operation, and performs accurate repairs based on the instruction manual. This allows the repair support system to analyze and summarize the instruction manual of the product, and operate the robot to perform repairs.

[0030] The acquisition unit acquires the contents of the product's instruction manual. For example, the acquisition unit photographs the instruction manual using a camera and acquires the image data. Specifically, the acquisition unit photographs each page of the instruction manual using a high-resolution camera and performs character recognition using image processing technology. This allows the contents of the instruction manual to be acquired as digital data. The acquisition unit can also directly acquire the instruction manual in digital format. For example, the acquisition unit acquires the instruction manual in PDF format or text format. This includes downloading from the Internet and reading from an external storage device such as a USB memory. Furthermore, the acquisition unit can acquire the instruction manual from a cloud storage service, which allows the latest instruction manual to be always available. The acquisition unit can centrally manage these data and cooperate with other departments and systems as necessary. For example, the acquired data of the instruction manual is stored in a cloud server and made accessible to the summary department and repair department. The acquisition unit also has a function of classifying the contents of the instruction manual and enabling the instruction manual of a specific product to be quickly searched for. This allows the acquisition unit to efficiently and effectively acquire the contents of the instruction manual and improve the performance of the entire system.

[0031] The summarizing unit uses the generation AI to summarize the contents of the instruction manual acquired by the acquisition unit. For example, the generation AI analyzes the contents of the instruction manual, extracts important information, and generates a summary. The generation AI performs summarization using a text generation AI (for example, LLM). Specifically, the generation AI uses natural language processing technology to analyze the text of the instruction manual and extracts important keywords and phrases. Next, based on these keywords and phrases, it generates a summary that concisely summarizes the main points of the instruction manual. For example, the generation AI receives a prompt such as "Please summarize the main points of this instruction manual" and generates a summary. The generation AI can learn from data on past instruction manuals and user feedback to improve the accuracy of the summary. Furthermore, the summarizing unit provides the generated summary to the user, allowing the user to quickly obtain the information he or she needs. For example, the summarizing unit displays the summary on a smartphone or tablet, allowing the user to easily access it. In addition, the summarizing unit also has a function of reading the summary content aloud, making it possible to accommodate users with visual impairments. This allows the summarizing unit to efficiently summarize the contents of the acquired instruction manual and provide it in a form that is easy for the user to use.

[0032] The repair unit operates the robot using the generation AI to repair the product based on the contents summarized by the summarization unit. The repair unit, for example, operates the robot's hand to repair the product. Specifically, the robot's hand has an arm capable of precise movement and accurately performs repairs based on the instruction manual. The generation AI analyzes the summarized contents of the instruction manual and instructs the robot on the repair procedure. For example, the generation AI receives a prompt such as "Please tell me the repair procedure for this product" and generates a repair procedure. The generation AI can learn past repair data and the contents of the instruction manual and suggest the optimal repair procedure. The repair unit controls the robot's arm based on the generated repair procedure to perform precise repair work. For example, the robot's arm can accurately perform operations such as turning screws, removing parts, and installing parts. Furthermore, the repair unit can monitor the progress of the repair work in real time and respond immediately if an abnormality occurs. For example, if the robot's sensor detects an abnormality, the repair work is paused, the cause of the abnormality is identified, and corrected. In addition, the repair unit can record the history of the repair work and use it as a reference for future repair work. This enables the repair department to repair the product efficiently and accurately based on the contents of the summarized instruction manual.

[0033] The repair unit can operate the robot hand to repair the product. The robot hand, for example, has an arm capable of precise movement and accurately performs repairs based on an instruction manual. For example, the robot hand can remove parts of the product and install new parts. The robot hand can also inspect the inside of the product and identify the defective part. Furthermore, the robot hand can check the operation of the product and determine whether the repair is complete. In this way, the repair of the product can be automated by operating the robot hand.

[0034] The reception unit can accept input of information indicating a malfunction symptom of the product from the user. The repair unit can operate the robot based on the summarized content and the information accepted by the reception unit. The reception unit, for example, accepts input of the malfunction symptom by the user in text format. The reception unit can also accept input of the malfunction symptom by the user in image format. Furthermore, the reception unit can also accept input of the malfunction symptom by the user in voice format. For example, when the user inputs "the product does not turn on", the reception unit accepts the information. The repair unit operates the robot to perform repairs based on the information accepted by the reception unit. For example, the repair unit operates the robot's hand to repair the power supply of the product based on the malfunction symptom input by the user. This allows more accurate repairs to be performed based on the information input by the user.

[0035] When acquiring the instruction manual, the acquisition unit can analyze the user's past repair history and select the acquisition method. For example, the acquisition unit preferentially acquires instruction manuals for products that the user has repaired in the past. The acquisition unit can also preferentially acquire instruction manuals for specific brands or models from the user's past repair history. Furthermore, the acquisition unit can analyze the user's past repair history and select the most efficient acquisition method. For example, the acquisition unit searches a database for instruction manuals for products that the user has repaired in the past and acquires them preferentially. This makes it possible to provide an optimal method for acquiring the instruction manual by taking into account the user's past repair history. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI.

[0036] The acquisition unit can perform filtering based on the user's repair skill level when acquiring the instruction manual. For example, if the user is a beginner, the acquisition unit preferentially acquires a simple instruction manual. In addition, if the user is an intermediate user, the acquisition unit can also acquire a detailed instruction manual. Furthermore, if the user is an advanced user, the acquisition unit can also acquire a specialized instruction manual. For example, the acquisition unit acquires the user's repair skill level from a database, and filters and provides instruction manuals according to that level. This can improve the success rate of repairs by providing an instruction manual according to the user's repair skill level. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI.

[0037] When acquiring the instruction manual, the acquisition unit can preferentially acquire an instruction manual with high relevance based on the geographical location information of the user. For example, when the user is in a specific area, the acquisition unit preferentially acquires an instruction manual for a product sold in that area. In addition, when the user is traveling, the acquisition unit can also preferentially acquire an instruction manual for a product to be used at the travel destination. Furthermore, the acquisition unit can acquire the most relevant instruction manual based on the geographical location information of the user. For example, the acquisition unit acquires the geographical location information of the user from GPS data, and filters and provides the instruction manual based on the information. In this way, by taking the geographical location information of the user into consideration, it is possible to provide an instruction manual with high relevance. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI.

