system

Abstract

The system according to this embodiment aims to streamline the creation and review of RFPs, RFIs, and specifications. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a review unit. The reception unit receives input for proposed conditions and supply conditions. The generation unit automatically creates RFPs, RFIs, and specifications based on the conditions entered by the reception unit. The review unit reads the specifications created by the generation unit and reviews their contents.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that a great deal of time and labor are required for the creation and review of RFPs, RFIs, and specifications.

[0005] The system according to the embodiment aims to improve the efficiency of creating and reviewing RFPs, RFIs, and specifications.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a review unit. The reception unit inputs proposal conditions and supply conditions. The generation unit automatically creates RFPs, RFIs, and specifications based on the conditions input by the reception unit. The review unit reads the specifications created by the generation unit and reviews the content.

Effects of the Invention

[0007] The system according to this embodiment can streamline the creation and review of RFPs, RFIs, and specifications. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The specification creation system according to an embodiment of the present invention automatically creates RFPs (Request for Proposal), RFIs (Request for Information), and specifications simply by inputting proposed conditions and service conditions, and further reads and scrutinizes the content of the created specifications. The specification creation system automatically creates RFPs, RFIs, and specifications simply by inputting proposed conditions and service conditions, and further reads and scrutinizes the content of the created specifications. This system is expected to significantly reduce the time and effort required for specification creation, improve the likelihood of securing orders, and prevent accidents. First, the user inputs the proposed conditions and service conditions. At this time, the user only needs to input specific conditions. For example, they input "implementation conditions for product A" or "service conditions for service B". This information is input into the generation AI. Next, the generation AI analyzes the input conditions and automatically creates RFPs, RFIs, and specifications. The generation AI generates the optimal text based on information from past specifications and contracts, and product information. For example, it automatically creates a specification for product A based on "implementation conditions for product A". At this time, text that incorporates unique expressions and technical terms is generated. Furthermore, the generating AI reads the generated specifications and scrutinizes their contents. The generating AI analyzes the content of the specifications and checks for errors or deficiencies. For example, if the specifications contain incorrect information, the generating AI will detect it and suggest corrections. It can also add favorable conditions to the specifications in natural language. This system can significantly reduce the time and effort required to create specifications. In particular, it is expected to greatly reduce the burden on personnel unfamiliar with specification creation and personnel in local governments that are regularly transferred. In addition, by scrutinizing the content of the generated specifications, the likelihood of winning the contract can be improved and accidents can be prevented. For example, by checking for errors in the specifications, the risk of re-bidding can be reduced. Furthermore, by utilizing the generating AI, favorable conditions can be added in natural language during bidding and RFP / RFI. This improves the likelihood of winning the contract and increases the chances of adoption. Also, by reducing the time required to create specifications, time can be allocated to other tasks.Thus, the specification creation system automatically generates RFPs, RFIs, and specifications simply by inputting the proposed conditions and service conditions, and further reads and scrutinizes the generated specifications. This significantly reduces the time and effort required for specification creation, and is expected to improve the likelihood of securing orders and prevent accidents.

[0029] The specification creation system according to this embodiment comprises a reception unit, a generation unit, and a review unit. The reception unit receives input for proposed conditions and provision conditions. Proposed conditions and provision conditions include, but are not limited to, product introduction conditions and service provision conditions. The reception unit receives the proposed conditions and provision conditions entered by the user in digital format, for example. The reception unit can also support multiple input methods, such as voice input and handwriting input. For example, the reception unit converts the user's voice input into text data using voice recognition technology. The reception unit can also receive handwritten proposed conditions and provision conditions using technology that converts handwritten input into digital data. The generation unit automatically creates RFPs, RFIs, and specifications based on the conditions entered by the reception unit using generation AI. The generation unit generates the most suitable text based on, for example, information from past specifications and contracts, and product information. The generation unit can, for example, refer to a database of past specifications and contracts and generate the most suitable text based on similar conditions. The generation unit can also generate text that includes detailed specifications of products and services based on product information. For example, the generation unit automatically creates a specification document for product A based on the introduction conditions for product A. The generation unit can also use generation AI to generate text that incorporates unique phrasing and technical terms. The review unit reads the specification document created by the generation unit and reviews its contents. The review unit, for example, analyzes the contents of the specification document and checks for errors or deficiencies. The review unit, for example, detects grammatical errors or inconsistencies in content and proposes corrections. The review unit can also add favorable conditions to the specification document in natural language. For example, the review unit adds favorable conditions to the specification document to improve the likelihood of securing the order. As a result, the specification document creation system according to this embodiment can automatically create RFPs, RFIs, and specification documents simply by inputting proposed conditions and supply conditions, and can also read the created specification documents and review their contents. Some or all of the above-described processes in the generation unit may be performed using generation AI, or they may be performed without using generation AI. For example, the generation unit can generate text by referring to a database of past specifications and contracts and using a generative AI model that generates the most suitable text based on similar conditions.Some or all of the above-described processes in the scrutiny unit may be performed using AI, or they may not be performed using AI. For example, the scrutiny unit can take the specifications created by the generation unit as input and scrutinize the contents using an AI model that detects errors and deficiencies.

[0030] The reception department inputs proposal and service conditions. These conditions include, but are not limited to, product implementation conditions and service provision conditions. The reception department receives the proposal and service conditions entered by the user in digital format. Specifically, it can not only receive text data entered by the user using a keyboard, but can also support multiple input methods such as voice input and handwriting input. For example, the reception department can convert the user's voice input into text data using speech recognition technology. Speech recognition technology uses advanced algorithms to analyze the user's speech in real time and convert it into accurate text data. This allows the user to input proposal and service conditions without using their hands. The reception department can also receive handwritten proposal and service conditions using technology that converts handwritten input into digital data. Optical character recognition (OCR) technology is used to digitize handwritten input, converting handwritten characters and figures into digital data with high accuracy. This allows the user to directly import what they have written on paper into the system. Furthermore, the reception department has a database to centrally manage this input data and smoothly hand it over to the subsequent generation and verification departments. The database organizes the entered proposal and service conditions, and allows for searching and filtering as needed. This enables the reception department to handle diverse input methods from users and collect data efficiently and accurately.

