system

Abstract

We provide the system. [Solution] A means of collecting and organizing past transaction estimate data and storing it in an information set, A means of having the user enter a new quote, A means of analyzing the entered quotation data, comparing it with past information, and identifying inappropriate items, A means of generating a consensus-building negotiation strategy for items deemed inappropriate, A means of notifying users of the generated strategy and supporting negotiations in real time using mobile devices, A system that includes this.

Description

Technical Field

[0005] ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes 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 as a 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] There is a problem that it is difficult for an orderer with little construction experience to judge the appropriateness of an estimate and conduct appropriate price reduction negotiations. In addition, since past cases have not been accumulated, there are situations where judgments based on experience cannot be made. In such situations, there is a need for a system that can identify inappropriate items in an estimate and provide an effective negotiation strategy.

Means for Solving the Problems

[0005] This invention provides a system for collecting and organizing past construction estimate data and storing it as a database. This system has means for analyzing newly entered estimate data by the user and identifying inappropriate items by comparing it with past data. It also provides means for automatically generating a cost reduction negotiation strategy based on the identified inappropriate items and notifying the user, and furthermore, by updating the database using user feedback and improving the accuracy of future analyses, it enables even clients with little construction experience to make appropriate decisions.

[0006] "Construction estimate data" refers to document data that lists the types, quantities, and unit prices of the work and materials required for construction.

[0007] A "user" is an individual or organization that uses the system to input estimates, check analysis results, and provide feedback.

[0008] A "database" is a system that organizes, stores, and manages collected data in a way that allows for efficient searching and analysis.

[0009] "Analysis" is the process of extracting information by using a computer to examine input data in detail, comparing and evaluating it.

[0010] An "inappropriate item" is an element of an estimate that shows an unusually high or unreasonable value compared to normal market prices or past examples.

[0011] A "negotiation strategy for price reduction" is a plan that outlines specific negotiation methods and goals proposed to correct inappropriate items.

[0012] "Notifying" refers to the act of informing users of analysis results and proposed strategies.

[0013] "Feedback" refers to the act of users reporting negotiation results and usage experiences to the system, and this information is used to improve the system and update data. [Brief explanation of the drawing]

[0014] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] The 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.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention relates to a system for efficiently managing construction estimates and evaluating their appropriateness. The system consists of a server, terminals, and users. The server collects and organizes past construction estimate data and stores it in a database. The information stored in the database is designed to enable efficient searching and comparison.

[0036] Users input new construction estimate information provided by partner companies and other sources into the system. The information entered by the user is first sent to the server, where it is compared and analyzed against the database. The purpose of the analysis is to identify items in the estimate that appear to be inappropriate. A program running on the server generates the analysis results using an algorithm for detecting anomalies.

[0037] For any detected inappropriate items, the server automatically creates a strategy for negotiating a price reduction. This strategy includes specific, actionable guidelines for the user, which are communicated to the user via their terminal as needed. For example, if the unit price of a certain estimate item is higher than the market average, the server will indicate how to proceed with negotiations regarding that item.

[0038] Users can receive analysis results and negotiation strategies from the server via their terminals and use them in negotiations with construction companies. The database is updated by user feedback on the actual negotiation results. This allows the system to continuously improve, providing more accurate analysis and strategies.

[0039] For example, if a user receives an estimate for a construction project and determines that the unit price for "foundation work costs" is unreasonable compared to past statistics, the server will generate a detailed negotiation guide for that item. The user can then use this guide to request a review of the foundation work costs from the construction company.

[0040] In this way, this system automates the entire process of analyzing and negotiating construction estimates, helping users make efficient and reliable decisions.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server automatically collects historical construction estimate data from internal databases and reliable external data sources. The collected data is deduplicated and stored in the database in a standardized format.

[0044] Step 2:

[0045] The user enters new construction estimate information provided by partner companies into the terminal. The details of the estimate are categorized by item and transferred to the server.

[0046] Step 3:

[0047] The server analyzes the received estimate data. This includes comparing the unit price and quantity of each entered estimate item with historical data in the database. The server uses algorithms to identify any inappropriate elements.

[0048] Step 4:

[0049] The server lists inappropriate items based on the comparison results. It particularly focuses on items that are expensive compared to market averages or past cases, and extracts items that are deemed abnormal.

[0050] Step 5:

[0051] The server generates negotiation strategies for price reductions for the listed unsuitable items. These strategies include target negotiation amounts, negotiation methods, and reference information from past successful negotiations.

[0052] Step 6:

[0053] The terminal displays analysis results and negotiation strategies from the server through the user interface. Based on this information, the user prepares to begin negotiations with the construction company.

[0054] Step 7:

[0055] Users conduct actual negotiations and input the results into the system as feedback. This feedback includes the success rate of the negotiations and information on the unit price that was actually agreed upon.

[0056] Step 8:

[0057] The server receives feedback from users and updates the database. This accumulates information that will improve the accuracy of future analyses and strategy generation.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] There is a problem in that it is not possible to efficiently identify inappropriate items in construction estimates and create negotiation strategies, and the accuracy of analysis does not improve by reflecting the results of negotiations in the system. This issue hinders the efficiency and transparency of the construction estimation process and is a factor that causes disadvantages for users.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes means for collecting and organizing past information and storing it in an information storage device; means for receiving new information from users; means for analyzing the input information, comparing it with past information, and identifying abnormal items; means for generating reduction negotiation strategies for the items determined to be abnormal; means for notifying users of the generated strategies; and means for collecting negotiation results from users and updating the information storage device to improve the accuracy of the analysis. As a result, the accuracy and efficiency of construction estimates are improved, and users are able to make more appropriate decisions.

[0063] An "information storage device" is a memory device for organizing and saving past information, and is a database designed to enable efficient searching and analysis.

[0064] "User" refers to an individual or organization that operates this system, inputs information, or receives analysis results.

[0065] An "abnormal item" is an element that, when the entered information is compared to past statistical data or standards, is judged to deviate from the expected value.

[0066] A "reduction negotiation strategy" is a plan that proposes specific negotiation methods and procedures to adjust abnormal items to appropriate prices and conditions.

[0067] "Analysis accuracy" is an indicator that represents the system's ability to accurately compare and analyze information and make appropriate decisions.

[0068] This invention relates to an information processing system aimed at the efficient management and analysis of construction estimates. The system consists of a server, terminals, and users. The server utilizes an information storage device and has the function of managing a database that organizes and stores past construction estimate data. The server uses this database to perform comparative analysis with new estimate information. During the analysis, it is desirable to use software that implements an algorithm for detecting anomalies. This software can be programmed using languages ​​such as Python or R.

[0069] Users input new construction estimate data into the system via a terminal. The terminal is responsible for transmitting the input data to the server. The terminal can also receive analysis results and negotiation strategies notified from the server. A standard personal computer or tablet-type information terminal can be used for this input.

[0070] A concrete example is when a user receives an estimate for a new construction project. In this case, the user enters information for the "foundation work cost" into the system, and the server compares this information with the database. The server compares it with historical statistical data and, if it is automatically determined to be unreasonable, generates a detailed guide for negotiation to reduce the cost.

[0071] An example of a prompt message would be, "If the unit price for 'foundation work costs' in a construction project estimate is too high, please suggest how to proceed with negotiations." This allows users to negotiate with construction companies with a concrete strategy.

[0072] Thus, the present invention automates the analysis and negotiation process for construction cost estimates, supporting users in making more appropriate and reliable decisions.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The server collects past construction estimate data from various sources. Since this data is often provided in different formats, the server converts it into a unified format and stores it in an information storage device as organized data. The input is estimate data from companies and projects, and the output is a database that enables efficient searching and analysis. This process involves data processing such as data cleaning and format conversion.

[0076] Step 2:

[0077] The user uses a terminal to input new construction estimate information into the system. Once input is complete, the terminal sends the information to the server. The input here is the specific estimate details, and the output is the new estimate information received by the server. This step includes actions to ensure that data is entered accurately through the user interface.

[0078] Step 3:

[0079] The server analyzes newly received estimate information by comparing it with existing databases. This analysis uses algorithms to detect outliers. The inputs are the new estimate data and historical database data, and the output is a list of identified anomalies. Specifically, data calculations are performed using statistical analysis and anomaly detection algorithms.

[0080] Step 4:

[0081] The server generates reduction negotiation strategies for anomalies identified through analysis. These strategies include specific negotiation points and rationale based on market data. The input is a list of anomalies, and the output is a detailed negotiation guide. The server utilizes a generative AI model to automatically generate more effective strategies.

[0082] Step 5:

[0083] The server notifies the terminal of the generated strategy. The user receives this strategy through the terminal. The input is the generated negotiation strategy, and the output is what the user receives. Specifically, this includes system notifications that present the user with visualized information.

[0084] Step 6:

[0085] The user negotiates with the construction company based on the negotiation strategy they receive. The user inputs the results of these negotiations into the system as feedback. The input is the negotiation results from the user, and the output is updated feedback information. Based on this feedback, the server updates the database to improve the accuracy of future analyses. Specific actions include the process of reflecting the input results in the database.

[0086] (Application Example 1)

[0087] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0088] This invention aims to automate the efficient management and evaluation of the appropriateness of estimation data in construction and transactions, and to provide real-time negotiation support. In particular, there is a need to quickly identify inappropriate items by comparing them with past data and provide that information to the user immediately, thereby enabling efficient and accurate negotiations on site. Another challenge is to continuously improve the entire system by utilizing user feedback to improve the accuracy of future estimations.

[0089] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0090] In this invention, the server includes means for collecting and organizing past transaction estimate data and storing it in an information set, means for allowing users to input new estimates, and means for analyzing the input estimate data, comparing it with past information, and identifying inappropriate items. This allows users to receive real-time support for identifying inappropriate items and negotiating agreements using a mobile terminal. Furthermore, by incorporating user feedback, the information set can be updated to improve strategic accuracy.

[0091] "Transaction estimate data" refers to detailed cost and resource estimates presented during construction or transactions.

[0092] An "information collection" refers to a series of information that has been systematically organized and integrated into a database.

[0093] "User" refers to anyone who uses this system to receive assistance with cost analysis or negotiation.

[0094] A "new quote" refers to newly submitted quote information that did not exist before it was provided by the user.

[0095] An "inappropriate item" refers to an element in the estimation data that is judged to be abnormal when compared to standards or past data.

[0096] A "consensus-building negotiation strategy" refers to specific negotiation methods and policies aimed at obtaining appropriate terms for items deemed inappropriate.

[0097] A "mobile device" refers to a portable information device such as a smartphone or tablet that a user can carry and use.

[0098] "Real-time" refers to the instantaneous and uninterrupted processing and communication of information.

[0099] "Feedback" refers to the act of a user providing results from using the negotiation support system and opinions based on those results.

[0100] "Strategic accuracy" refers to an indicator that shows how effective a consensus-building negotiation strategy is in correcting inappropriate items.

[0101] This invention is a system for automating the analysis of estimates and negotiation support for construction and transaction projects. The specific implementation method is described below.

[0102] The server first efficiently collects historical transaction estimate data, organizes it into an information set, and stores it. This information set is managed as a database using a cloud platform such as Amazon Web Services (AWS®). The server then uses a data analysis algorithm built in Python to make the stored data analyzable.

[0103] Users input new quotes into the system using mobile devices such as smartphones and tablets. This entered quote data is transmitted to the server in real time, where it is analyzed by comparing it with past data sets to identify inappropriate items. The results of this analysis are notified to the user's mobile device, and the user can receive a consensus-building negotiation strategy based on this information. This strategy proposes specific negotiation policies and provides guidance to facilitate negotiations.

[0104] Furthermore, users can input the results of actual negotiations into the system as feedback, and the server updates its information set based on this feedback. This updated information will enable the system to provide even more accurate analysis and effective strategies in the future.

[0105] For example, if a user enters new estimate information for "foundation work costs" into the system, the server can analyze it and indicate that "foundation work costs are higher than the market average," and then provide specific advice for negotiation.

[0106] An example of a prompt message for the generating AI model is, "Analyze the new foundation construction cost estimate and check for any inappropriate items."

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The server collects historical transaction quote data, organizes it into an information set, and stores it. The input here is historical quote data provided externally, and the output is a systematic information set stored in the cloud. The server uploads this data to AWS's database service, preparing it for efficient searching and comparison.

