system

Abstract

The system according to the embodiment aims to automatically digitize order forms received by fax, thereby reducing the amount of manual input work. [Solution] A system according to an embodiment includes a scanning unit, an analyzing unit, and an input unit. The scanning unit scans an order form received by fax and captures it as image data. The analyzing unit analyzes the image data captured by the scanning unit and converts the order details into text data. The input unit automatically inputs the text data converted by the analyzing unit into an order management system.

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 required a lot of manual input of order forms received by fax, which was inefficient.

[0005] The system according to the embodiment aims to automatically digitize order forms received by fax, thereby reducing the amount of manual input work. [Means for solving the problem]

[0006] The system according to the embodiment includes a scanning unit, an analyzing unit, and an input unit. The scanning unit scans an order form received by fax and captures it as image data. The analyzing unit analyzes the image data captured by the scanning unit and converts the order details into text data. The input unit automatically inputs the text data converted by the analyzing unit into the order management system. [Effects of the Invention]

[0007] The system according to the embodiment automatically digitizes order forms received by fax, reducing the amount of manual input work. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An order processing system according to an embodiment of the present invention automatically digitizes order forms received by fax, eliminating the need for manual data entry. The order processing system scans order forms received by fax and imports them as image data. AI analyzes the image data, converts the order details into text data, and automatically inputs the data into an order management system. For example, the order processing system scans order forms received by fax and imports them as image data. The order form is digitized using a scanner. For example, the order form is placed on the scanner and image data is generated by simply pressing a button. The order processing system then analyzes the image data using AI and converts the order details into text data. The AI ​​uses optical character recognition (OCR) technology to extract text information from the image data and recognizes the order details as text data. For example, the AI ​​reads information such as the product name, quantity, and price listed on the order form and converts it into text data. The order processing system then automatically inputs the converted text data into an order management system. For example, the AI-generated text data is sent to the order management system, and the system automatically registers the order details. This eliminates the need for manual data entry and improves order processing efficiency. This allows the order processing system to significantly reduce the amount of manual input required, enabling more efficient order processing. For example, the manual input of order details that was previously required is no longer necessary, shortening work time. It also reduces errors caused by manual input, allowing for more accurate order processing. Furthermore, it speeds up order processing, contributing to improved customer satisfaction.

[0029] An order processing system according to an embodiment includes a scanning unit, an analysis unit, and an input unit. The scanning unit scans an order form received by fax and captures it as image data. The scanning unit, for example, uses a scanner to digitize the order form. For example, image data is generated by simply placing the order form on the scanner and pressing a button. The scanning unit may also include information about the scanner's settings and type, such as resolution settings and color mode settings. The analysis unit uses AI to analyze the image data captured by the scanning unit and converts the order details into text data. The analysis unit may use OCR technology to extract character information from the image data and recognize the order details as text data. For example, OCR technology such as Tesseract or ABBYY FineReader may be used. The analysis unit may also use algorithms such as neural networks and support vector machines to improve analysis accuracy. The input unit automatically inputs the text data converted by the analysis unit into an order management system. For example, the input unit transmits text data generated by AI to the order management system, and the system automatically registers the order details. The input unit may also include a specific system configuration and data integration method. For example, this includes API integration, database integration, etc. As a result, the order processing system according to the embodiment can automatically digitize order forms received by fax, reducing the effort required for manual input.

[0030] The scanning unit may include information about the settings or type of scanner. Scanner settings include, for example, resolution settings and color mode settings. Scanner types include, for example, flatbed scanners and document scanners. The scanning unit may set the resolution to 300 dpi and the color mode to grayscale, for example. The scanning unit may also scan order forms using a flatbed scanner. The scanning unit may also scan multiple order forms at once using a document scanner. This allows for optimal scanning according to the settings and type of scanner.

[0031] The analysis unit may include details of OCR technology or algorithms. Details of OCR technology include specific OCR technologies such as Tesseract and ABBYY FineReader. Algorithms include, for example, neural networks and support vector machines. The analysis unit may extract character information from image data using, for example, Tesseract. The analysis unit may also perform highly accurate character recognition using ABBYY FineReader. Furthermore, the analysis unit may improve the accuracy of character recognition using a neural network. For example, a neural network learns from a large amount of character data and has advanced character recognition capabilities. This improves the analysis accuracy through the details of OCR technology and algorithms.

[0032] The input unit may include a specific system configuration or a data linkage method. The system configuration may include, for example, a hardware configuration or a software module. The data linkage method may include, for example, API linkage or database linkage. The input unit may, for example, perform API linkage with an order management system and automatically transmit text data. The input unit may also perform database linkage and directly register text data in a database. Furthermore, the input unit may include a server or network device as a hardware configuration. This allows for efficient data input through the specific system configuration and data linkage.

[0033] The order processing system according to the embodiment includes an effect section. The effect section shows the effect of reduced manual input using specific numerical values ​​or examples. For example, the effect section can display specific numerical values ​​showing the effect of reduced manual input. The effect section can also display specific examples showing the effect of reduced manual input. For example, the effect section can display the amount of time saved or the number of errors saved. The effect section can also display examples of implementation at specific companies and specific examples of improvements to business processes. This makes it possible to confirm the effectiveness of the system by specifically showing the effect of reduced manual input.

[0034] The scanning unit can automatically adjust optimal scan settings depending on the type and format of the order form. For example, if the order form is handwritten, the scanning unit can set a high resolution to improve the accuracy of reading the characters. Furthermore, if the order form is printed, the scanning unit can scan at a standard resolution. Furthermore, if the order form consists of multiple pages, the scanning unit can automatically combine the pages and import them as a single image data. This enables optimal scan settings depending on the type and format of the order form. Some or all of the above-described processing in the scanning unit may be performed using, or without, AI. For example, the scanning unit can input the type and format of the order form into AI and have the AI ​​adjust the optimal scan settings.

