system

Abstract

An object of a system according to an embodiment is to analyze an ALM system and past response history data, create a field investigation list, and propose variant replacement.SOLUTION: A system includes an analysis unit, a list creation unit, and a proposal unit. The analysis unit analyzes an ALM system and past response history data. The list creation unit creates a field investigation list on the basis of a result analyzed by the analysis unit. The proposal unit proposes the deformed replacement based on the field investigation list created by the list creation unit.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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology had the problem of not adequately utilizing ALM systems and past response history data to create on-site inspection lists and propose appropriate variant replacements.

[0005] The system according to the embodiment aims to analyze the ALM system and past response history data, create a site inspection list, and propose variant replacement. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a list creation unit, and a proposal unit. The analysis unit analyzes the ALM system and past response history data. The list creation unit creates an on-site inspection list based on the results of the analysis by the analysis unit. The proposal unit proposes variant replacement based on the on-site inspection list created by the list creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the ALM system and past response history data, create an on-site inspection list, and propose variant replacement. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The on-site investigation system according to the embodiment of the present invention is a system that analyzes the ALM system and past response history data, creates an on-site investigation list, and proposes alternative replacements. This allows the on-site investigation system to efficiently conduct on-site investigations and quickly propose necessary countermeasures.

[0029] An on-site inspection system according to an embodiment includes an analysis unit, a list creation unit, and a proposal unit. The analysis unit analyzes an ALM system and past response history data. For example, the analysis unit uses a generation AI to analyze past response history data and extract a deterioration pattern for a specific battery. The analysis unit can also use the generation AI to perform analysis taking into account battery usage environment data (temperature, humidity, etc.). The analysis unit can also use an emotion estimation function to analyze the emotions of technicians recorded in past response history and evaluate the severity of the problem. The list creation unit creates an on-site inspection list based on the results of the analysis by the analysis unit. For example, the list creation unit uses the generation AI to analyze the response history of other equipment (e.g., generators and cooling systems) and create a comprehensive maintenance list. The list creation unit can also use the generation AI to add external market data and technology trend information to the analyzed data and predict future problems. The list creation unit can also use the emotion estimation function to evaluate the technician's satisfaction with past responses and identify areas for improvement. The proposal unit proposes a variant replacement based on the on-site inspection list created by the list creation unit. For example, when proposing a variant replacement, the suggestion unit uses the generation AI to analyze the performance data of the replacement candidate battery and select the optimal replacement. The suggestion unit can also use the generation AI to refer to past success stories and failure stories when proposing a replacement procedure to minimize risk. The suggestion unit can also use an emotion estimation function to consider the technician's emotions and make suggestions to reduce the burden of the replacement work. This enables the on-site inspection system according to the embodiment to efficiently conduct on-site inspections and quickly propose necessary countermeasures. For example, the output unit displays the inspection results to the technician via a web application or mobile application. If feedback is desired in paper form, the results are printed using a printer. Sending the results via email directly to the technician or administrator provides quick feedback.

[0030] The analysis unit can analyze the deterioration pattern of a specific battery from past response history and build a predictive model based on the deterioration pattern. The analysis unit, for example, uses a generation AI to analyze past response history data and extract the deterioration pattern of a specific battery. For example, it predicts the progression of deterioration based on the battery's usage period and usage environment. The analysis unit also uses a generation AI to build a predictive model based on the deterioration pattern. For example, it predicts battery deterioration using a machine learning model. In this way, it is possible to predict the progression of deterioration by analyzing the battery's deterioration pattern from past response history and building a predictive model.

[0031] The analysis unit can perform analysis taking into account data on the battery's usage environment. For example, the generation AI performs analysis taking into account data on the battery's usage environment (temperature, humidity, etc.). For example, the analysis unit analyzes the rate of deterioration in a high-temperature environment and makes a highly accurate prediction. The analysis unit can also use the generation AI to analyze the rate of deterioration in a high-humidity environment. The analysis unit can also use the generation AI to analyze the rate of deterioration in an environment with a lot of vibration. As a result, by performing analysis taking into account data on the battery's usage environment, more accurate deterioration predictions are possible.

[0032] The list creation unit can also analyze the response history of other equipment and create a comprehensive maintenance list. The list creation unit can, for example, use generation AI to analyze the response history of other equipment such as generators and cooling systems and create a comprehensive maintenance list. For example, it can analyze the deterioration patterns of each piece of equipment and determine maintenance priorities. The list creation unit can also use generation AI to analyze usage environment data of other equipment and create a comprehensive maintenance list. The list creation unit can also use generation AI to analyze emotion data of other equipment and create a comprehensive maintenance list. In this way, efficient maintenance can be achieved by analyzing the response history of other equipment and creating a comprehensive maintenance list.

[0033] The list creation unit can add market data or technology trend information to the data to be analyzed and predict future problems. The list creation unit, for example, adds external market data or technology trend information to the data to be analyzed by the generation AI and predicts future problems. For example, the list creation unit can evaluate the impact of introducing a new technology. The list creation unit can also use the generation AI to predict future problems based on technology trend information. The list creation unit can also use the generation AI to predict future problems based on market data. In this way, future problems can be predicted by adding and analyzing market data and technology trend information.