[0110] Step 2:

[0111] The user enters new quote information using a mobile device. The quote data entered by the user through the form is sent to the server in its original format. The input here is new quote data, and the output is the data recorded on the server side. The input is validated field by field to confirm the consistency of the content.

[0112] Step 3:

[0113] The server analyzes the newly received quote data. It compares the input data with historical data to identify inappropriate items. This process uses an anomaly detection algorithm built in Python. The input is the new quote data and historical data, and the output is a list of inappropriate items. Heuristic analysis is performed on the data to extract values ​​that differ significantly from the market average.

[0114] Step 4:

[0115] The server generates a consensus-building negotiation strategy for items deemed inappropriate. This process utilizes a generative AI model to generate negotiation strategies. The input is a list of inappropriate items, and the output is a strategy containing specific negotiation guidelines. Through generated prompt messages, the AI ​​model creates the negotiation strategy and communicates it to the user.

[0116] Step 5:

[0117] The terminal notifies the user of the generated negotiation strategy. The user then conducts negotiations in real time based on this strategy. The input is the negotiation strategy received from the server, and the output is specific negotiation advice provided to the user. The terminal uses a notification function to communicate the strategy to the user and also allows for feedback input.

[0118] Step 6:

[0119] Users input negotiation results into the system as feedback. The input here represents the actual negotiation results, and the output is an updated set of information. Users input feedback via terminals, and the server updates the database based on this feedback, improving the accuracy of future analyses.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] This invention combines an emotion engine with a system for managing and analyzing construction estimates. The system consists of a server, terminals, and users. The server collects and organizes past construction estimate data and stores it in a database. Users, operating on terminals, input new construction estimates and send them to the server, where the estimates are analyzed and their appropriateness is evaluated.

[0122] The server compares the entered quotation data with past examples in the database and uses algorithms to identify inappropriate items. During this process, the emotion engine monitors the user's input behavior and choices and analyzes their emotional state. The results of this analysis are used to adjust the negotiation strategy.

[0123] The generated negotiation strategy is customized to take the user's emotional state into account. For example, if the user is feeling anxious, it can include more detailed and reassuring explanations. The device then presents the final strategy to the user and assists them in starting negotiations with the construction company.

[0124] Furthermore, user feedback includes emotional information recognized by the emotion engine, which is recorded in a database by the server. This information is used to improve the accuracy of future data analysis and negotiation strategy generation.

[0125] For example, when a user is preparing for a quote negotiation, if the emotion engine detects that the user is feeling stressed, the system adjusts its approach to present the strategy to the negotiating partner in a friendly and easy-to-understand manner. Furthermore, after the negotiation, a report is generated highlighting areas for improvement in the next negotiation based on the user's feedback.

[0126] This system aims to enable continuous learning and improvement that takes user emotions into account, thereby providing more effective and personalized support.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The server automatically collects and organizes past construction estimate data from internal databases and reliable external sources. The data is standardized and stored in the database for efficient searching.

[0130] Step 2:

[0131] The user uses a terminal to enter new construction estimate information provided by partner companies. The details of the estimate are entered for each required field and sent to the server.

[0132] Step 3:

[0133] The server activates the emotion engine to collect emotional data during the user's input operations. The emotion engine analyzes emotions based on factors such as input speed and the user interface selection history at that time.

[0134] Step 4:

[0135] The server analyzes the entered estimate data by comparing it with past database information. It uses algorithms to evaluate the validity of each item and identify any inappropriate items.

[0136] Step 5:

[0137] The server adjusts the negotiation strategy based on the user's emotional state, as analyzed by the emotion engine. For example, if the user is feeling anxious, the strategy will include more detailed explanations and support information.

[0138] Step 6:

[0139] The terminal presents the user with negotiation strategies tailored to their emotions and analysis results from the server. Based on this information, the user can smoothly negotiate with construction companies.

[0140] Step 7:

[0141] Users input feedback into the system regarding negotiation outcomes and their emotional responses during the process. This feedback includes analysis results from the emotion engine.

[0142] Step 8:

[0143] The server records user feedback in a database, which is then used to improve analysis accuracy and strengthen future negotiation strategies. The data is then used for statistical analysis to further refine future strategies.

[0144] (Example 2)

[0145] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0146] Conventional construction estimation systems simply compared past data, failing to take into account the user's emotions or the psychological aspects of negotiation. This resulted in standardized negotiation strategies and an inability to provide appropriate support to users. Furthermore, there was a lack of mechanisms to effectively utilize user feedback to improve the accuracy of strategies.

[0147] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0148] This invention includes a server that collects and organizes estimation data related to past construction activities and stores it in an information storage device; a server that performs sentiment analysis based on the user's input behavior and selections and customizes strategies that take into account the user's emotional state; and a server that receives user feedback and updates the information storage device to improve the accuracy of future strategies. This makes it possible to provide flexible and effective negotiation strategies that take into account the user's psychological aspects.

[0149] "Estimation documents related to past construction activities" refers to detailed information regarding expenses and plans for previously carried out construction work or projects.

[0150] An "information storage device" refers to a system or database for organizing and storing data.

[0151] "Users" refers to end users who use this system to enter new quotes or utilize negotiation strategies.

[0152] "Emotional analysis" refers to the process of evaluating and interpreting a user's psychological state based on their actions and choices.

[0153] "Customizing a strategy" refers to optimizing negotiation tactics and approaches according to the individual user's circumstances and feelings.

[0154] "Receiving feedback" refers to the process of collecting feedback and evaluations from users.

[0155] "Improving strategic accuracy" refers to enhancing the precision and effectiveness of negotiation strategies by utilizing newly acquired data and feedback.

[0156] The system based on this invention aims to efficiently manage construction cost estimation data and support users. Specifically, it consists of three components: a server, a terminal, and a user. The server is equipped with a database management system that collects estimation data related to past construction activities and organizes and stores it in an information storage device. This allows for the efficient organization of large amounts of data and the rapid extraction of necessary information.

[0157] Users input new quotation data via a terminal. The terminal provides a user interface that validates the format and required fields of the data entered by the user. The input data is immediately sent to the server for analysis.

[0158] The server runs a dedicated algorithm to compare the received estimate data with existing information. It also incorporates an emotion analysis engine that evaluates the user's emotional state based on their input actions and choices. Based on these results, a generative AI model creates a customized strategy for each user.

[0159] The generated strategy is presented to the user via the terminal. This allows the user to negotiate more effectively with the construction company. For example, if the user feels anxious or stressed while preparing for negotiations, the system will supplement with reassuring information and adjust the presentation of the strategy to be more user-friendly.

[0160] An example of a prompt message is, "How can we generate the optimal negotiation strategy based on the user's emotional state when negotiating a construction estimate?" This allows the system to provide advanced support that takes into account the user's psychological aspects.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The server automatically collects estimation data related to past construction activities from the internet and specific data sources. The collected data undergoes data cleansing operations and is processed into a proper format. Missing values ​​are imputed and outliers are removed, transforming it into highly reliable information. This is then stored in an information storage device and used in the analysis process described later.

[0164] Step 2:

[0165] The user enters a new construction estimate into the interface using a terminal. At this stage, the entered data is sent to a data validation program for formatting and checking required fields. As a result, accurate and complete estimate data is sent to the server, ready to proceed to the next analysis step.

[0166] Step 3:

[0167] The server compares the newly received estimate data with historical data stored in the information storage device. First, it runs a data analysis algorithm to detect inappropriate items. This analysis uses pattern recognition technology to identify abnormal costs and items and calculates an evaluation score based on them.

[0168] Step 4:

[0169] The server evaluates the user's emotions based on their input actions and choices, using an emotion analysis engine. It then customizes negotiation strategies that take the user's emotional state into account using a generative AI model. Specifically, it determines what information the user needs based on the emotion analysis results and structures that information in an appropriate format.

[0170] Step 5:

[0171] The terminal presents the user with strategies obtained from the server. This provides the user with the best course of action and advice when starting negotiations with construction companies. The information presented is detailed, user-friendly, and includes elements that alleviate anxiety.

[0172] Step 6:

[0173] Users provide feedback via a terminal after negotiations. The terminal sends this feedback, along with sentiment information, to a server. The server records the received feedback in an information storage device and uses it as input for future data analysis and strategy generation. Through this process, the system continuously learns and improves to provide highly accurate and customized assistance.

[0174] (Application Example 2)

[0175] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0176] Conventional planning and estimation systems merely compare data with past cases and do not generate strategies that take into account the user's emotional state. Therefore, users are more likely to feel anxious during negotiations, making effective negotiation support difficult. Furthermore, the lack of mechanisms to effectively utilize feedback and improve the accuracy of negotiation strategies is also a challenge.

[0177] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0178] In this invention, the server includes means for collecting and organizing past plan estimate data and storing it in information storage; means for allowing users to input new estimates; means for analyzing the input estimate information, comparing it with past information, and identifying inappropriate parts; means for monitoring and analyzing the user's emotional state and generating a negotiation strategy for price reduction based thereon; and means for notifying the user of the generated strategy and supporting the user in starting negotiations with the planning company. This enables reassuring negotiation support tailored to the user's emotional state, and further allows for continuous improvement of the strategy's accuracy through feedback.

[0179] "Planning and estimation data" refers to detailed estimation information from past projects, collected to analyze project costs and resource allocation.

[0180] "Information storage" refers to an electronic storage area for efficiently and securely storing digital information, and it serves the role of a database.

[0181] "Users" refers to individual people or organizations that operate this system and receive services such as quote input and negotiation support.

[0182] "Analysis" is the process of examining input data in detail and extracting its meaning and patterns, and it is a necessary step in making data-driven decisions.

[0183] "Inappropriate elements" refer to elements in new estimation data that are judged to be unreasonable or lacking in validity compared to past cases.

[0184] "Emotional state" refers to the psychological reactions and emotional tendencies that users exhibit when using the system, and is an important factor that influences the generation of negotiation strategies.

[0185] A "cost reduction negotiation strategy" refers to specific methods for reducing costs that are proposed for areas deemed unreasonable in order to successfully negotiate on fair terms.

[0186] "Notification" refers to the act of clearly communicating system-generated information and suggestions to users, supporting them in making appropriate decisions.

[0187] To implement this invention, a system is constructed in which the server, terminal, and user each play their respective roles.

[0188] The server is responsible for collecting historical planning and estimation data, organizing it, and storing it in information storage. This data is compared with new estimation information entered by users through their terminals. The server uses machine learning libraries (e.g., TENSORFLOW®, PyTorch) and database management systems (e.g., MySQL®, PostgreSQL) for data analysis to identify inaccuracies in the entered estimation information.

[0189] Meanwhile, the server runs an emotion engine to analyze the user's emotional state. Emotional analysis uses emotion recognition tools such as Google® Cloud Natural Language API and Affectiva. Based on the user's emotional state, the server leverages a generative AI model to generate an appropriate negotiation strategy for price reduction. This strategy is customized to provide a sense of security.

[0190] The generated negotiation strategy is communicated to the user via the device. The user then prepares to begin negotiations with the planning company based on this information. The device provides an intuitive and user-friendly interface and sends feedback to the server along with sentiment data.

[0191] As a concrete example, consider a situation where a person in charge of a construction project inputs new building construction estimate information. In this case, the system can refer to data from previous projects, sense the person's emotional state, and suggest strategies to alleviate their anxiety. These strategies include past success stories and detailed information that provides reassurance.

[0192] A concrete example of a prompt sentence to input into a generative AI model would be something like, "What strategies should be used to reassure the customer with this estimate?"

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The user enters new quote information using a terminal. This input includes data on the project's detailed specifications, budget, and resource allocation. This input data is then sent from the terminal to the server.

[0196] Step 2:

[0197] The server compares the received estimate information with historical data. The server uses a database management system such as MySQL or PostgreSQL to access past plan estimate databases. This comparison allows for data analysis to identify any parts deemed inappropriate. As a result, information about the detected inappropriate parts is obtained.

[0198] Step 3:

[0199] The server analyzes user emotion data received through the device. The emotion engine utilizes Google Cloud Natural Language API and Affectiva. It receives and analyzes user facial expressions and voice data perceived by the device as input. The output of this analysis is a detailed report on the user's emotional state.

[0200] Step 4:

[0201] The server generates negotiation strategies for price reductions using a generative AI model. This process takes a list of inappropriate elements and a user sentiment report as input. The AI ​​model constructs a customized strategy based on past successes and the user's emotional needs. The output is a reassuring negotiation strategy for the user.