[0038] When acquiring the instruction manual, the acquisition unit can acquire the relevant instruction manual based on the user's social media activity. For example, the acquisition unit preferentially acquires the instruction manual of the product mentioned by the user on social media. The acquisition unit can also acquire the instruction manual of the product in which the user is interested from the user's social media activity. Furthermore, the acquisition unit can analyze the user's social media activity and acquire the most relevant instruction manual. For example, the acquisition unit analyzes the content of the user's social media posts and provides the instruction manual by filtering based on the information. In this way, it is possible to provide the highly relevant instruction manual by analyzing the user's social media activity. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI.

[0039] The summarizing unit can adjust the level of detail of the summary based on the importance of the instruction manual when generating the summary. For example, in the case of an important instruction manual, the summarizing unit generates a detailed summary. In addition, in the case of a general instruction manual, the summarizing unit can also generate a concise summary. Furthermore, the summarizing unit can adjust the level of detail of the summary according to the importance of the instruction manual. For example, the summarizing unit obtains the importance of the instruction manual from a database and adjusts the level of detail of the summary based on that information. In this way, by adjusting the level of detail of the summary according to the importance of the instruction manual, it is possible to appropriately provide necessary information. Some or all of the above-mentioned processing in the summarizing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0040] The summarization unit can apply different summarization algorithms based on the category of the instruction manual when generating the summary. For example, in the case of an instruction manual for an electronic device, the summarization unit applies a technical summarization algorithm. In addition, in the case of an instruction manual for furniture, the summarization unit can also apply a summarization algorithm specialized for assembly procedures. Furthermore, the summarization unit can apply an optimal summarization algorithm depending on the category of the instruction manual. For example, the summarization unit obtains the category of the instruction manual from a database and selects a summarization algorithm based on that information. In this way, it is possible to provide an optimal summary by applying a summarization algorithm according to the category of the instruction manual. Some or all of the above-mentioned processing in the summarization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0041] When generating summaries, the summarizing unit can determine the priority of summaries based on the publication date of the instruction manual. For example, the summarizing unit prioritizes summarizing the latest instruction manual. In addition, the summarizing unit can lower the priority of summaries in the case of older instruction manuals. Furthermore, the summarizing unit can determine the priority of summaries based on the publication date of the instruction manual. For example, the summarizing unit obtains the publication date of the instruction manual from a database and determines the priority of summaries based on that information. In this way, by determining the priority of summaries based on the publication date of the instruction manual, it is possible to provide the latest information with priority. Some or all of the above-mentioned processing in the summarizing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0042] The summarization unit can adjust the order of summaries based on the relevance of the instruction manual when generating summaries. For example, the summarization unit summarizes the most relevant part first. The summarization unit can also postpone the less relevant part. Furthermore, the summarization unit can adjust the order of summaries based on the relevance of the instruction manual. For example, the summarization unit obtains the relevance of the instruction manual from a database and adjusts the order of summaries based on the information. In this way, important information can be provided preferentially by adjusting the order of summaries based on the relevance of the instruction manual. Some or all of the above-mentioned processing in the summarization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0043] At the time of repair, the repair unit can select a repair method by analyzing the user's past repair history. For example, the repair unit refers to the repair method of a product that the user has previously repaired. The repair unit can also select the most effective repair method from the user's past repair history. Furthermore, the repair unit can analyze the user's past repair history and select the optimal repair method. For example, the repair unit obtains the user's past repair history from a database and selects a repair method based on that information. In this way, the optimal repair method can be provided by taking into account the user's past repair history. Some or all of the above-mentioned processing in the repair unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0044] The repair unit can customize the repair procedure based on the user's repair skill level at the time of repair. For example, if the user is a beginner, the repair unit can provide a simple repair procedure. Furthermore, if the user is an intermediate user, the repair unit can also provide a detailed repair procedure. Furthermore, if the user is an advanced user, the repair unit can also provide a specialized repair procedure. For example, the repair unit can obtain the user's repair skill level from a database and customize and provide a repair procedure according to that level. This can improve the success rate of repairs by providing a repair procedure according to the user's repair skill level. Some or all of the above-mentioned processing in the repair unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0045] The repair unit can select a repair method based on the geographical location information of the user at the time of repair. For example, if the user is in a specific area, the repair unit selects a repair method available in that area. Also, if the user is traveling, the repair unit can select a repair method available at the travel destination. Furthermore, the repair unit can select an optimal repair method based on the geographical location information of the user. For example, the repair unit acquires the geographical location information of the user from GPS data and selects a repair method based on that information. In this way, it is possible to provide an optimal repair method by taking into account the geographical location information of the user. A part or all of the above-mentioned processing in the repair unit may be performed, for example, using AI, or may be performed without using AI.

[0046] The repair unit can suggest a repair procedure based on the user's social media activity at the time of repair. The repair unit, for example, suggests a repair procedure for a product mentioned by the user on social media. The repair unit can also suggest a repair procedure for a product in which the user is interested based on the user's social media activity. Furthermore, the repair unit can analyze the user's social media activity and suggest the most relevant repair procedure. For example, the repair unit analyzes the content of the user's social media posts and suggests a repair procedure based on that information. In this way, it is possible to provide a highly relevant repair procedure by analyzing the user's social media activity. Some or all of the above-mentioned processing in the repair unit may be performed, for example, using AI, or may be performed without using AI.

