system

Abstract

The system according to this embodiment aims to enable SMB companies to achieve a purchasing process at the level of large corporations. [Solution] The system according to the embodiment comprises an analysis unit, a selection unit, a negotiation unit, a proposal unit, and a continuous analysis unit. The analysis unit analyzes the issues in the purchasing process. The selection unit identifies the optimal supplier based on the issues analyzed by the analysis unit. The negotiation unit conducts price negotiations with the supplier identified by the selection unit. The proposal unit proposes a purchasing process based on the price negotiated by the negotiation unit. The continuous analysis unit continuously analyzes the purchasing process proposed by the proposal unit and continues to propose the optimal purchasing process.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 a 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 prior art, it is difficult for SMB enterprises to realize a purchasing process at the level of large enterprises, and there are problems in price negotiation power and selection of suppliers.

[0005] The system according to the embodiment aims to enable SMB enterprises to realize a purchasing process at the level of large enterprises.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a selection unit, a negotiation unit, a proposal unit, and a continuous analysis unit. The analysis unit analyzes the issues in the purchasing process. The selection unit identifies the optimal supplier based on the issues analyzed by the analysis unit. The negotiation unit conducts price negotiations with the supplier identified by the selection unit. The proposal unit proposes a purchasing process based on the price negotiated by the negotiation unit. The continuous analysis unit continuously analyzes the purchasing process proposed by the proposal unit and continues to propose the optimal purchasing process. [Effects of the Invention]

[0007] The system according to this embodiment enables SMBs to achieve a purchasing process at the level of large corporations. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 purchasing support system according to an embodiment of the present invention is a support service that utilizes generative AI to enable SMBs (small and medium-sized enterprises) to achieve purchasing processes at the level of large corporations. This purchasing support system solves the purchasing process challenges faced by SMB companies by using generative AI to achieve procurement at appropriate prices based on the purchasing activity history of the company being supported. This allows SMB companies to focus on business expansion and support the development and growth of each of their group trading partners. For example, the generative AI analyzes past purchasing data, identifies optimal suppliers, and conducts price negotiations to achieve procurement at appropriate prices. Next, based on the purchasing process proposed by the generative AI, SMB companies can focus on business expansion. For example, by purchasing goods at appropriate prices from suppliers proposed by the generative AI, costs can be reduced and profits increased. This allows SMB companies to focus on business expansion and support the development and growth of each of their group trading partners. Furthermore, the generative AI continuously analyzes the purchasing process challenges faced by SMB companies and continues to propose optimal purchasing processes. This allows SMB companies to always achieve optimal purchasing processes and focus on business expansion. For example, the generating AI identifies new suppliers and negotiates prices, ensuring that procurement is always conducted at the appropriate price. This system enables SMBs to achieve a purchasing process on par with large corporations, allowing them to focus on business expansion. This supports the development and growth of each group's trading partners and increases the transaction volume of each group. For instance, by procuring goods at appropriate prices based on the purchasing process proposed by the generating AI, SMBs can reduce costs and increase profits. This allows SMBs to focus on business expansion and support the development and growth of each group's trading partners. In short, the purchasing support system enables SMBs to achieve a purchasing process on par with large corporations and focus on business expansion.

[0029] The purchasing support system according to this embodiment comprises an analysis unit, a selection unit, a negotiation unit, a proposal unit, and a continuous analysis unit. The analysis unit analyzes the challenges of the purchasing process. The analysis unit analyzes challenges faced by SMB companies, such as insufficient price negotiation power, supplier selection, and lack of know-how regarding purchasing activities. The analysis unit uses generative AI to analyze past purchasing data and market data to identify specific challenges faced by SMB companies. The selection unit identifies the optimal supplier based on the challenges analyzed by the analysis unit. The selection unit, for example, analyzes past purchasing data to identify the optimal supplier. The selection unit uses generative AI to evaluate suppliers based on criteria such as price, quality, delivery time, and reliability, and selects the optimal supplier. The negotiation unit conducts price negotiations with the suppliers identified by the selection unit. The negotiation unit, for example, conducts price negotiations with the identified suppliers. The negotiation unit uses generative AI to optimize the timing and methods of negotiations to achieve purchasing at an appropriate price. The proposal unit proposes a purchasing process based on the price negotiated by the negotiation unit. The proposal department proposes a purchasing process based on negotiated prices, for example. The proposal department uses generative AI to optimize purchasing processes such as ordering, delivery, inspection, and payment, achieving efficient purchasing. The continuous analysis department continuously analyzes the purchasing process proposed by the proposal department and continues to propose the optimal purchasing process. The continuous analysis department, for example, identifies new suppliers and negotiates prices to ensure that purchasing is always done at appropriate prices. The continuous analysis department uses generative AI to collect data regularly and perform real-time analysis, constantly optimizing the purchasing process based on the latest information. As a result, the purchasing support system according to this embodiment enables SMBs to achieve purchasing processes at the level of large corporations, allowing them to focus on business expansion.

[0030] The analytics department analyzes challenges in the purchasing process. For example, it analyzes challenges faced by SMBs, such as insufficient price negotiation power, supplier selection, and lack of expertise in purchasing activities. Using generative AI, the analytics department analyzes historical purchasing data and market data to identify specific challenges faced by SMBs. Specifically, the generative AI receives historical purchasing history and market trend data as input and performs pattern recognition and anomaly detection based on this data. For example, it analyzes data on price fluctuation patterns and supplier reliability to identify when prices tend to rise and which suppliers are prone to delivery delays. Furthermore, the generative AI can also use natural language processing technology to analyze the content of purchasing managers' notes and emails to extract potential problems in the purchasing process. This allows the analytics department to analyze the specific challenges faced by SMBs from multiple angles and generate detailed reports. The analysis results are visualized in a dashboard format and provided in a way that is easily understandable to purchasing managers. This provides the analytics department with a foundation for SMBs to clearly understand the challenges in their purchasing process and take appropriate measures.

[0031] The Identification Department identifies the optimal supplier based on the issues analyzed by the Analysis Department. For example, the Identification Department analyzes past purchasing data to identify the best supplier. The Identification Department uses Generative AI to evaluate suppliers based on criteria such as price, quality, delivery time, and reliability, and selects the optimal supplier. Specifically, the Generative AI analyzes the past transaction data of each supplier and evaluates price fluctuation history, quality stability, and on-time delivery rates. Furthermore, the Generative AI refers to external market data and industry reports to comprehensively judge the market evaluation and competitiveness of suppliers. This allows the Identification Department to select suppliers that are not only inexpensive but also highly reliable in terms of quality and delivery time. The Identification Department also uses Generative AI to assess the risks of suppliers. For example, it can analyze the financial status and past trouble history of suppliers to eliminate high-risk suppliers in advance. This allows the Identification Department to identify the optimal suppliers for SMB companies to receive a stable supply and improve the reliability of the purchasing process.

[0032] The Negotiation Department conducts price negotiations with suppliers identified by the Designation Department. For example, the Negotiation Department negotiates prices with identified suppliers. Using Generative AI, the Negotiation Department optimizes the timing and methods of negotiations to achieve procurement at appropriate prices. Specifically, the Generative AI analyzes past negotiation data and market price trends to identify the optimal negotiation timing. For example, if prices tend to fall during a particular period, negotiating at that time can yield favorable terms. The Generative AI also suggests specific phrases and strategies to use during negotiations. For instance, in price negotiations, presenting compelling data based on past transaction history and market competition can secure favorable terms from suppliers. Furthermore, the Generative AI can monitor the progress of negotiations in real time and modify the negotiation strategy as needed. This allows the Negotiation Department to consistently use optimal negotiation methods, enabling SMBs to procure goods on favorable terms.

[0033] The Proposal Department proposes a purchasing process based on the price negotiated by the Negotiation Department. For example, the Proposal Department proposes a purchasing process based on the negotiated price. The Proposal Department uses generative AI to optimize purchasing processes such as ordering, delivery, inspection, and payment, thereby achieving efficient purchasing. Specifically, the generative AI analyzes past purchasing data and market trends to propose the optimal ordering timing and delivery schedule. For example, by predicting peak demand periods and placing orders beforehand, inventory shortages can be prevented. In addition, to optimize the inspection process at the time of delivery, the generative AI analyzes past inspection data and proposes key inspection items and efficient inspection methods. Furthermore, in the payment process, the generative AI can propose the optimal payment timing and payment method, thereby improving cash flow. As a result, the Proposal Department enables SMBs to achieve an efficient and effective purchasing process, leading to cost reductions and improved operational efficiency.

[0034] The Continuous Analysis Department continuously analyzes the purchasing process proposed by the Proposal Department and proposes the optimal purchasing process. For example, the Continuous Analysis Department identifies new suppliers and negotiates prices to ensure that procurement is always conducted at appropriate prices. The Continuous Analysis Department uses generative AI to perform regular data collection and real-time analysis, constantly optimizing the purchasing process based on the latest information. Specifically, the generative AI regularly collects market data and purchasing data and evaluates the performance of the purchasing process based on this data. For example, it monitors supplier performance and market price trends, and changes suppliers or negotiates prices as needed. The generative AI also evaluates the efficiency and cost at each step of the purchasing process and identifies areas for improvement. As a result, the Continuous Analysis Department can constantly optimize the purchasing process based on the latest information and support SMB companies in maintaining their competitiveness. Furthermore, the Continuous Analysis Department regularly proposes improvements to the purchasing process, enabling SMB companies to always implement the optimal purchasing strategy. In this way, the Continuous Analysis Department can play a crucial role in enabling SMB companies to respond quickly to market fluctuations and achieve sustainable growth.

[0035] The analysis unit can analyze challenges faced by SMB companies, such as insufficient price negotiation power, supplier selection, and lack of know-how regarding purchasing activities. For example, to analyze insufficient price negotiation power, the analysis unit analyzes past negotiation history and market data. Using generative AI, the analysis unit can evaluate negotiation success rates and negotiation methods to identify insufficient price negotiation power. Furthermore, to analyze challenges related to supplier selection, the analysis unit analyzes past purchasing data and supplier evaluation data. Using generative AI, the analysis unit can evaluate suppliers based on criteria such as quality, delivery time, and reliability to identify the optimal supplier. In addition, to analyze insufficient know-how regarding purchasing activities, the analysis unit analyzes the purchasing processes and workflows of SMB companies. Using generative AI, the analysis unit can evaluate the efficiency and effectiveness of the purchasing process to identify insufficient know-how. This allows for the proposal of appropriate purchasing processes by analyzing the unique challenges of SMB companies. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without using generative AI. For example, the analysis unit can input past purchase data into a generating AI, which can then analyze the data to identify problems.