[0202] Step 5:

[0203] The server sends the generated negotiation strategy to the terminal, which then notifies the user. The terminal displays the notification visually and audibly through an intuitive UI. This output provides information that enables the user to effectively begin negotiations with the planning company.

[0204] Step 6:

[0205] Users send feedback from their device to the server after negotiations. This feedback includes not only the negotiation results but also emotional information. The server records this in a database and uses it to generate future strategies.

[0206] Step 7:

[0207] The server uses feedback and new sentiment data to perform data analysis and improve its strategy generation algorithms. This process allows the system to continuously learn and provide users with more accurate support.

[0208] 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.

[0209] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0210] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] 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.

[0214] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0215] 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.

[0216] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0217] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0218] 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.

[0219] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0220] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0221] The 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.

[0222] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0223] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0224] This invention relates to a system for efficiently managing construction estimates and evaluating their appropriateness. The system consists of a server, terminals, and users. The server collects and organizes past construction estimate data and stores it in a database. The information stored in the database is designed to enable efficient searching and comparison.

[0225] Users input new construction estimate information provided by partner companies and other sources into the system. The information entered by the user is first sent to the server, where it is compared and analyzed against the database. The purpose of the analysis is to identify items in the estimate that appear to be inappropriate. A program running on the server generates the analysis results using an algorithm for detecting anomalies.

[0226] For any detected inappropriate items, the server automatically creates a strategy for negotiating a price reduction. This strategy includes specific, actionable guidelines for the user, which are communicated to the user via their terminal as needed. For example, if the unit price of a certain estimate item is higher than the market average, the server will indicate how to proceed with negotiations regarding that item.

[0227] Users can receive analysis results and negotiation strategies from the server via their terminals and use them in negotiations with construction companies. The database is updated by user feedback on the actual negotiation results. This allows the system to continuously improve, providing more accurate analysis and strategies.

[0228] For example, if a user receives an estimate for a construction project and determines that the unit price for "foundation work costs" is unreasonable compared to past statistics, the server will generate a detailed negotiation guide for that item. The user can then use this guide to request a review of the foundation work costs from the construction company.

[0229] In this way, this system automates the entire process of analyzing and negotiating construction estimates, helping users make efficient and reliable decisions.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The server automatically collects historical construction estimate data from internal databases and reliable external data sources. The collected data is deduplicated and stored in the database in a standardized format.

[0233] Step 2:

[0234] The user enters new construction estimate information provided by partner companies into the terminal. The details of the estimate are categorized by item and transferred to the server.

[0235] Step 3:

[0236] The server analyzes the received estimate data. This includes comparing the unit price and quantity of each entered estimate item with historical data in the database. The server uses algorithms to identify any inappropriate elements.

[0237] Step 4:

[0238] The server lists inappropriate items based on the comparison results. It particularly focuses on items that are expensive compared to market averages or past cases, and extracts items that are deemed abnormal.

[0239] Step 5:

[0240] The server generates negotiation strategies for price reductions for the listed unsuitable items. These strategies include target negotiation amounts, negotiation methods, and reference information from past successful negotiations.

[0241] Step 6:

[0242] The terminal displays analysis results and negotiation strategies from the server through the user interface. Based on this information, the user prepares to begin negotiations with the construction company.

[0243] Step 7:

[0244] Users conduct actual negotiations and input the results into the system as feedback. This feedback includes the success rate of the negotiations and information on the unit price that was actually agreed upon.

[0245] Step 8:

[0246] The server receives feedback from users and updates the database. This accumulates information that will improve the accuracy of future analyses and strategy generation.

[0247] (Example 1)

[0248] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0249] There is a problem in that it is not possible to efficiently identify inappropriate items in construction estimates and create negotiation strategies, and the accuracy of analysis does not improve by reflecting the results of negotiations in the system. This issue hinders the efficiency and transparency of the construction estimation process and is a factor that causes disadvantages for users.

[0250] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0251] In this invention, the server includes means for collecting and organizing past information and storing it in an information storage device; means for receiving new information from users; means for analyzing the input information, comparing it with past information, and identifying abnormal items; means for generating reduction negotiation strategies for the items determined to be abnormal; means for notifying users of the generated strategies; and means for collecting negotiation results from users and updating the information storage device to improve the accuracy of the analysis. As a result, the accuracy and efficiency of construction estimates are improved, and users are able to make more appropriate decisions.

[0252] An "information storage device" is a memory device for organizing and saving past information, and is a database designed to enable efficient searching and analysis.

[0253] "User" refers to an individual or organization that operates this system, inputs information, or receives analysis results.

[0254] An "abnormal item" is an element that, when the entered information is compared to past statistical data or standards, is judged to deviate from the expected value.

[0255] A "reduction negotiation strategy" is a plan that proposes specific negotiation methods and procedures to adjust abnormal items to appropriate prices and conditions.

[0256] "Analysis accuracy" is an indicator that represents the system's ability to accurately compare and analyze information and make appropriate decisions.

[0257] This invention relates to an information processing system aimed at the efficient management and analysis of construction estimates. The system consists of a server, terminals, and users. The server utilizes an information storage device and has the function of managing a database that organizes and stores past construction estimate data. The server uses this database to perform comparative analysis with new estimate information. During the analysis, it is desirable to use software that implements an algorithm for detecting anomalies. This software can be programmed using languages ​​such as Python or R.

[0258] Users input new construction estimate data into the system via a terminal. The terminal is responsible for transmitting the input data to the server. The terminal can also receive analysis results and negotiation strategies notified from the server. A standard personal computer or tablet-type information terminal can be used for this input.

[0259] A concrete example is when a user receives an estimate for a new construction project. In this case, the user enters information for the "foundation work cost" into the system, and the server compares this information with the database. The server compares it with historical statistical data and, if it is automatically determined to be unreasonable, generates a detailed guide for negotiation to reduce the cost.

[0260] An example of a prompt message would be, "If the unit price for 'foundation work costs' in a construction project estimate is too high, please suggest how to proceed with negotiations." This allows users to negotiate with construction companies with a concrete strategy.

[0261] Thus, the present invention automates the analysis and negotiation process for construction cost estimates, supporting users in making more appropriate and reliable decisions.

[0262] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0263] Step 1:

[0264] The server collects past construction estimate data from various sources. Since this data is often provided in different formats, the server converts it into a unified format and stores it in an information storage device as organized data. The input is estimate data from companies and projects, and the output is a database that enables efficient searching and analysis. This process involves data processing such as data cleaning and format conversion.

[0265] Step 2:

[0266] The user uses a terminal to input new construction estimate information into the system. Once input is complete, the terminal sends the information to the server. The input here is the specific estimate details, and the output is the new estimate information received by the server. This step includes actions to ensure that data is entered accurately through the user interface.

[0267] Step 3:

[0268] The server analyzes newly received estimate information by comparing it with existing databases. This analysis uses algorithms to detect outliers. The inputs are the new estimate data and historical database data, and the output is a list of identified anomalies. Specifically, data calculations are performed using statistical analysis and anomaly detection algorithms.

[0269] Step 4:

[0270] The server generates reduction negotiation strategies for anomalies identified through analysis. These strategies include specific negotiation points and rationale based on market data. The input is a list of anomalies, and the output is a detailed negotiation guide. The server utilizes a generative AI model to automatically generate more effective strategies.

[0271] Step 5:

[0272] The server notifies the terminal of the generated strategy. The user receives this strategy through the terminal. The input is the generated negotiation strategy, and the output is what the user receives. Specifically, this includes system notifications that present the user with visualized information.

[0273] Step 6:

[0274] The user negotiates with the construction company based on the negotiation strategy they receive. The user inputs the results of these negotiations into the system as feedback. The input is the negotiation results from the user, and the output is updated feedback information. Based on this feedback, the server updates the database to improve the accuracy of future analyses. Specific actions include the process of reflecting the input results in the database.

[0275] (Application Example 1)

[0276] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0277] This invention aims to automate the efficient management and evaluation of the appropriateness of estimation data in construction and transactions, and to provide real-time negotiation support. In particular, there is a need to quickly identify inappropriate items by comparing them with past data and provide that information to the user immediately, thereby enabling efficient and accurate negotiations on site. Another challenge is to continuously improve the entire system by utilizing user feedback to improve the accuracy of future estimations.

[0278] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0279] In this invention, the server includes means for collecting and organizing past transaction estimate data and storing it in an information set, means for allowing users to input new estimates, and means for analyzing the input estimate data, comparing it with past information, and identifying inappropriate items. This allows users to receive real-time support for identifying inappropriate items and negotiating agreements using a mobile terminal. Furthermore, by incorporating user feedback, the information set can be updated to improve strategic accuracy.

[0280] "Estimation data" refers to the detailed cost and resource estimation information presented during construction or transactions.

[0281] "Information collection" refers to a series of information groups that systematically organize the collected data and integrate it into a database.

[0282] "User" refers to a person who uses this system to receive estimation analysis and negotiation support.

[0283] "New estimate" refers to the newly submitted estimate information that did not exist before and is provided by the user.

[0284] "Improper item" refers to an element that is judged to be abnormal compared to the standards or past data within the estimation data.

[0285] "Consensus formation negotiation strategy" refers to the specific negotiation methods and guidelines for leading to appropriate conditions for the items judged to be improper.

[0286] "Mobile terminal" refers to a portable information terminal such as a smartphone or tablet that can be carried and used by the user.

[0287] "Real-time" refers to the situation where information processing and communication are carried out immediately without delay.

[0288] "Feedback" refers to the act of the user providing the usage results of the negotiation support system and opinions based on them.

[0289] "Strategy accuracy" refers to an indicator showing how effective the consensus formation negotiation strategy is for correcting improper items.

[0290] The present invention is a system for automating the estimation analysis and negotiation support of construction and transactions. The following shows a specific implementation method.

[0291] The server first efficiently collects historical transaction estimate data, organizes it into an information set, and stores it. This information set is managed as a database using a cloud platform such as Amazon Web Services (AWS). The server then uses a data analysis algorithm built in Python to make the stored data analyzable.

[0292] Users input new quotes into the system using mobile devices such as smartphones and tablets. This entered quote data is transmitted to the server in real time, where it is analyzed by comparing it with past data sets to identify inappropriate items. The results of this analysis are notified to the user's mobile device, and the user can receive a consensus-building negotiation strategy based on this information. This strategy proposes specific negotiation policies and provides guidance to facilitate negotiations.

[0293] Furthermore, users can input the results of actual negotiations into the system as feedback, and the server updates its information set based on this feedback. This updated information will enable the system to provide even more accurate analysis and effective strategies in the future.

[0294] For example, if a user enters new estimate information for "foundation work costs" into the system, the server can analyze it and indicate that "foundation work costs are higher than the market average," and then provide specific advice for negotiation.

[0295] An example of a prompt message for the generating AI model is, "Analyze the new foundation construction cost estimate and check for any inappropriate items."

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] The server collects historical transaction quote data, organizes it into an information set, and stores it. The input here is historical quote data provided externally, and the output is a systematic information set stored in the cloud. The server uploads this data to AWS's database service, preparing it for efficient searching and comparison.

[0299] Step 2:

[0300] The user enters new quote information using a mobile device. The quote data entered by the user through the form is sent to the server in its original format. The input here is new quote data, and the output is the data recorded on the server side. The input is validated field by field to confirm the consistency of the content.

[0301] Step 3:

[0302] The server analyzes the newly received quote data. It compares the input data with historical data to identify inappropriate items. This process uses an anomaly detection algorithm built in Python. The input is the new quote data and historical data, and the output is a list of inappropriate items. Heuristic analysis is performed on the data to extract values ​​that differ significantly from the market average.

[0303] Step 4:

[0304] The server generates a consensus-building negotiation strategy for items deemed inappropriate. This process utilizes a generative AI model to generate negotiation strategies. The input is a list of inappropriate items, and the output is a strategy containing specific negotiation guidelines. Through generated prompt messages, the AI ​​model creates the negotiation strategy and communicates it to the user.

[0305] Step 5:

[0306] The terminal notifies the user of the generated negotiation strategy. Based on this, the user conducts negotiations in real time. The input is the negotiation strategy received from the server, and the output is the specific negotiation advice provided to the user. The terminal uses the notification function to transmit the strategy to the user and also enables the input of feedback.

[0307] Step 6:

[0308] The user inputs the negotiation result into the system as feedback. The input here is the negotiation result in practice, and the output is the updated information set. The user inputs the feedback through the terminal, and the server updates the database based on this to improve the future analysis accuracy.