[0031] The generation unit uses a generation AI to automatically create RFPs, RFIs, and specifications based on conditions entered by the reception unit. For example, the generation unit generates optimal text based on information from past specifications and contracts, as well as product information. Specifically, the generation AI utilizes natural language processing (NLP) technology to analyze the input conditions and generate text in the appropriate context. The generation AI refers to a database of past specifications and contracts and generates optimal text based on similar conditions. For example, the generation AI learns from similar specifications created in the past and uses their patterns and phrasing to create new specifications. Furthermore, the generation unit can generate text containing detailed specifications of products and services based on product information. For example, the generation unit automatically creates a specification for product A based on the implementation conditions for product A. The generation AI describes the technical features and implementation procedures of product A in detail, covering all the information the user needs. In addition, the generation unit can use the generation AI to generate text that incorporates unique phrasing and technical terms. As a result, the generated specifications, while containing specialized content, are written in a readable and easy-to-understand manner. The generation unit also has an interface to temporarily store the generated specifications and allow the review unit to access them. This enables the generation unit to automatically create efficient and high-quality specifications, improving the overall productivity of the system.

[0032] The review unit reads the specifications created by the generation unit and reviews their contents. For example, the review unit analyzes the contents of the specifications to check for errors or deficiencies. Specifically, the review unit uses natural language processing (NLP) technology to detect grammatical errors and content inconsistencies in the generated text. For example, the review unit detects grammatical errors and content inconsistencies and proposes corrections. Using an AI model, the review unit takes the specifications created by the generation unit as input and detects errors and deficiencies. The AI ​​model learns from past data and can detect common grammatical errors and content inconsistencies with high accuracy. The review unit can also add favorable conditions to the specifications in natural language. For example, the review unit can add favorable conditions to the specifications to improve the likelihood of securing the order. The review unit analyzes the generated specifications in detail and enhances their competitiveness by adding favorable conditions and suggestions for the user. Furthermore, the review unit can receive feedback from users and continuously improve the content of the specifications. For example, the revisions and additional requests pointed out by users are incorporated into future specification document creation. This allows the review department to improve the quality of the generated specifications and increase user satisfaction. The review department works in conjunction with the generation department to quickly and accurately review the content of the generated specifications and complete them as final specifications.

[0033] The generation unit can generate optimal text based on information from past specifications and contracts, as well as product information. For example, the generation unit can refer to a database of past specifications and contracts and generate optimal text based on similar conditions. For example, the generation unit can generate text that includes detailed specifications of products and services based on product information. For example, the generation unit can automatically create a specification for product A based on the implementation conditions for product A. The generation unit can also use generation AI to generate text that incorporates unique phrasing and technical terms. This enables the creation of highly accurate RFPs, RFIs, and specifications by generating optimal text based on past information. Some or all of the above-described processes in the generation unit may be performed using generation AI, or they may not. For example, the generation unit can generate text using a generation AI model that refers to a database of past specifications and contracts and generates optimal text based on similar conditions.

[0034] The review unit can analyze the contents of the specification document and check for errors or deficiencies. For example, the review unit can detect grammatical errors or inconsistencies in content and propose corrections. The review unit can also use checklists to analyze the contents of the specification document and check for errors or deficiencies. The review unit can also use error checking algorithms to review the contents of the specification document. This allows for the provision of high-quality specifications by analyzing the contents of the specification document and checking for errors or deficiencies. Some or all of the above processes in the review unit may be performed using AI or not. For example, the review unit can take the specification document created by the generation unit as input and review its contents using an AI model that detects errors or deficiencies.

[0035] The review unit can add favorable conditions to the specifications in natural language. For example, the review unit can add favorable conditions to the specifications to improve the likelihood of securing the order. The review unit can also add favorable conditions in natural language, such as price advantages or shorter delivery times. The review unit can also use templates for adding favorable conditions to the specifications. This allows for an improvement in the likelihood of securing the order by adding favorable conditions to the specifications. Some or all of the above processing in the review unit may be performed using AI or not. For example, the review unit can take the specifications created by the generation unit as input and review the content using an AI model that adds favorable conditions.

[0036] The review unit can detect errors and deficiencies and propose corrections. For example, the review unit can detect grammatical errors and content inconsistencies and propose corrections. The review unit can also use error checking algorithms to detect errors and deficiencies. For example, the review unit can point out areas that need correction and present proposed corrections. This improves the quality of the specification by detecting errors and deficiencies and proposing corrections. Some or all of the above processes in the review unit may be performed using AI or not. For example, the review unit can take a specification created by the generation unit as input and review its content using an AI model that detects errors and deficiencies.

[0037] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display suggested conditions and service conditions that the user has frequently entered in the past as candidates. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest suggested conditions and service conditions that the user will use at a specific time of day based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method to the user. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input data into AI and have the AI ​​suggest the optimal input method.

[0038] The reception desk can filter the input of proposal and service conditions based on the user's industry and area of ​​expertise. For example, if the user belongs to the IT industry, the reception desk will prioritize displaying IT-related proposal and service conditions. If the user belongs to the medical industry, the reception desk can also prioritize displaying medical-related proposal and service conditions. If the user belongs to the education industry, the reception desk can also prioritize displaying education-related proposal and service conditions. By filtering based on the user's industry and area of ​​expertise, highly relevant conditions can be displayed preferentially. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input data on the user's industry and area of ​​expertise into the AI ​​and have the AI ​​perform the filtering.

[0039] The reception desk can prioritize inputting highly relevant conditions based on the user's geographical location when inputting suggested conditions or service conditions. For example, if the user is in a specific region, the reception desk will prioritize displaying suggested conditions or service conditions related to that region. For example, if the user is traveling, the reception desk can also prioritize displaying suggested conditions or service conditions related to the travel destination. For example, if the user is at home, the reception desk can also prioritize displaying suggested conditions or service conditions related to the user's home area. This allows for the provision of more appropriate conditions by prioritizing the input of highly relevant conditions based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into the AI ​​and have the AI ​​prioritize the display of highly relevant conditions.

[0040] The reception desk can analyze the user's social media activity and input relevant conditions when the user enters proposal and offer conditions. For example, the reception desk can prioritize displaying proposal and offer conditions related to topics the user frequently mentions on social media. The reception desk can also prioritize displaying proposal and offer conditions related to companies and brands the user follows on social media. The reception desk can also prioritize displaying proposal and offer conditions related to groups and communities the user participates in on social media. This allows the reception desk to prioritize displaying relevant conditions by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into AI and have the AI ​​prioritize displaying relevant conditions.

[0041] The generation unit can adjust the level of detail in the generated text based on the importance of the proposed conditions and conditions provided. For example, the generation unit can generate text with detailed explanations for important proposed conditions and conditions. For example, the generation unit can also generate text with concise explanations for less important proposed conditions and conditions. The generation unit can also adjust the length and level of detail of paragraphs according to the importance of the proposed conditions and conditions. This allows for the generation of more appropriate text by adjusting the level of detail in the text based on the importance of the proposed conditions and conditions. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input importance data of the proposed conditions and conditions provided into the generation AI and have the generation AI perform the adjustment of the level of detail in the text.