[0036] The identification unit can analyze past purchasing data to identify the optimal supplier. For example, the identification unit can analyze past purchasing data to identify the optimal supplier. The identification unit can use a generation AI to evaluate suppliers based on criteria such as price, quality, delivery time, and reliability, and select the optimal supplier. The identification unit uses a generation AI to analyze past purchasing data and evaluate the performance of suppliers. For example, the identification unit can use a generation AI to analyze past purchasing data and identify the most reliable supplier. The identification unit can also use a generation AI to identify cost-effective suppliers based on past purchasing data. Furthermore, the identification unit can use a generation AI to analyze past purchasing data and identify suppliers with stable delivery times. This enables efficient purchasing by identifying the optimal supplier based on past purchasing data. Some or all of the above processing in the identification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the identification unit can input past purchasing data into a generation AI, and the generation AI can analyze the data to identify the optimal supplier.

[0037] The Negotiation Department can conduct price negotiations with identified suppliers. For example, the Negotiation Department can conduct price negotiations with identified suppliers. The Negotiation Department can use generative AI to optimize the timing and methods of negotiations and achieve procurement at appropriate prices. The Negotiation Department can use generative AI to evaluate the success rate and methods of negotiations and formulate the optimal negotiation strategy. For example, the Negotiation Department can use generative AI to analyze past negotiation history and identify negotiation methods with a high success rate. The Negotiation Department can also use generative AI to identify negotiation methods that have a high cost reduction effect based on past negotiation history. Furthermore, the Negotiation Department can use generative AI to analyze past negotiation history and identify negotiation methods that have a high delivery time reduction effect. This makes it possible to procure at appropriate prices by conducting price negotiations with identified suppliers. Some or all of the above processes in the Negotiation Department may be performed using generative AI or not. For example, the Negotiation Department can input negotiation data with identified suppliers into generative AI, and the generative AI can analyze the data and formulate the optimal negotiation strategy.

[0038] The proposal department can propose a purchasing process based on the negotiated price. For example, the proposal department proposes a purchasing process based on the negotiated price. The proposal department can use generative AI to optimize purchasing processes such as ordering, delivery, inspection, and payment, thereby achieving efficient purchasing. The proposal department uses generative AI to formulate the optimal purchasing process based on the negotiated price. For example, the proposal department can use generative AI to propose a purchasing process that is highly effective in reducing costs based on the negotiated price. The proposal department can also use generative AI to propose a purchasing process that is highly effective in shortening delivery times based on the negotiated price. Furthermore, the proposal department can use generative AI to propose a purchasing process that is highly effective in improving quality based on the negotiated price. This makes efficient purchasing possible by proposing a purchasing process based on the negotiated price. Some or all of the above processing in the proposal department may be performed using generative AI or not. For example, the proposal department can input negotiated price data into generative AI, and the generative AI can analyze the data and propose the optimal purchasing process.

[0039] The Continuous Analysis Department can always ensure procurement at appropriate prices by identifying new suppliers and negotiating prices. For example, the Continuous Analysis Department can always ensure procurement at appropriate prices by identifying new suppliers and negotiating prices. The Continuous Analysis Department can use generative AI to perform periodic data collection and real-time analysis, constantly optimizing the purchasing process based on the latest information. The Continuous Analysis Department uses generative AI to identify new suppliers and negotiate prices. For example, the Continuous Analysis Department can use generative AI to analyze market data and identify new suppliers. Furthermore, the Continuous Analysis Department can use generative AI to negotiate prices with new suppliers, ensuring procurement at appropriate prices. In addition, the Continuous Analysis Department can use generative AI to evaluate the performance of new suppliers and select the optimal supplier. This allows for continuous proposal of the optimal purchasing process, enabling procurement at appropriate prices at all times. Some or all of the above processes in the Continuous Analysis Department may be performed using generative AI, or they may be performed without generative AI. For example, the continuous analysis unit inputs data on new suppliers into the generating AI, which then analyzes the data to identify the optimal supplier.

[0040] The analysis unit can analyze the past purchasing history of SMB companies in detail and extract specific patterns. For example, the analysis unit can use a generative AI to analyze past purchasing history and identify patterns of frequently purchased goods and services. The analysis unit uses the generative AI to analyze past purchasing history in detail and extract specific patterns. For example, the analysis unit can use the generative AI to analyze past purchasing history and extract seasonal purchasing patterns. The analysis unit can also use the generative AI to identify transaction patterns with specific suppliers based on past purchasing history. Furthermore, the analysis unit can use the generative AI to analyze past purchasing history and identify the purchase frequency and timing of specific products. This allows for the extraction of specific patterns through detailed analysis of past purchasing history, enabling more efficient purchasing. Some or all of the above-described processes in the analysis unit may be performed using the generative AI, or they may not be performed using the generative AI. For example, the analysis unit can input past purchasing history data into the generative AI, which can then analyze the data and extract specific patterns.

[0041] The analysis unit can apply industry-specific analytical methods, taking into account the industry characteristics of SMB companies. For example, the analysis unit can use a generative AI to analyze purchasing data of manufacturing SMB companies and identify purchasing patterns specific to the manufacturing industry. The analysis unit can use the generative AI to apply analytical methods that take industry characteristics into account. For example, the analysis unit can use a generative AI to analyze purchasing data of retail SMB companies and identify purchasing patterns specific to the retail industry. The analysis unit can also use a generative AI to analyze purchasing data of service industry SMB companies and identify purchasing patterns specific to the service industry. Furthermore, the analysis unit can use a generative AI to apply industry-specific analytical methods, taking industry characteristics into account. This allows for more appropriate analysis by applying analytical methods that take industry characteristics into account. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input data related to industry characteristics into a generative AI, and the generative AI can analyze the data and apply analytical methods specific to a particular industry.

[0042] The analysis unit can analyze region-specific challenges by considering the geographical location information of SMB companies. For example, the analysis unit uses a generative AI to identify region-specific purchasing challenges based on the location of SMB companies. The analysis unit uses the generative AI to analyze region-specific challenges while considering geographical location information. For example, the analysis unit can use the generative AI to propose an optimal purchasing strategy considering the regional economic situation. The analysis unit can also use the generative AI to analyze the regional competitive landscape and propose purchasing strategies to enhance competitiveness. Furthermore, the analysis unit can use the generative AI to propose an optimal purchasing process considering regional logistics costs and regulations. In this way, by considering geographical location information, region-specific challenges can be analyzed and an appropriate purchasing process can be proposed. Some or all of the above processing in the analysis unit may be performed using the generative AI, or it may be performed without the generative AI. For example, the analysis unit can input geographical location information into the generative AI, and the generative AI can analyze the data to identify region-specific challenges.

[0043] The analytics unit can analyze the social media activities of SMB companies and identify related issues. For example, the analytics unit can use generative AI to analyze social media posts of SMB companies and identify purchasing issues. The analytics unit uses generative AI to analyze social media activities and identify related issues. For example, the analytics unit can use generative AI to suggest improvements to purchasing strategies based on social media reactions. The analytics unit can also use generative AI to analyze social media trends and reflect them in purchasing strategies. Furthermore, the analytics unit can use generative AI to analyze social media engagement data and identify issues based on customer feedback. In this way, by analyzing social media activities, related issues can be identified and appropriate purchasing processes can be proposed. Some or all of the above processes in the analytics unit may be performed using generative AI or not. For example, the analytics unit can input social media post data into generative AI, and the generative AI can analyze the data and identify related issues.

[0044] The identification unit can analyze past purchase data in detail and identify the optimal supplier. For example, the identification unit can use a generating AI to analyze past purchase data and identify the most reliable supplier. The identification unit uses a generating AI to analyze past purchase data in detail and identify the optimal supplier. For example, the identification unit can use a generating AI to identify cost-effective suppliers based on past purchase data. The identification unit can also use a generating AI to analyze past purchase data and identify suppliers with stable delivery times. Furthermore, the identification unit can use a generating AI to analyze past purchase data and identify high-quality suppliers. This allows for the identification of optimal suppliers and efficient purchasing by analyzing past purchase data in detail. Some or all of the above-described processes in the identification unit may be performed using a generating AI or not. For example, the identification unit can input past purchase data into a generating AI, which can analyze the data and identify the optimal supplier.

[0045] The Identification Unit can select suppliers specializing in a particular industry by considering the industry characteristics of the suppliers. For example, the Identification Unit can use a generating AI to identify suppliers in the manufacturing industry and address the specific needs of the manufacturing industry. The Identification Unit uses a generating AI to select suppliers specializing in a particular industry by considering the industry characteristics of the suppliers. For example, the Identification Unit can use a generating AI to identify suppliers in the retail industry and address the specific needs of the retail industry. The Identification Unit can also use a generating AI to identify suppliers in the service industry and address the specific needs of the service industry. Furthermore, the Identification Unit can use a generating AI to select suppliers specializing in a particular industry by considering industry characteristics. This enables efficient purchasing by selecting suppliers specializing in a particular industry by considering industry characteristics. Some or all of the above processing in the Identification Unit may be performed using a generating AI or not. For example, the Identification Unit can input data on industry characteristics into a generating AI, and the generating AI can analyze the data to select suppliers specializing in a particular industry.

[0046] The identification unit can identify region-specific suppliers by considering the geographical location information of those suppliers. For example, the identification unit uses a generating AI to identify suppliers that can address region-specific challenges based on the supplier's location. The identification unit uses the generating AI to identify region-specific suppliers by considering geographical location information. For example, the identification unit can use the generating AI to identify the optimal supplier by considering the regional economic situation. The identification unit can also use the generating AI to analyze the regional competitive situation and identify suppliers that will enhance competitiveness. Furthermore, the identification unit can use the generating AI to identify the optimal supplier by considering regional logistics costs and regulations. This allows for the identification of region-specific suppliers and efficient purchasing by considering geographical location information. Some or all of the above processing in the identification unit may be performed using the generating AI or not. For example, the identification unit can input geographical location information into the generating AI, and the generating AI can analyze the data to identify region-specific suppliers.

[0047] The identification unit can analyze the social media activities of suppliers and identify relevant suppliers. For example, the identification unit can use a generative AI to analyze the social media posts of suppliers and identify reliable suppliers. The identification unit uses a generative AI to analyze social media activities and identify relevant suppliers. For example, the identification unit can use a generative AI to identify reputable suppliers based on social media reactions. The identification unit can also use a generative AI to analyze social media trends and identify suppliers that can meet the latest needs. Furthermore, the identification unit can use a generative AI to analyze social media engagement data and identify suppliers based on customer feedback. This allows for the identification of relevant suppliers by analyzing social media activities, enabling efficient purchasing. Some or all of the above processing in the identification unit may be performed using a generative AI or not. For example, the identification unit can input social media post data into a generative AI, which can analyze the data and identify relevant suppliers.

[0048] The negotiation department can analyze past negotiation history in detail and select the optimal negotiation method. For example, the negotiation department can use a generative AI to analyze past negotiation history and identify negotiation methods with a high success rate. The negotiation department uses a generative AI to analyze past negotiation history in detail and select the optimal negotiation method. For example, the negotiation department can use a generative AI to identify negotiation methods that are highly effective in reducing costs based on past negotiation history. The negotiation department can also use a generative AI to analyze past negotiation history and identify negotiation methods that are highly effective in shortening delivery times. Furthermore, the negotiation department can use a generative AI to analyze past negotiation history and identify negotiation methods that are highly effective in improving quality. In this way, by analyzing past negotiation history in detail, the optimal negotiation method can be selected, enabling efficient negotiations. Some or all of the above processes in the negotiation department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the negotiation department can input past negotiation history data into a generative AI, and the generative AI can analyze the data and select the optimal negotiation method.