[0034] When proposing a variant exchange, the suggestion unit can analyze the performance data of the replacement candidate battery and select the optimal replacement. For example, when the generation AI proposes a variant exchange, the suggestion unit analyzes the performance data of the replacement candidate battery and selects the optimal replacement. For example, the optimal replacement is selected based on the battery's capacity and lifespan. The suggestion unit can also use the generation AI to analyze the number of charge / discharge cycles of the battery and select the optimal replacement. The suggestion unit can also use the generation AI to analyze the battery's internal resistance and select the optimal replacement. In this way, appropriate variant exchange is possible by analyzing the performance data of the replacement candidate battery and selecting the optimal replacement.

[0035] The proposal unit can minimize risk by referring to past success stories and failure stories when proposing an exchange procedure. The proposal unit can minimize risk by referring to past success stories and failure stories when proposing an exchange procedure by the generation AI, for example. For example, the proposal unit can propose an optimal exchange procedure based on past data. The proposal unit can also use the generation AI to propose an exchange procedure based on success stories. The proposal unit can also use the generation AI to propose an exchange procedure based on failure stories. In this way, by referring to past success stories and failure stories and minimizing risk, it is possible to propose a safer exchange procedure.

[0036] When proposing variant replacement, the suggestion unit can simultaneously suggest variant replacement of other equipment, thereby improving maintenance efficiency. For example, when the generation AI proposes variant replacement, the suggestion unit can simultaneously suggest variant replacement of other equipment, thereby improving maintenance efficiency. For example, the suggestion unit can simultaneously suggest replacement of generators and cooling devices. The suggestion unit can also use the generation AI to propose replacement procedures for other equipment. The suggestion unit can also use the generation AI to propose replacement timing for other equipment. In this way, by simultaneously proposing variant replacement of other equipment, maintenance efficiency is improved.

[0037] The proposal unit can reflect the latest technological trends and market information in the replacement battery it proposes and take future upgrades into consideration. The proposal unit, for example, can reflect the latest technological trends and market information in the replacement battery proposed by the generation AI and take future upgrades into consideration. For example, it can propose a battery that employs new technology. The proposal unit can also use the generation AI to select a replacement battery based on technological trend information. The proposal unit can also use the generation AI to select a replacement battery based on market information. This makes it possible to propose a more appropriate replacement battery by reflecting the latest technological trends and market information and taking future upgrades into consideration.

[0038] The list creation unit can automatically evaluate the importance and urgency of each investigation item in the on-site investigation list and assign priorities to them. For example, when the generation AI creates the on-site investigation list, the list creation unit automatically evaluates the importance and urgency of each investigation item and assigns priorities to them. For example, batteries that are deteriorating are added to the list first. The list creation unit can also use the generation AI to evaluate the impact of investigation items and assign priorities to them. The list creation unit can also use the generation AI to evaluate the need to respond to investigation items and assign priorities to them. This enables efficient on-site investigations by evaluating the importance and urgency of each investigation item and assigning priorities to them.

[0039] When creating the on-site inspection list, the list creation unit can refer to the results of past inspections and evaluate the possibility of a similar problem recurring. For example, when the generation AI creates the on-site inspection list, the list creation unit can refer to the results of past inspections and evaluate the possibility of a similar problem recurring. For example, batteries that have previously experienced problems are added to the list preferentially. The list creation unit can also use the generation AI to evaluate the possibility of recurrence based on the results of past inspections. The list creation unit can also use the generation AI to identify the causes of recurrence based on the results of past inspections. In this way, by referring to the results of past inspections and evaluating the possibility of a similar problem recurring, measures to prevent recurrence can be taken.

[0040] The list creation unit can add geographical information of the survey target to the field survey list and propose an efficient route. For example, the list creation unit uses a generation AI to add geographical information of the survey target to the field survey list and propose an efficient route. For example, the list creation unit calculates the optimal route based on the location information of the survey target. The list creation unit can also use the generation AI to propose an efficient route based on geographical information. The list creation unit can also use the generation AI to propose a route that takes traffic conditions into consideration. In this way, by adding geographical information of the survey target and proposing an efficient route, the efficiency of the field survey is improved.

[0041] When creating the list, the list creation department can refer to best practices from other companies and industries and select the most appropriate survey items. For example, the list creation department uses a generation AI to refer to best practices from other companies and industries and select the most appropriate survey items. For example, the survey items are determined based on success stories from other companies. The list creation department can also use a generation AI to select survey items based on industry standards. The list creation department can also use a generation AI to select survey items based on success stories. In this way, by referring to best practices from other companies and industries and selecting the most appropriate survey items, the quality of the survey is improved.

[0042] When reporting the investigation results, the suggestion unit can add a comparative analysis with past data to evaluate the progress of the problem. For example, the suggestion unit can add a comparative analysis with past data to the investigation results reported by the generation AI to evaluate the progress of the problem. For example, the suggestion unit can evaluate the current situation by comparing it with past degradation data. The suggestion unit can also use the generation AI to evaluate the progress of the problem based on past data. The suggestion unit can also use the generation AI to evaluate the progress of the problem based on trend analysis. In this way, by adding a comparative analysis with past data and evaluating the progress of the problem, more appropriate measures can be taken.