[0035] The scanning unit can be equipped with a function to scan multiple order forms at once and separate them into individual image data. For example, the scanning unit can load multiple order forms into a scanner, scan them all at once, and then have AI separate them into individual image data. After scanning, the scanning unit can automatically save each order form in a different folder using AI. Furthermore, the scanning unit can recognize the boundaries of the order forms and accurately separate them during scanning. This allows multiple order forms to be scanned at once and processed efficiently. Some or all of the above-described processing in the scanning unit may be performed using AI, for example, or may be performed without using AI. For example, the scanning unit can input scanned image data into AI and have the AI ​​separate the data into individual image data.

[0036] The scanning unit can be added with a function to automatically adjust the quality (resolution, brightness, contrast, etc.) of an order form. For example, the scanning unit can use AI to automatically optimize the resolution of an order form when scanning. The scanning unit can also use AI to automatically adjust the brightness of an order form when scanning. Furthermore, the scanning unit can use AI to automatically adjust the contrast of an order form when scanning. This automatically adjusts the quality of the order form, thereby improving scanning accuracy. Some or all of the above-mentioned processing in the scanning unit may be performed using AI, for example, or may be performed without using AI. For example, the scanning unit can input scanned image data into AI and have the AI ​​adjust the resolution, brightness, and contrast.

[0037] The scanning unit can prioritize scanning highly relevant order forms by taking into account the geographical information of the order form. For example, the scanning unit can prioritize scanning nearby order forms based on the geographical information of the order form. The scanning unit can also postpone distant order forms based on the geographical information of the order form. Furthermore, the scanning unit can prioritize scanning order forms in a specific region based on the geographical information of the order form. This allows highly relevant order forms to be prioritized by taking geographical information into account. Some or all of the above-described processing in the scanning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scanning unit can input the geographical information of the order form into AI and have the AI ​​prioritize scanning highly relevant order forms.

[0038] The scanning unit can customize the scan resolution and settings based on the contents of the order form. For example, if the contents of the order form are detailed, the scanning unit can scan at a high resolution. On the other hand, if the contents of the order form are simple, the scanning unit can scan at a standard resolution. Furthermore, the scanning unit can automatically adjust the brightness and contrast according to the contents of the order form. This enables optimal scan settings according to the contents of the order form. Some or all of the above-mentioned processing in the scanning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scanning unit can input the contents of the order form into AI and have the AI ​​customize the resolution and settings.

[0039] The scanning unit can suggest optimal scan settings by referring to the user's past scan history. For example, the scanning unit can suggest optimal resolution based on the user's past scan history. The scanning unit can also suggest optimal brightness and contrast based on the user's past scan history. Furthermore, the scanning unit can automatically apply optimal scan settings based on the user's past scan history. This improves scanning efficiency by suggesting optimal scan settings based on the past scan history. Some or all of the above-described processing in the scanning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scanning unit can input the user's past scan history into AI and have the AI ​​suggest optimal scan settings.

[0040] The analysis unit can apply different OCR algorithms depending on the format and layout of the order form. For example, if the order form is handwritten, the analysis unit can apply an OCR algorithm specialized for handwritten characters. Furthermore, if the order form is printed, the analysis unit can apply an OCR algorithm specialized for printed characters. Furthermore, if the order form has a complex layout, the analysis unit can perform layout analysis and apply the optimal OCR algorithm. This allows the optimal OCR algorithm to be applied depending on the format and layout of the order form. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the format and layout of the order form into AI and have the AI ​​apply the optimal OCR algorithm.

[0041] The analysis unit can automatically classify the contents of the order form (such as product name, quantity, and price) and convert them into text data. For example, the analysis unit can automatically classify the contents of the order form using AI and convert information such as product name, quantity, and price into text data. The analysis unit can also analyze the contents of the order form using AI, classify them by category, and convert them into text data. Furthermore, the analysis unit can analyze the contents of the order form using AI and convert them into text data according to a specific format. This enables efficient data processing by automatically classifying the contents of the order form and converting them into text data. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the contents of the order form into AI and have the AI ​​perform the classification and conversion into text data.

[0042] The analysis unit can introduce an additional algorithm for recognizing handwritten characters and special characters. The analysis unit can, for example, introduce an additional algorithm for recognizing handwritten characters to improve analysis accuracy. The analysis unit can also introduce an additional algorithm for recognizing special characters to improve analysis accuracy. Furthermore, the analysis unit can introduce a combined algorithm for recognizing both handwritten characters and special characters to improve analysis accuracy. This improves the recognition accuracy of handwritten characters and special characters. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data of handwritten characters and special characters into AI and have the AI ​​apply the recognition algorithm.

[0043] The analysis unit can determine the priority of analysis based on the time of submission of the order form. For example, the analysis unit can prioritize analysis of order forms submitted earlier. The analysis unit can also postpone analysis of order forms submitted later. Furthermore, the analysis unit can prioritize analysis of order forms submitted within a specific period based on the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission into AI and have the AI ​​determine the priority of analysis.

[0044] The analysis unit can improve the accuracy of the analysis by referring to literature and databases related to the order form. For example, when analyzing the contents of the order form, the analysis unit can improve the accuracy by referring to related literature and databases. Furthermore, when analyzing the contents of the order form, the analysis unit can improve the accuracy by referring to past databases. Furthermore, when analyzing the contents of the order form, the analysis unit can improve the accuracy by referring to databases in related industries. In this way, the accuracy of the analysis is improved by referring to related literature and databases. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from related literature and databases into AI and have the AI ​​improve the accuracy of the analysis.

[0045] The analysis unit can weight the analysis taking into account the market value and importance of the order form. For example, the analysis unit can prioritize analysis of order forms with high market value. The analysis unit can also prioritize analysis of order forms with high importance. Furthermore, the analysis unit can weight the analysis taking into account both market value and importance. In this way, by weighting the analysis based on market value and importance, important order forms can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on market value and importance into AI and have the AI ​​perform the weighting of the analysis.

[0046] The input unit can convert data into an optimal data format depending on the database structure of the order management system. For example, the input unit can convert text data into an optimal format depending on the database structure of the order management system. The input unit can also perform conversion to maintain data consistency depending on the database structure of the order management system. Furthermore, the input unit can automatically adjust the format of data depending on the database structure of the order management system. This maintains data consistency by converting data into an optimal data format depending on the database structure. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input information about the database structure to AI and have the AI ​​perform conversion into the optimal data format.