[0049] The Negotiation Department can apply negotiation techniques specific to a particular industry, taking into account the industry characteristics of the supplier. For example, the Negotiation Department's generative AI can apply negotiation techniques that address the specific needs of the manufacturing industry when negotiating with a manufacturing supplier. The Negotiation Department can use generative AI to apply negotiation techniques specific to a particular industry, taking into account the industry characteristics of the supplier. For example, the Negotiation Department can use generative AI to apply negotiation techniques that address the specific needs of the retail industry when negotiating with a retail supplier. The Negotiation Department can also use generative AI to apply negotiation techniques that address the specific needs of the service industry when negotiating with a service supplier. Furthermore, the Negotiation Department can use generative AI to apply negotiation techniques specific to a particular industry, taking into account industry characteristics. This allows for efficient negotiation by applying negotiation techniques specific to a particular industry, by considering industry characteristics. Some or all of the above-described processes in the Negotiation Department may be performed using generative AI or not. For example, the Negotiation Department can input data on industry characteristics into the generative AI, and the generative AI can analyze the data and apply negotiation techniques specific to a particular industry.

[0050] The negotiation department can apply region-specific negotiation methods by considering the geographical location information of suppliers. For example, the negotiation department can use a generative AI to apply negotiation methods that address region-specific challenges based on the supplier's location. The negotiation department uses a generative AI to apply region-specific negotiation methods while considering geographical location information. For example, the negotiation department can use a generative AI to apply the optimal negotiation method by considering the regional economic situation. The negotiation department can also use a generative AI to analyze the regional competitive situation and apply negotiation methods to enhance competitiveness. Furthermore, the negotiation department can use a generative AI to apply the optimal negotiation method by considering regional logistics costs and regulations. This enables efficient negotiation by applying region-specific negotiation methods while considering geographical location information. Some or all of the above processes in the negotiation department may be performed using a generative AI or not. For example, the negotiation department can input geographical location information into a generative AI, which can analyze the data and apply region-specific negotiation methods.

[0051] The negotiation department can analyze the social media activities of suppliers and identify relevant negotiation techniques. For example, the negotiation department can use generative AI to analyze suppliers' social media posts and identify reliable negotiation techniques. The negotiation department can use generative AI to analyze social media activities and identify relevant negotiation techniques. For example, the negotiation department can use generative AI to identify well-regarded negotiation techniques based on social media reactions. The negotiation department can also use generative AI to analyze social media trends and identify negotiation techniques that can address the latest needs. Furthermore, the negotiation department can use generative AI to analyze social media engagement data and identify negotiation techniques based on customer feedback. This allows for the identification of relevant negotiation techniques by analyzing social media activities, enabling more efficient negotiations. Some or all of the above processes in the negotiation department may be performed using generative AI or not. For example, the negotiation department can input social media post data into generative AI, which can analyze the data and identify relevant negotiation techniques.

[0052] The proposal department can adjust the level of detail of its proposals based on the negotiated price. For example, the proposal department can use a generation AI to generate proposals with high cost-reduction potential based on the negotiated price. The proposal department can use a generation AI to adjust the level of detail of its proposals based on the negotiated price. For example, the proposal department can use a generation AI to generate proposals with high delivery time reduction potential based on the negotiated price. The proposal department can also use a generation AI to generate proposals with high quality improvement potential based on the negotiated price. Furthermore, the proposal department can use a generation AI to propose the optimal purchasing process based on the negotiated price. By adjusting the level of detail of proposals based on the negotiated price, more appropriate proposals become possible. Some or all of the above processes in the proposal department may be performed using a generation AI, or they may not be performed using a generation AI. For example, the proposal department can input negotiated price data into a generation AI, which can analyze the data and generate the optimal proposal.

[0053] The proposal department can apply industry-specific proposal methods by considering the industry characteristics of its suppliers. For example, the proposal department's generating AI can make proposals to manufacturing suppliers that address the specific needs of the manufacturing industry. The proposal department uses the generating AI to apply industry-specific proposal methods by considering the industry characteristics of its suppliers. For example, the proposal department's generating AI can make proposals to retail suppliers that address the specific needs of the retail industry. The proposal department can also make proposals to service industry suppliers that address the specific needs of the service industry. Furthermore, the proposal department can apply industry-specific proposal methods by considering industry characteristics. This enables efficient proposals by considering industry characteristics and applying industry-specific proposal methods. Some or all of the above processing in the proposal department may be performed using the generating AI, or it may be performed without using the generating AI. For example, the proposal department can input data on industry characteristics into the generating AI, and the generating AI can analyze the data and apply industry-specific proposal methods.

[0054] The proposal department can determine the priority of proposals based on the timing of the submitted negotiated prices. For example, the proposal department can use a generation AI to prioritize proposals that require the fastest response based on the timing of the submitted negotiated prices. The proposal department uses a generation AI to determine the priority of proposals based on the timing of the submitted negotiated prices. For example, the proposal department can use a generation AI to prioritize proposals that offer a high cost reduction effect based on the timing of the submitted negotiated prices. The proposal department can also use a generation AI to prioritize proposals that offer a high delivery time reduction effect based on the timing of the submitted negotiated prices. Furthermore, the proposal department can use a generation AI to prioritize proposals that offer a high quality improvement effect based on the timing of the submitted negotiated prices. This allows for more appropriate proposals by determining the priority of proposals based on the timing of the submitted negotiated prices. Some or all of the above processing in the proposal department may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal department can input the data on the timing of the submitted negotiated prices into a generation AI, and the generation AI can analyze the data to determine the priority of proposals.

[0055] The proposal department can adjust the order of proposals based on the relationships between suppliers. For example, the proposal department's generating AI can prioritize the most important proposals based on the relationships between suppliers. The proposal department uses the generating AI to adjust the order of proposals based on the relationships between suppliers. For example, the proposal department can use the generating AI to prioritize proposals with high cost reduction effects based on the relationships between suppliers. The proposal department can also use the generating AI to prioritize proposals with high delivery time reduction effects based on the relationships between suppliers. Furthermore, the proposal department can use the generating AI to prioritize proposals with high quality improvement effects based on the relationships between suppliers. By adjusting the order of proposals based on the relationships between suppliers, more appropriate proposals become possible. Some or all of the above processing in the proposal department may be performed using the generating AI, or it may be performed without using the generating AI. For example, the proposal department can input supplier relationship data into the generating AI, and the generating AI can analyze the data and adjust the order of proposals.

[0056] The Continuous Analysis Unit can analyze past analysis data in detail and select the optimal analysis method. For example, the Continuous Analysis Unit uses a generating AI to analyze past analysis data and identify analysis methods with a high success rate. The Continuous Analysis Unit uses the generating AI to analyze past analysis data in detail and select the optimal analysis method. For example, the Continuous Analysis Unit can use the generating AI to identify analysis methods that have a high cost reduction effect based on past analysis data. The Continuous Analysis Unit can also use the generating AI to analyze past analysis data and identify analysis methods that have a high effect on shortening delivery times. Furthermore, the Continuous Analysis Unit can use the generating AI to analyze past analysis data and identify analysis methods that have a high effect on improving quality. In this way, by analyzing past analysis data in detail, the optimal analysis method can be selected, enabling efficient analysis. Some or all of the above processes in the Continuous Analysis Unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the Continuous Analysis Unit can input past analysis data into the generating AI, and the generating AI can analyze the data and select the optimal analysis method.

[0057] The Continuous Analysis Unit can apply analytical methods specific to a particular industry, taking into account the industry characteristics of its suppliers. For example, the Continuous Analysis Unit's generating AI can analyze data from manufacturing suppliers and apply analytical methods that address the specific needs of the manufacturing industry. The Continuous Analysis Unit uses the generating AI to apply analytical methods specific to a particular industry, taking into account the industry characteristics of its suppliers. For example, the Continuous Analysis Unit's generating AI can analyze data from retail suppliers and apply analytical methods that address the specific needs of the retail industry. The Continuous Analysis Unit can also use the generating AI to analyze data from service suppliers and apply analytical methods that address the specific needs of the service industry. Furthermore, the Continuous Analysis Unit can apply analytical methods specific to a particular industry, taking industry characteristics into account. This enables efficient analysis by applying analytical methods specific to a particular industry, taking industry characteristics into account. Some or all of the above-described processes in the Continuous Analysis Unit may be performed using the generating AI or not. For example, the Continuous Analysis Unit can input data related to industry characteristics into the generating AI, and the generating AI can analyze the data and apply analytical methods specific to a particular industry.

[0058] The continuous analysis unit can apply region-specific analysis methods by considering the geographical location information of suppliers. For example, the continuous analysis unit's generating AI can apply analysis methods that address region-specific challenges based on the supplier's location. The continuous analysis unit uses the generating AI to apply region-specific analysis methods while considering geographical location information. For example, the continuous analysis unit's generating AI can apply the optimal analysis method by considering the region's economic situation. The continuous analysis unit can also have the generating AI analyze the regional competitive situation and apply analysis methods to enhance competitiveness. Furthermore, the continuous analysis unit can have the generating AI apply the optimal analysis method by considering regional logistics costs and regulations. This enables efficient analysis by considering geographical location information and applying region-specific analysis methods. Some or all of the above processing in the continuous analysis unit may be performed using the generating AI or not. For example, the continuous analysis unit can input geographical location information into the generating AI, and the generating AI can analyze the data and apply region-specific analysis methods.

[0059] The Continuous Analysis Unit can analyze the social media activities of suppliers and identify relevant analytical methods. For example, the Continuous Analysis Unit can use a generative AI to analyze the social media posts of suppliers and identify reliable analytical methods. The Continuous Analysis Unit uses a generative AI to analyze social media activities and identify relevant analytical methods. For example, the Continuous Analysis Unit can use a generative AI to identify well-regarded analytical methods based on social media reactions. The Continuous Analysis Unit can also use a generative AI to analyze social media trends and identify analytical methods that can meet the latest needs. Furthermore, the Continuous Analysis Unit can use a generative AI to analyze social media engagement data and identify analytical methods based on customer feedback. This enables efficient analysis by identifying relevant analytical methods through the analysis of social media activities. Some or all of the above processes in the Continuous Analysis Unit may be performed using a generative AI or not. For example, the Continuous Analysis Unit can input social media post data into a generative AI, which can analyze the data and identify relevant analytical methods.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The purchasing support system can also include a forecasting unit. This unit predicts future purchasing demand and supports timely procurement. For example, it can use generative AI to analyze past purchasing data and market trends to predict the next demand peak. It can also consider seasonal demand fluctuations and suggest appropriate inventory management. Furthermore, it can forecast demand based on specific events or campaigns and optimize procurement plans. This allows SMBs to procure appropriately in line with demand, preventing inventory shortages and excesses.