[0047] The reception unit can select an input method based on the user's past failure history when inputting failure information. For example, the reception unit automatically displays failure information that the user frequently inputs in the past as candidates. The reception unit can also preferentially suggest an input method (such as voice or text) that the user has used in the past. Furthermore, the reception unit can predict and suggest an input method to be used in a specific time period based on the user's past failure history. For example, the reception unit acquires the user's past failure history from a database and selects an input method based on the information. In this way, the optimal input method can be provided by taking into account the user's past failure history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0048] The reception unit can select an input method based on the device information of the user when inputting the failure information. For example, when the user is using a smartphone, the reception unit provides an input method that matches the screen size. In addition, when the user is using a tablet, the reception unit can also provide an input method optimized for a large screen. Furthermore, when the user is using a smart watch, the reception unit can also provide an input method that is simple and highly visible. For example, the reception unit acquires the device information of the user from a database and selects an input method based on the information. In this way, it is possible to provide an optimal input method by taking into account the device information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The repair assistance system may further include a diagnostic unit. The diagnostic unit analyzes the sensor data to identify the faulty part of the product. For example, the diagnostic unit acquires data such as temperature, vibration, and current from a sensor installed inside the product and detects anomalies. The diagnostic unit may also identify the cause of the fault by comparing with past fault data. Furthermore, the diagnostic unit may detect signs of a fault and suggest preventive repairs. In this way, the repair assistance system may realize early detection and prevention of faults and extend the life of the product.

[0051] When operating the robot's hand, the repair unit can select the optimal repair procedure by referring to the user's repair history. For example, the repair unit can refer to a database of products that the user has repaired in the past, and suggest the optimal repair procedure in the event of a similar failure. The repair unit can also analyze the probability that a specific repair procedure will be successful from the user's repair history, and select that procedure with priority. Furthermore, the repair unit can customize the repair procedure based on the user's repair history, and provide the most efficient repair method for the user. In this way, the repair unit can realize more effective repairs by utilizing the user's past experience.

[0052] The acquisition unit can preferentially acquire highly relevant instruction manuals based on the geographical location information of the user. For example, when the user is in a specific area, instruction manuals for products sold in that area are acquired preferentially. Also, when the user is traveling, instruction manuals for products to be used at the travel destination can be acquired preferentially. Furthermore, the acquisition unit can also acquire the most relevant instruction manual based on the geographical location information of the user. In this way, it is possible to provide highly relevant instruction manuals by taking into account the geographical location information of the user.

[0053] The repair unit can customize the repair procedure based on the user's repair skill level during repair. For example, if the user is a beginner, a simple repair procedure can be provided. If the user is an intermediate user, a detailed repair procedure can be provided. Furthermore, if the user is an advanced user, a specialized repair procedure can be provided. This can improve the success rate of repairs by providing a repair procedure according to the user's repair skill level.

[0054] When making repairs, the repair department can select a repair method by analyzing the user's past repair history. For example, the repair method is referred to as the repair method for a product that the user has previously repaired. Also, the repair department can select the most effective repair method from the user's past repair history. Furthermore, the repair department can analyze the user's past repair history and select the optimal repair method. In this way, the optimal repair method can be provided by taking into account the user's past repair history.

[0055] When acquiring the instruction manual, the acquisition unit can acquire relevant instruction manuals based on the user's social media activity. For example, instruction manuals for products that the user has mentioned on social media are acquired preferentially. Also, instruction manuals for products in which the user is interested can be acquired from the user's social media activity. Furthermore, the user's social media activity can be analyzed to acquire the most relevant instruction manual. In this way, it is possible to provide highly relevant instruction manuals by analyzing the user's social media activity.

[0056] When generating the summary, the summarization unit can apply different summarization algorithms based on the category of the instruction manual. For example, for an instruction manual for an electronic device, a technical summarization algorithm can be applied. For an instruction manual for furniture, a summarization algorithm specialized for assembly steps can be applied. Furthermore, the most suitable summarization algorithm can be applied depending on the category of the instruction manual. In this way, the most suitable summary can be provided by applying a summarization algorithm according to the category of the instruction manual.

[0057] The process flow of the first embodiment will be briefly described below.

[0058] Step 1: The acquisition unit acquires the contents of the product's instruction manual. For example, the acquisition unit takes a picture of the instruction manual using a camera and acquires the image data. The acquisition unit can also directly acquire a digital version of the instruction manual. For example, the acquisition unit acquires a PDF or text version of the instruction manual. Step 2: The summarization unit uses the generation AI to summarize the contents of the instruction manual acquired by the acquisition unit. For example, the generation AI analyzes the contents of the instruction manual, extracts important information, and generates a summary. The generation AI performs the summarization using a text generation AI (e.g., LLM). For example, the generation AI receives a prompt such as "Please summarize the main points of this instruction manual" and generates a summary. Step 3: The repair unit operates the robot using the generation AI based on the content summarized by the summarization unit to repair the product. The repair unit operates, for example, the robot's hand to repair the product. The robot's hand is equipped with, for example, an arm capable of precise movements, and accurately performs repairs based on the instruction manual.

[0059] (Example 2) The repair support system according to the embodiment of the present invention is a system that analyzes and summarizes the instruction manual of a product, and operates a robot to perform repairs. This repair support system analyzes the instruction manual of the product with a camera, and the generation AI summarizes the contents. Next, the robot is operated based on the contents summarized by the generation AI to repair the product. For example, when a user takes a picture of the instruction manual of a product with a camera, the system acquires the contents, and the generation AI summarizes them. Based on the summarized contents, the robot's hands are operated to repair the product. In addition, the user can also input symptoms of the product's malfunction, and that information is also reflected in the repair. This allows the repair support system to analyze and summarize the instruction manual of the product, and operate the robot to perform repairs.

[0060] The repair support system according to the embodiment includes an acquisition unit, a summarization unit, and a repair unit. The acquisition unit acquires the contents of the instruction manual of the product. For example, the acquisition unit photographs the instruction manual using a camera and acquires the image data. The acquisition unit can also directly acquire the instruction manual in a digital format. For example, the acquisition unit acquires the instruction manual in a PDF format or a text format. The summarization unit summarizes the contents of the instruction manual acquired by the acquisition unit using a generation AI. For example, the summarization unit generates a summary by having the generation AI analyze the contents of the instruction manual and extract important information. The generation AI performs the summarization using a text generation AI (for example, LLM). For example, the generation AI receives a prompt such as "Please summarize the main points of this instruction manual" and generates a summary. The repair unit repairs the product by operating a robot using the generation AI based on the contents summarized by the summarization unit. The repair unit repairs the product by operating, for example, the robot's hand. The robot's hand is equipped with, for example, an arm capable of precise operation, and performs accurate repairs based on the instruction manual. This allows the repair support system to analyze and summarize the instruction manual of the product, and operate the robot to perform repairs.