[0043] The proposal unit can include cost and time estimates in the proposed countermeasures and evaluate their feasibility. The proposal unit, for example, includes cost and time estimates in the countermeasures proposed by the generation AI and evaluates their feasibility. For example, the proposal unit estimates the cost and time required for replacement work. The proposal unit can also use the generation AI to make an estimate based on a cost calculation method. The proposal unit can also use the generation AI to make an estimate based on a time estimation method. In this way, by evaluating feasibility including cost and time estimates, more realistic countermeasures can be proposed.

[0044] The proposal unit can integrate the investigation results with other related equipment and systems to propose a comprehensive maintenance plan. For example, the proposal unit can integrate the investigation results reported by the generation AI with other related equipment and systems to propose a comprehensive maintenance plan. For example, the proposal unit can integrate maintenance plans for generators and cooling equipment. The proposal unit can also use the generation AI to integrate maintenance plans for related equipment. The proposal unit can also use the generation AI to integrate maintenance plans for related systems. In this way, by integrating the investigation results with other related equipment and systems and proposing a comprehensive maintenance plan, maintenance efficiency is improved.

[0045] The proposal unit can refer to best practices from other companies and industries when proposing countermeasures and select the optimal solution. For example, the proposal unit uses a generation AI to refer to best practices from other companies and industries and select the optimal solution. For example, the proposal unit determines countermeasures based on success stories from other companies. The proposal unit can also use a generation AI to select countermeasures based on industry standards. The proposal unit can also use a generation AI to select countermeasures based on success stories. In this way, by referring to best practices from other companies and industries and selecting the optimal solution, the quality of the countermeasures is improved.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] When analyzing past response history data, the analysis unit can also use an anomaly detection algorithm to identify undiscovered problems. For example, the analysis unit can use generative AI to detect abnormal patterns from past data and predict potential problems. The analysis unit can also use an anomaly detection algorithm to identify abnormal values ​​in the data and take early action. The analysis unit can also use an anomaly detection algorithm to detect abnormal behavior and identify the cause of the problem. This makes it possible to identify undiscovered problems early and respond quickly.

[0048] When creating the on-site inspection list, the list creation unit can predict the lifespan of the equipment being inspected and suggest replacement times. For example, the list creation unit uses generation AI to analyze the equipment's usage history data and predict its lifespan. The list creation unit can also use a lifespan prediction algorithm to evaluate the rate of equipment deterioration and suggest the optimal replacement time. The list creation unit can also use a lifespan prediction algorithm to consider equipment usage environment data and suggest replacement times. This allows for efficient maintenance by predicting the lifespan of equipment and suggesting the optimal replacement time.

[0049] When proposing a variant exchange, the proposal unit can evaluate the environmental impact of the candidate battery and select an environmentally friendly alternative. For example, the proposal unit can use a generative AI to evaluate the environmental impact of the battery's manufacturing process and disposal treatment. The proposal unit can also use an environmental impact assessment algorithm to evaluate the environmental impact of the battery during use and select the optimal alternative. The proposal unit can also use an environmental impact assessment algorithm to evaluate the recyclability of the battery and select an environmentally friendly alternative. This makes it possible to conduct variant exchange that takes environmental impact into consideration.

[0050] When creating the on-site inspection list, the list creation unit can analyze the energy consumption data of the equipment to be inspected and propose improvements to energy efficiency. For example, the list creation unit can use a generation AI to analyze the energy consumption data of the equipment and identify equipment with low energy efficiency. The list creation unit can also use an energy efficiency evaluation algorithm to propose measures to reduce energy consumption. The list creation unit can also use an energy efficiency evaluation algorithm to propose the timing of replacing equipment where improvements in energy efficiency can be expected. This makes it possible to improve energy efficiency.

[0051] When analyzing past response history data, the analysis unit can evaluate the reliability of the data and exclude unreliable data. For example, the analysis unit can use generative AI to evaluate the consistency and accuracy of the data. The analysis unit can also use a data reliability evaluation algorithm to identify unreliable data and exclude it from the analysis. The analysis unit can also use a data reliability evaluation algorithm to detect missing data and outliers, thereby improving the accuracy of the analysis results. This makes it possible to obtain more accurate results by evaluating the reliability of the data and performing analysis based on highly reliable data.