[0047] The input unit can be added with a function to check the consistency of the order details and automatically correct any errors. For example, the input unit can use AI to check the consistency of the order details and automatically correct any errors. The input unit can also use AI to check the consistency of the order details and notify the user of any errors and prompt them to correct them. The input unit can also use AI to check the consistency of the order details and suggest corrections if any errors are found. This automatically checks the consistency of the order details and corrects any errors, thereby improving the accuracy of the data. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the order details data into AI and have the AI ​​perform consistency checks and error corrections.

[0048] The input unit can adjust the order of input based on the priority of the order contents. For example, the input unit can prioritize input of important orders based on the priority of the order contents. The input unit can also prioritize input of urgent orders based on the priority of the order contents. Furthermore, the input unit can postpone regular orders based on the priority of the order contents. In this way, by adjusting the order of input based on the priority of the order contents, important orders can be processed with priority. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input priority data of the order contents to AI and have the AI ​​adjust the order of input.

[0049] The input unit can prioritize input of highly relevant data by taking into account the geographical information of the order form. The input unit can, for example, prioritize input of nearby data based on the geographical information of the order form. The input unit can also postpone data from distant locations based on the geographical information of the order form. Furthermore, the input unit can prioritize input of data from a specific region based on the geographical information of the order form. This allows highly relevant data to be prioritized by taking geographical information into account. Some or all of the above-described processing in the input unit may be performed using AI, for example, or may be performed without using AI. For example, the input unit can input the geographical information of the order form to AI and cause the AI ​​to execute the prioritized input of highly relevant data.

[0050] The input unit can customize the input format and settings based on the contents of the order form. The input unit can, for example, automatically adjust the input format based on the contents of the order form. The input unit can also customize the input settings based on the contents of the order form. Furthermore, the input unit can perform input according to a specific format based on the contents of the order form. This enables the optimal input format and settings according to the contents of the order form. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the contents of the order form into AI and have the AI ​​customize the format and settings.

[0051] The input unit can suggest an optimal input method by referring to the user's past input history. The input unit can suggest an optimal input method based on the user's past input history, for example. The input unit can also automatically adjust the input format based on the user's past input history. Furthermore, the input unit can customize input settings based on the user's past input history. This improves input efficiency by suggesting an optimal input method based on the past input history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past input history into AI and have the AI ​​suggest an optimal input method.

[0052] The effect unit can be added with a function to update specific numerical values ​​and examples of manual input reduction in real time. The effect unit can, for example, display specific numerical values ​​of manual input reduction in real time. The effect unit can also update and display examples of manual input reduction in real time. Furthermore, the effect unit can visually display the effect of manual input reduction in graphs and charts. This allows the effect of the system to be immediately confirmed by updating specific numerical values ​​and examples of manual input reduction in real time. Some or all of the above-mentioned processing in the effect unit may be performed, for example, using AI, or may be performed without using AI. For example, the effect unit can input data of manual input reduction into AI and have the AI ​​perform real-time updates.

[0053] The effect unit can introduce graphs and charts to visually display the effect of the reduction in manual input. For example, the effect unit can display a graph showing the effect of the reduction in manual input. The effect unit can also display a chart showing the effect of the reduction in manual input. Furthermore, the effect unit can provide a dashboard showing the effect of the reduction in manual input. This allows the effect of the system to be intuitively understood by visually displaying the effect of the reduction in manual input. Some or all of the above-described processing in the effect unit can be performed using, for example, AI, or can be performed without using AI. For example, the effect unit can input data on the reduction in manual input into AI and have the AI ​​generate graphs and charts.

[0054] The effect section can add a function for comparing the effect of manual input reduction with other business processes. For example, the effect section can display the effect of manual input reduction in comparison with other business processes. The effect section can also generate a report for comparing the effect of manual input reduction with other business processes. Furthermore, the effect section can provide graphs and charts for comparing the effect of manual input reduction with other business processes. This allows the effectiveness of the system to be evaluated from multiple angles by comparing the effect of manual input reduction with other business processes. Some or all of the above-described processing in the effect section can be performed using, for example, AI, or can be performed without using AI. For example, the effect section can input data on manual input reduction into AI and have the AI ​​compare it with other business processes.

[0055] The effect unit can add a function to display the effect of manual input reduction in association with geographical information. The effect unit can, for example, display the effect of manual input reduction in association with geographical information. The effect unit can also analyze the effect of manual input reduction based on the geographical information. Furthermore, the effect unit can generate a report on the effect of manual input reduction based on the geographical information. This allows the effect for each region to be understood by displaying the effect in association with geographical information. Some or all of the above-described processing in the effect unit may be performed using, for example, AI, or may be performed without using AI. For example, the effect unit can input data on manual input reduction into AI and have the AI ​​associate the data with geographical information.

[0056] The effect section can add a function to display the effect of manual input reduction in cooperation with other business processes. The effect section can, for example, display the effect of manual input reduction in cooperation with other business processes. The effect section can also analyze the effect of manual input reduction in cooperation with other business processes. Furthermore, the effect section can generate a report on the effect of manual input reduction in cooperation with other business processes. This allows the effect of the system to be comprehensively evaluated by displaying the effect in cooperation with other business processes. Some or all of the above-mentioned processing in the effect section can be performed, for example, using AI or without AI. For example, the effect section can input data on manual input reduction into AI and have the AI ​​execute cooperation with other business processes.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The order processing system may further include a voice recognition unit. The voice recognition unit allows a user to input order details by voice. For example, when a user transmits an order detail over the phone, the voice recognition unit converts the voice into text data, which can then be input into the order management system. The voice recognition unit may also recognize voices in different languages, translate them, and convert them into text data. Furthermore, the voice recognition unit may have a function to remove background noise and obtain clear voice data. This allows orders to be processed by voice input, improving user convenience.

[0059] The order processing system may further include a user authentication unit. The user authentication unit provides a function for verifying the identity of the user and enhancing security. For example, the user authentication unit may authenticate the user using fingerprint authentication or face authentication. The user authentication unit may also authenticate the user using a password or PIN code. Furthermore, the user authentication unit may have a function for encrypting and storing authentication information to prevent unauthorized access. This enhances security and allows order processing to be carried out with peace of mind.