[0042] The generation unit can apply different generation algorithms depending on the category of the proposed conditions or conditions to be provided. For example, for IT-related proposed conditions or conditions to be provided, the generation unit can apply a generation algorithm that includes technical details. For example, for medical-related proposed conditions or conditions to be provided, the generation unit can also apply a generation algorithm that includes technical terms. For example, for education-related proposed conditions or conditions to be provided, the generation unit can also apply a generation algorithm that includes an educational perspective. By applying a generation algorithm according to the category of the proposed conditions or conditions to be provided, more appropriate text can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input category data of proposed conditions or conditions to a generation AI and have the generation AI execute the application of the generation algorithm.

[0043] The generation unit can determine the priority of the generated text based on the submission timing of the proposed conditions and service conditions. For example, the generation unit will prioritize generating text for proposed conditions and service conditions with approaching submission deadlines. The generation unit can also postpone generating text for proposed conditions and service conditions with distant submission deadlines. The generation unit can also adjust the priority of the generated text according to the submission timing of the proposed conditions and service conditions. This enables efficient text generation by determining the priority of text based on the submission timing of the proposed conditions and service conditions. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the submission timing of the proposed conditions and service conditions into the generation AI and have the generation AI determine the priority of the text.

[0044] The generation unit can adjust the order of the generated sentences based on the relationships between the proposed conditions and the provided conditions. For example, the generation unit can prioritize placing highly related proposed conditions and provided conditions in the first half of the sentence. The generation unit can also, for example, place less related proposed conditions and provided conditions in the latter half of the sentence. The generation unit can also adjust the order of the generated sentences according to the relationships between the proposed conditions and provided conditions. By adjusting the order of sentences based on the relationships between the proposed conditions and provided conditions, more appropriate sentences can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input relationship data between proposed conditions and provided conditions into a generation AI and have the generation AI perform the adjustment of the sentence order.

[0045] The scrutiny unit can optimize the scrutiny algorithm by referring to past scrutiny data. For example, the scrutiny unit can learn patterns of errors and deficiencies based on past scrutiny data and optimize the scrutiny algorithm. For example, the scrutiny unit can extract errors and deficiencies specific to a particular industry or field from past scrutiny data and optimize the scrutiny algorithm. For example, the scrutiny unit can analyze past scrutiny data to improve the accuracy of the scrutiny algorithm. In this way, the scrutiny algorithm can be optimized and its accuracy improved by referring to past scrutiny data. Some or all of the above processes in the scrutiny unit may be performed using AI or not. For example, the scrutiny unit can input past scrutiny data into AI and have AI perform the optimization of the scrutiny algorithm.

[0046] The scrutiny department can conduct scrutiny by considering the attribute information of the submitter of the proposed terms and conditions. For example, if the submitter of the proposed terms and conditions is a new customer, the scrutiny department will conduct a rigorous scrutiny. For example, if the submitter of the proposed terms and conditions is an existing customer, the scrutiny department can also conduct scrutiny by considering past transaction history. For example, if the submitter of the proposed terms and conditions belongs to a specific industry or field, the scrutiny department can also apply scrutiny criteria specific to that industry or field. This allows for more appropriate scrutiny by considering the submitter's attribute information. Some or all of the above processes in the scrutiny department may be performed using AI or not. For example, the scrutiny department can input the submitter's attribute information into AI and have the AI ​​perform the scrutiny.

[0047] The scrutiny unit can perform scrutiny considering the geographical distribution of proposed conditions and conditions of provision. For example, if the proposed conditions and conditions of provision are concentrated in a particular region, the scrutiny unit will perform scrutiny considering the characteristics of that region. For example, if the proposed conditions and conditions of provision are dispersed across multiple regions, the scrutiny unit can also perform scrutiny considering the characteristics of each region. For example, the scrutiny unit can adjust the focus of the scrutiny according to the geographical distribution of the proposed conditions and conditions of provision. This makes it possible to perform more appropriate scrutiny by considering the geographical distribution of the proposed conditions and conditions of provision. Some or all of the above processing in the scrutiny unit may be performed using AI or not. For example, the scrutiny unit can input geographical distribution data of proposed conditions and conditions of provision into AI and have the AI ​​perform the scrutiny.

[0048] The scrutiny unit can improve the accuracy of its scrutiny by referring to relevant literature on the proposed conditions and terms of service. For example, the scrutiny unit can improve the accuracy of its scrutiny by referring to academic papers related to the proposed conditions and terms of service. The scrutiny unit can also improve the accuracy of its scrutiny by referring to industry reports related to the proposed conditions and terms of service. The scrutiny unit can also improve the accuracy of its scrutiny by referring to patent documents related to the proposed conditions and terms of service. In this way, the accuracy of the scrutiny can be improved by referring to relevant literature. Some or all of the above processing in the scrutiny unit may be performed using AI or not. For example, the scrutiny unit can input relevant literature data on the proposed conditions and terms of service into AI and have the AI ​​perform the scrutiny.

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

[0050] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display suggested conditions and service conditions that the user has frequently entered in the past as candidates. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest suggested conditions and service conditions that the user will use at a specific time of day based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method to the user. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input data into AI and have the AI ​​suggest the optimal input method.

[0051] The reception desk can filter the input of proposal and service conditions based on the user's industry and area of ​​expertise. For example, if the user belongs to the IT industry, the reception desk will prioritize displaying IT-related proposal and service conditions. If the user belongs to the medical industry, the reception desk can also prioritize displaying medical-related proposal and service conditions. If the user belongs to the education industry, the reception desk can also prioritize displaying education-related proposal and service conditions. By filtering based on the user's industry and area of ​​expertise, highly relevant conditions can be displayed preferentially. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input data on the user's industry and area of ​​expertise into the AI ​​and have the AI ​​perform the filtering.

[0052] The reception desk can prioritize inputting highly relevant conditions based on the user's geographical location when inputting suggested conditions or service conditions. For example, if the user is in a specific region, the reception desk will prioritize displaying suggested conditions or service conditions related to that region. For example, if the user is traveling, the reception desk can also prioritize displaying suggested conditions or service conditions related to the travel destination. For example, if the user is at home, the reception desk can also prioritize displaying suggested conditions or service conditions related to the user's home area. This allows for the provision of more appropriate conditions by prioritizing the input of highly relevant conditions based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into the AI ​​and have the AI ​​prioritize the display of highly relevant conditions.