[0062] The purchasing support system can also include an evaluation unit. This unit regularly assesses the performance of suppliers to maintain reliable suppliers. For example, the evaluation unit can use generative AI to evaluate suppliers' on-time delivery rates and quality control performance. It can also analyze suppliers' cost-effectiveness and propose optimal trading terms. Furthermore, the evaluation unit can evaluate suppliers' customer service and after-sales service to determine overall reliability. This allows SMBs to build long-term business relationships with reliable suppliers.

[0063] The purchasing support system can also include a notification unit. This unit notifies users of important purchasing information and alerts in real time. For example, it can use AI to detect sudden fluctuations in market prices and immediately notify users. It can also detect delays in supplier deliveries or quality issues and quickly propose countermeasures. Furthermore, it can remind users of important contract renewal dates and payment deadlines, helping them to act at the appropriate time. This allows SMBs to avoid missing important purchasing information and respond quickly and appropriately.

[0064] The purchasing support system can also include a training department. This department provides purchasing managers at SMB companies with training on purchasing processes and negotiation techniques. For example, the training department can use generative AI to analyze past successes and failures and learn effective negotiation methods. It can also improve practical negotiation skills through simulations. Furthermore, the training department can provide information on the latest market trends and purchasing strategies to update the managers' knowledge. This enables purchasing managers at SMB companies to conduct more effective purchasing activities.

[0065] The purchasing support system can also include a feedback section. This section collects user feedback and uses it to improve the system. For example, the feedback section can use generative AI to analyze user opinions and requests, improving the system's functionality and usability. It can also periodically survey user satisfaction and evaluate the quality of service. Furthermore, based on user feedback, the feedback section can propose new features and services, increasing the system's value. This ensures that SMBs always have access to a purchasing support system that meets their latest needs.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The analysis department analyzes the challenges in the purchasing process. Using generative AI, the analysis department analyzes historical purchasing data and market data to identify specific challenges faced by SMB companies. For example, it analyzes challenges such as insufficient price negotiation power, supplier selection, and lack of know-how regarding purchasing activities. Step 2: The Identification Unit identifies the optimal supplier based on the issues analyzed by the Analysis Unit. The Identification Unit uses Generative AI to evaluate suppliers based on criteria such as price, quality, delivery time, and reliability, and selects the optimal supplier. For example, it may analyze past purchasing data to identify the optimal supplier. Step 3: The Negotiation Department conducts price negotiations with suppliers identified by the Identification Department. The Negotiation Department uses generative AI to optimize the timing and methods of negotiations, thereby achieving procurement at appropriate prices. For example, it conducts price negotiations with identified suppliers. Step 4: The Proposal Department proposes a purchasing process based on the price negotiated by the Negotiation Department. The Proposal Department uses generative AI to optimize purchasing processes such as ordering, delivery, inspection, and payment, thereby achieving efficient purchasing. For example, it proposes a purchasing process based on the negotiated price. Step 5: The Continuous Analysis Department continuously analyzes the purchasing process proposed by the Proposal Department and proposes the optimal purchasing process. The Continuous Analysis Department uses generative AI to collect data regularly and perform real-time analysis, constantly optimizing the purchasing process based on the latest information. For example, by identifying new suppliers and negotiating prices, it ensures that procurement is always conducted at appropriate prices.

[0068] (Example of form 2) The purchasing support system according to an embodiment of the present invention is a support service that utilizes generative AI to enable SMBs (small and medium-sized enterprises) to achieve purchasing processes at the level of large corporations. This purchasing support system solves the purchasing process challenges faced by SMB companies by using generative AI to achieve procurement at appropriate prices based on the purchasing activity history of the company being supported. This allows SMB companies to focus on business expansion and support the development and growth of each of their group trading partners. For example, the generative AI analyzes past purchasing data, identifies optimal suppliers, and conducts price negotiations to achieve procurement at appropriate prices. Next, based on the purchasing process proposed by the generative AI, SMB companies can focus on business expansion. For example, by purchasing goods at appropriate prices from suppliers proposed by the generative AI, costs can be reduced and profits increased. This allows SMB companies to focus on business expansion and support the development and growth of each of their group trading partners. Furthermore, the generative AI continuously analyzes the purchasing process challenges faced by SMB companies and continues to propose optimal purchasing processes. This allows SMB companies to always achieve optimal purchasing processes and focus on business expansion. For example, the generating AI identifies new suppliers and negotiates prices, ensuring that procurement is always conducted at the appropriate price. This system enables SMBs to achieve a purchasing process on par with large corporations, allowing them to focus on business expansion. This supports the development and growth of each group's trading partners and increases the transaction volume of each group. For instance, by procuring goods at appropriate prices based on the purchasing process proposed by the generating AI, SMBs can reduce costs and increase profits. This allows SMBs to focus on business expansion and support the development and growth of each group's trading partners. In short, the purchasing support system enables SMBs to achieve a purchasing process on par with large corporations and focus on business expansion.

[0069] The purchasing support system according to this embodiment comprises an analysis unit, a selection unit, a negotiation unit, a proposal unit, and a continuous analysis unit. The analysis unit analyzes the challenges of the purchasing process. The analysis unit analyzes challenges faced by SMB companies, such as insufficient price negotiation power, supplier selection, and lack of know-how regarding purchasing activities. The analysis unit uses generative AI to analyze past purchasing data and market data to identify specific challenges faced by SMB companies. The selection unit identifies the optimal supplier based on the challenges analyzed by the analysis unit. The selection unit, for example, analyzes past purchasing data to identify the optimal supplier. The selection unit uses generative AI to evaluate suppliers based on criteria such as price, quality, delivery time, and reliability, and selects the optimal supplier. The negotiation unit conducts price negotiations with the suppliers identified by the selection unit. The negotiation unit, for example, conducts price negotiations with the identified suppliers. The negotiation unit uses generative AI to optimize the timing and methods of negotiations to achieve purchasing at an appropriate price. The proposal unit proposes a purchasing process based on the price negotiated by the negotiation unit. The proposal department proposes a purchasing process based on negotiated prices, for example. The proposal department uses generative AI to optimize purchasing processes such as ordering, delivery, inspection, and payment, achieving efficient purchasing. The continuous analysis department continuously analyzes the purchasing process proposed by the proposal department and continues to propose the optimal purchasing process. The continuous analysis department, for example, identifies new suppliers and negotiates prices to ensure that purchasing is always done at appropriate prices. The continuous analysis department uses generative AI to collect data regularly and perform real-time analysis, constantly optimizing the purchasing process based on the latest information. As a result, the purchasing support system according to this embodiment enables SMBs to achieve purchasing processes at the level of large corporations, allowing them to focus on business expansion.

[0070] The analytics department analyzes challenges in the purchasing process. For example, it analyzes challenges faced by SMBs, such as insufficient price negotiation power, supplier selection, and lack of expertise in purchasing activities. Using generative AI, the analytics department analyzes historical purchasing data and market data to identify specific challenges faced by SMBs. Specifically, the generative AI receives historical purchasing history and market trend data as input and performs pattern recognition and anomaly detection based on this data. For example, it analyzes data on price fluctuation patterns and supplier reliability to identify when prices tend to rise and which suppliers are prone to delivery delays. Furthermore, the generative AI can also use natural language processing technology to analyze the content of purchasing managers' notes and emails to extract potential problems in the purchasing process. This allows the analytics department to analyze the specific challenges faced by SMBs from multiple angles and generate detailed reports. The analysis results are visualized in a dashboard format and provided in a way that is easily understandable to purchasing managers. This provides the analytics department with a foundation for SMBs to clearly understand the challenges in their purchasing process and take appropriate measures.

[0071] The Identification Department identifies the optimal supplier based on the issues analyzed by the Analysis Department. For example, the Identification Department analyzes past purchasing data to identify the best supplier. The Identification Department uses Generative AI to evaluate suppliers based on criteria such as price, quality, delivery time, and reliability, and selects the optimal supplier. Specifically, the Generative AI analyzes the past transaction data of each supplier and evaluates price fluctuation history, quality stability, and on-time delivery rates. Furthermore, the Generative AI refers to external market data and industry reports to comprehensively judge the market evaluation and competitiveness of suppliers. This allows the Identification Department to select suppliers that are not only inexpensive but also highly reliable in terms of quality and delivery time. The Identification Department also uses Generative AI to assess the risks of suppliers. For example, it can analyze the financial status and past trouble history of suppliers to eliminate high-risk suppliers in advance. This allows the Identification Department to identify the optimal suppliers for SMB companies to receive a stable supply and improve the reliability of the purchasing process.

[0072] The Negotiation Department conducts price negotiations with suppliers identified by the Designation Department. For example, the Negotiation Department negotiates prices with identified suppliers. Using Generative AI, the Negotiation Department optimizes the timing and methods of negotiations to achieve procurement at appropriate prices. Specifically, the Generative AI analyzes past negotiation data and market price trends to identify the optimal negotiation timing. For example, if prices tend to fall during a particular period, negotiating at that time can yield favorable terms. The Generative AI also suggests specific phrases and strategies to use during negotiations. For instance, in price negotiations, presenting compelling data based on past transaction history and market competition can secure favorable terms from suppliers. Furthermore, the Generative AI can monitor the progress of negotiations in real time and modify the negotiation strategy as needed. This allows the Negotiation Department to consistently use optimal negotiation methods, enabling SMBs to procure goods on favorable terms.

[0073] The Proposal Department proposes a purchasing process based on the price negotiated by the Negotiation Department. For example, the Proposal Department proposes a purchasing process based on the negotiated price. The Proposal Department uses generative AI to optimize purchasing processes such as ordering, delivery, inspection, and payment, thereby achieving efficient purchasing. Specifically, the generative AI analyzes past purchasing data and market trends to propose the optimal ordering timing and delivery schedule. For example, by predicting peak demand periods and placing orders beforehand, inventory shortages can be prevented. In addition, to optimize the inspection process at the time of delivery, the generative AI analyzes past inspection data and proposes key inspection items and efficient inspection methods. Furthermore, in the payment process, the generative AI can propose the optimal payment timing and payment method, thereby improving cash flow. As a result, the Proposal Department enables SMBs to achieve an efficient and effective purchasing process, leading to cost reductions and improved operational efficiency.

[0074] The Continuous Analysis Department continuously analyzes the purchasing process proposed by the Proposal Department and proposes the optimal purchasing process. For example, the Continuous Analysis Department identifies new suppliers and negotiates prices to ensure that procurement is always conducted at appropriate prices. The Continuous Analysis Department uses generative AI to perform regular data collection and real-time analysis, constantly optimizing the purchasing process based on the latest information. Specifically, the generative AI regularly collects market data and purchasing data and evaluates the performance of the purchasing process based on this data. For example, it monitors supplier performance and market price trends, and changes suppliers or negotiates prices as needed. The generative AI also evaluates the efficiency and cost at each step of the purchasing process and identifies areas for improvement. As a result, the Continuous Analysis Department can constantly optimize the purchasing process based on the latest information and support SMB companies in maintaining their competitiveness. Furthermore, the Continuous Analysis Department regularly proposes improvements to the purchasing process, enabling SMB companies to always implement the optimal purchasing strategy. In this way, the Continuous Analysis Department can play a crucial role in enabling SMB companies to respond quickly to market fluctuations and achieve sustainable growth.