[0060] The order processing system may further include a predictive analysis unit. The predictive analysis unit analyzes past order data and provides the ability to predict future orders. For example, the predictive analysis unit may analyze seasonal order trends and optimize inventory management. The predictive analysis unit may also predict when specific products will sell well and plan promotional activities. Furthermore, the predictive analysis unit may analyze customer purchasing histories and make personalized proposals to individual customers. This allows for efficient inventory management and marketing strategies.

[0061] The order processing system may further include an energy management unit. The energy management unit monitors the energy consumption of the entire system and achieves efficient energy use. For example, the energy management unit can automatically turn off functions that are used less frequently to reduce energy consumption. The energy management unit can also adjust the operation of the system to reduce energy consumption during peak hours. Furthermore, the energy management unit can analyze energy consumption data and make suggestions to improve energy efficiency. This enables environmentally friendly system operation.

[0062] The order processing system may further include a data backup unit. The data backup unit regularly backs up data in the system to ensure data safety. For example, the data backup unit can automatically back up data every day to prepare for the unlikely event of data loss. The data backup unit can also store data in different locations to protect data in the event of a disaster. Furthermore, the data backup unit can check the integrity of the backed up data and correct it if necessary. This improves the safety and reliability of the data.

[0063] The order processing system may further include a data analysis unit. The data analysis unit analyzes data within the system and provides insights for business improvement. For example, the data analysis unit may analyze order data to understand sales trends and customer purchasing behavior. The data analysis unit may also analyze inventory data to support the optimization of inventory management. Furthermore, the data analysis unit may analyze customer feedback to identify areas for service improvement. This enables business improvement based on data.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The scanning unit scans the order form received by fax and captures it as image data. The scanning unit digitizes the order form using, for example, a scanner. Simply place the order form on the scanner and press a button to generate image data. The scanning unit can also include information about the scanner settings and type. For example, this can include resolution settings and color mode settings. Step 2: The analysis unit analyzes the image data captured by the scanning unit and converts the order details into text data. For example, the analysis unit uses OCR technology to extract character information from the image data and recognize the order details as text data. OCR technology used includes Tesseract and ABBYY FineReader. The analysis unit can also improve analysis accuracy by using algorithms such as neural networks and support vector machines. Step 3: The input unit automatically inputs the text data converted by the analysis unit into the order management system. For example, the input unit sends text data generated by AI to the order management system, and the system automatically registers the order details. The input unit can also include specific system configurations and data integration methods. For example, API integration and database integration are included.

[0066] (Example 2) An order processing system according to an embodiment of the present invention automatically digitizes order forms received by fax, eliminating the need for manual data entry. The order processing system scans order forms received by fax and imports them as image data. AI analyzes the image data, converts the order details into text data, and automatically inputs the data into an order management system. For example, the order processing system scans order forms received by fax and imports them as image data. The order form is digitized using a scanner. For example, the order form is placed on the scanner and image data is generated by simply pressing a button. The order processing system then analyzes the image data using AI and converts the order details into text data. The AI ​​uses optical character recognition (OCR) technology to extract text information from the image data and recognizes the order details as text data. For example, the AI ​​reads information such as the product name, quantity, and price listed on the order form and converts it into text data. The order processing system then automatically inputs the converted text data into an order management system. For example, the AI-generated text data is sent to the order management system, and the system automatically registers the order details. This eliminates the need for manual data entry and improves order processing efficiency. This allows the order processing system to significantly reduce the amount of manual input required, enabling more efficient order processing. For example, the manual input of order details that was previously required is no longer necessary, shortening work time. It also reduces errors caused by manual input, allowing for more accurate order processing. Furthermore, it speeds up order processing, contributing to improved customer satisfaction.

[0067] An order processing system according to an embodiment includes a scanning unit, an analysis unit, and an input unit. The scanning unit scans an order form received by fax and captures it as image data. The scanning unit, for example, uses a scanner to digitize the order form. For example, image data is generated by simply placing the order form on the scanner and pressing a button. The scanning unit may also include information about the scanner's settings and type, such as resolution settings and color mode settings. The analysis unit uses AI to analyze the image data captured by the scanning unit and converts the order details into text data. The analysis unit may use OCR technology to extract character information from the image data and recognize the order details as text data. For example, OCR technology such as Tesseract or ABBYY FineReader may be used. The analysis unit may also use algorithms such as neural networks and support vector machines to improve analysis accuracy. The input unit automatically inputs the text data converted by the analysis unit into an order management system. For example, the input unit transmits text data generated by AI to the order management system, and the system automatically registers the order details. The input unit may also include a specific system configuration and data integration method. For example, this includes API integration, database integration, etc. As a result, the order processing system according to the embodiment can automatically digitize order forms received by fax, reducing the effort required for manual input.

[0068] The scanning unit may include information about the settings or type of scanner. Scanner settings include, for example, resolution settings and color mode settings. Scanner types include, for example, flatbed scanners and document scanners. The scanning unit may set the resolution to 300 dpi and the color mode to grayscale, for example. The scanning unit may also scan order forms using a flatbed scanner. The scanning unit may also scan multiple order forms at once using a document scanner. This allows for optimal scanning according to the settings and type of scanner.

[0069] The analysis unit may include details of OCR technology or algorithms. Details of OCR technology include specific OCR technologies such as Tesseract and ABBYY FineReader. Algorithms include, for example, neural networks and support vector machines. The analysis unit may extract character information from image data using, for example, Tesseract. The analysis unit may also perform highly accurate character recognition using ABBYY FineReader. Furthermore, the analysis unit may improve the accuracy of character recognition using a neural network. For example, a neural network learns from a large amount of character data and has advanced character recognition capabilities. This improves the analysis accuracy through the details of OCR technology and algorithms.

[0070] The input unit may include a specific system configuration or a data linkage method. The system configuration may include, for example, a hardware configuration or a software module. The data linkage method may include, for example, API linkage or database linkage. The input unit may, for example, perform API linkage with an order management system and automatically transmit text data. The input unit may also perform database linkage and directly register text data in a database. Furthermore, the input unit may include a server or network device as a hardware configuration. This allows for efficient data input through the specific system configuration and data linkage.