[0053] The generation unit can adjust the level of detail in the generated text based on the importance of the proposed conditions and conditions provided. For example, the generation unit can generate text with detailed explanations for important proposed conditions and conditions. For example, the generation unit can also generate text with concise explanations for less important proposed conditions and conditions. The generation unit can also adjust the length and level of detail of paragraphs according to the importance of the proposed conditions and conditions. This allows for the generation of more appropriate text by adjusting the level of detail in the text based on the importance of the proposed conditions and conditions. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input importance data of the proposed conditions and conditions provided into the generation AI and have the generation AI perform the adjustment of the level of detail in the text.

[0054] The generation unit can apply different generation algorithms depending on the category of the proposed conditions or conditions to be provided. For example, for IT-related proposed conditions or conditions to be provided, the generation unit can apply a generation algorithm that includes technical details. For example, for medical-related proposed conditions or conditions to be provided, the generation unit can also apply a generation algorithm that includes technical terms. For example, for education-related proposed conditions or conditions to be provided, the generation unit can also apply a generation algorithm that includes an educational perspective. By applying a generation algorithm according to the category of the proposed conditions or conditions to be provided, more appropriate text can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input category data of proposed conditions or conditions to a generation AI and have the generation AI execute the application of the generation algorithm.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The reception desk inputs the proposed conditions and service conditions. These conditions include product implementation requirements and service provision conditions. The reception desk receives the proposed conditions and service conditions entered by the user in digital format. It also supports multiple input methods, such as voice input and handwriting input. For example, it can use voice recognition technology to convert the user's voice input into text data, and technology to convert handwritten input into digital data to receive handwritten proposed conditions and service conditions. Step 2: The generation unit automatically creates RFPs, RFIs, and specifications based on the conditions entered by the reception unit. The generation unit uses generation AI to generate the most suitable text based on information from past specifications and contracts, as well as product information. For example, it can refer to a database of past specifications and contracts and generate the most suitable text based on similar conditions. It can also generate text that includes detailed specifications of products and services based on product information. The generation unit can also generate text that incorporates unique phrasing and technical terms. Step 3: The review unit reads the specification document created by the generation unit and reviews its contents. The review unit analyzes the contents of the specification document and checks for errors or deficiencies. For example, it detects grammatical errors and inconsistencies in content and proposes corrections. It can also add favorable conditions to the specification document in natural language. This improves the likelihood of securing the order.

[0057] (Example of form 2) The specification creation system according to an embodiment of the present invention automatically creates RFPs (Request for Proposal), RFIs (Request for Information), and specifications simply by inputting proposed conditions and service conditions, and further reads and scrutinizes the content of the created specifications. The specification creation system automatically creates RFPs, RFIs, and specifications simply by inputting proposed conditions and service conditions, and further reads and scrutinizes the content of the created specifications. This system is expected to significantly reduce the time and effort required for specification creation, improve the likelihood of securing orders, and prevent accidents. First, the user inputs the proposed conditions and service conditions. At this time, the user only needs to input specific conditions. For example, they input "implementation conditions for product A" or "service conditions for service B". This information is input into the generation AI. Next, the generation AI analyzes the input conditions and automatically creates RFPs, RFIs, and specifications. The generation AI generates the optimal text based on information from past specifications and contracts, and product information. For example, it automatically creates a specification for product A based on "implementation conditions for product A". At this time, text that incorporates unique expressions and technical terms is generated. Furthermore, the generating AI reads the generated specifications and scrutinizes their contents. The generating AI analyzes the content of the specifications and checks for errors or deficiencies. For example, if the specifications contain incorrect information, the generating AI will detect it and suggest corrections. It can also add favorable conditions to the specifications in natural language. This system can significantly reduce the time and effort required to create specifications. In particular, it is expected to greatly reduce the burden on personnel unfamiliar with specification creation and personnel in local governments that are regularly transferred. In addition, by scrutinizing the content of the generated specifications, the likelihood of winning the contract can be improved and accidents can be prevented. For example, by checking for errors in the specifications, the risk of re-bidding can be reduced. Furthermore, by utilizing the generating AI, favorable conditions can be added in natural language during bidding and RFP / RFI. This improves the likelihood of winning the contract and increases the chances of adoption. Also, by reducing the time required to create specifications, time can be allocated to other tasks.Thus, the specification creation system automatically generates RFPs, RFIs, and specifications simply by inputting the proposed conditions and service conditions, and further reads and scrutinizes the generated specifications. This significantly reduces the time and effort required for specification creation, and is expected to improve the likelihood of securing orders and prevent accidents.

[0058] The specification creation system according to this embodiment comprises a reception unit, a generation unit, and a review unit. The reception unit receives input for proposed conditions and provision conditions. Proposed conditions and provision conditions include, but are not limited to, product introduction conditions and service provision conditions. The reception unit receives the proposed conditions and provision conditions entered by the user in digital format, for example. The reception unit can also support multiple input methods, such as voice input and handwriting input. For example, the reception unit converts the user's voice input into text data using voice recognition technology. The reception unit can also receive handwritten proposed conditions and provision conditions using technology that converts handwritten input into digital data. The generation unit automatically creates RFPs, RFIs, and specifications based on the conditions entered by the reception unit using generation AI. The generation unit generates the most suitable text based on, for example, information from past specifications and contracts, and product information. The generation unit can, for example, refer to a database of past specifications and contracts and generate the most suitable text based on similar conditions. The generation unit can also generate text that includes detailed specifications of products and services based on product information. For example, the generation unit automatically creates a specification document for product A based on the introduction conditions for product A. The generation unit can also use generation AI to generate text that incorporates unique phrasing and technical terms. The review unit reads the specification document created by the generation unit and reviews its contents. The review unit, for example, analyzes the contents of the specification document and checks for errors or deficiencies. The review unit, for example, detects grammatical errors or inconsistencies in content and proposes corrections. The review unit can also add favorable conditions to the specification document in natural language. For example, the review unit adds favorable conditions to the specification document to improve the likelihood of securing the order. As a result, the specification document creation system according to this embodiment can automatically create RFPs, RFIs, and specification documents simply by inputting proposed conditions and supply conditions, and can also read the created specification documents and review their contents. Some or all of the above-described processes in the generation unit may be performed using generation AI, or they may be performed without using generation AI. For example, the generation unit can generate text by referring to a database of past specifications and contracts and using a generative AI model that generates the most suitable text based on similar conditions.Some or all of the above-described processes in the scrutiny unit may be performed using AI, or they may not be performed using AI. For example, the scrutiny unit can take the specifications created by the generation unit as input and scrutinize the contents using an AI model that detects errors and deficiencies.