[0075] The analysis unit can analyze challenges faced by SMB companies, such as insufficient price negotiation power, supplier selection, and lack of know-how regarding purchasing activities. For example, to analyze insufficient price negotiation power, the analysis unit analyzes past negotiation history and market data. Using generative AI, the analysis unit can evaluate negotiation success rates and negotiation methods to identify insufficient price negotiation power. Furthermore, to analyze challenges related to supplier selection, the analysis unit analyzes past purchasing data and supplier evaluation data. Using generative AI, the analysis unit can evaluate suppliers based on criteria such as quality, delivery time, and reliability to identify the optimal supplier. In addition, to analyze insufficient know-how regarding purchasing activities, the analysis unit analyzes the purchasing processes and workflows of SMB companies. Using generative AI, the analysis unit can evaluate the efficiency and effectiveness of the purchasing process to identify insufficient know-how. This allows for the proposal of appropriate purchasing processes by analyzing the unique challenges of SMB companies. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without using generative AI. For example, the analysis unit can input past purchase data into a generating AI, which can then analyze the data to identify problems.

[0076] The identification unit can analyze past purchasing data to identify the optimal supplier. For example, the identification unit can analyze past purchasing data to identify the optimal supplier. The identification unit can use a generation AI to evaluate suppliers based on criteria such as price, quality, delivery time, and reliability, and select the optimal supplier. The identification unit uses a generation AI to analyze past purchasing data and evaluate the performance of suppliers. For example, the identification unit can use a generation AI to analyze past purchasing data and identify the most reliable supplier. The identification unit can also use a generation AI to identify cost-effective suppliers based on past purchasing data. Furthermore, the identification unit can use a generation AI to analyze past purchasing data and identify suppliers with stable delivery times. This enables efficient purchasing by identifying the optimal supplier based on past purchasing data. Some or all of the above processing in the identification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the identification unit can input past purchasing data into a generation AI, and the generation AI can analyze the data to identify the optimal supplier.

[0077] The Negotiation Department can conduct price negotiations with identified suppliers. For example, the Negotiation Department can conduct price negotiations with identified suppliers. The Negotiation Department can use generative AI to optimize the timing and methods of negotiations and achieve procurement at appropriate prices. The Negotiation Department can use generative AI to evaluate the success rate and methods of negotiations and formulate the optimal negotiation strategy. For example, the Negotiation Department can use generative AI to analyze past negotiation history and identify negotiation methods with a high success rate. The Negotiation Department can also use generative AI to identify negotiation methods that have a high cost reduction effect based on past negotiation history. Furthermore, the Negotiation Department can use generative AI to analyze past negotiation history and identify negotiation methods that have a high delivery time reduction effect. This makes it possible to procure at appropriate prices by conducting price negotiations with identified suppliers. Some or all of the above processes in the Negotiation Department may be performed using generative AI or not. For example, the Negotiation Department can input negotiation data with identified suppliers into generative AI, and the generative AI can analyze the data and formulate the optimal negotiation strategy.

[0078] The proposal department can propose a purchasing process based on the negotiated price. For example, the proposal department proposes a purchasing process based on the negotiated price. The proposal department can use generative AI to optimize purchasing processes such as ordering, delivery, inspection, and payment, thereby achieving efficient purchasing. The proposal department uses generative AI to formulate the optimal purchasing process based on the negotiated price. For example, the proposal department can use generative AI to propose a purchasing process that is highly effective in reducing costs based on the negotiated price. The proposal department can also use generative AI to propose a purchasing process that is highly effective in shortening delivery times based on the negotiated price. Furthermore, the proposal department can use generative AI to propose a purchasing process that is highly effective in improving quality based on the negotiated price. This makes efficient purchasing possible by proposing a purchasing process based on the negotiated price. Some or all of the above processing in the proposal department may be performed using generative AI or not. For example, the proposal department can input negotiated price data into generative AI, and the generative AI can analyze the data and propose the optimal purchasing process.

[0079] The Continuous Analysis Department can always ensure procurement at appropriate prices by identifying new suppliers and negotiating prices. For example, the Continuous Analysis Department can always ensure procurement at appropriate prices by identifying new suppliers and negotiating prices. The Continuous Analysis Department can use generative AI to perform periodic data collection and real-time analysis, constantly optimizing the purchasing process based on the latest information. The Continuous Analysis Department uses generative AI to identify new suppliers and negotiate prices. For example, the Continuous Analysis Department can use generative AI to analyze market data and identify new suppliers. Furthermore, the Continuous Analysis Department can use generative AI to negotiate prices with new suppliers, ensuring procurement at appropriate prices. In addition, the Continuous Analysis Department can use generative AI to evaluate the performance of new suppliers and select the optimal supplier. This allows for continuous proposal of the optimal purchasing process, enabling procurement at appropriate prices at all times. Some or all of the above processes in the Continuous Analysis Department may be performed using generative AI, or they may be performed without generative AI. For example, the continuous analysis unit inputs data on new suppliers into the generating AI, which then analyzes the data to identify the optimal supplier.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit's generative AI can quickly perform an analysis and provide a solution promptly. The analysis unit uses the generative AI to estimate the user's emotions and adjust the analysis priority. For example, the analysis unit can have the generative AI analyze the user's facial expression data to estimate the stress level. The analysis unit can also have the generative AI analyze the user's voice data to estimate the emotional state. Furthermore, the analysis unit can have the generative AI analyze the user's biometric data (heart rate and skin electrical activity) to estimate the emotional state. This allows for more appropriate analysis by adjusting the analysis priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input the user's facial expression data into a generating AI, which can then analyze the data to estimate the emotional state.

[0081] The analysis unit can analyze the past purchasing history of SMB companies in detail and extract specific patterns. For example, the analysis unit can use a generative AI to analyze past purchasing history and identify patterns of frequently purchased goods and services. The analysis unit uses the generative AI to analyze past purchasing history in detail and extract specific patterns. For example, the analysis unit can use the generative AI to analyze past purchasing history and extract seasonal purchasing patterns. The analysis unit can also use the generative AI to identify transaction patterns with specific suppliers based on past purchasing history. Furthermore, the analysis unit can use the generative AI to analyze past purchasing history and identify the purchase frequency and timing of specific products. This allows for the extraction of specific patterns through detailed analysis of past purchasing history, enabling more efficient purchasing. Some or all of the above-described processes in the analysis unit may be performed using the generative AI, or they may not be performed using the generative AI. For example, the analysis unit can input past purchasing history data into the generative AI, which can then analyze the data and extract specific patterns.

[0082] The analysis unit can apply industry-specific analytical methods, taking into account the industry characteristics of SMB companies. For example, the analysis unit can use a generative AI to analyze purchasing data of manufacturing SMB companies and identify purchasing patterns specific to the manufacturing industry. The analysis unit can use the generative AI to apply analytical methods that take industry characteristics into account. For example, the analysis unit can use a generative AI to analyze purchasing data of retail SMB companies and identify purchasing patterns specific to the retail industry. The analysis unit can also use a generative AI to analyze purchasing data of service industry SMB companies and identify purchasing patterns specific to the service industry. Furthermore, the analysis unit can use a generative AI to apply industry-specific analytical methods, taking industry characteristics into account. This allows for more appropriate analysis by applying analytical methods that take industry characteristics into account. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input data related to industry characteristics into a generative AI, and the generative AI can analyze the data and apply analytical methods specific to a particular industry.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can use a generating AI to provide a simple and highly visible display method. The analysis unit uses the generating AI to estimate the user's emotions and adjust the display method of the analysis results. For example, the analysis unit can use the generating AI to analyze the user's facial expression data and estimate their state of tension. The analysis unit can also use the generating AI to analyze the user's voice data and estimate their emotional state. Furthermore, the analysis unit can use the generating AI to analyze the user's biometric data (heart rate and skin electrical activity) and estimate their emotional state. This allows for a more appropriate display by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using the generating AI or not. For example, the analysis unit can input the user's facial expression data into a generating AI, which can then analyze the data to estimate the emotional state.

[0084] The analysis unit can analyze region-specific challenges by considering the geographical location information of SMB companies. For example, the analysis unit uses a generative AI to identify region-specific purchasing challenges based on the location of SMB companies. The analysis unit uses the generative AI to analyze region-specific challenges while considering geographical location information. For example, the analysis unit can use the generative AI to propose an optimal purchasing strategy considering the regional economic situation. The analysis unit can also use the generative AI to analyze the regional competitive landscape and propose purchasing strategies to enhance competitiveness. Furthermore, the analysis unit can use the generative AI to propose an optimal purchasing process considering regional logistics costs and regulations. In this way, by considering geographical location information, region-specific challenges can be analyzed and an appropriate purchasing process can be proposed. Some or all of the above processing in the analysis unit may be performed using the generative AI, or it may be performed without the generative AI. For example, the analysis unit can input geographical location information into the generative AI, and the generative AI can analyze the data to identify region-specific challenges.

[0085] The analytics unit can analyze the social media activities of SMB companies and identify related issues. For example, the analytics unit can use generative AI to analyze social media posts of SMB companies and identify purchasing issues. The analytics unit uses generative AI to analyze social media activities and identify related issues. For example, the analytics unit can use generative AI to suggest improvements to purchasing strategies based on social media reactions. The analytics unit can also use generative AI to analyze social media trends and reflect them in purchasing strategies. Furthermore, the analytics unit can use generative AI to analyze social media engagement data and identify issues based on customer feedback. In this way, by analyzing social media activities, related issues can be identified and appropriate purchasing processes can be proposed. Some or all of the above processes in the analytics unit may be performed using generative AI or not. For example, the analytics unit can input social media post data into generative AI, and the generative AI can analyze the data and identify related issues.

[0086] The identification unit can estimate the user's emotions and determine the priority of suppliers to identify based on the estimated user emotions. For example, if the user is stressed, the identification unit will prioritize suppliers that can respond quickly using the generating AI. The identification unit uses the generating AI to estimate the user's emotions and determine the priority of suppliers. For example, the identification unit can have the generating AI analyze the user's facial expression data to estimate the stress level. The identification unit can also have the generating AI analyze the user's voice data to estimate the emotional state. Furthermore, the identification unit can have the generating AI analyze the user's biometric data (heart rate and skin electrical activity) to estimate the emotional state. This allows for the identification of more appropriate suppliers by determining the priority of suppliers according to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generating AI. The generating 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 identification unit may be performed using the generating AI or not. For example, a specific unit can input user facial expression data into a generating AI, which then analyzes the data to estimate the emotional state and determine the priority of suppliers.