[0061] The acquisition unit acquires the contents of the product's instruction manual. For example, the acquisition unit photographs the instruction manual using a camera and acquires the image data. Specifically, the acquisition unit photographs each page of the instruction manual using a high-resolution camera and performs character recognition using image processing technology. This allows the contents of the instruction manual to be acquired as digital data. The acquisition unit can also directly acquire the instruction manual in digital format. For example, the acquisition unit acquires the instruction manual in PDF format or text format. This includes downloading from the Internet and reading from an external storage device such as a USB memory. Furthermore, the acquisition unit can acquire the instruction manual from a cloud storage service, which allows the latest instruction manual to be always available. The acquisition unit can centrally manage these data and cooperate with other departments and systems as necessary. For example, the acquired data of the instruction manual is stored in a cloud server and made accessible to the summary department and repair department. The acquisition unit also has a function of classifying the contents of the instruction manual and enabling the instruction manual of a specific product to be quickly searched for. This allows the acquisition unit to efficiently and effectively acquire the contents of the instruction manual and improve the performance of the entire system.

[0062] The summarizing unit uses the generation AI to summarize the contents of the instruction manual acquired by the acquisition unit. For example, the generation AI analyzes the contents of the instruction manual, extracts important information, and generates a summary. The generation AI performs summarization using a text generation AI (for example, LLM). Specifically, the generation AI uses natural language processing technology to analyze the text of the instruction manual and extracts important keywords and phrases. Next, based on these keywords and phrases, it generates a summary that concisely summarizes the main points of the instruction manual. For example, the generation AI receives a prompt such as "Please summarize the main points of this instruction manual" and generates a summary. The generation AI can learn from data on past instruction manuals and user feedback to improve the accuracy of the summary. Furthermore, the summarizing unit provides the generated summary to the user, allowing the user to quickly obtain the information he or she needs. For example, the summarizing unit displays the summary on a smartphone or tablet, allowing the user to easily access it. In addition, the summarizing unit also has a function of reading the summary content aloud, making it possible to accommodate users with visual impairments. This allows the summarizing unit to efficiently summarize the contents of the acquired instruction manual and provide it in a form that is easy for the user to use.

[0063] The repair unit operates the robot using the generation AI to repair the product based on the contents summarized by the summarization unit. The repair unit, for example, operates the robot's hand to repair the product. Specifically, the robot's hand has an arm capable of precise movement and accurately performs repairs based on the instruction manual. The generation AI analyzes the summarized contents of the instruction manual and instructs the robot on the repair procedure. For example, the generation AI receives a prompt such as "Please tell me the repair procedure for this product" and generates a repair procedure. The generation AI can learn past repair data and the contents of the instruction manual and suggest the optimal repair procedure. The repair unit controls the robot's arm based on the generated repair procedure to perform precise repair work. For example, the robot's arm can accurately perform operations such as turning screws, removing parts, and installing parts. Furthermore, the repair unit can monitor the progress of the repair work in real time and respond immediately if an abnormality occurs. For example, if the robot's sensor detects an abnormality, the repair work is paused, the cause of the abnormality is identified, and corrected. In addition, the repair unit can record the history of the repair work and use it as a reference for future repair work. This enables the repair department to repair the product efficiently and accurately based on the contents of the summarized instruction manual.

[0064] The repair unit can operate the robot hand to repair the product. The robot hand, for example, has an arm capable of precise movement and accurately performs repairs based on an instruction manual. For example, the robot hand can remove parts of the product and install new parts. The robot hand can also inspect the inside of the product and identify the defective part. Furthermore, the robot hand can check the operation of the product and determine whether the repair is complete. In this way, the repair of the product can be automated by operating the robot hand.

[0065] The reception unit can accept input of information indicating a malfunction symptom of the product from the user. The repair unit can operate the robot based on the summarized content and the information accepted by the reception unit. The reception unit, for example, accepts input of the malfunction symptom by the user in text format. The reception unit can also accept input of the malfunction symptom by the user in image format. Furthermore, the reception unit can also accept input of the malfunction symptom by the user in voice format. For example, when the user inputs "the product does not turn on", the reception unit accepts the information. The repair unit operates the robot to perform repairs based on the information accepted by the reception unit. For example, the repair unit operates the robot's hand to repair the power supply of the product based on the malfunction symptom input by the user. This allows more accurate repairs to be performed based on the information input by the user.

[0066] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring the instruction manual based on the emotion. The acquisition unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, when the user is stressed, the acquisition unit immediately acquires the instruction manual and starts analysis. Also, when the user is relaxed, the acquisition unit can wait for the user's instruction before acquiring the instruction manual. Furthermore, when the user is in a hurry, the acquisition unit can quickly acquire the instruction manual. This makes it possible to reduce the user's stress by adjusting the timing of acquiring the instruction manual according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0067] When acquiring the instruction manual, the acquisition unit can analyze the user's past repair history and select the acquisition method. For example, the acquisition unit preferentially acquires instruction manuals for products that the user has repaired in the past. The acquisition unit can also preferentially acquire instruction manuals for specific brands or models from the user's past repair history. Furthermore, the acquisition unit can analyze the user's past repair history and select the most efficient acquisition method. For example, the acquisition unit searches a database for instruction manuals for products that the user has repaired in the past and acquires them preferentially. This makes it possible to provide an optimal method for acquiring the instruction manual by taking into account the user's past repair history. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI.