[0309] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0310] The present invention is in a form that combines an emotion engine with a system for managing and analyzing construction estimates. This system is composed of a server, a terminal, and a user. The server collects and organizes past construction estimate data and stores it in a database. The user operating on the terminal inputs a new construction estimate and transmits it to the server, thereby enabling the analysis and appropriateness evaluation of the estimate.

[0311] The server compares the input estimate data with past cases in the database and identifies inappropriate items using an algorithm. In this process, the emotion engine monitors the user's input actions and selections and analyzes the emotional state. The analysis result is used to adjust the negotiation strategy.

[0312] The generated negotiation strategy is customized to take the user's emotional state into account. For example, if the user is feeling anxious, it can include more detailed and reassuring explanations. The device then presents the final strategy to the user and assists them in starting negotiations with the construction company.

[0313] Furthermore, user feedback includes emotional information recognized by the emotion engine, which is recorded in a database by the server. This information is used to improve the accuracy of future data analysis and negotiation strategy generation.

[0314] For example, when a user is preparing for a quote negotiation, if the emotion engine detects that the user is feeling stressed, the system adjusts its approach to present the strategy to the negotiating partner in a friendly and easy-to-understand manner. Furthermore, after the negotiation, a report is generated highlighting areas for improvement in the next negotiation based on the user's feedback.

[0315] This system aims to enable continuous learning and improvement that takes user emotions into account, thereby providing more effective and personalized support.

[0316] The following describes the processing flow.

[0317] Step 1:

[0318] The server automatically collects and organizes past construction estimate data from internal databases and reliable external sources. The data is standardized and stored in the database for efficient searching.

[0319] Step 2:

[0320] The user uses a terminal to enter new construction estimate information provided by partner companies. The details of the estimate are entered for each required field and sent to the server.

[0321] Step 3:

[0322] The server activates the emotion engine to collect emotional data during the user's input operations. The emotion engine analyzes emotions based on factors such as input speed and the user interface selection history at that time.

[0323] Step 4:

[0324] The server analyzes the entered estimate data by comparing it with past database information. It uses algorithms to evaluate the validity of each item and identify any inappropriate items.

[0325] Step 5:

[0326] The server adjusts the negotiation strategy based on the user's emotional state, as analyzed by the emotion engine. For example, if the user is feeling anxious, the strategy will include more detailed explanations and support information.

[0327] Step 6:

[0328] The terminal presents the user with negotiation strategies tailored to their emotions and analysis results from the server. Based on this information, the user can smoothly negotiate with construction companies.

[0329] Step 7:

[0330] Users input feedback into the system regarding negotiation outcomes and their emotional responses during the process. This feedback includes analysis results from the emotion engine.

[0331] Step 8:

[0332] The server records user feedback in a database, which is then used to improve analysis accuracy and strengthen future negotiation strategies. The data is then used for statistical analysis to further refine future strategies.

[0333] (Example 2)

[0334] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0335] Conventional construction estimation systems simply compared past data, failing to take into account the user's emotions or the psychological aspects of negotiation. This resulted in standardized negotiation strategies and an inability to provide appropriate support to users. Furthermore, there was a lack of mechanisms to effectively utilize user feedback to improve the accuracy of strategies.

[0336] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0337] This invention includes a server that collects and organizes estimation data related to past construction activities and stores it in an information storage device; a server that performs sentiment analysis based on the user's input behavior and selections and customizes strategies that take into account the user's emotional state; and a server that receives user feedback and updates the information storage device to improve the accuracy of future strategies. This makes it possible to provide flexible and effective negotiation strategies that take into account the user's psychological aspects.

[0338] "Estimation documents related to past construction activities" refers to detailed information regarding expenses and plans for previously carried out construction work or projects.

[0339] An "information storage device" refers to a system or database for organizing and storing data.

[0340] "Users" refers to end users who use this system to enter new quotes or utilize negotiation strategies.

[0341] "Emotional analysis" refers to the process of evaluating and interpreting a user's psychological state based on their actions and choices.

[0342] "Customizing a strategy" refers to optimizing negotiation tactics and approaches according to the individual user's circumstances and feelings.

[0343] "Receiving feedback" refers to the process of collecting feedback and evaluations from users.

[0344] "Improving strategic accuracy" refers to enhancing the precision and effectiveness of negotiation strategies by utilizing newly acquired data and feedback.

[0345] The system based on this invention aims to efficiently manage construction cost estimation data and support users. Specifically, it consists of three components: a server, a terminal, and a user. The server is equipped with a database management system that collects estimation data related to past construction activities and organizes and stores it in an information storage device. This allows for the efficient organization of large amounts of data and the rapid extraction of necessary information.

[0346] Users input new quotation data via a terminal. The terminal provides a user interface that validates the format and required fields of the data entered by the user. The input data is immediately sent to the server for analysis.

[0347] The server runs a dedicated algorithm to compare the received estimate data with existing information. It also incorporates an emotion analysis engine that evaluates the user's emotional state based on their input actions and choices. Based on these results, a generative AI model creates a customized strategy for each user.

[0348] The generated strategy is presented to the user via the terminal. This allows the user to negotiate more effectively with the construction company. For example, if the user feels anxious or stressed while preparing for negotiations, the system will supplement with reassuring information and adjust the presentation of the strategy to be more user-friendly.

[0349] An example of a prompt message is, "How can we generate the optimal negotiation strategy based on the user's emotional state when negotiating a construction estimate?" This allows the system to provide advanced support that takes into account the user's psychological aspects.

[0350] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0351] Step 1:

[0352] The server automatically collects estimation data related to past construction activities from the internet and specific data sources. The collected data undergoes data cleansing operations and is processed into a proper format. Missing values ​​are imputed and outliers are removed, transforming it into highly reliable information. This is then stored in an information storage device and used in the analysis process described later.

[0353] Step 2:

[0354] The user enters a new construction estimate into the interface using a terminal. At this stage, the entered data is sent to a data validation program for formatting and checking required fields. As a result, accurate and complete estimate data is sent to the server, ready to proceed to the next analysis step.

[0355] Step 3:

[0356] The server compares the newly received estimate data with historical data stored in the information storage device. First, it runs a data analysis algorithm to detect inappropriate items. This analysis uses pattern recognition technology to identify abnormal costs and items and calculates an evaluation score based on them.

[0357] Step 4:

[0358] The server evaluates the user's emotions based on their input actions and choices, using an emotion analysis engine. It then customizes negotiation strategies that take the user's emotional state into account using a generative AI model. Specifically, it determines what information the user needs based on the emotion analysis results and structures that information in an appropriate format.

[0359] Step 5:

[0360] The terminal presents the user with strategies obtained from the server. This provides the user with the best course of action and advice when starting negotiations with construction companies. The information presented is detailed, user-friendly, and includes elements that alleviate anxiety.

[0361] Step 6:

[0362] Users provide feedback via a terminal after negotiations. The terminal sends this feedback, along with sentiment information, to a server. The server records the received feedback in an information storage device and uses it as input for future data analysis and strategy generation. Through this process, the system continuously learns and improves to provide highly accurate and customized assistance.

[0363] (Application Example 2)

[0364] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0365] Conventional planning and estimation systems merely compare data with past cases and do not generate strategies that take into account the user's emotional state. Therefore, users are more likely to feel anxious during negotiations, making effective negotiation support difficult. Furthermore, the lack of mechanisms to effectively utilize feedback and improve the accuracy of negotiation strategies is also a challenge.

[0366] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0367] In this invention, the server includes means for collecting and organizing past plan estimate data and storing it in information storage; means for allowing users to input new estimates; means for analyzing the input estimate information, comparing it with past information, and identifying inappropriate parts; means for monitoring and analyzing the user's emotional state and generating a negotiation strategy for price reduction based thereon; and means for notifying the user of the generated strategy and supporting the user in starting negotiations with the planning company. This enables reassuring negotiation support tailored to the user's emotional state, and further allows for continuous improvement of the strategy's accuracy through feedback.

[0368] "Planning and estimation data" refers to detailed estimation information from past projects, collected to analyze project costs and resource allocation.

[0369] "Information storage" refers to an electronic storage area for efficiently and securely storing digital information, and it serves the role of a database.

[0370] "Users" refers to individual people or organizations that operate this system and receive services such as quote input and negotiation support.

[0371] "Analysis" is the process of examining input data in detail and extracting its meaning and patterns, and it is a necessary step in making data-driven decisions.

[0372] "Inappropriate elements" refer to elements in new estimation data that are judged to be unreasonable or lacking in validity compared to past cases.

[0373] "Emotional state" refers to the psychological reactions and emotional tendencies that users exhibit when using the system, and is an important factor that influences the generation of negotiation strategies.

[0374] A "cost reduction negotiation strategy" refers to specific methods for reducing costs that are proposed for areas deemed unreasonable in order to successfully negotiate on fair terms.

[0375] "Notification" refers to the act of clearly communicating system-generated information and suggestions to users, supporting them in making appropriate decisions.

[0376] To implement this invention, a system is constructed in which the server, terminal, and user each play their respective roles.

[0377] The server is responsible for collecting historical planning and estimation data, organizing it, and storing it in information storage. This data is compared with new estimation information entered by users through their terminals. The server uses machine learning libraries (e.g., TensorFlow, PyTorch) and database management systems (e.g., MySQL, PostgreSQL) for data analysis to identify inaccuracies in the entered estimation information.

[0378] Meanwhile, the server runs an emotion engine to analyze the user's emotional state. Emotional analysis uses emotion recognition tools such as the Google Cloud Natural Language API and Affectiva. Based on the user's emotional state, the server leverages a generative AI model to generate an appropriate negotiation strategy for price reduction. This strategy is customized to provide a sense of security.

[0379] The generated negotiation strategy is communicated to the user via the device. The user then prepares to begin negotiations with the planning company based on this information. The device provides an intuitive and user-friendly interface and sends feedback to the server along with sentiment data.

[0380] As a concrete example, consider a situation where a person in charge of a construction project inputs new building construction estimate information. In this case, the system can refer to data from previous projects, sense the person's emotional state, and suggest strategies to alleviate their anxiety. These strategies include past success stories and detailed information that provides reassurance.

[0381] A concrete example of a prompt sentence to input into a generative AI model would be something like, "What strategies should be used to reassure the customer with this estimate?"

[0382] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0383] Step 1:

[0384] The user enters new quote information using a terminal. This input includes data on the project's detailed specifications, budget, and resource allocation. This input data is then sent from the terminal to the server.

[0385] Step 2:

[0386] The server compares the received estimate information with historical data. The server uses a database management system such as MySQL or PostgreSQL to access past plan estimate databases. This comparison allows for data analysis to identify any parts deemed inappropriate. As a result, information about the detected inappropriate parts is obtained.

[0387] Step 3:

[0388] The server analyzes user emotion data received through the device. The emotion engine utilizes Google Cloud Natural Language API and Affectiva. It receives and analyzes user facial expressions and voice data perceived by the device as input. The output of this analysis is a detailed report on the user's emotional state.

[0389] Step 4:

[0390] The server generates negotiation strategies for price reductions using a generative AI model. This process takes a list of inappropriate elements and a user sentiment report as input. The AI ​​model constructs a customized strategy based on past successes and the user's emotional needs. The output is a reassuring negotiation strategy for the user.

[0391] Step 5:

[0392] The server sends the generated negotiation strategy to the terminal, which then notifies the user. The terminal displays the notification visually and audibly through an intuitive UI. This output provides information that enables the user to effectively begin negotiations with the planning company.

[0393] Step 6:

[0394] Users send feedback from their device to the server after negotiations. This feedback includes not only the negotiation results but also emotional information. The server records this in a database and uses it to generate future strategies.

[0395] Step 7:

[0396] The server uses feedback and new sentiment data to perform data analysis and improve its strategy generation algorithms. This process allows the system to continuously learn and provide users with more accurate support.

[0397] 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.

[0398] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0399] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0400] [Third Embodiment]

[0401] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0402] 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.

[0403] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0404] 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.

[0405] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0406] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0407] 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.

[0408] 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.

[0409] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0410] The 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.

[0411] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0412] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0413] This invention relates to a system for efficiently managing construction estimates and evaluating their appropriateness. The system consists of a server, terminals, and users. The server collects and organizes past construction estimate data and stores it in a database. The information stored in the database is designed to enable efficient searching and comparison.