[0052] When creating the on-site inspection list, the list creation unit can analyze the energy consumption data of the equipment to be inspected and propose improvements to energy efficiency. For example, the list creation unit can use a generation AI to analyze the energy consumption data of the equipment and identify equipment with low energy efficiency. The list creation unit can also use an energy efficiency evaluation algorithm to propose measures to reduce energy consumption. The list creation unit can also use an energy efficiency evaluation algorithm to propose the timing of replacing equipment where improvements in energy efficiency can be expected. This makes it possible to improve energy efficiency.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The analysis unit analyzes the ALM system and past response history data. For example, the analysis unit uses generative AI to analyze past response history data and extract the deterioration pattern of a specific battery. The analysis unit can also take into account battery usage environment data (temperature, humidity, etc.). Furthermore, the analysis unit can use emotion estimation functionality to analyze the emotions of technicians recorded in past response history and evaluate the severity of the problem. Step 2: The list creation unit creates an on-site inspection list based on the results of the analysis by the analysis unit. For example, the list creation unit uses generative AI to analyze the response history of other equipment (e.g., generators and cooling equipment) to create a comprehensive maintenance list. The list creation unit can also add external market data and technology trend information to the analyzed data to predict future problems. Furthermore, the list creation unit can use emotion estimation functionality to evaluate the satisfaction level of technicians with past responses and identify areas for improvement. Step 3: The proposal unit proposes an alternative replacement based on the on-site inspection list created by the list creation unit. For example, when proposing an alternative replacement, the proposal unit uses generative AI to analyze the performance data of the battery candidate for replacement and select the optimal replacement. The proposal unit can also minimize risks by referring to past success stories and failure stories when proposing replacement procedures. Furthermore, the proposal unit can use an emotion estimation function to take into account the emotions of the technician and make proposals to reduce the burden of the replacement work.

[0055] (Example 2) The on-site investigation system according to the embodiment of the present invention is a system that analyzes the ALM system and past response history data, creates an on-site investigation list, and proposes alternative replacements. This allows the on-site investigation system to efficiently conduct on-site investigations and quickly propose necessary countermeasures.

[0056] An on-site inspection system according to an embodiment includes an analysis unit, a list creation unit, and a proposal unit. The analysis unit analyzes an ALM system and past response history data. For example, the analysis unit uses a generation AI to analyze past response history data and extract a deterioration pattern for a specific battery. The analysis unit can also use the generation AI to perform analysis taking into account battery usage environment data (temperature, humidity, etc.). The analysis unit can also use an emotion estimation function to analyze the emotions of technicians recorded in past response history and evaluate the severity of the problem. The list creation unit creates an on-site inspection list based on the results of the analysis by the analysis unit. For example, the list creation unit uses the generation AI to analyze the response history of other equipment (e.g., generators and cooling systems) and create a comprehensive maintenance list. The list creation unit can also use the generation AI to add external market data and technology trend information to the analyzed data and predict future problems. The list creation unit can also use the emotion estimation function to evaluate the technician's satisfaction with past responses and identify areas for improvement. The proposal unit proposes a variant replacement based on the on-site inspection list created by the list creation unit. For example, when proposing a variant replacement, the suggestion unit uses the generation AI to analyze the performance data of the replacement candidate battery and select the optimal replacement. The suggestion unit can also use the generation AI to refer to past success stories and failure stories when proposing a replacement procedure to minimize risk. The suggestion unit can also use an emotion estimation function to consider the technician's emotions and make suggestions to reduce the burden of the replacement work. This enables the on-site inspection system according to the embodiment to efficiently conduct on-site inspections and quickly propose necessary countermeasures. For example, the output unit displays the inspection results to the technician via a web application or mobile application. If feedback is desired in paper form, the results are printed using a printer. Sending the results via email directly to the technician or administrator provides quick feedback.

[0057] The analysis unit can analyze the deterioration pattern of a specific battery from past response history and build a predictive model based on the deterioration pattern. The analysis unit, for example, uses a generation AI to analyze past response history data and extract the deterioration pattern of a specific battery. For example, it predicts the progression of deterioration based on the battery's usage period and usage environment. The analysis unit also uses a generation AI to build a predictive model based on the deterioration pattern. For example, it predicts battery deterioration using a machine learning model. In this way, it is possible to predict the progression of deterioration by analyzing the battery's deterioration pattern from past response history and building a predictive model.

[0058] The analysis unit can perform analysis taking into account data on the battery's usage environment. For example, the generation AI performs analysis taking into account data on the battery's usage environment (temperature, humidity, etc.). For example, the analysis unit analyzes the rate of deterioration in a high-temperature environment and makes a highly accurate prediction. The analysis unit can also use the generation AI to analyze the rate of deterioration in a high-humidity environment. The analysis unit can also use the generation AI to analyze the rate of deterioration in an environment with a lot of vibration. As a result, by performing analysis taking into account data on the battery's usage environment, more accurate deterioration predictions are possible.

[0059] The analysis unit can use the emotion estimation function to analyze the emotion of the engineer recorded in the past response history and evaluate the severity of the problem. The analysis unit, for example, uses the emotion estimation function to analyze the emotion of the engineer recorded in the past response history. For example, the analysis unit calculates an emotion score from the engineer's comments and evaluates the severity of the problem. The analysis unit can also use the emotion estimation function to analyze the engineer's facial expression and calculate an emotion score. The analysis unit can also use the emotion estimation function to analyze the engineer's voice and calculate an emotion score. In this way, by analyzing the engineer's emotion and evaluating the severity of the problem, more appropriate response is possible.