[0068] The acquisition unit can perform filtering based on the user's repair skill level when acquiring the instruction manual. For example, if the user is a beginner, the acquisition unit preferentially acquires a simple instruction manual. In addition, if the user is an intermediate user, the acquisition unit can also acquire a detailed instruction manual. Furthermore, if the user is an advanced user, the acquisition unit can also acquire a specialized instruction manual. For example, the acquisition unit acquires the user's repair skill level from a database, and filters and provides instruction manuals according to that level. This can improve the success rate of repairs by providing an instruction manual according to the user's repair skill level. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI.

[0069] The acquisition unit can estimate the user's emotions and determine the priority of the instruction manual to be acquired based on the emotions. The acquisition unit, for example, captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, when the user is stressed, the acquisition unit preferentially acquires a simple instruction manual. In addition, when the user is relaxed, the acquisition unit can also acquire a detailed instruction manual. Furthermore, when the user is in a hurry, the acquisition unit can also preferentially acquire an instruction manual including the most important part. In this way, by determining the priority of the instruction manual according to the user's emotions, it is possible to provide information according to the user's needs. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0070] When acquiring the instruction manual, the acquisition unit can preferentially acquire an instruction manual with high relevance based on the geographical location information of the user. For example, when the user is in a specific area, the acquisition unit preferentially acquires an instruction manual for a product sold in that area. In addition, when the user is traveling, the acquisition unit can also preferentially acquire an instruction manual for a product to be used at the travel destination. Furthermore, the acquisition unit can acquire the most relevant instruction manual based on the geographical location information of the user. For example, the acquisition unit acquires the geographical location information of the user from GPS data, and filters and provides the instruction manual based on the information. In this way, by taking the geographical location information of the user into consideration, it is possible to provide an instruction manual with high relevance. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI.

[0071] When acquiring the instruction manual, the acquisition unit can acquire the relevant instruction manual based on the user's social media activity. For example, the acquisition unit preferentially acquires the instruction manual of the product mentioned by the user on social media. The acquisition unit can also acquire the instruction manual of the product in which the user is interested from the user's social media activity. Furthermore, the acquisition unit can analyze the user's social media activity and acquire the most relevant instruction manual. For example, the acquisition unit analyzes the content of the user's social media posts and provides the instruction manual by filtering based on the information. In this way, it is possible to provide the highly relevant instruction manual by analyzing the user's social media activity. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI.

[0072] The summarization unit can estimate the user's emotion and adjust the expression method of the summary based on the emotion. For example, the summarization unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the summarization unit generates a concise and easy-to-understand summary when the user is stressed. The summarization unit can also generate a detailed summary when the user is relaxed. Furthermore, the summarization unit can also generate a summary that emphasizes the most important parts when the user is in a hurry. This makes it possible to provide a summary that is easy for the user to understand by adjusting the expression method of the summary according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0073] The summarizing unit can adjust the level of detail of the summary based on the importance of the instruction manual when generating the summary. For example, in the case of an important instruction manual, the summarizing unit generates a detailed summary. In addition, in the case of a general instruction manual, the summarizing unit can also generate a concise summary. Furthermore, the summarizing unit can adjust the level of detail of the summary according to the importance of the instruction manual. For example, the summarizing unit obtains the importance of the instruction manual from a database and adjusts the level of detail of the summary based on that information. In this way, by adjusting the level of detail of the summary according to the importance of the instruction manual, it is possible to appropriately provide necessary information. Some or all of the above-mentioned processing in the summarizing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0074] The summarization unit can apply different summarization algorithms based on the category of the instruction manual when generating the summary. For example, in the case of an instruction manual for an electronic device, the summarization unit applies a technical summarization algorithm. In addition, in the case of an instruction manual for furniture, the summarization unit can also apply a summarization algorithm specialized for assembly procedures. Furthermore, the summarization unit can apply an optimal summarization algorithm depending on the category of the instruction manual. For example, the summarization unit obtains the category of the instruction manual from a database and selects a summarization algorithm based on that information. In this way, it is possible to provide an optimal summary by applying a summarization algorithm according to the category of the instruction manual. Some or all of the above-mentioned processing in the summarization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0075] The summarization unit can estimate the user's emotion and adjust the length of the summary based on the emotion. For example, the summarization unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the summarization unit generates a short summary that is to the point when the user is stressed. The summarization unit can also generate a detailed summary when the user is relaxed. Furthermore, the summarization unit can also generate a short summary that emphasizes the most important parts when the user is in a hurry. This allows the length of the summary to be adjusted according to the user's emotion, thereby providing an optimal summary for the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0076] When generating summaries, the summarizing unit can determine the priority of summaries based on the publication date of the instruction manual. For example, the summarizing unit prioritizes summarizing the latest instruction manual. In addition, the summarizing unit can lower the priority of summaries in the case of older instruction manuals. Furthermore, the summarizing unit can determine the priority of summaries based on the publication date of the instruction manual. For example, the summarizing unit obtains the publication date of the instruction manual from a database and determines the priority of summaries based on that information. In this way, by determining the priority of summaries based on the publication date of the instruction manual, it is possible to provide the latest information with priority. Some or all of the above-mentioned processing in the summarizing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0077] The summarization unit can adjust the order of summaries based on the relevance of the instruction manual when generating summaries. For example, the summarization unit summarizes the most relevant part first. The summarization unit can also postpone the less relevant part. Furthermore, the summarization unit can adjust the order of summaries based on the relevance of the instruction manual. For example, the summarization unit obtains the relevance of the instruction manual from a database and adjusts the order of summaries based on the information. In this way, important information can be provided preferentially by adjusting the order of summaries based on the relevance of the instruction manual. Some or all of the above-mentioned processing in the summarization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0078] The repair unit can estimate the user's emotions and adjust the repair method based on the emotions. The repair unit, for example, captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the repair unit selects a simple and quick repair method when the user is stressed. The repair unit can also provide detailed repair procedures when the user is relaxed. Furthermore, the repair unit can also select the most efficient repair method when the user is in a hurry. In this way, the repair method can be adjusted according to the user's emotions, thereby providing the optimal repair method for the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0079] At the time of repair, the repair unit can select a repair method by analyzing the user's past repair history. For example, the repair unit refers to the repair method of a product that the user has previously repaired. The repair unit can also select the most effective repair method from the user's past repair history. Furthermore, the repair unit can analyze the user's past repair history and select the optimal repair method. For example, the repair unit obtains the user's past repair history from a database and selects a repair method based on that information. In this way, the optimal repair method can be provided by taking into account the user's past repair history. Some or all of the above-mentioned processing in the repair unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0080] The repair unit can customize the repair procedure based on the user's repair skill level at the time of repair. For example, if the user is a beginner, the repair unit can provide a simple repair procedure. Furthermore, if the user is an intermediate user, the repair unit can also provide a detailed repair procedure. Furthermore, if the user is an advanced user, the repair unit can also provide a specialized repair procedure. For example, the repair unit can obtain the user's repair skill level from a database and customize and provide a repair procedure according to that level. This can improve the success rate of repairs by providing a repair procedure according to the user's repair skill level. Some or all of the above-mentioned processing in the repair unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0081] The repair unit can estimate the user's emotions and determine the priority of repairs based on the emotions. The repair unit, for example, captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the repair unit prioritizes the simplest repairs when the user is stressed. The repair unit can also prioritize detailed repairs when the user is relaxed. Furthermore, the repair unit can also prioritize the most important repairs when the user is in a hurry. In this way, repairs can be provided according to the needs of the user by determining the priority of repairs according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0082] The repair unit can select a repair method based on the geographical location information of the user at the time of repair. For example, if the user is in a specific area, the repair unit selects a repair method available in that area. Also, if the user is traveling, the repair unit can select a repair method available at the travel destination. Furthermore, the repair unit can select an optimal repair method based on the geographical location information of the user. For example, the repair unit acquires the geographical location information of the user from GPS data and selects a repair method based on that information. In this way, it is possible to provide an optimal repair method by taking into account the geographical location information of the user. A part or all of the above-mentioned processing in the repair unit may be performed, for example, using AI, or may be performed without using AI.