[0087] The identification unit can analyze past purchase data in detail and identify the optimal supplier. For example, the identification unit can use a generating AI to analyze past purchase data and identify the most reliable supplier. The identification unit uses a generating AI to analyze past purchase data in detail and identify the optimal supplier. For example, the identification unit can use a generating AI to identify cost-effective suppliers based on past purchase data. The identification unit can also use a generating AI to analyze past purchase data and identify suppliers with stable delivery times. Furthermore, the identification unit can use a generating AI to analyze past purchase data and identify high-quality suppliers. This allows for the identification of optimal suppliers and efficient purchasing by analyzing past purchase data in detail. Some or all of the above-described processes in the identification unit may be performed using a generating AI or not. For example, the identification unit can input past purchase data into a generating AI, which can analyze the data and identify the optimal supplier.

[0088] The Identification Unit can select suppliers specializing in a particular industry by considering the industry characteristics of the suppliers. For example, the Identification Unit can use a generating AI to identify suppliers in the manufacturing industry and address the specific needs of the manufacturing industry. The Identification Unit uses a generating AI to select suppliers specializing in a particular industry by considering the industry characteristics of the suppliers. For example, the Identification Unit can use a generating AI to identify suppliers in the retail industry and address the specific needs of the retail industry. The Identification Unit can also use a generating AI to identify suppliers in the service industry and address the specific needs of the service industry. Furthermore, the Identification Unit can use a generating AI to select suppliers specializing in a particular industry by considering industry characteristics. This enables efficient purchasing by selecting suppliers specializing in a particular industry by considering industry characteristics. Some or all of the above processing in the Identification Unit may be performed using a generating AI or not. For example, the Identification Unit can input data on industry characteristics into a generating AI, and the generating AI can analyze the data to select suppliers specializing in a particular industry.

[0089] The identification unit can estimate the user's emotions and adjust the display method of the identified suppliers based on the estimated user emotions. For example, if the user is nervous, the identification unit can use a generating AI to provide a simple and highly visible display method. The identification unit uses a generating AI to estimate the user's emotions and adjust the display method of the suppliers. For example, the identification unit can use a generating AI to analyze the user's facial expression data and estimate their state of tension. The identification unit can also use a generating AI to analyze the user's voice data and estimate their emotional state. Furthermore, the identification unit can use a generating AI to analyze the user's biometric data (heart rate and skin electrical activity) and estimate their emotional state. This allows for a more appropriate display by adjusting the supplier display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the identification unit may be performed using a generating AI or not. For example, a specific unit can input user facial expression data into a generating AI, which then analyzes the data to estimate the emotional state and adjust the way suppliers are displayed.

[0090] The identification unit can identify region-specific suppliers by considering the geographical location information of those suppliers. For example, the identification unit uses a generating AI to identify suppliers that can address region-specific challenges based on the supplier's location. The identification unit uses the generating AI to identify region-specific suppliers by considering geographical location information. For example, the identification unit can use the generating AI to identify the optimal supplier by considering the regional economic situation. The identification unit can also use the generating AI to analyze the regional competitive situation and identify suppliers that will enhance competitiveness. Furthermore, the identification unit can use the generating AI to identify the optimal supplier by considering regional logistics costs and regulations. This allows for the identification of region-specific suppliers and efficient purchasing by considering geographical location information. Some or all of the above processing in the identification unit may be performed using the generating AI or not. For example, the identification unit can input geographical location information into the generating AI, and the generating AI can analyze the data to identify region-specific suppliers.

[0091] The identification unit can analyze the social media activities of suppliers and identify relevant suppliers. For example, the identification unit can use a generative AI to analyze the social media posts of suppliers and identify reliable suppliers. The identification unit uses a generative AI to analyze social media activities and identify relevant suppliers. For example, the identification unit can use a generative AI to identify reputable suppliers based on social media reactions. The identification unit can also use a generative AI to analyze social media trends and identify suppliers that can meet the latest needs. Furthermore, the identification unit can use a generative AI to analyze social media engagement data and identify suppliers based on customer feedback. This allows for the identification of relevant suppliers by analyzing social media activities, enabling efficient purchasing. Some or all of the above processing in the identification unit may be performed using a generative AI or not. For example, the identification unit can input social media post data into a generative AI, which can analyze the data and identify relevant suppliers.

[0092] The negotiation unit can estimate the user's emotions and adjust its negotiation strategy based on those emotions. For example, if the user is feeling stressed, the negotiation unit will employ a strategy in which the generative AI quickly advances the negotiation. The negotiation unit uses the generative AI to estimate the user's emotions and adjust its negotiation strategy. For example, the negotiation unit can use the generative AI to analyze the user's facial expression data and estimate their stress level. The negotiation unit can also use the generative AI to analyze the user's voice data and estimate their emotional state. Furthermore, the negotiation unit can use the generative AI to analyze the user's biometric data (heart rate and skin electrical activity) and estimate their emotional state. This allows for more appropriate negotiations by adjusting the negotiation strategy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the negotiation unit may be performed using or without the generative AI. For example, the negotiation department can input user facial expression data into a generating AI, which then analyzes the data to estimate the emotional state and adjust the negotiation strategy accordingly.

[0093] The negotiation department can analyze past negotiation history in detail and select the optimal negotiation method. For example, the negotiation department can use a generative AI to analyze past negotiation history and identify negotiation methods with a high success rate. The negotiation department uses a generative AI to analyze past negotiation history in detail and select the optimal negotiation method. For example, the negotiation department can use a generative AI to identify negotiation methods that are highly effective in reducing costs based on past negotiation history. The negotiation department can also use a generative AI to analyze past negotiation history and identify negotiation methods that are highly effective in shortening delivery times. Furthermore, the negotiation department can use a generative AI to analyze past negotiation history and identify negotiation methods that are highly effective in improving quality. In this way, by analyzing past negotiation history in detail, the optimal negotiation method can be selected, enabling efficient negotiations. Some or all of the above processes in the negotiation department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the negotiation department can input past negotiation history data into a generative AI, and the generative AI can analyze the data and select the optimal negotiation method.

[0094] The Negotiation Department can apply negotiation techniques specific to a particular industry, taking into account the industry characteristics of the supplier. For example, the Negotiation Department's generative AI can apply negotiation techniques that address the specific needs of the manufacturing industry when negotiating with a manufacturing supplier. The Negotiation Department can use generative AI to apply negotiation techniques specific to a particular industry, taking into account the industry characteristics of the supplier. For example, the Negotiation Department can use generative AI to apply negotiation techniques that address the specific needs of the retail industry when negotiating with a retail supplier. The Negotiation Department can also use generative AI to apply negotiation techniques that address the specific needs of the service industry when negotiating with a service supplier. Furthermore, the Negotiation Department can use generative AI to apply negotiation techniques specific to a particular industry, taking into account industry characteristics. This allows for efficient negotiation by applying negotiation techniques specific to a particular industry, by considering industry characteristics. Some or all of the above-described processes in the Negotiation Department may be performed using generative AI or not. For example, the Negotiation Department can input data on industry characteristics into the generative AI, and the generative AI can analyze the data and apply negotiation techniques specific to a particular industry.

[0095] The negotiation unit can estimate the user's emotions and adjust how the negotiation results are displayed based on the estimated emotions. For example, if the user is nervous, the negotiation unit can use a generative AI to provide a simple and highly visible display method. The negotiation unit uses generative AI to estimate the user's emotions and adjust how the negotiation results are displayed. For example, the negotiation unit can use generative AI to analyze the user's facial expression data and estimate their level of tension. The negotiation unit can also use generative AI to analyze the user's voice data and estimate their emotional state. Furthermore, the negotiation unit can use generative AI to analyze the user's biometric data (heart rate and skin electrical activity) and estimate their emotional state. This allows for a more appropriate display by adjusting how the negotiation results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the negotiation unit may be performed using generative AI or not. For example, the negotiation department can input user facial expression data into a generating AI, which then analyzes the data to estimate the emotional state and adjust how the negotiation results are displayed.

[0096] The negotiation department can apply region-specific negotiation methods by considering the geographical location information of suppliers. For example, the negotiation department can use a generative AI to apply negotiation methods that address region-specific challenges based on the supplier's location. The negotiation department uses a generative AI to apply region-specific negotiation methods while considering geographical location information. For example, the negotiation department can use a generative AI to apply the optimal negotiation method by considering the regional economic situation. The negotiation department can also use a generative AI to analyze the regional competitive situation and apply negotiation methods to enhance competitiveness. Furthermore, the negotiation department can use a generative AI to apply the optimal negotiation method by considering regional logistics costs and regulations. This enables efficient negotiation by applying region-specific negotiation methods while considering geographical location information. Some or all of the above processes in the negotiation department may be performed using a generative AI or not. For example, the negotiation department can input geographical location information into a generative AI, which can analyze the data and apply region-specific negotiation methods.

[0097] The negotiation department can analyze the social media activities of suppliers and identify relevant negotiation techniques. For example, the negotiation department can use generative AI to analyze suppliers' social media posts and identify reliable negotiation techniques. The negotiation department can use generative AI to analyze social media activities and identify relevant negotiation techniques. For example, the negotiation department can use generative AI to identify well-regarded negotiation techniques based on social media reactions. The negotiation department can also use generative AI to analyze social media trends and identify negotiation techniques that can address the latest needs. Furthermore, the negotiation department can use generative AI to analyze social media engagement data and identify negotiation techniques based on customer feedback. This allows for the identification of relevant negotiation techniques by analyzing social media activities, enabling more efficient negotiations. Some or all of the above processes in the negotiation department may be performed using generative AI or not. For example, the negotiation department can input social media post data into generative AI, which can analyze the data and identify relevant negotiation techniques.

[0098] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is feeling stressed, the suggestion unit's generative AI will provide simple and easily understandable suggestions. The suggestion unit uses the generative AI to estimate the user's emotions and adjust the way suggestions are presented. For example, the suggestion unit can use the generative AI to analyze the user's facial expression data and estimate their stress level. The suggestion unit can also use the generative AI to analyze the user's voice data and estimate their emotional state. Furthermore, the suggestion unit can use the generative AI to analyze the user's biometric data (heart rate and skin electrical activity) and estimate their emotional state. This allows for more appropriate suggestions by adjusting the way suggestions are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using or without the generative AI. For example, the proposal unit can input user facial expression data into a generation AI, which then analyzes the data to estimate the emotional state and adjust the way the proposal is expressed.

[0099] The proposal department can adjust the level of detail of its proposals based on the negotiated price. For example, the proposal department can use a generation AI to generate proposals with high cost-reduction potential based on the negotiated price. The proposal department can use a generation AI to adjust the level of detail of its proposals based on the negotiated price. For example, the proposal department can use a generation AI to generate proposals with high delivery time reduction potential based on the negotiated price. The proposal department can also use a generation AI to generate proposals with high quality improvement potential based on the negotiated price. Furthermore, the proposal department can use a generation AI to propose the optimal purchasing process based on the negotiated price. By adjusting the level of detail of proposals based on the negotiated price, more appropriate proposals become possible. Some or all of the above processes in the proposal department may be performed using a generation AI, or they may not be performed using a generation AI. For example, the proposal department can input negotiated price data into a generation AI, which can analyze the data and generate the optimal proposal.