[0060] The list creation unit can also analyze the response history of other equipment and create a comprehensive maintenance list. The list creation unit can, for example, use generation AI to analyze the response history of other equipment such as generators and cooling systems and create a comprehensive maintenance list. For example, it can analyze the deterioration patterns of each piece of equipment and determine maintenance priorities. The list creation unit can also use generation AI to analyze usage environment data of other equipment and create a comprehensive maintenance list. The list creation unit can also use generation AI to analyze emotion data of other equipment and create a comprehensive maintenance list. In this way, efficient maintenance can be achieved by analyzing the response history of other equipment and creating a comprehensive maintenance list.

[0061] The list creation unit can add market data or technology trend information to the data to be analyzed and predict future problems. The list creation unit, for example, adds external market data or technology trend information to the data to be analyzed by the generation AI and predicts future problems. For example, the list creation unit can evaluate the impact of introducing a new technology. The list creation unit can also use the generation AI to predict future problems based on technology trend information. The list creation unit can also use the generation AI to predict future problems based on market data. In this way, future problems can be predicted by adding and analyzing market data and technology trend information.

[0062] The list creation unit can use the emotion estimation function to evaluate the satisfaction level of the technician's past responses and identify areas for improvement. The list creation unit, for example, uses the emotion estimation function to evaluate the satisfaction level of the technician's past responses. For example, the list creation unit calculates an emotion score from the technician's comments and evaluates the satisfaction level. The list creation unit can also use the emotion estimation function to analyze the technician's facial expression and calculate an emotion score. The list creation unit can also use the emotion estimation function to analyze the technician's voice and calculate an emotion score. This makes it possible to evaluate the technician's satisfaction level and identify areas for improvement, thereby improving the quality of the service.

[0063] When proposing a variant exchange, the suggestion unit can analyze the performance data of the replacement candidate battery and select the optimal replacement. For example, when the generation AI proposes a variant exchange, the suggestion unit analyzes the performance data of the replacement candidate battery and selects the optimal replacement. For example, the optimal replacement is selected based on the battery's capacity and lifespan. The suggestion unit can also use the generation AI to analyze the number of charge / discharge cycles of the battery and select the optimal replacement. The suggestion unit can also use the generation AI to analyze the battery's internal resistance and select the optimal replacement. In this way, appropriate variant exchange is possible by analyzing the performance data of the replacement candidate battery and selecting the optimal replacement.

[0064] The proposal unit can minimize risk by referring to past success stories and failure stories when proposing an exchange procedure. The proposal unit can minimize risk by referring to past success stories and failure stories when proposing an exchange procedure by the generation AI, for example. For example, the proposal unit can propose an optimal exchange procedure based on past data. The proposal unit can also use the generation AI to propose an exchange procedure based on success stories. The proposal unit can also use the generation AI to propose an exchange procedure based on failure stories. In this way, by referring to past success stories and failure stories and minimizing risk, it is possible to propose a safer exchange procedure.

[0065] The suggestion unit can use the emotion estimation function to consider the emotion of the technician and make suggestions to reduce the burden of the replacement work. The suggestion unit, for example, uses the emotion estimation function to consider the emotion of the technician and make suggestions to reduce the burden of the replacement work. For example, the suggestion unit can propose a procedure with less burden based on the technician's emotion score. The suggestion unit can also use the emotion estimation function to analyze the technician's facial expression and propose a procedure with less burden. The suggestion unit can also use the emotion estimation function to analyze the technician's voice and propose a procedure with less burden. In this way, the work efficiency is improved by considering the emotion of the technician and reducing the burden of the replacement work.

[0066] When proposing variant replacement, the suggestion unit can simultaneously suggest variant replacement of other equipment, thereby improving maintenance efficiency. For example, when the generation AI proposes variant replacement, the suggestion unit can simultaneously suggest variant replacement of other equipment, thereby improving maintenance efficiency. For example, the suggestion unit can simultaneously suggest replacement of generators and cooling devices. The suggestion unit can also use the generation AI to propose replacement procedures for other equipment. The suggestion unit can also use the generation AI to propose replacement timing for other equipment. In this way, by simultaneously proposing variant replacement of other equipment, maintenance efficiency is improved.

[0067] The proposal unit can reflect the latest technological trends and market information in the replacement battery it proposes and take future upgrades into consideration. The proposal unit, for example, can reflect the latest technological trends and market information in the replacement battery proposed by the generation AI and take future upgrades into consideration. For example, it can propose a battery that employs new technology. The proposal unit can also use the generation AI to select a replacement battery based on technological trend information. The proposal unit can also use the generation AI to select a replacement battery based on market information. This makes it possible to propose a more appropriate replacement battery by reflecting the latest technological trends and market information and taking future upgrades into consideration.

[0068] The suggestion unit can use the emotion estimation function to evaluate the user's emotion and suggest an alternative battery that will satisfy the user most. The suggestion unit, for example, uses the emotion estimation function to evaluate the user's emotion and suggest an alternative battery that will satisfy the user most. For example, the suggestion unit selects an optimal battery based on the user's emotion score. The suggestion unit can also use the emotion estimation function to analyze the user's facial expression and select an optimal battery. The suggestion unit can also use the emotion estimation function to analyze the user's voice and select an optimal battery. In this way, by evaluating the user's emotion and suggesting an alternative battery that will satisfy the user most, user satisfaction is improved.