[0083] The repair unit can suggest a repair procedure based on the user's social media activity at the time of repair. The repair unit, for example, suggests a repair procedure for a product mentioned by the user on social media. The repair unit can also suggest a repair procedure for a product in which the user is interested based on the user's social media activity. Furthermore, the repair unit can analyze the user's social media activity and suggest the most relevant repair procedure. For example, the repair unit analyzes the content of the user's social media posts and suggests a repair procedure based on that information. In this way, it is possible to provide a highly relevant repair procedure by analyzing the user's social media activity. Some or all of the above-mentioned processing in the repair unit may be performed, for example, using AI, or may be performed without using AI.

[0084] The reception unit can estimate the user's emotion and adjust the input method of the malfunction information based on the emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, when the user is stressed, the reception unit provides a simple interface and minimizes the input procedure. In addition, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input the malfunction information. In this way, by adjusting the input method of the malfunction information according to the user's emotion, an interface that is easy for the user to use can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0085] The reception unit can select an input method based on the user's past failure history when inputting failure information. For example, the reception unit automatically displays failure information that the user frequently inputs in the past as candidates. The reception unit can also preferentially suggest an input method (such as voice or text) that the user has used in the past. Furthermore, the reception unit can predict and suggest an input method to be used in a specific time period based on the user's past failure history. For example, the reception unit acquires the user's past failure history from a database and selects an input method based on the information. In this way, the optimal input method can be provided by taking into account the user's past failure history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0086] The reception unit can estimate the user's emotions and determine the priority of the malfunction information based on the emotions. The reception unit, for example, captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, when the user is stressed, the reception unit preferentially processes the simplest malfunction information. In addition, when the user is relaxed, the reception unit can also preferentially process detailed malfunction information. Furthermore, when the user is in a hurry, the reception unit can also preferentially process the most important malfunction information. This makes it possible to respond according to the user's needs by determining the priority of the malfunction information according to the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0087] The reception unit can select an input method based on the device information of the user when inputting the failure information. For example, when the user is using a smartphone, the reception unit provides an input method that matches the screen size. In addition, when the user is using a tablet, the reception unit can also provide an input method optimized for a large screen. Furthermore, when the user is using a smart watch, the reception unit can also provide an input method that is simple and highly visible. For example, the reception unit acquires the device information of the user from a database and selects an input method based on the information. In this way, it is possible to provide an optimal input method by taking into account the device information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The repair assistance system may further include a diagnostic unit. The diagnostic unit analyzes the sensor data to identify the faulty part of the product. For example, the diagnostic unit acquires data such as temperature, vibration, and current from a sensor installed inside the product and detects anomalies. The diagnostic unit may also identify the cause of the fault by comparing with past fault data. Furthermore, the diagnostic unit may detect signs of a fault and suggest preventive repairs. In this way, the repair assistance system may realize early detection and prevention of faults and extend the life of the product.

[0090] When operating the robot's hand, the repair unit can select the optimal repair procedure by referring to the user's repair history. For example, the repair unit can refer to a database of products that the user has repaired in the past, and suggest the optimal repair procedure in the event of a similar failure. The repair unit can also analyze the probability that a specific repair procedure will be successful from the user's repair history, and select that procedure with priority. Furthermore, the repair unit can customize the repair procedure based on the user's repair history, and provide the most efficient repair method for the user. In this way, the repair unit can realize more effective repairs by utilizing the user's past experience.

[0091] The reception unit can estimate the user's emotion and adjust the input method of the malfunction information based on the emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. If the user is stressed, a simple interface is provided to minimize the input procedure. If the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable the user to input the malfunction information quickly. In this way, an easy-to-use interface can be provided for the user by adjusting the input method of the malfunction information according to the user's emotion.

[0092] The acquisition unit can preferentially acquire highly relevant instruction manuals based on the geographical location information of the user. For example, when the user is in a specific area, instruction manuals for products sold in that area are acquired preferentially. Also, when the user is traveling, instruction manuals for products to be used at the travel destination can be acquired preferentially. Furthermore, the acquisition unit can also acquire the most relevant instruction manual based on the geographical location information of the user. In this way, it is possible to provide highly relevant instruction manuals by taking into account the geographical location information of the user.