[0071] The order processing system according to the embodiment includes an effect section. The effect section shows the effect of reduced manual input using specific numerical values ​​or examples. For example, the effect section can display specific numerical values ​​showing the effect of reduced manual input. The effect section can also display specific examples showing the effect of reduced manual input. For example, the effect section can display the amount of time saved or the number of errors saved. The effect section can also display examples of implementation at specific companies and specific examples of improvements to business processes. This makes it possible to confirm the effectiveness of the system by specifically showing the effect of reduced manual input.

[0072] The scanning unit can estimate the user's emotions and adjust the timing of scanning based on the estimated user emotions. For example, if the user is feeling stressed, the scanning unit can delay the timing of scanning and wait until the user is relaxed. Furthermore, if the user is in a hurry, the scanning unit can speed up the timing of scanning to perform processing quickly. Furthermore, if the user is relaxed, the scanning unit can perform scanning at a normal timing. This allows the user's stress to be reduced by adjusting the timing of scanning according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the scanning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the scanning unit can input image data of the user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0073] The scanning unit can automatically adjust optimal scan settings depending on the type and format of the order form. For example, if the order form is handwritten, the scanning unit can set a high resolution to improve the accuracy of reading the characters. Furthermore, if the order form is printed, the scanning unit can scan at a standard resolution. Furthermore, if the order form consists of multiple pages, the scanning unit can automatically combine the pages and import them as a single image data. This enables optimal scan settings depending on the type and format of the order form. Some or all of the above-described processing in the scanning unit may be performed using, or without, AI. For example, the scanning unit can input the type and format of the order form into AI and have the AI ​​adjust the optimal scan settings.

[0074] The scanning unit can be equipped with a function to scan multiple order forms at once and separate them into individual image data. For example, the scanning unit can load multiple order forms into a scanner, scan them all at once, and then have AI separate them into individual image data. After scanning, the scanning unit can automatically save each order form in a different folder using AI. Furthermore, the scanning unit can recognize the boundaries of the order forms and accurately separate them during scanning. This allows multiple order forms to be scanned at once and processed efficiently. Some or all of the above-described processing in the scanning unit may be performed using AI, for example, or may be performed without using AI. For example, the scanning unit can input scanned image data into AI and have the AI ​​separate the data into individual image data.

[0075] The scanning unit can be added with a function to automatically adjust the quality (resolution, brightness, contrast, etc.) of an order form. For example, the scanning unit can use AI to automatically optimize the resolution of an order form when scanning. The scanning unit can also use AI to automatically adjust the brightness of an order form when scanning. Furthermore, the scanning unit can use AI to automatically adjust the contrast of an order form when scanning. This automatically adjusts the quality of the order form, thereby improving scanning accuracy. Some or all of the above-mentioned processing in the scanning unit may be performed using AI, for example, or may be performed without using AI. For example, the scanning unit can input scanned image data into AI and have the AI ​​adjust the resolution, brightness, and contrast.

[0076] The scanning unit can estimate the user's emotions and determine the priority of scanning based on the estimated user emotions. For example, if the user is in a hurry, the scanning unit can set the scanning priority to high. Furthermore, if the user is relaxed, the scanning unit can perform scanning at a normal priority. Furthermore, if the user is stressed, the scanning unit can set the scanning priority to low. This enables scanning according to the user's needs by determining the scanning priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the scanning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the scanning unit can input image data of the user captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0077] The scanning unit can prioritize scanning highly relevant order forms by taking into account the geographical information of the order form. For example, the scanning unit can prioritize scanning nearby order forms based on the geographical information of the order form. The scanning unit can also postpone distant order forms based on the geographical information of the order form. Furthermore, the scanning unit can prioritize scanning order forms in a specific region based on the geographical information of the order form. This allows highly relevant order forms to be prioritized by taking geographical information into account. Some or all of the above-described processing in the scanning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scanning unit can input the geographical information of the order form into AI and have the AI ​​prioritize scanning highly relevant order forms.

[0078] The scanning unit can customize the scan resolution and settings based on the contents of the order form. For example, if the contents of the order form are detailed, the scanning unit can scan at a high resolution. On the other hand, if the contents of the order form are simple, the scanning unit can scan at a standard resolution. Furthermore, the scanning unit can automatically adjust the brightness and contrast according to the contents of the order form. This enables optimal scan settings according to the contents of the order form. Some or all of the above-mentioned processing in the scanning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scanning unit can input the contents of the order form into AI and have the AI ​​customize the resolution and settings.

[0079] The scanning unit can suggest optimal scan settings by referring to the user's past scan history. For example, the scanning unit can suggest optimal resolution based on the user's past scan history. The scanning unit can also suggest optimal brightness and contrast based on the user's past scan history. Furthermore, the scanning unit can automatically apply optimal scan settings based on the user's past scan history. This improves scanning efficiency by suggesting optimal scan settings based on the past scan history. Some or all of the above-described processing in the scanning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scanning unit can input the user's past scan history into AI and have the AI ​​suggest optimal scan settings.

[0080] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can increase the accuracy of the analysis to perform faster processing. Furthermore, if the user is relaxed, the analysis unit can perform analysis with normal accuracy. Furthermore, if the user is stressed, the analysis unit can increase the accuracy of the analysis to provide accurate results. This allows analysis tailored to the user's needs by adjusting the analysis accuracy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the analysis accuracy.

[0081] The analysis unit can apply different OCR algorithms depending on the format and layout of the order form. For example, if the order form is handwritten, the analysis unit can apply an OCR algorithm specialized for handwritten characters. Furthermore, if the order form is printed, the analysis unit can apply an OCR algorithm specialized for printed characters. Furthermore, if the order form has a complex layout, the analysis unit can perform layout analysis and apply the optimal OCR algorithm. This allows the optimal OCR algorithm to be applied depending on the format and layout of the order form. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the format and layout of the order form into AI and have the AI ​​apply the optimal OCR algorithm.