[0059] The reception department inputs proposal and service conditions. These conditions include, but are not limited to, product implementation conditions and service provision conditions. The reception department receives the proposal and service conditions entered by the user in digital format. Specifically, it can not only receive text data entered by the user using a keyboard, but can also support multiple input methods such as voice input and handwriting input. For example, the reception department can convert the user's voice input into text data using speech recognition technology. Speech recognition technology uses advanced algorithms to analyze the user's speech in real time and convert it into accurate text data. This allows the user to input proposal and service conditions without using their hands. The reception department can also receive handwritten proposal and service conditions using technology that converts handwritten input into digital data. Optical character recognition (OCR) technology is used to digitize handwritten input, converting handwritten characters and figures into digital data with high accuracy. This allows the user to directly import what they have written on paper into the system. Furthermore, the reception department has a database to centrally manage this input data and smoothly hand it over to the subsequent generation and verification departments. The database organizes the entered proposal and service conditions, and allows for searching and filtering as needed. This enables the reception department to handle diverse input methods from users and collect data efficiently and accurately.

[0060] The generation unit uses a generation AI to automatically create RFPs, RFIs, and specifications based on conditions entered by the reception unit. For example, the generation unit generates optimal text based on information from past specifications and contracts, as well as product information. Specifically, the generation AI utilizes natural language processing (NLP) technology to analyze the input conditions and generate text in the appropriate context. The generation AI refers to a database of past specifications and contracts and generates optimal text based on similar conditions. For example, the generation AI learns from similar specifications created in the past and uses their patterns and phrasing to create new specifications. Furthermore, the generation unit can generate text containing detailed specifications of products and services based on product information. For example, the generation unit automatically creates a specification for product A based on the implementation conditions for product A. The generation AI describes the technical features and implementation procedures of product A in detail, covering all the information the user needs. In addition, the generation unit can use the generation AI to generate text that incorporates unique phrasing and technical terms. As a result, the generated specifications, while containing specialized content, are written in a readable and easy-to-understand manner. The generation unit also has an interface to temporarily store the generated specifications and allow the review unit to access them. This enables the generation unit to automatically create efficient and high-quality specifications, improving the overall productivity of the system.

[0061] The review unit reads the specifications created by the generation unit and reviews their contents. For example, the review unit analyzes the contents of the specifications to check for errors or deficiencies. Specifically, the review unit uses natural language processing (NLP) technology to detect grammatical errors and content inconsistencies in the generated text. For example, the review unit detects grammatical errors and content inconsistencies and proposes corrections. Using an AI model, the review unit takes the specifications created by the generation unit as input and detects errors and deficiencies. The AI ​​model learns from past data and can detect common grammatical errors and content inconsistencies with high accuracy. The review unit can also add favorable conditions to the specifications in natural language. For example, the review unit can add favorable conditions to the specifications to improve the likelihood of securing the order. The review unit analyzes the generated specifications in detail and enhances their competitiveness by adding favorable conditions and suggestions for the user. Furthermore, the review unit can receive feedback from users and continuously improve the content of the specifications. For example, the revisions and additional requests pointed out by users are incorporated into future specification document creation. This allows the review department to improve the quality of the generated specifications and increase user satisfaction. The review department works in conjunction with the generation department to quickly and accurately review the content of the generated specifications and complete them as final specifications.

[0062] The generation unit can generate optimal text based on information from past specifications and contracts, as well as product information. For example, the generation unit can refer to a database of past specifications and contracts and generate optimal text based on similar conditions. For example, the generation unit can generate text that includes detailed specifications of products and services based on product information. For example, the generation unit can automatically create a specification for product A based on the implementation conditions for product A. The generation unit can also use generation AI to generate text that incorporates unique phrasing and technical terms. This enables the creation of highly accurate RFPs, RFIs, and specifications by generating optimal text based on past information. Some or all of the above-described processes in the generation unit may be performed using generation AI, or they may not. For example, the generation unit can generate text using a generation AI model that refers to a database of past specifications and contracts and generates optimal text based on similar conditions.

[0063] The review unit can analyze the contents of the specification document and check for errors or deficiencies. For example, the review unit can detect grammatical errors or inconsistencies in content and propose corrections. The review unit can also use checklists to analyze the contents of the specification document and check for errors or deficiencies. The review unit can also use error checking algorithms to review the contents of the specification document. This allows for the provision of high-quality specifications by analyzing the contents of the specification document and checking for errors or deficiencies. Some or all of the above processes in the review unit may be performed using AI or not. For example, the review unit can take the specification document created by the generation unit as input and review its contents using an AI model that detects errors or deficiencies.

[0064] The review unit can add favorable conditions to the specifications in natural language. For example, the review unit can add favorable conditions to the specifications to improve the likelihood of securing the order. The review unit can also add favorable conditions in natural language, such as price advantages or shorter delivery times. The review unit can also use templates for adding favorable conditions to the specifications. This allows for an improvement in the likelihood of securing the order by adding favorable conditions to the specifications. Some or all of the above processing in the review unit may be performed using AI or not. For example, the review unit can take the specifications created by the generation unit as input and review the content using an AI model that adds favorable conditions.

[0065] The review unit can detect errors and deficiencies and propose corrections. For example, the review unit can detect grammatical errors and content inconsistencies and propose corrections. The review unit can also use error checking algorithms to detect errors and deficiencies. For example, the review unit can point out areas that need correction and present proposed corrections. This improves the quality of the specification by detecting errors and deficiencies and proposing corrections. Some or all of the above processes in the review unit may be performed using AI or not. For example, the review unit can take a specification created by the generation unit as input and review its content using an AI model that detects errors and deficiencies.

[0066] The reception desk can estimate the user's emotions and adjust the input interface for suggested and offered conditions based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, for example, the reception desk can provide detailed input options and suggest a customizable input method. If the user is in a hurry, for example, the reception desk can prioritize voice input to allow for quick input of suggested and offered conditions. This reduces the burden on the user by adjusting the input interface according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0067] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display suggested conditions and service conditions that the user has frequently entered in the past as candidates. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest suggested conditions and service conditions that the user will use at a specific time of day based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method to the user. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input data into AI and have the AI ​​suggest the optimal input method.