[0069] The list creation unit can automatically evaluate the importance and urgency of each investigation item in the on-site investigation list and assign priorities to them. For example, when the generation AI creates the on-site investigation list, the list creation unit automatically evaluates the importance and urgency of each investigation item and assigns priorities to them. For example, batteries that are deteriorating are added to the list first. The list creation unit can also use the generation AI to evaluate the impact of investigation items and assign priorities to them. The list creation unit can also use the generation AI to evaluate the need to respond to investigation items and assign priorities to them. This enables efficient on-site investigations by evaluating the importance and urgency of each investigation item and assigning priorities to them.

[0070] When creating the on-site inspection list, the list creation unit can refer to the results of past inspections and evaluate the possibility of a similar problem recurring. For example, when the generation AI creates the on-site inspection list, the list creation unit can refer to the results of past inspections and evaluate the possibility of a similar problem recurring. For example, batteries that have previously experienced problems are added to the list preferentially. The list creation unit can also use the generation AI to evaluate the possibility of recurrence based on the results of past inspections. The list creation unit can also use the generation AI to identify the causes of recurrence based on the results of past inspections. In this way, by referring to the results of past inspections and evaluating the possibility of a similar problem recurring, measures to prevent recurrence can be taken.

[0071] The list creation unit can use the emotion estimation function to consider the emotions of the engineer and propose a less stressful investigation schedule. The list creation unit, for example, uses the emotion estimation function to consider the emotions of the engineer and propose a less stressful investigation schedule. For example, the list creation unit creates a less stressful schedule based on the engineer's emotion score. The list creation unit can also use the emotion estimation function to analyze the engineer's facial expressions and create a less stressful schedule. The list creation unit can also use the emotion estimation function to analyze the engineer's voice and create a less stressful schedule. In this way, work efficiency is improved by considering the emotions of the engineer and proposing a less stressful investigation schedule.

[0072] The list creation unit can add geographical information of the survey target to the field survey list and propose an efficient route. For example, the list creation unit uses a generation AI to add geographical information of the survey target to the field survey list and propose an efficient route. For example, the list creation unit calculates the optimal route based on the location information of the survey target. The list creation unit can also use the generation AI to propose an efficient route based on geographical information. The list creation unit can also use the generation AI to propose a route that takes traffic conditions into consideration. In this way, by adding geographical information of the survey target and proposing an efficient route, the efficiency of the field survey is improved.

[0073] When creating the list, the list creation department can refer to best practices from other companies and industries and select the most appropriate survey items. For example, the list creation department uses a generation AI to refer to best practices from other companies and industries and select the most appropriate survey items. For example, the survey items are determined based on success stories from other companies. The list creation department can also use a generation AI to select survey items based on industry standards. The list creation department can also use a generation AI to select survey items based on success stories. In this way, by referring to best practices from other companies and industries and selecting the most appropriate survey items, the quality of the survey is improved.

[0074] The list creation unit can use the emotion estimation function to evaluate the user's emotions toward the equipment being surveyed and add survey items that will improve user satisfaction. The list creation unit, for example, uses the emotion estimation function to evaluate the user's emotions toward the equipment being surveyed and add survey items that will improve user satisfaction. For example, the list creation unit determines survey items based on the user's emotion score. The list creation unit can also use the emotion estimation function to analyze the user's facial expressions and determine survey items. The list creation unit can also use the emotion estimation function to analyze the user's voice and determine survey items. In this way, user satisfaction is improved by evaluating the user's emotions and adding survey items that will improve user satisfaction.

[0075] When reporting the investigation results, the suggestion unit can add a comparative analysis with past data to evaluate the progress of the problem. For example, the suggestion unit can add a comparative analysis with past data to the investigation results reported by the generation AI to evaluate the progress of the problem. For example, the suggestion unit can evaluate the current situation by comparing it with past degradation data. The suggestion unit can also use the generation AI to evaluate the progress of the problem based on past data. The suggestion unit can also use the generation AI to evaluate the progress of the problem based on trend analysis. In this way, by adding a comparative analysis with past data and evaluating the progress of the problem, more appropriate measures can be taken.

[0076] The proposal unit can include cost and time estimates in the proposed countermeasures and evaluate their feasibility. The proposal unit, for example, includes cost and time estimates in the countermeasures proposed by the generation AI and evaluates their feasibility. For example, the proposal unit estimates the cost and time required for replacement work. The proposal unit can also use the generation AI to make an estimate based on a cost calculation method. The proposal unit can also use the generation AI to make an estimate based on a time estimation method. In this way, by evaluating feasibility including cost and time estimates, more realistic countermeasures can be proposed.