[0082] The analysis unit can automatically classify the contents of the order form (such as product name, quantity, and price) and convert them into text data. For example, the analysis unit can automatically classify the contents of the order form using AI and convert information such as product name, quantity, and price into text data. The analysis unit can also analyze the contents of the order form using AI, classify them by category, and convert them into text data. Furthermore, the analysis unit can analyze the contents of the order form using AI and convert them into text data according to a specific format. This enables efficient data processing by automatically classifying the contents of the order form and converting them into text data. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the contents of the order form into AI and have the AI ​​perform the classification and conversion into text data.

[0083] The analysis unit can introduce an additional algorithm for recognizing handwritten characters and special characters. The analysis unit can, for example, introduce an additional algorithm for recognizing handwritten characters to improve analysis accuracy. The analysis unit can also introduce an additional algorithm for recognizing special characters to improve analysis accuracy. Furthermore, the analysis unit can introduce a combined algorithm for recognizing both handwritten characters and special characters to improve analysis accuracy. This improves the recognition accuracy of handwritten characters and special characters. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data of handwritten characters and special characters into AI and have the AI ​​apply the recognition algorithm.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the display method of the analysis results to be adjusted according to the user's emotions, making it easier for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the display method.

[0085] The analysis unit can determine the priority of analysis based on the time of submission of the order form. For example, the analysis unit can prioritize analysis of order forms submitted earlier. The analysis unit can also postpone analysis of order forms submitted later. Furthermore, the analysis unit can prioritize analysis of order forms submitted within a specific period based on the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission into AI and have the AI ​​determine the priority of analysis.

[0086] The analysis unit can improve the accuracy of the analysis by referring to literature and databases related to the order form. For example, when analyzing the contents of the order form, the analysis unit can improve the accuracy by referring to related literature and databases. Furthermore, when analyzing the contents of the order form, the analysis unit can improve the accuracy by referring to past databases. Furthermore, when analyzing the contents of the order form, the analysis unit can improve the accuracy by referring to databases in related industries. In this way, the accuracy of the analysis is improved by referring to related literature and databases. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from related literature and databases into AI and have the AI ​​improve the accuracy of the analysis.

[0087] The analysis unit can weight the analysis taking into account the market value and importance of the order form. For example, the analysis unit can prioritize analysis of order forms with high market value. The analysis unit can also prioritize analysis of order forms with high importance. Furthermore, the analysis unit can weight the analysis taking into account both market value and importance. In this way, by weighting the analysis based on market value and importance, important order forms can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on market value and importance into AI and have the AI ​​perform the weighting of the analysis.

[0088] The input unit can estimate the user's emotion and adjust the timing of input based on the estimated user's emotion. For example, if the user is in a hurry, the input unit can accelerate the timing of input to perform processing quickly. Furthermore, if the user is relaxed, the input unit can perform input at a normal timing. Furthermore, if the user is feeling stressed, the input unit can delay the timing of input and wait until the user is relaxed. This adjusts the timing of input according to the user's emotion, thereby reducing the user's stress. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the input unit can input the user's emotion data to an AI and have the AI ​​adjust the timing of input.

[0089] The input unit can convert data into an optimal data format depending on the database structure of the order management system. For example, the input unit can convert text data into an optimal format depending on the database structure of the order management system. The input unit can also perform conversion to maintain data consistency depending on the database structure of the order management system. Furthermore, the input unit can automatically adjust the format of data depending on the database structure of the order management system. This maintains data consistency by converting data into an optimal data format depending on the database structure. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input information about the database structure to AI and have the AI ​​perform conversion into the optimal data format.

[0090] The input unit can be added with a function to check the consistency of the order details and automatically correct any errors. For example, the input unit can use AI to check the consistency of the order details and automatically correct any errors. The input unit can also use AI to check the consistency of the order details and notify the user of any errors and prompt them to correct them. The input unit can also use AI to check the consistency of the order details and suggest corrections if any errors are found. This automatically checks the consistency of the order details and corrects any errors, thereby improving the accuracy of the data. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the order details data into AI and have the AI ​​perform consistency checks and error corrections.

[0091] The input unit can adjust the order of input based on the priority of the order contents. For example, the input unit can prioritize input of important orders based on the priority of the order contents. The input unit can also prioritize input of urgent orders based on the priority of the order contents. Furthermore, the input unit can postpone regular orders based on the priority of the order contents. In this way, by adjusting the order of input based on the priority of the order contents, important orders can be processed with priority. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input priority data of the order contents to AI and have the AI ​​adjust the order of input.

[0092] The input unit can estimate the user's emotions and determine the priority of inputs based on the estimated user emotions. For example, if the user is in a hurry, the input unit can set a high priority for the input. Furthermore, if the user is relaxed, the input unit can perform inputs at a normal priority. Furthermore, if the user is stressed, the input unit can set a low priority for the input. This enables inputs tailored to the user's needs by determining the priority of inputs according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the input unit can input the user's emotion data into an AI and have the AI ​​determine the priority of the inputs.

[0093] The input unit can prioritize input of highly relevant data by taking into account the geographical information of the order form. The input unit can, for example, prioritize input of nearby data based on the geographical information of the order form. The input unit can also postpone data from distant locations based on the geographical information of the order form. Furthermore, the input unit can prioritize input of data from a specific region based on the geographical information of the order form. This allows highly relevant data to be prioritized by taking geographical information into account. Some or all of the above-described processing in the input unit may be performed using AI, for example, or may be performed without using AI. For example, the input unit can input the geographical information of the order form to AI and cause the AI ​​to execute the prioritized input of highly relevant data.

[0094] The input unit can customize the input format and settings based on the contents of the order form. The input unit can, for example, automatically adjust the input format based on the contents of the order form. The input unit can also customize the input settings based on the contents of the order form. Furthermore, the input unit can perform input according to a specific format based on the contents of the order form. This enables the optimal input format and settings according to the contents of the order form. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the contents of the order form into AI and have the AI ​​customize the format and settings.