[0414] Users input new construction estimate information provided by partner companies and other sources into the system. The information entered by the user is first sent to the server, where it is compared and analyzed against the database. The purpose of the analysis is to identify items in the estimate that appear to be inappropriate. A program running on the server generates the analysis results using an algorithm for detecting anomalies.

[0415] For any detected inappropriate items, the server automatically creates a strategy for negotiating a price reduction. This strategy includes specific, actionable guidelines for the user, which are communicated to the user via their terminal as needed. For example, if the unit price of a certain estimate item is higher than the market average, the server will indicate how to proceed with negotiations regarding that item.

[0416] Users can receive analysis results and negotiation strategies from the server via their terminals and use them in negotiations with construction companies. The database is updated by user feedback on the actual negotiation results. This allows the system to continuously improve, providing more accurate analysis and strategies.

[0417] For example, if a user receives an estimate for a construction project and determines that the unit price for "foundation work costs" is unreasonable compared to past statistics, the server will generate a detailed negotiation guide for that item. The user can then use this guide to request a review of the foundation work costs from the construction company.

[0418] In this way, this system automates the entire process of analyzing and negotiating construction estimates, helping users make efficient and reliable decisions.

[0419] The following describes the processing flow.

[0420] Step 1:

[0421] The server automatically collects historical construction estimate data from internal databases and reliable external data sources. The collected data is deduplicated and stored in the database in a standardized format.

[0422] Step 2:

[0423] The user enters new construction estimate information provided by partner companies into the terminal. The details of the estimate are categorized by item and transferred to the server.

[0424] Step 3:

[0425] The server analyzes the received estimate data. This includes comparing the unit price and quantity of each entered estimate item with historical data in the database. The server uses algorithms to identify any inappropriate elements.

[0426] Step 4:

[0427] The server lists inappropriate items based on the comparison results. It particularly focuses on items that are expensive compared to market averages or past cases, and extracts items that are deemed abnormal.

[0428] Step 5:

[0429] The server generates negotiation strategies for price reductions for the listed unsuitable items. These strategies include target negotiation amounts, negotiation methods, and reference information from past successful negotiations.

[0430] Step 6:

[0431] The terminal displays analysis results and negotiation strategies from the server through the user interface. Based on this information, the user prepares to begin negotiations with the construction company.

[0432] Step 7:

[0433] Users conduct actual negotiations and input the results into the system as feedback. This feedback includes the success rate of the negotiations and information on the unit price that was actually agreed upon.

[0434] Step 8:

[0435] The server receives feedback from users and updates the database. This accumulates information that will improve the accuracy of future analyses and strategy generation.

[0436] (Example 1)

[0437] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0438] There is a problem in that it is not possible to efficiently identify inappropriate items in construction estimates and create negotiation strategies, and the accuracy of analysis does not improve by reflecting the results of negotiations in the system. This issue hinders the efficiency and transparency of the construction estimation process and is a factor that causes disadvantages for users.

[0439] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0440] In this invention, the server includes means for collecting and organizing past information and storing it in an information storage device; means for receiving new information from users; means for analyzing the input information, comparing it with past information, and identifying abnormal items; means for generating reduction negotiation strategies for the items determined to be abnormal; means for notifying users of the generated strategies; and means for collecting negotiation results from users and updating the information storage device to improve the accuracy of the analysis. As a result, the accuracy and efficiency of construction estimates are improved, and users are able to make more appropriate decisions.

[0441] An "information storage device" is a memory device for organizing and saving past information, and is a database designed to enable efficient searching and analysis.

[0442] "User" refers to an individual or organization that operates this system, inputs information, or receives analysis results.

[0443] An "abnormal item" is an element that, when the entered information is compared to past statistical data or standards, is judged to deviate from the expected value.

[0444] A "reduction negotiation strategy" is a plan that proposes specific negotiation methods and procedures to adjust abnormal items to appropriate prices and conditions.

[0445] "Analysis accuracy" is an indicator that represents the system's ability to accurately compare and analyze information and make appropriate decisions.

[0446] This invention relates to an information processing system aimed at the efficient management and analysis of construction estimates. The system consists of a server, terminals, and users. The server utilizes an information storage device and has the function of managing a database that organizes and stores past construction estimate data. The server uses this database to perform comparative analysis with new estimate information. During the analysis, it is desirable to use software that implements an algorithm for detecting anomalies. This software can be programmed using languages ​​such as Python or R.

[0447] Users input new construction estimate data into the system via a terminal. The terminal is responsible for transmitting the input data to the server. The terminal can also receive analysis results and negotiation strategies notified from the server. A standard personal computer or tablet-type information terminal can be used for this input.

[0448] A concrete example is when a user receives an estimate for a new construction project. In this case, the user enters information for the "foundation work cost" into the system, and the server compares this information with the database. The server compares it with historical statistical data and, if it is automatically determined to be unreasonable, generates a detailed guide for negotiation to reduce the cost.

[0449] An example of a prompt message would be, "If the unit price for 'foundation work costs' in a construction project estimate is too high, please suggest how to proceed with negotiations." This allows users to negotiate with construction companies with a concrete strategy.

[0450] Thus, the present invention automates the analysis and negotiation process for construction cost estimates, supporting users in making more appropriate and reliable decisions.

[0451] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0452] Step 1:

[0453] The server collects past construction estimate data from various sources. Since this data is often provided in different formats, the server converts it into a unified format and stores it in an information storage device as organized data. The input is estimate data from companies and projects, and the output is a database that enables efficient searching and analysis. This process involves data processing such as data cleaning and format conversion.

[0454] Step 2:

[0455] The user uses a terminal to input new construction estimate information into the system. Once input is complete, the terminal sends the information to the server. The input here is the specific estimate details, and the output is the new estimate information received by the server. This step includes actions to ensure that data is entered accurately through the user interface.

[0456] Step 3:

[0457] The server analyzes newly received estimate information by comparing it with existing databases. This analysis uses algorithms to detect outliers. The inputs are the new estimate data and historical database data, and the output is a list of identified anomalies. Specifically, data calculations are performed using statistical analysis and anomaly detection algorithms.

[0458] Step 4:

[0459] The server generates reduction negotiation strategies for anomalies identified through analysis. These strategies include specific negotiation points and rationale based on market data. The input is a list of anomalies, and the output is a detailed negotiation guide. The server utilizes a generative AI model to automatically generate more effective strategies.

[0460] Step 5:

[0461] The server notifies the terminal of the generated strategy. The user receives this strategy through the terminal. The input is the generated negotiation strategy, and the output is what the user receives. Specifically, this includes system notifications that present the user with visualized information.

[0462] Step 6:

[0463] The user negotiates with the construction company based on the negotiation strategy they receive. The user inputs the results of these negotiations into the system as feedback. The input is the negotiation results from the user, and the output is updated feedback information. Based on this feedback, the server updates the database to improve the accuracy of future analyses. Specific actions include the process of reflecting the input results in the database.

[0464] (Application Example 1)

[0465] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0466] This invention aims to automate the efficient management and evaluation of the appropriateness of estimation data in construction and transactions, and to provide real-time negotiation support. In particular, there is a need to quickly identify inappropriate items by comparing them with past data and provide that information to the user immediately, thereby enabling efficient and accurate negotiations on site. Another challenge is to continuously improve the entire system by utilizing user feedback to improve the accuracy of future estimations.

[0467] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0468] In this invention, the server includes means for collecting and organizing past transaction estimate data and storing it in an information set, means for allowing users to input new estimates, and means for analyzing the input estimate data, comparing it with past information, and identifying inappropriate items. This allows users to receive real-time support for identifying inappropriate items and negotiating agreements using a mobile terminal. Furthermore, by incorporating user feedback, the information set can be updated to improve strategic accuracy.

[0469] "Transaction estimate data" refers to detailed cost and resource estimates presented during construction or transactions.

[0470] An "information collection" refers to a series of information that has been systematically organized and integrated into a database.

[0471] "User" refers to anyone who uses this system to receive assistance with cost analysis or negotiation.

[0472] A "new quote" refers to newly submitted quote information that did not exist before it was provided by the user.

[0473] An "inappropriate item" refers to an element in the estimation data that is judged to be abnormal when compared to standards or past data.

[0474] A "consensus-building negotiation strategy" refers to specific negotiation methods and policies aimed at obtaining appropriate terms for items deemed inappropriate.

[0475] A "mobile device" refers to a portable information device such as a smartphone or tablet that a user can carry and use.

[0476] "Real-time" refers to the instantaneous and uninterrupted processing and communication of information.

[0477] "Feedback" refers to the act of a user providing results from using the negotiation support system and opinions based on those results.

[0478] "Strategic accuracy" refers to an indicator that shows how effective a consensus-building negotiation strategy is in correcting inappropriate items.

[0479] This invention is a system for automating the analysis of estimates and negotiation support for construction and transaction projects. The specific implementation method is described below.

[0480] The server first efficiently collects historical transaction estimate data, organizes it into an information set, and stores it. This information set is managed as a database using a cloud platform such as Amazon Web Services (AWS). The server then uses a data analysis algorithm built in Python to make the stored data analyzable.

[0481] Users input new quotes into the system using mobile devices such as smartphones and tablets. This entered quote data is transmitted to the server in real time, where it is analyzed by comparing it with past data sets to identify inappropriate items. The results of this analysis are notified to the user's mobile device, and the user can receive a consensus-building negotiation strategy based on this information. This strategy proposes specific negotiation policies and provides guidance to facilitate negotiations.

[0482] Furthermore, users can input the results of actual negotiations into the system as feedback, and the server updates its information set based on this feedback. This updated information will enable the system to provide even more accurate analysis and effective strategies in the future.

[0483] For example, if a user enters new estimate information for "foundation work costs" into the system, the server can analyze it and indicate that "foundation work costs are higher than the market average," and then provide specific advice for negotiation.

[0484] An example of a prompt message for the generating AI model is, "Analyze the new foundation construction cost estimate and check for any inappropriate items."

[0485] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0486] Step 1:

[0487] The server collects historical transaction quote data, organizes it into an information set, and stores it. The input here is historical quote data provided externally, and the output is a systematic information set stored in the cloud. The server uploads this data to AWS's database service, preparing it for efficient searching and comparison.

[0488] Step 2:

[0489] The user enters new quote information using a mobile device. The quote data entered by the user through the form is sent to the server in its original format. The input here is new quote data, and the output is the data recorded on the server side. The input is validated field by field to confirm the consistency of the content.

[0490] Step 3:

[0491] The server analyzes the newly received quote data. It compares the input data with historical data to identify inappropriate items. This process uses an anomaly detection algorithm built in Python. The input is the new quote data and historical data, and the output is a list of inappropriate items. Heuristic analysis is performed on the data to extract values ​​that differ significantly from the market average.

[0492] Step 4:

[0493] The server generates a consensus-building negotiation strategy for items deemed inappropriate. This process utilizes a generative AI model to generate negotiation strategies. The input is a list of inappropriate items, and the output is a strategy containing specific negotiation guidelines. Through generated prompt messages, the AI ​​model creates the negotiation strategy and communicates it to the user.

[0494] Step 5:

[0495] The terminal notifies the user of the generated negotiation strategy. The user then conducts negotiations in real time based on this strategy. The input is the negotiation strategy received from the server, and the output is specific negotiation advice provided to the user. The terminal uses a notification function to communicate the strategy to the user and also allows for feedback input.

[0496] Step 6:

[0497] Users input negotiation results into the system as feedback. The input here represents the actual negotiation results, and the output is an updated set of information. Users input feedback via terminals, and the server updates the database based on this feedback, improving the accuracy of future analyses.

[0498] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0499] This invention combines an emotion engine with a system for managing and analyzing construction estimates. The system consists of a server, terminals, and users. The server collects and organizes past construction estimate data and stores it in a database. Users, operating on terminals, input new construction estimates and send them to the server, where the estimates are analyzed and their appropriateness is evaluated.

[0500] The server compares the entered quotation data with past examples in the database and uses algorithms to identify inappropriate items. During this process, the emotion engine monitors the user's input behavior and choices and analyzes their emotional state. The results of this analysis are used to adjust the negotiation strategy.

[0501] The generated negotiation strategy is customized to take the user's emotional state into account. For example, if the user is feeling anxious, it can include more detailed and reassuring explanations. The device then presents the final strategy to the user and assists them in starting negotiations with the construction company.