[0100] The proposal department can apply industry-specific proposal methods by considering the industry characteristics of its suppliers. For example, the proposal department's generating AI can make proposals to manufacturing suppliers that address the specific needs of the manufacturing industry. The proposal department uses the generating AI to apply industry-specific proposal methods by considering the industry characteristics of its suppliers. For example, the proposal department's generating AI can make proposals to retail suppliers that address the specific needs of the retail industry. The proposal department can also make proposals to service industry suppliers that address the specific needs of the service industry. Furthermore, the proposal department can apply industry-specific proposal methods by considering industry characteristics. This enables efficient proposals by considering industry characteristics and applying industry-specific proposal methods. Some or all of the above processing in the proposal department may be performed using the generating AI, or it may be performed without using the generating AI. For example, the proposal department can input data on industry characteristics into the generating AI, and the generating AI can analyze the data and apply industry-specific proposal methods.

[0101] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit's generating AI will provide a short, concise suggestion. The suggestion unit uses the generating AI to estimate the user's emotions and adjust the length of the suggestion. For example, the suggestion unit can use the generating AI to analyze the user's facial expression data and estimate that the user is in a hurry. The suggestion unit can also use the generating AI to analyze the user's voice data and estimate their emotional state. Furthermore, the suggestion unit can use the generating AI to analyze the user's biometric data (heart rate and skin electrical activity) and estimate their emotional state. This allows for more appropriate suggestions by adjusting the length of the suggestion according to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using or without the generating AI. For example, the suggestion unit can input user facial expression data into a generating AI, which then analyzes the data to estimate the emotional state and adjust the length of the suggestion.

[0102] The proposal department can determine the priority of proposals based on the timing of the submitted negotiated prices. For example, the proposal department can use a generation AI to prioritize proposals that require the fastest response based on the timing of the submitted negotiated prices. The proposal department uses a generation AI to determine the priority of proposals based on the timing of the submitted negotiated prices. For example, the proposal department can use a generation AI to prioritize proposals that offer a high cost reduction effect based on the timing of the submitted negotiated prices. The proposal department can also use a generation AI to prioritize proposals that offer a high delivery time reduction effect based on the timing of the submitted negotiated prices. Furthermore, the proposal department can use a generation AI to prioritize proposals that offer a high quality improvement effect based on the timing of the submitted negotiated prices. This allows for more appropriate proposals by determining the priority of proposals based on the timing of the submitted negotiated prices. Some or all of the above processing in the proposal department may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal department can input the data on the timing of the submitted negotiated prices into a generation AI, and the generation AI can analyze the data to determine the priority of proposals.

[0103] The proposal department can adjust the order of proposals based on the relationships between suppliers. For example, the proposal department's generating AI can prioritize the most important proposals based on the relationships between suppliers. The proposal department uses the generating AI to adjust the order of proposals based on the relationships between suppliers. For example, the proposal department can use the generating AI to prioritize proposals with high cost reduction effects based on the relationships between suppliers. The proposal department can also use the generating AI to prioritize proposals with high delivery time reduction effects based on the relationships between suppliers. Furthermore, the proposal department can use the generating AI to prioritize proposals with high quality improvement effects based on the relationships between suppliers. By adjusting the order of proposals based on the relationships between suppliers, more appropriate proposals become possible. Some or all of the above processing in the proposal department may be performed using the generating AI, or it may be performed without using the generating AI. For example, the proposal department can input supplier relationship data into the generating AI, and the generating AI can analyze the data and adjust the order of proposals.

[0104] The continuous analysis unit can estimate the user's emotions and adjust the frequency of continuous analysis based on the estimated emotions. For example, if the user is feeling stressed, the continuous analysis unit will have the generative AI perform frequent analysis and provide solutions quickly. The continuous analysis unit uses the generative AI to estimate the user's emotions and adjust the frequency of continuous analysis. For example, the continuous analysis unit can have the generative AI analyze the user's facial expression data to estimate the stress level. The continuous analysis unit can also have the generative AI analyze the user's voice data to estimate the emotional state. Furthermore, the continuous analysis unit can have the generative AI analyze the user's biometric data (heart rate and skin electrical activity) to estimate the emotional state. This allows for more appropriate analysis by adjusting the frequency of continuous analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the continuous analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the continuous analysis unit can input user facial expression data into a generating AI, which can analyze the data to estimate the emotional state and adjust the frequency of the continuous analysis.

[0105] The Continuous Analysis Unit can analyze past analysis data in detail and select the optimal analysis method. For example, the Continuous Analysis Unit uses a generating AI to analyze past analysis data and identify analysis methods with a high success rate. The Continuous Analysis Unit uses the generating AI to analyze past analysis data in detail and select the optimal analysis method. For example, the Continuous Analysis Unit can use the generating AI to identify analysis methods that have a high cost reduction effect based on past analysis data. The Continuous Analysis Unit can also use the generating AI to analyze past analysis data and identify analysis methods that have a high effect on shortening delivery times. Furthermore, the Continuous Analysis Unit can use the generating AI to analyze past analysis data and identify analysis methods that have a high effect on improving quality. In this way, by analyzing past analysis data in detail, the optimal analysis method can be selected, enabling efficient analysis. Some or all of the above processes in the Continuous Analysis Unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the Continuous Analysis Unit can input past analysis data into the generating AI, and the generating AI can analyze the data and select the optimal analysis method.

[0106] The Continuous Analysis Unit can apply analytical methods specific to a particular industry, taking into account the industry characteristics of its suppliers. For example, the Continuous Analysis Unit's generating AI can analyze data from manufacturing suppliers and apply analytical methods that address the specific needs of the manufacturing industry. The Continuous Analysis Unit uses the generating AI to apply analytical methods specific to a particular industry, taking into account the industry characteristics of its suppliers. For example, the Continuous Analysis Unit's generating AI can analyze data from retail suppliers and apply analytical methods that address the specific needs of the retail industry. The Continuous Analysis Unit can also use the generating AI to analyze data from service suppliers and apply analytical methods that address the specific needs of the service industry. Furthermore, the Continuous Analysis Unit can apply analytical methods specific to a particular industry, taking industry characteristics into account. This enables efficient analysis by applying analytical methods specific to a particular industry, taking industry characteristics into account. Some or all of the above-described processes in the Continuous Analysis Unit may be performed using the generating AI or not. For example, the Continuous Analysis Unit can input data related to industry characteristics into the generating AI, and the generating AI can analyze the data and apply analytical methods specific to a particular industry.

[0107] The continuous analysis unit can estimate the user's emotions and adjust the display method of the continuous analysis results based on the estimated user emotions. For example, if the user is nervous, the continuous analysis unit can use a generating AI to provide a simple and highly visible display method. The continuous analysis unit uses a generating AI to estimate the user's emotions and adjust the display method of the continuous analysis results. For example, the continuous analysis unit can use a generating AI to analyze the user's facial expression data and estimate their state of tension. The continuous analysis unit can also use a generating AI to analyze the user's voice data and estimate their emotional state. Furthermore, the continuous analysis unit can use a generating AI to analyze the user's biometric data (heart rate and skin electrical activity) and estimate their emotional state. This allows for a more appropriate display by adjusting the display method of the continuous analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the continuous analysis unit may be performed using a generating AI, or they may be performed without using a generating AI. For example, the continuous analysis unit can input user facial expression data into a generating AI, the generating AI can analyze the data to estimate the emotional state, and the display method of the continuous analysis results can be adjusted.

[0108] The continuous analysis unit can apply region-specific analysis methods by considering the geographical location information of suppliers. For example, the continuous analysis unit's generating AI can apply analysis methods that address region-specific challenges based on the supplier's location. The continuous analysis unit uses the generating AI to apply region-specific analysis methods while considering geographical location information. For example, the continuous analysis unit's generating AI can apply the optimal analysis method by considering the region's economic situation. The continuous analysis unit can also have the generating AI analyze the regional competitive situation and apply analysis methods to enhance competitiveness. Furthermore, the continuous analysis unit can have the generating AI apply the optimal analysis method by considering regional logistics costs and regulations. This enables efficient analysis by considering geographical location information and applying region-specific analysis methods. Some or all of the above processing in the continuous analysis unit may be performed using the generating AI or not. For example, the continuous analysis unit can input geographical location information into the generating AI, and the generating AI can analyze the data and apply region-specific analysis methods.

[0109] The Continuous Analysis Unit can analyze the social media activities of suppliers and identify relevant analytical methods. For example, the Continuous Analysis Unit can use a generative AI to analyze the social media posts of suppliers and identify reliable analytical methods. The Continuous Analysis Unit uses a generative AI to analyze social media activities and identify relevant analytical methods. For example, the Continuous Analysis Unit can use a generative AI to identify well-regarded analytical methods based on social media reactions. The Continuous Analysis Unit can also use a generative AI to analyze social media trends and identify analytical methods that can meet the latest needs. Furthermore, the Continuous Analysis Unit can use a generative AI to analyze social media engagement data and identify analytical methods based on customer feedback. This enables efficient analysis by identifying relevant analytical methods through the analysis of social media activities. Some or all of the above processes in the Continuous Analysis Unit may be performed using a generative AI or not. For example, the Continuous Analysis Unit can input social media post data into a generative AI, which can analyze the data and identify relevant analytical methods.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The purchasing support system can also include a forecasting unit. This unit predicts future purchasing demand and supports timely procurement. For example, it can use generative AI to analyze past purchasing data and market trends to predict the next demand peak. It can also consider seasonal demand fluctuations and suggest appropriate inventory management. Furthermore, it can forecast demand based on specific events or campaigns and optimize procurement plans. This allows SMBs to procure appropriately in line with demand, preventing inventory shortages and excesses.

[0112] The purchasing support system can also include an evaluation unit. This unit regularly assesses the performance of suppliers to maintain reliable suppliers. For example, the evaluation unit can use generative AI to evaluate suppliers' on-time delivery rates and quality control performance. It can also analyze suppliers' cost-effectiveness and propose optimal trading terms. Furthermore, the evaluation unit can evaluate suppliers' customer service and after-sales service to determine overall reliability. This allows SMBs to build long-term business relationships with reliable suppliers.

[0113] The purchasing support system can also include a notification unit. This unit notifies users of important purchasing information and alerts in real time. For example, it can use AI to detect sudden fluctuations in market prices and immediately notify users. It can also detect delays in supplier deliveries or quality issues and quickly propose countermeasures. Furthermore, it can remind users of important contract renewal dates and payment deadlines, helping them to act at the appropriate time. This allows SMBs to avoid missing important purchasing information and respond quickly and appropriately.

[0114] The purchasing support system can also include a training department. This department provides purchasing managers at SMB companies with training on purchasing processes and negotiation techniques. For example, the training department can use generative AI to analyze past successes and failures and learn effective negotiation methods. It can also improve practical negotiation skills through simulations. Furthermore, the training department can provide information on the latest market trends and purchasing strategies to update the managers' knowledge. This enables purchasing managers at SMB companies to conduct more effective purchasing activities.