[0095] The input unit can suggest an optimal input method by referring to the user's past input history. The input unit can suggest an optimal input method based on the user's past input history, for example. The input unit can also automatically adjust the input format based on the user's past input history. Furthermore, the input unit can customize input settings based on the user's past input history. This improves input efficiency by suggesting an optimal input method based on the past input history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past input history into AI and have the AI ​​suggest an optimal input method.

[0096] The effect unit can estimate the user's emotions and adjust the display method of the effects based on the estimated user emotions. For example, if the user is nervous, the effect unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the effect unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the effect unit can provide a display method that focuses on the main points. This allows the display method of the effects to be adjusted according to the user's emotions, resulting in a display that is easy for the user to view. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the effect unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the effect unit can input the user's emotion data into an AI and have the AI ​​adjust the display method.

[0097] The effect unit can be added with a function to update specific numerical values ​​and examples of manual input reduction in real time. The effect unit can, for example, display specific numerical values ​​of manual input reduction in real time. The effect unit can also update and display examples of manual input reduction in real time. Furthermore, the effect unit can visually display the effect of manual input reduction in graphs and charts. This allows the effect of the system to be immediately confirmed by updating specific numerical values ​​and examples of manual input reduction in real time. Some or all of the above-mentioned processing in the effect unit may be performed, for example, using AI, or may be performed without using AI. For example, the effect unit can input data of manual input reduction into AI and have the AI ​​perform real-time updates.

[0098] The effect unit can introduce graphs and charts to visually display the effect of the reduction in manual input. For example, the effect unit can display a graph showing the effect of the reduction in manual input. The effect unit can also display a chart showing the effect of the reduction in manual input. Furthermore, the effect unit can provide a dashboard showing the effect of the reduction in manual input. This allows the effect of the system to be intuitively understood by visually displaying the effect of the reduction in manual input. Some or all of the above-described processing in the effect unit can be performed using, for example, AI, or can be performed without using AI. For example, the effect unit can input data on the reduction in manual input into AI and have the AI ​​generate graphs and charts.

[0099] The effect section can add a function for comparing the effect of manual input reduction with other business processes. For example, the effect section can display the effect of manual input reduction in comparison with other business processes. The effect section can also generate a report for comparing the effect of manual input reduction with other business processes. Furthermore, the effect section can provide graphs and charts for comparing the effect of manual input reduction with other business processes. This allows the effectiveness of the system to be evaluated from multiple angles by comparing the effect of manual input reduction with other business processes. Some or all of the above-described processing in the effect section can be performed using, for example, AI, or can be performed without using AI. For example, the effect section can input data on manual input reduction into AI and have the AI ​​compare it with other business processes.

[0100] The effect unit can estimate the user's emotions and determine the priority of effects based on the estimated user emotions. For example, if the user is in a hurry, the effect unit can set the priority of an effect to a high level. Furthermore, if the user is relaxed, the effect unit can display an effect with a normal priority level. Furthermore, if the user is stressed, the effect unit can set the priority of an effect to a low level. This enables effects to be displayed according to the user's needs by determining the priority of effects according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the effect unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the effect unit can input the user's emotion data into an AI and have the AI ​​determine the priority of effects.

[0101] The effect unit can add a function to display the effect of manual input reduction in association with geographical information. The effect unit can, for example, display the effect of manual input reduction in association with geographical information. The effect unit can also analyze the effect of manual input reduction based on the geographical information. Furthermore, the effect unit can generate a report on the effect of manual input reduction based on the geographical information. This allows the effect for each region to be understood by displaying the effect in association with geographical information. Some or all of the above-described processing in the effect unit may be performed using, for example, AI, or may be performed without using AI. For example, the effect unit can input data on manual input reduction into AI and have the AI ​​associate the data with geographical information.

[0102] The effect section can add a function to display the effect of manual input reduction in cooperation with other business processes. The effect section can, for example, display the effect of manual input reduction in cooperation with other business processes. The effect section can also analyze the effect of manual input reduction in cooperation with other business processes. Furthermore, the effect section can generate a report on the effect of manual input reduction in cooperation with other business processes. This allows the effect of the system to be comprehensively evaluated by displaying the effect in cooperation with other business processes. Some or all of the above-mentioned processing in the effect section can be performed, for example, using AI or without AI. For example, the effect section can input data on manual input reduction into AI and have the AI ​​execute cooperation with other business processes. === Hard Collateral 1-1 === Each of the multiple elements, including the scanning unit, analyzing unit, input unit, and effect unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the scanning unit digitizes an order form using the scanner of the smart device 14. The analyzing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts text information from image data using OCR technology. The input unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and transmits the generated text data to the order management system. The effect unit is realized, for example, by the control unit 46A of the smart device 14 and displays the effect of reducing manual input using specific numerical values ​​or examples. === Hard Collateral 1-2 === Each of the multiple elements, including the scanning unit, analyzing unit, input unit, and effect unit, described above, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the scanning unit digitizes an order form using a scanner in the smart glasses 214. The analyzing unit is implemented, for example, by the specific processing unit 290 in the data processing device 12 and extracts text information from image data using OCR technology. The input unit is implemented, for example, by the specific processing unit 290 in the data processing device 12 and transmits the generated text data to the order management system. The effect unit is implemented, for example, by the control unit 46A in the smart glasses 214 and displays the effect of reducing manual input using specific numerical values ​​or examples. === Hard Collateral 1-3 === Each of the multiple elements, including the scanning unit, analyzing unit, input unit, and effect unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the scanning unit digitizes an order form using the scanner of the headset terminal 314. The analyzing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts text information from image data using OCR technology. The input unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and transmits the generated text data to the order management system. The effect unit is realized, for example, by the control unit 46A of the headset terminal 314 and displays the effect of reducing manual input using specific numerical values ​​or examples. === Hard Collateral 1-4 === Each of the multiple elements, including the scanning unit, analyzing unit, input unit, and effect unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the scanning unit digitizes an order form using a scanner in the robot 414. The analyzing unit is realized, for example, by the specific processing unit 290 in the data processing device 12 and extracts text information from image data using OCR technology. The input unit is realized, for example, by the specific processing unit 290 in the data processing device 12 and transmits the generated text data to the order management system. The effect unit is realized, for example, by the control unit 46A of the robot 414 and displays the effect of reducing manual input using specific numerical values ​​or examples.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The order processing system may further include a voice recognition unit. The voice recognition unit allows a user to input order details by voice. For example, when a user transmits an order detail over the phone, the voice recognition unit converts the voice into text data, which can then be input into the order management system. The voice recognition unit may also recognize voices in different languages, translate them, and convert them into text data. Furthermore, the voice recognition unit may have a function to remove background noise and obtain clear voice data. This allows orders to be processed by voice input, improving user convenience.