[0502] Furthermore, user feedback includes emotional information recognized by the emotion engine, which is recorded in a database by the server. This information is used to improve the accuracy of future data analysis and negotiation strategy generation.

[0503] For example, when a user is preparing for a quote negotiation, if the emotion engine detects that the user is feeling stressed, the system adjusts its approach to present the strategy to the negotiating partner in a friendly and easy-to-understand manner. Furthermore, after the negotiation, a report is generated highlighting areas for improvement in the next negotiation based on the user's feedback.

[0504] This system aims to enable continuous learning and improvement that takes user emotions into account, thereby providing more effective and personalized support.

[0505] The following describes the processing flow.

[0506] Step 1:

[0507] The server automatically collects and organizes past construction estimate data from internal databases and reliable external sources. The data is standardized and stored in the database for efficient searching.

[0508] Step 2:

[0509] The user uses a terminal to enter new construction estimate information provided by partner companies. The details of the estimate are entered for each required field and sent to the server.

[0510] Step 3:

[0511] The server activates the emotion engine to collect emotional data during the user's input operations. The emotion engine analyzes emotions based on factors such as input speed and the user interface selection history at that time.

[0512] Step 4:

[0513] The server analyzes the entered estimate data by comparing it with past database information. It uses algorithms to evaluate the validity of each item and identify any inappropriate items.

[0514] Step 5:

[0515] The server adjusts the negotiation strategy based on the user's emotional state, as analyzed by the emotion engine. For example, if the user is feeling anxious, the strategy will include more detailed explanations and support information.

[0516] Step 6:

[0517] The terminal presents the user with negotiation strategies tailored to their emotions and analysis results from the server. Based on this information, the user can smoothly negotiate with construction companies.

[0518] Step 7:

[0519] Users input feedback into the system regarding negotiation outcomes and their emotional responses during the process. This feedback includes analysis results from the emotion engine.

[0520] Step 8:

[0521] The server records user feedback in a database, which is then used to improve analysis accuracy and strengthen future negotiation strategies. The data is then used for statistical analysis to further refine future strategies.

[0522] (Example 2)

[0523] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0524] Conventional construction estimation systems simply compared past data, failing to take into account the user's emotions or the psychological aspects of negotiation. This resulted in standardized negotiation strategies and an inability to provide appropriate support to users. Furthermore, there was a lack of mechanisms to effectively utilize user feedback to improve the accuracy of strategies.

[0525] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0526] This invention includes a server that collects and organizes estimation data related to past construction activities and stores it in an information storage device; a server that performs sentiment analysis based on the user's input behavior and selections and customizes strategies that take into account the user's emotional state; and a server that receives user feedback and updates the information storage device to improve the accuracy of future strategies. This makes it possible to provide flexible and effective negotiation strategies that take into account the user's psychological aspects.

[0527] "Estimation documents related to past construction activities" refers to detailed information regarding expenses and plans for previously carried out construction work or projects.

[0528] An "information storage device" refers to a system or database for organizing and storing data.

[0529] "Users" refers to end users who use this system to enter new quotes or utilize negotiation strategies.

[0530] "Emotional analysis" refers to the process of evaluating and interpreting a user's psychological state based on their actions and choices.

[0531] "Customizing a strategy" refers to optimizing negotiation tactics and approaches according to the individual user's circumstances and feelings.

[0532] "Receiving feedback" refers to the process of collecting feedback and evaluations from users.

[0533] "Improving strategic accuracy" refers to enhancing the precision and effectiveness of negotiation strategies by utilizing newly acquired data and feedback.

[0534] The system based on this invention aims to efficiently manage construction cost estimation data and support users. Specifically, it consists of three components: a server, a terminal, and a user. The server is equipped with a database management system that collects estimation data related to past construction activities and organizes and stores it in an information storage device. This allows for the efficient organization of large amounts of data and the rapid extraction of necessary information.

[0535] Users input new quotation data via a terminal. The terminal provides a user interface that validates the format and required fields of the data entered by the user. The input data is immediately sent to the server for analysis.

[0536] The server runs a dedicated algorithm to compare the received estimate data with existing information. It also incorporates an emotion analysis engine that evaluates the user's emotional state based on their input actions and choices. Based on these results, a generative AI model creates a customized strategy for each user.

[0537] The generated strategy is presented to the user via the terminal. This allows the user to negotiate more effectively with the construction company. For example, if the user feels anxious or stressed while preparing for negotiations, the system will supplement with reassuring information and adjust the presentation of the strategy to be more user-friendly.

[0538] An example of a prompt message is, "How can we generate the optimal negotiation strategy based on the user's emotional state when negotiating a construction estimate?" This allows the system to provide advanced support that takes into account the user's psychological aspects.

[0539] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0540] Step 1:

[0541] The server automatically collects estimation data related to past construction activities from the internet and specific data sources. The collected data undergoes data cleansing operations and is processed into a proper format. Missing values ​​are imputed and outliers are removed, transforming it into highly reliable information. This is then stored in an information storage device and used in the analysis process described later.

[0542] Step 2:

[0543] The user enters a new construction estimate into the interface using a terminal. At this stage, the entered data is sent to a data validation program for formatting and checking required fields. As a result, accurate and complete estimate data is sent to the server, ready to proceed to the next analysis step.

[0544] Step 3:

[0545] The server compares the newly received estimate data with historical data stored in the information storage device. First, it runs a data analysis algorithm to detect inappropriate items. This analysis uses pattern recognition technology to identify abnormal costs and items and calculates an evaluation score based on them.

[0546] Step 4:

[0547] The server evaluates the user's emotions based on their input actions and choices, using an emotion analysis engine. It then customizes negotiation strategies that take the user's emotional state into account using a generative AI model. Specifically, it determines what information the user needs based on the emotion analysis results and structures that information in an appropriate format.

[0548] Step 5:

[0549] The terminal presents the user with strategies obtained from the server. This provides the user with the best course of action and advice when starting negotiations with construction companies. The information presented is detailed, user-friendly, and includes elements that alleviate anxiety.

[0550] Step 6:

[0551] Users provide feedback via a terminal after negotiations. The terminal sends this feedback, along with sentiment information, to a server. The server records the received feedback in an information storage device and uses it as input for future data analysis and strategy generation. Through this process, the system continuously learns and improves to provide highly accurate and customized assistance.

[0552] (Application Example 2)

[0553] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0554] Conventional planning and estimation systems merely compare data with past cases and do not generate strategies that take into account the user's emotional state. Therefore, users are more likely to feel anxious during negotiations, making effective negotiation support difficult. Furthermore, the lack of mechanisms to effectively utilize feedback and improve the accuracy of negotiation strategies is also a challenge.

[0555] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0556] In this invention, the server includes means for collecting and organizing past plan estimate data and storing it in information storage; means for allowing users to input new estimates; means for analyzing the input estimate information, comparing it with past information, and identifying inappropriate parts; means for monitoring and analyzing the user's emotional state and generating a negotiation strategy for price reduction based thereon; and means for notifying the user of the generated strategy and supporting the user in starting negotiations with the planning company. This enables reassuring negotiation support tailored to the user's emotional state, and further allows for continuous improvement of the strategy's accuracy through feedback.

[0557] "Planning and estimation data" refers to detailed estimation information from past projects, collected to analyze project costs and resource allocation.

[0558] "Information storage" refers to an electronic storage area for efficiently and securely storing digital information, and it serves the role of a database.

[0559] "Users" refers to individual people or organizations that operate this system and receive services such as quote input and negotiation support.

[0560] "Analysis" is the process of examining input data in detail and extracting its meaning and patterns, and it is a necessary step in making data-driven decisions.

[0561] "Inappropriate elements" refer to elements in new estimation data that are judged to be unreasonable or lacking in validity compared to past cases.

[0562] "Emotional state" refers to the psychological reactions and emotional tendencies that users exhibit when using the system, and is an important factor that influences the generation of negotiation strategies.

[0563] A "cost reduction negotiation strategy" refers to specific methods for reducing costs that are proposed for areas deemed unreasonable in order to successfully negotiate on fair terms.

[0564] "Notification" refers to the act of clearly communicating system-generated information and suggestions to users, supporting them in making appropriate decisions.

[0565] To implement this invention, a system is constructed in which the server, terminal, and user each play their respective roles.

[0566] The server is responsible for collecting historical planning and estimation data, organizing it, and storing it in information storage. This data is compared with new estimation information entered by users through their terminals. The server uses machine learning libraries (e.g., TensorFlow, PyTorch) and database management systems (e.g., MySQL, PostgreSQL) for data analysis to identify inaccuracies in the entered estimation information.

[0567] Meanwhile, the server runs an emotion engine to analyze the user's emotional state. Emotional analysis uses emotion recognition tools such as the Google Cloud Natural Language API and Affectiva. Based on the user's emotional state, the server leverages a generative AI model to generate an appropriate negotiation strategy for price reduction. This strategy is customized to provide a sense of security.

[0568] The generated negotiation strategy is communicated to the user via the device. The user then prepares to begin negotiations with the planning company based on this information. The device provides an intuitive and user-friendly interface and sends feedback to the server along with sentiment data.

[0569] As a concrete example, consider a situation where a person in charge of a construction project inputs new building construction estimate information. In this case, the system can refer to data from previous projects, sense the person's emotional state, and suggest strategies to alleviate their anxiety. These strategies include past success stories and detailed information that provides reassurance.

[0570] A concrete example of a prompt sentence to input into a generative AI model would be something like, "What strategies should be used to reassure the customer with this estimate?"

[0571] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0572] Step 1:

[0573] The user enters new quote information using a terminal. This input includes data on the project's detailed specifications, budget, and resource allocation. This input data is then sent from the terminal to the server.

[0574] Step 2:

[0575] The server compares the received estimate information with historical data. The server uses a database management system such as MySQL or PostgreSQL to access past plan estimate databases. This comparison allows for data analysis to identify any parts deemed inappropriate. As a result, information about the detected inappropriate parts is obtained.

[0576] Step 3:

[0577] The server analyzes user emotion data received through the device. The emotion engine utilizes Google Cloud Natural Language API and Affectiva. It receives and analyzes user facial expressions and voice data perceived by the device as input. The output of this analysis is a detailed report on the user's emotional state.

[0578] Step 4:

[0579] The server generates negotiation strategies for price reductions using a generative AI model. This process takes a list of inappropriate elements and a user sentiment report as input. The AI ​​model constructs a customized strategy based on past successes and the user's emotional needs. The output is a reassuring negotiation strategy for the user.

[0580] Step 5:

[0581] The server sends the generated negotiation strategy to the terminal, which then notifies the user. The terminal displays the notification visually and audibly through an intuitive UI. This output provides information that enables the user to effectively begin negotiations with the planning company.

[0582] Step 6:

[0583] Users send feedback from their device to the server after negotiations. This feedback includes not only the negotiation results but also emotional information. The server records this in a database and uses it to generate future strategies.

[0584] Step 7:

[0585] The server uses feedback and new sentiment data to perform data analysis and improve its strategy generation algorithms. This process allows the system to continuously learn and provide users with more accurate support.

[0586] 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.

[0587] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0588] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0589] [Fourth Embodiment]

[0590] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0591] 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.

[0592] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0593] 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.

[0594] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0595] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0596] 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.

[0597] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0598] 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.

[0599] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0600] The 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.

[0601] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0602] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0603] This invention relates to a system for efficiently managing construction estimates and evaluating their appropriateness. The system consists of a server, terminals, and users. The server collects and organizes past construction estimate data and stores it in a database. The information stored in the database is designed to enable efficient searching and comparison.

[0604] Users input new construction estimate information provided by partner companies and other sources into the system. The information entered by the user is first sent to the server, where it is compared and analyzed against the database. The purpose of the analysis is to identify items in the estimate that appear to be inappropriate. A program running on the server generates the analysis results using an algorithm for detecting anomalies.

[0605] For any detected inappropriate items, the server automatically creates a strategy for negotiating a price reduction. This strategy includes specific, actionable guidelines for the user, which are communicated to the user via their terminal as needed. For example, if the unit price of a certain estimate item is higher than the market average, the server will indicate how to proceed with negotiations regarding that item.

[0606] Users can receive analysis results and negotiation strategies from the server via their terminals and use them in negotiations with construction companies. The database is updated by user feedback on the actual negotiation results. This allows the system to continuously improve, providing more accurate analysis and strategies.