[0068] The reception desk can filter the input of proposal and service conditions based on the user's industry and area of ​​expertise. For example, if the user belongs to the IT industry, the reception desk will prioritize displaying IT-related proposal and service conditions. If the user belongs to the medical industry, the reception desk can also prioritize displaying medical-related proposal and service conditions. If the user belongs to the education industry, the reception desk can also prioritize displaying education-related proposal and service conditions. By filtering based on the user's industry and area of ​​expertise, highly relevant conditions can be displayed preferentially. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input data on the user's industry and area of ​​expertise into the AI ​​and have the AI ​​perform the filtering.

[0069] The reception desk can estimate the user's emotions and determine the priority of input conditions based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize inputting important conditions and postpone other conditions. For example, if the user is relaxed, the reception desk may prioritize inputting detailed conditions. For example, if the user is in a hurry, the reception desk may prioritize inputting only the most important conditions. This enables efficient input by determining the priority of input conditions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0070] The reception desk can prioritize inputting highly relevant conditions based on the user's geographical location when inputting suggested conditions or service conditions. For example, if the user is in a specific region, the reception desk will prioritize displaying suggested conditions or service conditions related to that region. For example, if the user is traveling, the reception desk can also prioritize displaying suggested conditions or service conditions related to the travel destination. For example, if the user is at home, the reception desk can also prioritize displaying suggested conditions or service conditions related to the user's home area. This allows for the provision of more appropriate conditions by prioritizing the input of highly relevant conditions based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into the AI ​​and have the AI ​​prioritize the display of highly relevant conditions.

[0071] The reception desk can analyze the user's social media activity and input relevant conditions when the user enters proposal and offer conditions. For example, the reception desk can prioritize displaying proposal and offer conditions related to topics the user frequently mentions on social media. The reception desk can also prioritize displaying proposal and offer conditions related to companies and brands the user follows on social media. The reception desk can also prioritize displaying proposal and offer conditions related to groups and communities the user participates in on social media. This allows the reception desk to prioritize displaying relevant conditions by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into AI and have the AI ​​prioritize displaying relevant conditions.

[0072] The generation unit can estimate the user's emotions and adjust the expression of the generated RFP, RFI, and specifications based on the estimated user emotions. For example, if the user is relaxed, the generation unit will generate an RFP, RFI, or specification using soft language. If the user is in a hurry, for example, the generation unit can also generate a concise and to-the-point RFP, RFI, or specification. If the user is excited, for example, the generation unit can also generate an RFP, RFI, or specification with visually stimulating effects. This allows for the generation of more appropriate RFPs, RFIs, and specifications by adjusting the expression according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform the adjustment of the expression.

[0073] The generation unit can adjust the level of detail in the generated text based on the importance of the proposed conditions and conditions provided. For example, the generation unit can generate text with detailed explanations for important proposed conditions and conditions. For example, the generation unit can also generate text with concise explanations for less important proposed conditions and conditions. The generation unit can also adjust the length and level of detail of paragraphs according to the importance of the proposed conditions and conditions. This allows for the generation of more appropriate text by adjusting the level of detail in the text based on the importance of the proposed conditions and conditions. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input importance data of the proposed conditions and conditions provided into the generation AI and have the generation AI perform the adjustment of the level of detail in the text.

[0074] The generation unit can apply different generation algorithms depending on the category of the proposed conditions or conditions to be provided. For example, for IT-related proposed conditions or conditions to be provided, the generation unit can apply a generation algorithm that includes technical details. For example, for medical-related proposed conditions or conditions to be provided, the generation unit can also apply a generation algorithm that includes technical terms. For example, for education-related proposed conditions or conditions to be provided, the generation unit can also apply a generation algorithm that includes an educational perspective. By applying a generation algorithm according to the category of the proposed conditions or conditions to be provided, more appropriate text can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input category data of proposed conditions or conditions to a generation AI and have the generation AI execute the application of the generation algorithm.

[0075] The generation unit can estimate the user's emotions and adjust the length of the generated text based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise text. If the user is relaxed, for example, the generation unit can also generate longer text with detailed explanations. If the user is excited, for example, the generation unit can also generate text with visually stimulating effects. By adjusting the length of the text according to the user's emotions, more appropriate text can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the text.

[0076] The generation unit can determine the priority of the generated text based on the submission timing of the proposed conditions and service conditions. For example, the generation unit will prioritize generating text for proposed conditions and service conditions with approaching submission deadlines. The generation unit can also postpone generating text for proposed conditions and service conditions with distant submission deadlines. The generation unit can also adjust the priority of the generated text according to the submission timing of the proposed conditions and service conditions. This enables efficient text generation by determining the priority of text based on the submission timing of the proposed conditions and service conditions. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the submission timing of the proposed conditions and service conditions into the generation AI and have the generation AI determine the priority of the text.

[0077] The generation unit can adjust the order of the generated sentences based on the relationships between the proposed conditions and the provided conditions. For example, the generation unit can prioritize placing highly related proposed conditions and provided conditions in the first half of the sentence. The generation unit can also, for example, place less related proposed conditions and provided conditions in the latter half of the sentence. The generation unit can also adjust the order of the generated sentences according to the relationships between the proposed conditions and provided conditions. By adjusting the order of sentences based on the relationships between the proposed conditions and provided conditions, more appropriate sentences can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input relationship data between proposed conditions and provided conditions into a generation AI and have the generation AI perform the adjustment of the sentence order.

[0078] The scrutiny unit can estimate the user's emotions and adjust the scrutiny criteria based on the estimated emotions. For example, if the user is tense, the scrutiny unit can apply strict scrutiny criteria to thoroughly check for errors and deficiencies. For example, if the user is relaxed, the scrutiny unit can apply flexible scrutiny criteria and focus on the overall flow. For example, if the user is in a hurry, the scrutiny unit can apply scrutiny criteria focused on key points. This allows for more appropriate scrutiny by adjusting the scrutiny criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scrutiny unit may be performed using AI or not. For example, the scrutiny unit can input user emotion data into a generative AI and have the generative AI adjust the scrutiny criteria.

[0079] The scrutiny unit can optimize the scrutiny algorithm by referring to past scrutiny data. For example, the scrutiny unit can learn patterns of errors and deficiencies based on past scrutiny data and optimize the scrutiny algorithm. For example, the scrutiny unit can extract errors and deficiencies specific to a particular industry or field from past scrutiny data and optimize the scrutiny algorithm. For example, the scrutiny unit can analyze past scrutiny data to improve the accuracy of the scrutiny algorithm. In this way, the scrutiny algorithm can be optimized and its accuracy improved by referring to past scrutiny data. Some or all of the above processes in the scrutiny unit may be performed using AI or not. For example, the scrutiny unit can input past scrutiny data into AI and have AI perform the optimization of the scrutiny algorithm.