[0093] The summarization unit can estimate the user's emotion and adjust the way the summary is expressed based on the emotion. For example, the summarization unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. If the user is stressed, a concise and easy-to-understand summary is generated. If the user is relaxed, a detailed summary can also be generated. Furthermore, if the user is in a hurry, a summary that emphasizes the most important parts can also be generated. In this way, a summary that is easy for the user to understand can be provided by adjusting the way the summary is expressed according to the user's emotion.

[0094] The repair unit can estimate the user's emotions and adjust the repair method based on the emotions. For example, the repair unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. If the user is stressed, a simple and quick repair method is selected. Also, if the user is relaxed, detailed repair procedures can be provided. Furthermore, if the user is in a hurry, the most efficient repair method can be selected. In this way, the repair method can be adjusted according to the user's emotions, and the optimal repair method for the user can be provided.

[0095] The repair unit can customize the repair procedure based on the user's repair skill level during repair. For example, if the user is a beginner, a simple repair procedure can be provided. If the user is an intermediate user, a detailed repair procedure can be provided. Furthermore, if the user is an advanced user, a specialized repair procedure can be provided. This can improve the success rate of repairs by providing a repair procedure according to the user's repair skill level.

[0096] When making repairs, the repair department can select a repair method by analyzing the user's past repair history. For example, the repair method is referred to as the repair method for a product that the user has previously repaired. Also, the repair department can select the most effective repair method from the user's past repair history. Furthermore, the repair department can analyze the user's past repair history and select the optimal repair method. In this way, the optimal repair method can be provided by taking into account the user's past repair history.

[0097] When acquiring the instruction manual, the acquisition unit can acquire relevant instruction manuals based on the user's social media activity. For example, instruction manuals for products that the user has mentioned on social media are acquired preferentially. Also, instruction manuals for products in which the user is interested can be acquired from the user's social media activity. Furthermore, the user's social media activity can be analyzed to acquire the most relevant instruction manual. In this way, it is possible to provide highly relevant instruction manuals by analyzing the user's social media activity.

[0098] When generating the summary, the summarization unit can apply different summarization algorithms based on the category of the instruction manual. For example, for an instruction manual for an electronic device, a technical summarization algorithm can be applied. For an instruction manual for furniture, a summarization algorithm specialized for assembly steps can be applied. Furthermore, the most suitable summarization algorithm can be applied depending on the category of the instruction manual. In this way, the most suitable summary can be provided by applying a summarization algorithm according to the category of the instruction manual.

[0099] The process flow of the second embodiment will be briefly described below.

[0100] Step 1: The acquisition unit acquires the contents of the product's instruction manual. For example, the acquisition unit takes a picture of the instruction manual using a camera and acquires the image data. The acquisition unit can also directly acquire a digital version of the instruction manual. For example, the acquisition unit acquires a PDF or text version of the instruction manual. Step 2: The summarization unit uses the generation AI to summarize the contents of the instruction manual acquired by the acquisition unit. For example, the generation AI analyzes the contents of the instruction manual, extracts important information, and generates a summary. The generation AI performs the summarization using a text generation AI (e.g., LLM). For example, the generation AI receives a prompt such as "Please summarize the main points of this instruction manual" and generates a summary. Step 3: The repair unit operates the robot using the generation AI based on the content summarized by the summarization unit to repair the product. The repair unit operates, for example, the robot's hand to repair the product. The robot's hand is equipped with, for example, an arm capable of precise movements, and accurately performs repairs based on the instruction manual.

[0101] 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.

[0102] 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<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 (e.g., still image data or video data) are 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 one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0103] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0104] For example, the acquisition unit photographs the instruction manual using the camera 42 of the smart device 14 and acquires the image data. The acquisition unit can also directly acquire the instruction manual in digital format by the specific processing unit 290 of the data processing device 12. For example, the summarization unit summarizes the contents of the instruction manual acquired by the acquisition unit using a generated AI by the specific processing unit 290 of the data processing device 12. The repair unit operates a robot to repair the product based on the contents summarized by the control unit 46A of the smart device 14, for example. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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, 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 WAN and / or a LAN.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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 performed in a secure state.

[0112] 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.

[0113] 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.

[0114] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0116] 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 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 (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0117] 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.

[0118] 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. 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 (e.g., still image data or video data) 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 one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0119] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0120] For example, the acquisition unit photographs the instruction manual using the camera 42 of the smart glasses 214 and acquires the image data. The acquisition unit can also directly acquire the instruction manual in digital format by the specific processing unit 290 of the data processing device 12. For example, the summarization unit summarizes the contents of the instruction manual acquired by the acquisition unit using a generated AI by the specific processing unit 290 of the data processing device 12. The repair unit operates a robot to repair the product based on the contents summarized by the control unit 46A of the smart glasses 214, for example. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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, 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 WAN and / or a LAN.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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 performed in a secure state.

[0128] 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.

[0129] 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.

[0130] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0132] 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 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 (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0133] 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.

[0134] 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. 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 (e.g., still image data or video data) 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 one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0135] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0136] For example, the acquisition unit photographs the instruction manual using the camera 42 of the headset terminal 314 and acquires the image data. The acquisition unit can also directly acquire the instruction manual in digital format by the specific processing unit 290 of the data processing device 12. For example, the summarization unit summarizes the contents of the instruction manual acquired by the acquisition unit using a generated AI by the specific processing unit 290 of the data processing device 12. The repair unit repairs the product by operating a robot based on the contents summarized by the control unit 46A of the headset terminal 314, for example. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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, 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 WAN and / or a LAN.

[0140] 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.

[0141] 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.

[0142] 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 image sensor or a CCD 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 a typical healthy person).

[0143] 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 performed in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0145] 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.

[0146] 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.

[0147] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0148] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0149] 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 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 (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0150] 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.