[0607] For example, if a user receives an estimate for a construction project and determines that the unit price for "foundation work costs" is unreasonable compared to past statistics, the server will generate a detailed negotiation guide for that item. The user can then use this guide to request a review of the foundation work costs from the construction company.

[0608] In this way, this system automates the entire process of analyzing and negotiating construction estimates, helping users make efficient and reliable decisions.

[0609] The following describes the processing flow.

[0610] Step 1:

[0611] The server automatically collects historical construction estimate data from internal databases and reliable external data sources. The collected data is deduplicated and stored in the database in a standardized format.

[0612] Step 2:

[0613] The user enters new construction estimate information provided by partner companies into the terminal. The details of the estimate are categorized by item and transferred to the server.

[0614] Step 3:

[0615] The server analyzes the received estimate data. This includes comparing the unit price and quantity of each entered estimate item with historical data in the database. The server uses algorithms to identify any inappropriate elements.

[0616] Step 4:

[0617] The server lists inappropriate items based on the comparison results. It particularly focuses on items that are expensive compared to market averages or past cases, and extracts items that are deemed abnormal.

[0618] Step 5:

[0619] The server generates negotiation strategies for price reductions for the listed unsuitable items. These strategies include target negotiation amounts, negotiation methods, and reference information from past successful negotiations.

[0620] Step 6:

[0621] The terminal displays analysis results and negotiation strategies from the server through the user interface. Based on this information, the user prepares to begin negotiations with the construction company.

[0622] Step 7:

[0623] Users conduct actual negotiations and input the results into the system as feedback. This feedback includes the success rate of the negotiations and information on the unit price that was actually agreed upon.

[0624] Step 8:

[0625] The server receives feedback from users and updates the database. This accumulates information that will improve the accuracy of future analyses and strategy generation.

[0626] (Example 1)

[0627] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0628] There is a problem in that it is not possible to efficiently identify inappropriate items in construction estimates and create negotiation strategies, and the accuracy of analysis does not improve by reflecting the results of negotiations in the system. This issue hinders the efficiency and transparency of the construction estimation process and is a factor that causes disadvantages for users.

[0629] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0630] In this invention, the server includes means for collecting and organizing past information and storing it in an information storage device; means for receiving new information from users; means for analyzing the input information, comparing it with past information, and identifying abnormal items; means for generating reduction negotiation strategies for the items determined to be abnormal; means for notifying users of the generated strategies; and means for collecting negotiation results from users and updating the information storage device to improve the accuracy of the analysis. As a result, the accuracy and efficiency of construction estimates are improved, and users are able to make more appropriate decisions.

[0631] An "information storage device" is a memory device for organizing and saving past information, and is a database designed to enable efficient searching and analysis.

[0632] "User" refers to an individual or organization that operates this system, inputs information, or receives analysis results.

[0633] An "abnormal item" is an element that, when the entered information is compared to past statistical data or standards, is judged to deviate from the expected value.

[0634] A "reduction negotiation strategy" is a plan that proposes specific negotiation methods and procedures to adjust abnormal items to appropriate prices and conditions.

[0635] "Analysis accuracy" is an indicator that represents the system's ability to accurately compare and analyze information and make appropriate decisions.

[0636] This invention relates to an information processing system aimed at the efficient management and analysis of construction estimates. The system consists of a server, terminals, and users. The server utilizes an information storage device and has the function of managing a database that organizes and stores past construction estimate data. The server uses this database to perform comparative analysis with new estimate information. During the analysis, it is desirable to use software that implements an algorithm for detecting anomalies. This software can be programmed using languages ​​such as Python or R.

[0637] Users input new construction estimate data into the system via a terminal. The terminal is responsible for transmitting the input data to the server. The terminal can also receive analysis results and negotiation strategies notified from the server. A standard personal computer or tablet-type information terminal can be used for this input.

[0638] A concrete example is when a user receives an estimate for a new construction project. In this case, the user enters information for the "foundation work cost" into the system, and the server compares this information with the database. The server compares it with historical statistical data and, if it is automatically determined to be unreasonable, generates a detailed guide for negotiation to reduce the cost.

[0639] An example of a prompt message would be, "If the unit price for 'foundation work costs' in a construction project estimate is too high, please suggest how to proceed with negotiations." This allows users to negotiate with construction companies with a concrete strategy.

[0640] Thus, the present invention automates the analysis and negotiation process for construction cost estimates, supporting users in making more appropriate and reliable decisions.

[0641] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0642] Step 1:

[0643] The server collects past construction estimate data from various sources. Since this data is often provided in different formats, the server converts it into a unified format and stores it in an information storage device as organized data. The input is estimate data from companies and projects, and the output is a database that enables efficient searching and analysis. This process involves data processing such as data cleaning and format conversion.

[0644] Step 2:

[0645] The user uses a terminal to input new construction estimate information into the system. Once input is complete, the terminal sends the information to the server. The input here is the specific estimate details, and the output is the new estimate information received by the server. This step includes actions to ensure that data is entered accurately through the user interface.

[0646] Step 3:

[0647] The server analyzes newly received estimate information by comparing it with existing databases. This analysis uses algorithms to detect outliers. The inputs are the new estimate data and historical database data, and the output is a list of identified anomalies. Specifically, data calculations are performed using statistical analysis and anomaly detection algorithms.

[0648] Step 4:

[0649] The server generates reduction negotiation strategies for anomalies identified through analysis. These strategies include specific negotiation points and rationale based on market data. The input is a list of anomalies, and the output is a detailed negotiation guide. The server utilizes a generative AI model to automatically generate more effective strategies.

[0650] Step 5:

[0651] The server notifies the terminal of the generated strategy. The user receives this strategy through the terminal. The input is the generated negotiation strategy, and the output is what the user receives. Specifically, this includes system notifications that present the user with visualized information.

[0652] Step 6:

[0653] The user negotiates with the construction company based on the negotiation strategy they receive. The user inputs the results of these negotiations into the system as feedback. The input is the negotiation results from the user, and the output is updated feedback information. Based on this feedback, the server updates the database to improve the accuracy of future analyses. Specific actions include the process of reflecting the input results in the database.

[0654] (Application Example 1)

[0655] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0656] This invention aims to automate the efficient management and evaluation of the appropriateness of estimation data in construction and transactions, and to provide real-time negotiation support. In particular, there is a need to quickly identify inappropriate items by comparing them with past data and provide that information to the user immediately, thereby enabling efficient and accurate negotiations on site. Another challenge is to continuously improve the entire system by utilizing user feedback to improve the accuracy of future estimations.

[0657] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0658] In this invention, the server includes means for collecting and organizing past transaction estimate data and storing it in an information set, means for allowing users to input new estimates, and means for analyzing the input estimate data, comparing it with past information, and identifying inappropriate items. This allows users to receive real-time support for identifying inappropriate items and negotiating agreements using a mobile terminal. Furthermore, by incorporating user feedback, the information set can be updated to improve strategic accuracy.

[0659] "Transaction estimate data" refers to detailed cost and resource estimates presented during construction or transactions.

[0660] An "information collection" refers to a series of information that has been systematically organized and integrated into a database.

[0661] "User" refers to anyone who uses this system to receive assistance with cost analysis or negotiation.

[0662] A "new quote" refers to newly submitted quote information that did not exist before it was provided by the user.

[0663] An "inappropriate item" refers to an element in the estimation data that is judged to be abnormal when compared to standards or past data.

[0664] A "consensus-building negotiation strategy" refers to specific negotiation methods and policies aimed at obtaining appropriate terms for items deemed inappropriate.

[0665] A "mobile device" refers to a portable information device such as a smartphone or tablet that a user can carry and use.

[0666] "Real-time" refers to the instantaneous and uninterrupted processing and communication of information.

[0667] "Feedback" refers to the act of a user providing results from using the negotiation support system and opinions based on those results.

[0668] "Strategic accuracy" refers to an indicator that shows how effective a consensus-building negotiation strategy is in correcting inappropriate items.

[0669] This invention is a system for automating the analysis of estimates and negotiation support for construction and transaction projects. The specific implementation method is described below.

[0670] The server first efficiently collects historical transaction estimate data, organizes it into an information set, and stores it. This information set is managed as a database using a cloud platform such as Amazon Web Services (AWS). The server then uses a data analysis algorithm built in Python to make the stored data analyzable.

[0671] Users input new quotes into the system using mobile devices such as smartphones and tablets. This entered quote data is transmitted to the server in real time, where it is analyzed by comparing it with past data sets to identify inappropriate items. The results of this analysis are notified to the user's mobile device, and the user can receive a consensus-building negotiation strategy based on this information. This strategy proposes specific negotiation policies and provides guidance to facilitate negotiations.

[0672] Furthermore, users can input the results of actual negotiations into the system as feedback, and the server updates its information set based on this feedback. This updated information will enable the system to provide even more accurate analysis and effective strategies in the future.

[0673] For example, if a user enters new estimate information for "foundation work costs" into the system, the server can analyze it and indicate that "foundation work costs are higher than the market average," and then provide specific advice for negotiation.

[0674] An example of a prompt message for the generating AI model is, "Analyze the new foundation construction cost estimate and check for any inappropriate items."

[0675] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0676] Step 1:

[0677] The server collects historical transaction quote data, organizes it into an information set, and stores it. The input here is historical quote data provided externally, and the output is a systematic information set stored in the cloud. The server uploads this data to AWS's database service, preparing it for efficient searching and comparison.

[0678] Step 2:

[0679] The user enters new quote information using a mobile device. The quote data entered by the user through the form is sent to the server in its original format. The input here is new quote data, and the output is the data recorded on the server side. The input is validated field by field to confirm the consistency of the content.

[0680] Step 3:

[0681] The server analyzes the newly received quote data. It compares the input data with historical data to identify inappropriate items. This process uses an anomaly detection algorithm built in Python. The input is the new quote data and historical data, and the output is a list of inappropriate items. Heuristic analysis is performed on the data to extract values ​​that differ significantly from the market average.

[0682] Step 4:

[0683] The server generates a consensus-building negotiation strategy for items deemed inappropriate. This process utilizes a generative AI model to generate negotiation strategies. The input is a list of inappropriate items, and the output is a strategy containing specific negotiation guidelines. Through generated prompt messages, the AI ​​model creates the negotiation strategy and communicates it to the user.

[0684] Step 5:

[0685] The terminal notifies the user of the generated negotiation strategy. The user then conducts negotiations in real time based on this strategy. The input is the negotiation strategy received from the server, and the output is specific negotiation advice provided to the user. The terminal uses a notification function to communicate the strategy to the user and also allows for feedback input.

[0686] Step 6:

[0687] Users input negotiation results into the system as feedback. The input here represents the actual negotiation results, and the output is an updated set of information. Users input feedback via terminals, and the server updates the database based on this feedback, improving the accuracy of future analyses.

[0688] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0689] This invention combines an emotion engine with a system for managing and analyzing construction estimates. The system consists of a server, terminals, and users. The server collects and organizes past construction estimate data and stores it in a database. Users, operating on terminals, input new construction estimates and send them to the server, where the estimates are analyzed and their appropriateness is evaluated.

[0690] The server compares the entered quotation data with past examples in the database and uses algorithms to identify inappropriate items. During this process, the emotion engine monitors the user's input behavior and choices and analyzes their emotional state. The results of this analysis are used to adjust the negotiation strategy.

[0691] The generated negotiation strategy is customized to take the user's emotional state into account. For example, if the user is feeling anxious, it can include more detailed and reassuring explanations. The device then presents the final strategy to the user and assists them in starting negotiations with the construction company.

[0692] Furthermore, user feedback includes emotional information recognized by the emotion engine, which is recorded in a database by the server. This information is used to improve the accuracy of future data analysis and negotiation strategy generation.

[0693] For example, when a user is preparing for a quote negotiation, if the emotion engine detects that the user is feeling stressed, the system adjusts its approach to present the strategy to the negotiating partner in a friendly and easy-to-understand manner. Furthermore, after the negotiation, a report is generated highlighting areas for improvement in the next negotiation based on the user's feedback.

[0694] This system aims to enable continuous learning and improvement that takes user emotions into account, thereby providing more effective and personalized support.

[0695] The following describes the processing flow.

[0696] Step 1:

[0697] The server automatically collects and organizes past construction estimate data from internal databases and reliable external sources. The data is standardized and stored in the database for efficient searching.

[0698] Step 2:

[0699] The user uses a terminal to enter new construction estimate information provided by partner companies. The details of the estimate are entered for each required field and sent to the server.