[0105] The order processing system may further include a user authentication unit. The user authentication unit provides a function for verifying the identity of the user and enhancing security. For example, the user authentication unit may authenticate the user using fingerprint authentication or face authentication. The user authentication unit may also authenticate the user using a password or PIN code. Furthermore, the user authentication unit may have a function for encrypting and storing authentication information to prevent unauthorized access. This enhances security and allows order processing to be carried out with peace of mind.

[0106] The order processing system may further include an emotion feedback unit. The emotion feedback unit monitors the user's emotions in real time and adjusts the system's response. For example, if the user is feeling stressed, the emotion feedback unit may make the system's response gentler, allowing the user to relax. Alternatively, if the user is happy, the emotion feedback unit may make the system's response more proactive, increasing user satisfaction. Furthermore, the emotion feedback unit may analyze the user's emotion data and use the results to improve the system. This allows for flexible responses according to the user's emotions.

[0107] The order processing system may further include a predictive analysis unit. The predictive analysis unit analyzes past order data and provides the ability to predict future orders. For example, the predictive analysis unit may analyze seasonal order trends and optimize inventory management. The predictive analysis unit may also predict when specific products will sell well and plan promotional activities. Furthermore, the predictive analysis unit may analyze customer purchasing histories and make personalized proposals to individual customers. This allows for efficient inventory management and marketing strategies.

[0108] The order processing system can further use the emotion estimation function to adjust the order confirmation process based on the user's emotions. For example, if the user feels anxious, the system can display a detailed confirmation message, allowing the user to complete the order with peace of mind. If the user is in a hurry, the system can display a concise confirmation message, allowing the user to complete the order quickly. If the user feels relaxed, the system can perform a regular confirmation process. This allows for flexible order confirmation based on the user's emotions.

[0109] The order processing system may further include an energy management unit. The energy management unit monitors the energy consumption of the entire system and achieves efficient energy use. For example, the energy management unit can automatically turn off functions that are used less frequently to reduce energy consumption. The energy management unit can also adjust the operation of the system to reduce energy consumption during peak hours. Furthermore, the energy management unit can analyze energy consumption data and make suggestions to improve energy efficiency. This enables environmentally friendly system operation.

[0110] The order processing system can further use the emotion estimation function to adjust customer support responses based on the user's emotions. For example, if the user is dissatisfied, the system can quickly notify customer support and provide priority support. If the user is satisfied, the system can provide a standard response. Furthermore, if the user is confused, the system can provide a detailed explanation to help the user understand. This enables flexible customer support that responds to the user's emotions.

[0111] The order processing system may further include a data backup unit. The data backup unit regularly backs up data in the system to ensure data safety. For example, the data backup unit can automatically back up data every day to prepare for the unlikely event of data loss. The data backup unit can also store data in different locations to protect data in the event of a disaster. Furthermore, the data backup unit can check the integrity of the backed up data and correct it if necessary. This improves the safety and reliability of the data.

[0112] The order processing system can further use the emotion estimation function to customize the system interface based on the user's emotions. For example, if the user is feeling stressed, the system can provide a simple and intuitive interface to reduce the user's burden. If the user is relaxed, the system can provide an interface with detailed information to meet the user's needs. If the user is in a hurry, the system can provide an interface that allows for quick operation. This allows for a flexible interface that responds to the user's emotions.

[0113] The order processing system may further include a data analysis unit. The data analysis unit analyzes data within the system and provides insights for business improvement. For example, the data analysis unit may analyze order data to understand sales trends and customer purchasing behavior. The data analysis unit may also analyze inventory data to support the optimization of inventory management. Furthermore, the data analysis unit may analyze customer feedback to identify areas for service improvement. This enables business improvement based on data.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: The scanning unit scans the order form received by fax and captures it as image data. The scanning unit digitizes the order form using, for example, a scanner. Simply place the order form on the scanner and press a button to generate image data. The scanning unit can also include information about the scanner settings and type. For example, this can include resolution settings and color mode settings. Step 2: The analysis unit analyzes the image data captured by the scanning unit and converts the order details into text data. For example, the analysis unit uses OCR technology to extract character information from the image data and recognize the order details as text data. OCR technology used includes Tesseract and ABBYY FineReader. The analysis unit can also improve analysis accuracy by using algorithms such as neural networks and support vector machines. Step 3: The input unit automatically inputs the text data converted by the analysis unit into the order management system. For example, the input unit sends text data generated by AI to the order management system, and the system automatically registers the order details. The input unit can also include specific system configurations and data integration methods. For example, API integration and database integration are included.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] [Explanation of symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A scanning unit that scans the order form received by fax and imports it as image data; an analysis unit that analyzes the image data captured by the scanning unit and converts the order details into text data; an input unit that automatically inputs the text data converted by the analysis unit into an order management system. A system characterized by:

2. The scanning unit Contains information about the scanner configuration or type 2. The system of claim 1.

3. The analysis unit Contains details of OCR technology or algorithms 2. The system of claim 1.

4. The input unit Includes specific system configuration or data integration methods 2. The system of claim 1.

5. Equipped with an effect section that shows the effect of reducing manual input with specific figures or examples 2. The system of claim 1.

6. The scanning unit Estimate the user's emotions and adjust the timing of scans based on the estimated user emotions 2. The system of claim 1.

7. The scanning unit Automatically adjusts scan settings to best suit the type and format of your order 2. The system of claim 1.

8. The scanning unit Add the ability to scan multiple purchase orders at once and split them into individual image data.

2. The system of claim 1.

9. The scanning unit Add functionality to automatically adjust purchase order quality 2. The system of claim 1.

Images