[0080] The scrutiny department can conduct scrutiny by considering the attribute information of the submitter of the proposed terms and conditions. For example, if the submitter of the proposed terms and conditions is a new customer, the scrutiny department will conduct a rigorous scrutiny. For example, if the submitter of the proposed terms and conditions is an existing customer, the scrutiny department can also conduct scrutiny by considering past transaction history. For example, if the submitter of the proposed terms and conditions belongs to a specific industry or field, the scrutiny department can also apply scrutiny criteria specific to that industry or field. This allows for more appropriate scrutiny by considering the submitter's attribute information. Some or all of the above processes in the scrutiny department may be performed using AI or not. For example, the scrutiny department can input the submitter's attribute information into AI and have the AI ​​perform the scrutiny.

[0081] The analysis unit can estimate the user's emotions and adjust the order in which the analysis results are displayed based on the estimated emotions. For example, if the user is nervous, the analysis unit may display important results first to provide reassurance. If the user is relaxed, the analysis unit may also display detailed results sequentially. If the user is in a hurry, the analysis unit may also display concise results first. By adjusting the order in which the analysis results are displayed according to the user's emotions, more appropriate results can be displayed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI execute the order in which the results are displayed.

[0082] The scrutiny unit can perform scrutiny considering the geographical distribution of proposed conditions and conditions of provision. For example, if the proposed conditions and conditions of provision are concentrated in a particular region, the scrutiny unit will perform scrutiny considering the characteristics of that region. For example, if the proposed conditions and conditions of provision are dispersed across multiple regions, the scrutiny unit can also perform scrutiny considering the characteristics of each region. For example, the scrutiny unit can adjust the focus of the scrutiny according to the geographical distribution of the proposed conditions and conditions of provision. This makes it possible to perform more appropriate scrutiny by considering the geographical distribution of the proposed conditions and conditions of provision. Some or all of the above processing in the scrutiny unit may be performed using AI or not. For example, the scrutiny unit can input geographical distribution data of proposed conditions and conditions of provision into AI and have the AI ​​perform the scrutiny.

[0083] The scrutiny unit can improve the accuracy of its scrutiny by referring to relevant literature on the proposed conditions and terms of service. For example, the scrutiny unit can improve the accuracy of its scrutiny by referring to academic papers related to the proposed conditions and terms of service. The scrutiny unit can also improve the accuracy of its scrutiny by referring to industry reports related to the proposed conditions and terms of service. The scrutiny unit can also improve the accuracy of its scrutiny by referring to patent documents related to the proposed conditions and terms of service. In this way, the accuracy of the scrutiny can be improved by referring to relevant literature. Some or all of the above processing in the scrutiny unit may be performed using AI or not. For example, the scrutiny unit can input relevant literature data on the proposed conditions and terms of service into AI and have the AI ​​perform the scrutiny.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The reception desk can estimate the user's emotions and adjust the input interface for suggested and offered conditions based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, for example, the reception desk can provide detailed input options and suggest a customizable input method. If the user is in a hurry, for example, the reception desk can prioritize voice input to allow for quick input of suggested and offered conditions. This reduces the burden on the user by adjusting the input interface according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display suggested conditions and service conditions that the user has frequently entered in the past as candidates. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest suggested conditions and service conditions that the user will use at a specific time of day based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method to the user. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input data into AI and have the AI ​​suggest the optimal input method.

[0087] The reception desk can filter the input of proposal and service conditions based on the user's industry and area of ​​expertise. For example, if the user belongs to the IT industry, the reception desk will prioritize displaying IT-related proposal and service conditions. If the user belongs to the medical industry, the reception desk can also prioritize displaying medical-related proposal and service conditions. If the user belongs to the education industry, the reception desk can also prioritize displaying education-related proposal and service conditions. By filtering based on the user's industry and area of ​​expertise, highly relevant conditions can be displayed preferentially. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input data on the user's industry and area of ​​expertise into the AI ​​and have the AI ​​perform the filtering.

[0088] The reception desk can estimate the user's emotions and determine the priority of input conditions based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize inputting important conditions and postpone other conditions. For example, if the user is relaxed, the reception desk may prioritize inputting detailed conditions. For example, if the user is in a hurry, the reception desk may prioritize inputting only the most important conditions. This enables efficient input by determining the priority of input conditions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The reception desk can prioritize inputting highly relevant conditions based on the user's geographical location when inputting suggested conditions or service conditions. For example, if the user is in a specific region, the reception desk will prioritize displaying suggested conditions or service conditions related to that region. For example, if the user is traveling, the reception desk can also prioritize displaying suggested conditions or service conditions related to the travel destination. For example, if the user is at home, the reception desk can also prioritize displaying suggested conditions or service conditions related to the user's home area. This allows for the provision of more appropriate conditions by prioritizing the input of highly relevant conditions based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into the AI ​​and have the AI ​​prioritize the display of highly relevant conditions.

[0090] The generation unit can estimate the user's emotions and adjust the expression of the generated RFP, RFI, and specifications based on the estimated user emotions. For example, if the user is relaxed, the generation unit will generate an RFP, RFI, or specification using soft language. If the user is in a hurry, for example, the generation unit can also generate a concise and to-the-point RFP, RFI, or specification. If the user is excited, for example, the generation unit can also generate an RFP, RFI, or specification with visually stimulating effects. This allows for the generation of more appropriate RFPs, RFIs, and specifications by adjusting the expression according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform the adjustment of the expression.

[0091] The generation unit can adjust the level of detail in the generated text based on the importance of the proposed conditions and conditions provided. For example, the generation unit can generate text with detailed explanations for important proposed conditions and conditions. For example, the generation unit can also generate text with concise explanations for less important proposed conditions and conditions. The generation unit can also adjust the length and level of detail of paragraphs according to the importance of the proposed conditions and conditions. This allows for the generation of more appropriate text by adjusting the level of detail in the text based on the importance of the proposed conditions and conditions. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input importance data of the proposed conditions and conditions provided into the generation AI and have the generation AI perform the adjustment of the level of detail in the text.