[0077] The suggestion unit can use the emotion estimation function to evaluate the engineer's emotion regarding the report content and create an easy-to-understand report. The suggestion unit, for example, uses the emotion estimation function to evaluate the engineer's emotion regarding the report content and create an easy-to-understand report. For example, the suggestion unit adjusts the content of the report based on the engineer's emotion score. The suggestion unit can also use the emotion estimation function to analyze the engineer's facial expression and adjust the content of the report. The suggestion unit can also use the emotion estimation function to analyze the engineer's voice and adjust the content of the report. In this way, the engineer's emotion is evaluated and an easy-to-understand report is created, thereby improving understanding of the report content.

[0078] The proposal unit can integrate the investigation results with other related equipment and systems to propose a comprehensive maintenance plan. For example, the proposal unit can integrate the investigation results reported by the generation AI with other related equipment and systems to propose a comprehensive maintenance plan. For example, the proposal unit can integrate maintenance plans for generators and cooling equipment. The proposal unit can also use the generation AI to integrate maintenance plans for related equipment. The proposal unit can also use the generation AI to integrate maintenance plans for related systems. In this way, by integrating the investigation results with other related equipment and systems and proposing a comprehensive maintenance plan, maintenance efficiency is improved.

[0079] The proposal unit can refer to best practices from other companies and industries when proposing countermeasures and select the optimal solution. For example, the proposal unit uses a generation AI to refer to best practices from other companies and industries and select the optimal solution. For example, the proposal unit determines countermeasures based on success stories from other companies. The proposal unit can also use a generation AI to select countermeasures based on industry standards. The proposal unit can also use a generation AI to select countermeasures based on success stories. In this way, by referring to best practices from other companies and industries and selecting the optimal solution, the quality of the countermeasures is improved.

[0080] The suggestion unit can use the emotion estimation function to evaluate the user's emotion and propose a countermeasure that is most satisfactory to the user. The suggestion unit, for example, uses the emotion estimation function to evaluate the user's emotion and propose a countermeasure that is most satisfactory to the user. For example, the suggestion unit selects an optimal countermeasure based on the user's emotion score. The suggestion unit can also use the emotion estimation function to analyze the user's facial expression and select an optimal countermeasure. The suggestion unit can also use the emotion estimation function to analyze the user's voice and select an optimal countermeasure. In this way, by evaluating the user's emotion and proposing a countermeasure that is most satisfactory to the user, user satisfaction is improved.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] When analyzing past response history data, the analysis unit can also use an anomaly detection algorithm to identify undiscovered problems. For example, the analysis unit can use generative AI to detect abnormal patterns from past data and predict potential problems. The analysis unit can also use an anomaly detection algorithm to identify abnormal values ​​in the data and take early action. The analysis unit can also use an anomaly detection algorithm to detect abnormal behavior and identify the cause of the problem. This makes it possible to identify undiscovered problems early and respond quickly.

[0083] When creating the on-site inspection list, the list creation unit can predict the lifespan of the equipment being inspected and suggest replacement times. For example, the list creation unit uses generation AI to analyze the equipment's usage history data and predict its lifespan. The list creation unit can also use a lifespan prediction algorithm to evaluate the rate of equipment deterioration and suggest the optimal replacement time. The list creation unit can also use a lifespan prediction algorithm to consider equipment usage environment data and suggest replacement times. This allows for efficient maintenance by predicting the lifespan of equipment and suggesting the optimal replacement time.

[0084] When proposing a variant exchange, the proposal unit can evaluate the environmental impact of the candidate battery and select an environmentally friendly alternative. For example, the proposal unit can use a generative AI to evaluate the environmental impact of the battery's manufacturing process and disposal treatment. The proposal unit can also use an environmental impact assessment algorithm to evaluate the environmental impact of the battery during use and select the optimal alternative. The proposal unit can also use an environmental impact assessment algorithm to evaluate the recyclability of the battery and select an environmentally friendly alternative. This makes it possible to conduct variant exchange that takes environmental impact into consideration.

[0085] The suggestion unit can use the emotion estimation function to consider the emotion of the engineer and propose a procedure that allows the engineer to work most efficiently. For example, the suggestion unit can propose a work procedure that causes less stress based on the emotion score of the engineer. The suggestion unit can also use the emotion estimation function to analyze the facial expression of the engineer and propose an optimal work procedure. The suggestion unit can also use the emotion estimation function to analyze the voice of the engineer and propose an efficient work procedure. In this way, by considering the emotion of the engineer and proposing an efficient work procedure, work efficiency is improved.

[0086] When creating the on-site inspection list, the list creation unit can analyze the energy consumption data of the equipment to be inspected and propose improvements to energy efficiency. For example, the list creation unit can use a generation AI to analyze the energy consumption data of the equipment and identify equipment with low energy efficiency. The list creation unit can also use an energy efficiency evaluation algorithm to propose measures to reduce energy consumption. The list creation unit can also use an energy efficiency evaluation algorithm to propose the timing of replacing equipment where improvements in energy efficiency can be expected. This makes it possible to improve energy efficiency.