[0115] The purchasing support system can also include a feedback section. This section collects user feedback and uses it to improve the system. For example, the feedback section can use generative AI to analyze user opinions and requests, improving the system's functionality and usability. It can also periodically survey user satisfaction and evaluate the quality of service. Furthermore, based on user feedback, the feedback section can propose new features and services, increasing the system's value. This ensures that SMBs always have access to a purchasing support system that meets their latest needs.

[0116] The purchasing support system can be further customized based on the user's emotions using emotion estimation capabilities. For example, if the user is feeling stressed, the analysis unit can use its generating AI to quickly analyze the situation and provide immediate solutions. If the user is relaxed, the identification unit can provide detailed information, allowing for careful consideration. Furthermore, if the user is feeling tense, the negotiation unit can propose a simple and highly visual negotiation strategy. This enables optimal purchasing support tailored to the user's emotions.

[0117] The purchasing support system can further utilize emotion estimation capabilities to provide notifications based on the user's emotions. For example, if the user is feeling stressed, the notification unit can provide concise summaries of important notifications. If the user is relaxed, it can provide notifications with more detailed information for deeper understanding. Furthermore, if the user is feeling anxious, it can provide visually clear notifications to enable quick responses. This allows for optimal notifications tailored to the user's emotions.

[0118] The purchasing support system can further utilize emotion estimation capabilities to provide training based on the user's emotions. For example, if the user is feeling stressed, the training unit can provide training in a relaxing environment. If the user is relaxed, it can provide more advanced training to improve their skills. Furthermore, if the user is feeling tense, it can provide simple and easy-to-understand training to ensure effective learning. This enables optimal training tailored to the user's emotions.

[0119] The purchasing support system can further collect user-emotion-based feedback using emotion estimation capabilities. For example, the feedback section can provide a concise feedback form if the user is stressed, or request more detailed feedback if the user is relaxed. Furthermore, if the user is tense, it can provide a visually easy-to-understand feedback form to quickly gather opinions. This enables the collection of optimal feedback tailored to the user's emotions.

[0120] The purchasing support system can further utilize emotion estimation capabilities to make predictions based on the user's emotions. For example, if the user is feeling stressed, the prediction unit can quickly forecast demand and propose immediate countermeasures. If the user is relaxed, it can provide detailed prediction data to allow for careful consideration. Furthermore, if the user is tense, it can provide simple and highly visual prediction results to enable quick action. This allows for optimal predictions tailored to the user's emotions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The analysis department analyzes the challenges in the purchasing process. Using generative AI, the analysis department analyzes historical purchasing data and market data to identify specific challenges faced by SMB companies. For example, it analyzes challenges such as insufficient price negotiation power, supplier selection, and lack of know-how regarding purchasing activities. Step 2: The Identification Unit identifies the optimal supplier based on the issues analyzed by the Analysis Unit. The Identification Unit uses Generative AI to evaluate suppliers based on criteria such as price, quality, delivery time, and reliability, and selects the optimal supplier. For example, it may analyze past purchasing data to identify the optimal supplier. Step 3: The Negotiation Department conducts price negotiations with suppliers identified by the Identification Department. The Negotiation Department uses generative AI to optimize the timing and methods of negotiations, thereby achieving procurement at appropriate prices. For example, it conducts price negotiations with identified suppliers. Step 4: The Proposal Department proposes a purchasing process based on the price negotiated by the Negotiation Department. The Proposal Department uses generative AI to optimize purchasing processes such as ordering, delivery, inspection, and payment, thereby achieving efficient purchasing. For example, it proposes a purchasing process based on the negotiated price. Step 5: The Continuous Analysis Department continuously analyzes the purchasing process proposed by the Proposal Department and proposes the optimal purchasing process. The Continuous Analysis Department uses generative AI to collect data regularly and perform real-time analysis, constantly optimizing the purchasing process based on the latest information. For example, by identifying new suppliers and negotiating prices, it ensures that procurement is always conducted at appropriate prices.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the analysis unit, identification unit, negotiation unit, proposal unit, and continuous analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the identification unit 290 of the data processing unit 12 and analyzes past purchase data and market data. The identification unit is implemented by the control unit 46A of the smart device 14 and identifies the optimal supplier. The negotiation unit is implemented by the identification unit 290 of the data processing unit 12 and negotiates prices with suppliers. The proposal unit is implemented by the control unit 46A of the smart device 14 and proposes a purchase process. The continuous analysis unit is implemented by the identification unit 290 of the data processing unit 12 and continuously analyzes the purchase process. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the analysis unit, identification unit, negotiation unit, proposal unit, and continuous analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the identification unit 290 of the data processing unit 12 and analyzes past purchase data and market data. The identification unit is implemented by the control unit 46A of the smart glasses 214 and identifies the optimal supplier. The negotiation unit is implemented by the identification unit 290 of the data processing unit 12 and negotiates prices with suppliers. The proposal unit is implemented by the control unit 46A of the smart glasses 214 and proposes a purchase process. The continuous analysis unit is implemented by the identification unit 290 of the data processing unit 12 and continuously analyzes the purchase process. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the analysis unit, identification unit, negotiation unit, proposal unit, and continuous analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the identification unit 290 of the data processing unit 12 and analyzes past purchase data and market data. The identification unit is implemented by the control unit 46A of the headset terminal 314 and identifies the optimal supplier. The negotiation unit is implemented by the identification unit 290 of the data processing unit 12 and negotiates prices with suppliers. The proposal unit is implemented by the control unit 46A of the headset terminal 314 and proposes a purchase process. The continuous analysis unit is implemented by the identification unit 290 of the data processing unit 12 and continuously analyzes the purchase process. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the analysis unit, identification unit, negotiation unit, proposal unit, and continuous analysis unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the identification unit 290 of the data processing unit 12 and analyzes past purchase data and market data. The identification unit is implemented by the control unit 46A of the robot 414 and identifies the optimal supplier. The negotiation unit is implemented by the identification unit 290 of the data processing unit 12 and negotiates prices with suppliers. The proposal unit is implemented by the control unit 46A of the robot 414 and proposes a purchasing process. The continuous analysis unit is implemented by the identification unit 290 of the data processing unit 12 and continuously analyzes the purchasing process. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) The analysis unit analyzes the challenges in the purchasing process, A selection unit identifies the optimal supplier based on the problem analyzed by the aforementioned analysis unit, A negotiation department that conducts price negotiations with suppliers identified by the aforementioned identification department, A proposal department that proposes a purchasing process based on the price negotiated by the aforementioned negotiation department, The system includes a continuous analysis unit that continuously analyzes the purchasing process proposed by the proposal unit and continues to propose the optimal purchasing process. A system characterized by the following features. (Note 2) The aforementioned analysis unit, This analysis examines the challenges faced by SMBs, such as a lack of price negotiation power, insufficient supplier selection, and a lack of expertise in purchasing activities. The system described in Appendix 1, characterized by the features described herein. (Note 3) The specified part is, We analyze past purchasing data to identify the optimal supplier. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned negotiating body said, Negotiate prices with the identified suppliers. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose a purchasing process based on the negotiated price. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned continuous analysis unit, By identifying new suppliers and negotiating prices, we can consistently ensure that we purchase goods at appropriate prices. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, We will conduct a detailed analysis of the past purchasing history of SMB companies and extract specific patterns. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, Considering the industry characteristics of SMB companies, we apply analytical methods specific to that particular industry. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, Analyze region-specific challenges by considering the geographical location information of SMB companies. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, Analyze the social media activities of SMB companies and identify related issues. The system described in Appendix 1, characterized by the features described herein. (Note 13) The specified part is, It estimates user sentiment and determines the priority of suppliers based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The specified part is, We analyze past purchasing data in detail to identify the optimal supplier. The system described in Appendix 1, characterized by the features described herein. (Note 15) The specified part is, Considering the industry characteristics of our suppliers, we select suppliers that specialize in specific industries. The system described in Appendix 1, characterized by the features described herein. (Note 16) The specified part is, We estimate user sentiment and adjust how suppliers are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The specified part is, Identify region-specific suppliers by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The specified part is, Analyze the social media activity of suppliers and identify relevant suppliers. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned negotiating body said, It estimates the user's emotions and adjusts negotiation strategies based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned negotiating body said, We will conduct a detailed analysis of past negotiation history and select the most suitable negotiation method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned negotiating body said, Considering the industry characteristics of our suppliers, we apply negotiation techniques specific to that particular industry. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned negotiating body said, The system estimates the user's emotions and adjusts how negotiation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned negotiating body said, We take into account the geographical location of our suppliers and apply region-specific negotiation techniques. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned negotiating body said, Analyze suppliers' social media activity and identify relevant negotiation techniques. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, Adjust the level of detail in the proposal based on the negotiated price. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, Considering the industry characteristics of our suppliers, we apply proposal methods tailored to specific industries. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, The priority of proposals will be determined based on the timing of the submitted negotiated prices. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, Adjust the order of proposals based on the relevance of the suppliers. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned continuous analysis unit, The system estimates the user's emotions and adjusts the frequency of subsequent analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned continuous analysis unit, We will analyze past analytical data in detail and select the optimal analytical method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned continuous analysis unit, Considering the industry characteristics of our suppliers, we apply analytical methods specific to that particular industry. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned continuous analysis unit, It estimates the user's emotions and adjusts how the continuous analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned continuous analysis unit, Considering the geographical location of suppliers, apply region-specific analytical methods. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned continuous analysis unit, Analyze the social media activity of our suppliers and identify relevant analytical methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 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 analysis unit analyzes the challenges in the purchasing process, A selection unit identifies the optimal supplier based on the problem analyzed by the aforementioned analysis unit, A negotiation department that conducts price negotiations with suppliers identified by the aforementioned identification department, A proposal department that proposes a purchasing process based on the price negotiated by the aforementioned negotiation department, The system includes a continuous analysis unit that continuously analyzes the purchasing process proposed by the proposal unit and continues to propose the optimal purchasing process. A system characterized by the following features.

2. The aforementioned analysis unit, This analysis examines the challenges faced by SMBs, such as a lack of price negotiation power, insufficient supplier selection, and a lack of expertise in purchasing activities. The system according to feature 1.

3. The specified part is, We analyze past purchasing data to identify the optimal supplier. The system according to feature 1.

4. The aforementioned negotiating body said, Negotiate prices with the identified suppliers. The system according to feature 1.

5. The aforementioned proposal section is, We propose a purchasing process based on the negotiated price. The system according to feature 1.

6. The aforementioned continuous analysis unit, By identifying new suppliers and negotiating prices, we can consistently ensure that we purchase goods at appropriate prices. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis priority based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit, We will conduct a detailed analysis of the past purchasing history of SMB companies and extract specific patterns. The system according to feature 1.

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