[0151] 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. 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 (e.g., still image data or video data) 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 one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0152] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0153] For example, the acquisition unit photographs the instruction manual using the camera 42 of the robot 414 and acquires the image data. The acquisition unit can also directly acquire the instruction manual in digital format by the specific processing unit 290 of the data processing device 12. For example, the summarization unit summarizes the contents of the instruction manual acquired by the acquisition unit using a generated AI by the specific processing unit 290 of the data processing device 12. The repair unit operates the robot to repair the product based on the contents summarized by the control unit 46A of the robot 414, for example. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0154] 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.

[0155] 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.

[0156] These emotions are distributed in the three 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.

[0157] 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).

[0158] 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. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that 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, for example, based on 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). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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 a 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0170] 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 replaced with respect to the description and illustrations shown above, 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 description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0171] 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, and standard was specifically and individually indicated to be incorporated by reference.

[0172] (Appendix 1) An acquisition unit for acquiring the contents of an instruction manual for a product; A unit for summarizing the contents of the instruction manual acquired by the acquisition unit using a generation AI; A unit for operating a robot using a generation AI based on the content summarized by the summarizing unit to repair the product. A system characterized by: (Appendix 2) The repair department includes: Activate the robot hand to repair the item 2. The system of claim 1, (Appendix 3) a reception unit that receives input of information indicating a symptom of a malfunction of the product from a user, The repair department includes: Operate the robot based on the summarized content and the information input by the reception unit 2. The system of claim 1, (Appendix 4) The acquisition unit is Estimating user emotions and adjusting the timing of obtaining instruction manuals based on emotions 2. The system of claim 1, (Appendix 5) The acquisition unit is When obtaining an instruction manual, the method of obtaining it is selected based on the user's past repair history. 2. The system of claim 1, (Appendix 6) The acquisition unit is Filtering user manuals based on the user's repair skill level 2. The system of claim 1, (Appendix 7) The acquisition unit is Estimate the user's emotions and prioritize the instruction manuals to be retrieved based on the emotions. 2. The system of claim 1, (Appendix 8) The acquisition unit is When retrieving instruction manuals, the most relevant instruction manuals are retrieved based on the user's geographic location information. 2. The system of claim 1, (Appendix 9) The acquisition unit is When retrieving an instruction manual, retrieve relevant instruction manuals based on the user's social media activity 2. The system of claim 1, (Appendix 10) The summary portion comprises: Estimate user emotions and adjust the way summaries are presented based on those emotions 2. The system of claim 1, (Appendix 11) The summary portion comprises: When generating a summary, adjust the level of detail in the summary based on the importance of the instruction manual. 2. The system of claim 1, (Appendix 12) The summary portion comprises: When generating summaries, different summarization algorithms are applied based on the category of the instruction manual. 2. The system of claim 1, (Appendix 13) The summary portion comprises: Estimate user sentiment and adjust summary length based on sentiment 2. The system of claim 1, (Appendix 14) The summary portion comprises: When generating the summary, after-claim is generated based on the publication date of the instruction manual. (Appendix 15) The summary portion comprises: When generating summaries, adjust the order of summaries based on the relevance of the instruction manual 2. The system of claim 1, (Appendix 16) The repair department includes: Infer user emotions and adjust repair methods based on emotions 2. The system of claim 1, (Appendix 17) The repair department includes: When repairing, the repair method is selected by analyzing the user's past repair history. 2. The system of claim 1, (Appendix 18) The repair department includes: During repair, customize repair instructions based on the user's repair skill level 2. The system of claim 1, (Appendix 19) The repair department includes: Estimate user emotions and prioritize repairs based on emotions 2. The system of claim 1, (Appendix 20) The repair department includes: When repairing, the repair method is selected based on the user's geographic location information 2. The system of claim 1, (Appendix 21) The repair department includes: When repairs are made, the system suggests repair procedures based on the user's social media activity. 2. The system of claim 1, (Appendix 22) The reception unit is Estimate user emotions and adjust the way fault information is entered based on emotions 2. The system of claim 1, (Appendix 23) The reception unit is When entering failure information, the input method is selected based on the user's past failure history. 2. The system of claim 1, (Appendix 24) The reception unit is Estimate user emotions and prioritize fault information based on emotions 2. The system of claim 1, (Appendix 25) The reception unit is When entering failure information, select the input method based on the user's device information. 2. The system of claim 1, [Explanation of symbols]

[0173] 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. An acquisition unit for acquiring the contents of an instruction manual for a product; A summarizing unit that summarizes the contents of the instruction manual acquired by the acquisition unit using a generation AI; A repair unit that operates a robot using a generating AI based on the content summarized by the summarizing unit to repair the product. A system characterized by:

2. The repair department includes: Activating the robotic hand to repair the item 2. The system of claim 1 .

3. a reception unit that receives input of information indicating a symptom of a malfunction of the product from a user, The repair department includes: Operate the robot based on the content summarized by the summarizing unit and the information input and received by the receiving unit.

2. The system of claim 1 .

4. The acquisition unit is The user's emotion is estimated, and the timing of obtaining the instruction manual is adjusted based on the emotion.

2. The system of claim 1 .

5. The acquisition unit is When obtaining the instruction manual, the user's past repair history is analyzed and a method of obtaining the instruction manual is selected.

2. The system of claim 1 .

6. The acquisition unit is When obtaining the instruction manual, filtering is performed based on the user's repair skill level.

2. The system of claim 1 .

7. The acquisition unit is The user's emotion is estimated, and a priority order for the instruction manual to be acquired is determined based on the emotion.

2. The system of claim 1 .

8. The acquisition unit is When acquiring the instruction manual, the instruction manual having high relevance is acquired preferentially based on the geographical location information of the user.

2. The system of claim 1 .

9. The acquisition unit is When the instruction manual is acquired, the instruction manual related to the user is acquired based on the user's social media activity.

2. The system of claim 1 .

10. The summary portion comprises: Estimate user emotions and adjust the way summaries are presented based on those emotions 2. The system of claim 1 .

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