[0087] The suggestion unit can use the emotion estimation function to evaluate the user's emotions and propose a maintenance plan that is most convincing to the user. For example, the suggestion unit can propose a maintenance plan that is highly convincing based on the user's emotion score. The suggestion unit can also use the emotion estimation function to analyze the user's facial expression and propose an optimal maintenance plan. The suggestion unit can also use the emotion estimation function to analyze the user's voice and propose a maintenance plan that is highly convincing. In this way, by evaluating the user's emotions and proposing a maintenance plan that is highly convincing, user satisfaction is improved.

[0088] When analyzing past response history data, the analysis unit can evaluate the reliability of the data and exclude unreliable data. For example, the analysis unit can use generative AI to evaluate the consistency and accuracy of the data. The analysis unit can also use a data reliability evaluation algorithm to identify unreliable data and exclude it from the analysis. The analysis unit can also use a data reliability evaluation algorithm to detect missing data and outliers, thereby improving the accuracy of the analysis results. This makes it possible to obtain more accurate results by evaluating the reliability of the data and performing analysis based on highly reliable data.

[0089] The suggestion unit can use the emotion estimation function to consider the emotion of the engineer and propose a procedure that allows the engineer to work most efficiently. For example, the suggestion unit can propose a work procedure that causes less stress based on the emotion score of the engineer. The suggestion unit can also use the emotion estimation function to analyze the facial expression of the engineer and propose an optimal work procedure. The suggestion unit can also use the emotion estimation function to analyze the voice of the engineer and propose an efficient work procedure. In this way, by considering the emotion of the engineer and proposing an efficient work procedure, work efficiency is improved.

[0090] When creating the on-site inspection list, the list creation unit can analyze the energy consumption data of the equipment to be inspected and propose improvements to energy efficiency. For example, the list creation unit can use a generation AI to analyze the energy consumption data of the equipment and identify equipment with low energy efficiency. The list creation unit can also use an energy efficiency evaluation algorithm to propose measures to reduce energy consumption. The list creation unit can also use an energy efficiency evaluation algorithm to propose the timing of replacing equipment where improvements in energy efficiency can be expected. This makes it possible to improve energy efficiency.

[0091] The suggestion unit can use the emotion estimation function to evaluate the user's emotions and propose a maintenance plan that is most convincing to the user. For example, the suggestion unit can propose a maintenance plan that is highly convincing based on the user's emotion score. The suggestion unit can also use the emotion estimation function to analyze the user's facial expression and propose an optimal maintenance plan. The suggestion unit can also use the emotion estimation function to analyze the user's voice and propose a maintenance plan that is highly convincing. In this way, by evaluating the user's emotions and proposing a maintenance plan that is highly convincing, user satisfaction is improved.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The analysis unit analyzes the ALM system and past response history data. For example, the analysis unit uses generative AI to analyze past response history data and extract the deterioration pattern of a specific battery. The analysis unit can also take into account battery usage environment data (temperature, humidity, etc.). Furthermore, the analysis unit can use emotion estimation functionality to analyze the emotions of technicians recorded in past response history and evaluate the severity of the problem. Step 2: The list creation unit creates an on-site inspection list based on the results of the analysis by the analysis unit. For example, the list creation unit uses generative AI to analyze the response history of other equipment (e.g., generators and cooling equipment) to create a comprehensive maintenance list. The list creation unit can also add external market data and technology trend information to the analyzed data to predict future problems. Furthermore, the list creation unit can use emotion estimation functionality to evaluate the satisfaction level of technicians with past responses and identify areas for improvement. Step 3: The proposal unit proposes an alternative replacement based on the on-site inspection list created by the list creation unit. For example, when proposing an alternative replacement, the proposal unit uses generative AI to analyze the performance data of the battery candidate for replacement and select the optimal replacement. The proposal unit can also minimize risks by referring to past success stories and failure stories when proposing replacement procedures. Furthermore, the proposal unit can use an emotion estimation function to take into account the emotions of the technician and make proposals to reduce the burden of the replacement work.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] 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.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] 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.

[0106] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] 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.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] 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.

[0120] 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.

[0121] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] 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.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] 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.

[0136] 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.

[0137] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] 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.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0153] 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.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 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 analysis department that analyzes the ALM system and past response history data, a list creation unit that creates a field survey list based on the results of the analysis by the analysis unit; a proposal unit that proposes an alternative exchange based on the field survey list created by the list creation unit. A system characterized by:

2. The list creation unit Analyze the response history of the other equipment and create a comprehensive maintenance list 2. The system of claim 1.

3. The proposal unit When proposing the replacement of a different battery type, the performance data of the replacement candidate battery is analyzed to select the optimal replacement.

2. The system of claim 1.

4. The list creation unit Automatically evaluate the importance and urgency of each survey item in the on-site survey list and assign priorities to them.

2. The system of claim 1.

5. The analysis unit Analyzing the emotions of engineers recorded in the past response history and assessing the seriousness of the problem 2. The system of claim 1.

6. The list creation unit Evaluate technician satisfaction with past interactions and identify areas for improvement 2. The system of claim 1.

7. The proposal unit Considering the feelings of the technician, we make suggestions to reduce the burden of replacement work.

2. The system of claim 1.

8. The proposal unit Evaluate engineers' feelings about the report content and create easy-to-understand reports 2. The system of claim 1.

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