[0092] The generation unit can apply different generation algorithms depending on the category of the proposed conditions or conditions to be provided. For example, for IT-related proposed conditions or conditions to be provided, the generation unit can apply a generation algorithm that includes technical details. For example, for medical-related proposed conditions or conditions to be provided, the generation unit can also apply a generation algorithm that includes technical terms. For example, for education-related proposed conditions or conditions to be provided, the generation unit can also apply a generation algorithm that includes an educational perspective. By applying a generation algorithm according to the category of the proposed conditions or conditions to be provided, more appropriate text can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input category data of proposed conditions or conditions to a generation AI and have the generation AI execute the application of the generation algorithm.

[0093] The generation unit can estimate the user's emotions and adjust the length of the generated text based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise text. If the user is relaxed, for example, the generation unit can also generate longer text with detailed explanations. If the user is excited, for example, the generation unit can also generate text with visually stimulating effects. By adjusting the length of the text according to the user's emotions, more appropriate text can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the text.

[0094] The scrutiny unit can estimate the user's emotions and adjust the scrutiny criteria based on the estimated emotions. For example, if the user is tense, the scrutiny unit can apply strict scrutiny criteria to thoroughly check for errors and deficiencies. For example, if the user is relaxed, the scrutiny unit can apply flexible scrutiny criteria and focus on the overall flow. For example, if the user is in a hurry, the scrutiny unit can apply scrutiny criteria focused on key points. This allows for more appropriate scrutiny by adjusting the scrutiny criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scrutiny unit may be performed using AI or not. For example, the scrutiny unit can input user emotion data into a generative AI and have the generative AI adjust the scrutiny criteria.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The reception desk inputs the proposed conditions and service conditions. These conditions include product implementation requirements and service provision conditions. The reception desk receives the proposed conditions and service conditions entered by the user in digital format. It also supports multiple input methods, such as voice input and handwriting input. For example, it can use voice recognition technology to convert the user's voice input into text data, and technology to convert handwritten input into digital data to receive handwritten proposed conditions and service conditions. Step 2: The generation unit automatically creates RFPs, RFIs, and specifications based on the conditions entered by the reception unit. The generation unit uses generation AI to generate the most suitable text based on information from past specifications and contracts, as well as product information. For example, it can refer to a database of past specifications and contracts and generate the most suitable text based on similar conditions. It can also generate text that includes detailed specifications of products and services based on product information. The generation unit can also generate text that incorporates unique phrasing and technical terms. Step 3: The review unit reads the specification document created by the generation unit and reviews its contents. The review unit analyzes the contents of the specification document and checks for errors or deficiencies. For example, it detects grammatical errors and inconsistencies in content and proposes corrections. It can also add favorable conditions to the specification document in natural language. This improves the likelihood of securing the order.

[0097] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0098] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0100] Each of the multiple elements, including the reception unit, generation unit, and scrutiny unit described above, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, which receives the proposed conditions and provision conditions entered by the user in digital format. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates the optimal document based on information from past specifications and contracts. The scrutiny unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the content of the generated specification document and checks for errors or deficiencies. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] As shown in Figure 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.

[0103] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0107] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0108] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0109] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0110] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0111] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0113] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0115] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements, including the reception unit, generation unit, and verification unit described above, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and converts the user's voice input into text data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the optimal text based on information from past specifications and contracts. The verification unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the generated specifications to check for errors or deficiencies. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements, including the reception unit, generation unit, and verification unit described above, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and converts the user's voice input into text data. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates the optimal text based on information from past specifications and contracts. The verification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the content of the generated specifications to check for errors or deficiencies. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0141] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements, including the reception unit, generation unit, and verification unit described above, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and converts the user's voice input into text data. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates the optimal text based on information from past specifications and contracts. The verification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the content of the generated specifications to check for errors or deficiencies. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0150] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0152] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0153] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0154] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0158] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0159] 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.

[0160] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0161] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0162] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0163] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0165] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0166] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0167] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0168] (Note 1) The reception desk where you input the proposed conditions and service conditions, A generation unit that automatically creates RFPs, RFIs, and specifications based on the conditions entered by the reception unit, The system includes a review unit that reads the specifications created by the generation unit and examines their contents. A system characterized by the following features. (Note 2) The generating unit is Based on past specifications, contracts, and product information, the system generates the most suitable text. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned inspection unit, Analyze the contents of the specifications and check for any errors or deficiencies. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned inspection unit, Add favorable conditions to the specifications in natural language. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned inspection unit, It detects errors and deficiencies and suggests corrections. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface for suggested conditions and service conditions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering proposal conditions or service conditions, filtering is performed based on the user's industry and area of ​​expertise. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input conditions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering proposal conditions or service conditions, the system prioritizes inputting conditions that are highly relevant based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering proposal conditions and service conditions, the system analyzes the user's social media activity and inputs relevant conditions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is We estimate user sentiment and adjust the way RFPs, RFIs, and specifications are written based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is Adjust the level of detail in the generated text based on the importance of the proposed conditions and terms of service. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is Different generation algorithms are applied depending on the category of proposed conditions and delivery conditions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the length of the generated text based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is Prioritize the documents generated based on the submission timing of proposal conditions and terms of service. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is Adjust the order of sentences generated based on the relevance of proposed conditions and terms of service. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned inspection unit, We estimate the user's emotions and adjust the scrutiny criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned inspection unit, Optimize the scrutiny algorithm by referring to past scrutiny data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned inspection unit, The proposal and terms of service will be reviewed carefully, taking into account the attribute information of the submitter. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned inspection unit, It estimates the user's sentiment and adjusts the order in which the analysis results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned inspection unit, We will conduct a thorough review, taking into account the geographical distribution of the proposed conditions and terms of service. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned inspection unit, We will improve the accuracy of our review by referring to relevant literature regarding the proposed conditions and terms of service. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk where you input the proposed conditions and service conditions, A generation unit that automatically creates RFPs, RFIs, and specifications based on the conditions entered by the reception unit, The system includes a review unit that reads the specifications created by the generation unit and examines their contents. A system characterized by the following features.

2. The generating unit is Based on past specifications, contracts, and product information, the system generates the most suitable text. The system according to feature 1.

3. The aforementioned inspection unit, Analyze the contents of the specifications and check for any errors or deficiencies. The system according to feature 1.

4. The aforementioned inspection unit, Add favorable conditions to the specifications in natural language. The system according to feature 1.

5. The aforementioned inspection unit, It detects errors and deficiencies and suggests corrections. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface for suggested conditions and service conditions based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When entering proposal conditions or service conditions, filtering is performed based on the user's industry and area of ​​expertise. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of input conditions based on the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is When entering proposal conditions or service conditions, the system prioritizes inputting conditions that are highly relevant based on the user's geographical location information. The system according to feature 1.

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