[0700] Step 3:

[0701] The server activates the emotion engine to collect emotional data during the user's input operations. The emotion engine analyzes emotions based on factors such as input speed and the user interface selection history at that time.

[0702] Step 4:

[0703] The server analyzes the entered estimate data by comparing it with past database information. It uses algorithms to evaluate the validity of each item and identify any inappropriate items.

[0704] Step 5:

[0705] The server adjusts the negotiation strategy based on the user's emotional state, as analyzed by the emotion engine. For example, if the user is feeling anxious, the strategy will include more detailed explanations and support information.

[0706] Step 6:

[0707] The terminal presents the user with negotiation strategies tailored to their emotions and analysis results from the server. Based on this information, the user can smoothly negotiate with construction companies.

[0708] Step 7:

[0709] Users input feedback into the system regarding negotiation outcomes and their emotional responses during the process. This feedback includes analysis results from the emotion engine.

[0710] Step 8:

[0711] The server records user feedback in a database, which is then used to improve analysis accuracy and strengthen future negotiation strategies. The data is then used for statistical analysis to further refine future strategies.

[0712] (Example 2)

[0713] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0714] Conventional construction estimation systems simply compared past data, failing to take into account the user's emotions or the psychological aspects of negotiation. This resulted in standardized negotiation strategies and an inability to provide appropriate support to users. Furthermore, there was a lack of mechanisms to effectively utilize user feedback to improve the accuracy of strategies.

[0715] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0716] This invention includes a server that collects and organizes estimation data related to past construction activities and stores it in an information storage device; a server that performs sentiment analysis based on the user's input behavior and selections and customizes strategies that take into account the user's emotional state; and a server that receives user feedback and updates the information storage device to improve the accuracy of future strategies. This makes it possible to provide flexible and effective negotiation strategies that take into account the user's psychological aspects.

[0717] "Estimation documents related to past construction activities" refers to detailed information regarding expenses and plans for previously carried out construction work or projects.

[0718] An "information storage device" refers to a system or database for organizing and storing data.

[0719] "Users" refers to end users who use this system to enter new quotes or utilize negotiation strategies.

[0720] "Emotional analysis" refers to the process of evaluating and interpreting a user's psychological state based on their actions and choices.

[0721] "Customizing a strategy" refers to optimizing negotiation tactics and approaches according to the individual user's circumstances and feelings.

[0722] "Receiving feedback" refers to the process of collecting feedback and evaluations from users.

[0723] "Improving strategic accuracy" refers to enhancing the precision and effectiveness of negotiation strategies by utilizing newly acquired data and feedback.

[0724] The system based on this invention aims to efficiently manage construction cost estimation data and support users. Specifically, it consists of three components: a server, a terminal, and a user. The server is equipped with a database management system that collects estimation data related to past construction activities and organizes and stores it in an information storage device. This allows for the efficient organization of large amounts of data and the rapid extraction of necessary information.

[0725] Users input new quotation data via a terminal. The terminal provides a user interface that validates the format and required fields of the data entered by the user. The input data is immediately sent to the server for analysis.

[0726] The server runs a dedicated algorithm to compare the received estimate data with existing information. It also incorporates an emotion analysis engine that evaluates the user's emotional state based on their input actions and choices. Based on these results, a generative AI model creates a customized strategy for each user.

[0727] The generated strategy is presented to the user via the terminal. This allows the user to negotiate more effectively with the construction company. For example, if the user feels anxious or stressed while preparing for negotiations, the system will supplement with reassuring information and adjust the presentation of the strategy to be more user-friendly.

[0728] An example of a prompt message is, "How can we generate the optimal negotiation strategy based on the user's emotional state when negotiating a construction estimate?" This allows the system to provide advanced support that takes into account the user's psychological aspects.

[0729] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0730] Step 1:

[0731] The server automatically collects estimation data related to past construction activities from the internet and specific data sources. The collected data undergoes data cleansing operations and is processed into a proper format. Missing values ​​are imputed and outliers are removed, transforming it into highly reliable information. This is then stored in an information storage device and used in the analysis process described later.

[0732] Step 2:

[0733] The user enters a new construction estimate into the interface using a terminal. At this stage, the entered data is sent to a data validation program for formatting and checking required fields. As a result, accurate and complete estimate data is sent to the server, ready to proceed to the next analysis step.

[0734] Step 3:

[0735] The server compares the newly received estimate data with historical data stored in the information storage device. First, it runs a data analysis algorithm to detect inappropriate items. This analysis uses pattern recognition technology to identify abnormal costs and items and calculates an evaluation score based on them.

[0736] Step 4:

[0737] The server evaluates the user's emotions based on their input actions and choices, using an emotion analysis engine. It then customizes negotiation strategies that take the user's emotional state into account using a generative AI model. Specifically, it determines what information the user needs based on the emotion analysis results and structures that information in an appropriate format.

[0738] Step 5:

[0739] The terminal presents the user with strategies obtained from the server. This provides the user with the best course of action and advice when starting negotiations with construction companies. The information presented is detailed, user-friendly, and includes elements that alleviate anxiety.

[0740] Step 6:

[0741] Users provide feedback via a terminal after negotiations. The terminal sends this feedback, along with sentiment information, to a server. The server records the received feedback in an information storage device and uses it as input for future data analysis and strategy generation. Through this process, the system continuously learns and improves to provide highly accurate and customized assistance.

[0742] (Application Example 2)

[0743] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0744] Conventional planning and estimation systems merely compare data with past cases and do not generate strategies that take into account the user's emotional state. Therefore, users are more likely to feel anxious during negotiations, making effective negotiation support difficult. Furthermore, the lack of mechanisms to effectively utilize feedback and improve the accuracy of negotiation strategies is also a challenge.

[0745] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0746] In this invention, the server includes means for collecting and organizing past plan estimate data and storing it in information storage; means for allowing users to input new estimates; means for analyzing the input estimate information, comparing it with past information, and identifying inappropriate parts; means for monitoring and analyzing the user's emotional state and generating a negotiation strategy for price reduction based thereon; and means for notifying the user of the generated strategy and supporting the user in starting negotiations with the planning company. This enables reassuring negotiation support tailored to the user's emotional state, and further allows for continuous improvement of the strategy's accuracy through feedback.

[0747] "Planning and estimation data" refers to detailed estimation information from past projects, collected to analyze project costs and resource allocation.

[0748] "Information storage" refers to an electronic storage area for efficiently and securely storing digital information, and it serves the role of a database.

[0749] "Users" refers to individual people or organizations that operate this system and receive services such as quote input and negotiation support.

[0750] "Analysis" is the process of examining input data in detail and extracting its meaning and patterns, and it is a necessary step in making data-driven decisions.

[0751] "Inappropriate elements" refer to elements in new estimation data that are judged to be unreasonable or lacking in validity compared to past cases.

[0752] "Emotional state" refers to the psychological reactions and emotional tendencies that users exhibit when using the system, and is an important factor that influences the generation of negotiation strategies.

[0753] A "cost reduction negotiation strategy" refers to specific methods for reducing costs that are proposed for areas deemed unreasonable in order to successfully negotiate on fair terms.

[0754] "Notification" refers to the act of clearly communicating system-generated information and suggestions to users, supporting them in making appropriate decisions.

[0755] To implement this invention, a system is constructed in which the server, terminal, and user each play their respective roles.

[0756] The server is responsible for collecting historical planning and estimation data, organizing it, and storing it in information storage. This data is compared with new estimation information entered by users through their terminals. The server uses machine learning libraries (e.g., TensorFlow, PyTorch) and database management systems (e.g., MySQL, PostgreSQL) for data analysis to identify inaccuracies in the entered estimation information.

[0757] Meanwhile, the server runs an emotion engine to analyze the user's emotional state. Emotional analysis uses emotion recognition tools such as the Google Cloud Natural Language API and Affectiva. Based on the user's emotional state, the server leverages a generative AI model to generate an appropriate negotiation strategy for price reduction. This strategy is customized to provide a sense of security.

[0758] The generated negotiation strategy is communicated to the user via the device. The user then prepares to begin negotiations with the planning company based on this information. The device provides an intuitive and user-friendly interface and sends feedback to the server along with sentiment data.

[0759] As a concrete example, consider a situation where a person in charge of a construction project inputs new building construction estimate information. In this case, the system can refer to data from previous projects, sense the person's emotional state, and suggest strategies to alleviate their anxiety. These strategies include past success stories and detailed information that provides reassurance.

[0760] A concrete example of a prompt sentence to input into a generative AI model would be something like, "What strategies should be used to reassure the customer with this estimate?"

[0761] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0762] Step 1:

[0763] The user enters new quote information using a terminal. This input includes data on the project's detailed specifications, budget, and resource allocation. This input data is then sent from the terminal to the server.

[0764] Step 2:

[0765] The server compares the received estimate information with historical data. The server uses a database management system such as MySQL or PostgreSQL to access past plan estimate databases. This comparison allows for data analysis to identify any parts deemed inappropriate. As a result, information about the detected inappropriate parts is obtained.

[0766] Step 3:

[0767] The server analyzes user emotion data received through the device. The emotion engine utilizes Google Cloud Natural Language API and Affectiva. It receives and analyzes user facial expressions and voice data perceived by the device as input. The output of this analysis is a detailed report on the user's emotional state.

[0768] Step 4:

[0769] The server generates negotiation strategies for price reductions using a generative AI model. This process takes a list of inappropriate elements and a user sentiment report as input. The AI ​​model constructs a customized strategy based on past successes and the user's emotional needs. The output is a reassuring negotiation strategy for the user.

[0770] Step 5:

[0771] The server sends the generated negotiation strategy to the terminal, which then notifies the user. The terminal displays the notification visually and audibly through an intuitive UI. This output provides information that enables the user to effectively begin negotiations with the planning company.

[0772] Step 6:

[0773] Users send feedback from their device to the server after negotiations. This feedback includes not only the negotiation results but also emotional information. The server records this in a database and uses it to generate future strategies.

[0774] Step 7:

[0775] The server uses feedback and new sentiment data to perform data analysis and improve its strategy generation algorithms. This process allows the system to continuously learn and provide users with more accurate support.

[0776] 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.

[0777] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0778] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0779] 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.

[0780] Figure 9 shows an 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.

[0781] 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.

[0782] 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.

[0783] 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, motorcycles, etc., 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, for example, based 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.

[0784] 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."

[0785] 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.

[0786] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0787] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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 the like 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.

[0796] 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.

[0797] The following is further disclosed regarding the embodiments described above.

[0798] (Claim 1)

[0799] A means of collecting, organizing, and storing past construction estimate data in a database,

[0800] A means of having users enter new quotes,

[0801] A means of analyzing the entered quotation data, comparing it with past data, and identifying inappropriate items,

[0802] A means of generating negotiation strategies for reducing the amount for items deemed inappropriate,

[0803] A means of notifying the user of the generated strategy,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, comprising means for automatically listing inappropriate items based on the analysis results.

[0807] (Claim 3)

[0808] The system according to claim 1, comprising means for receiving user feedback and updating the database to improve the accuracy of future strategies.

[0809] "Example 1"

[0810] (Claim 1)

[0811] A means for collecting and organizing past information and storing it in an information storage device,

[0812] A means of getting users to input new information,

[0813] A means of analyzing the input information, comparing it with past information, and identifying abnormal items,

[0814] A means of generating reduction negotiation strategies for items deemed abnormal,

[0815] A means of notifying the user of the generated strategy,

[0816] A means of collecting negotiation results from users, updating the information storage device, and improving the accuracy of the analysis,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, comprising means for automatically listing abnormal items based on the analysis results.

[0820] (Claim 3)

[0821] The system according to claim 1, comprising means for providing the user with the generated strategy as a concrete negotiation guide.

[0822] "Application Example 1"

[0823] (Claim 1)

[0824] A means of collecting and organizing past transaction estimate data and storing it in an information set,

[0825] A means of having the user enter a new quote,

[0826] A means of analyzing the entered quotation data, comparing it with past information, and identifying inappropriate items,

[0827] A means of generating a consensus-building negotiation strategy for items deemed inappropriate,

[0828] A means of notifying users of the generated strategy and supporting negotiations in real time using mobile devices,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, comprising means for automatically listing inappropriate items based on analysis results and providing them via a mobile terminal.

[0832] (Claim 3)

[0833] The system according to claim 1, which includes means for receiving feedback from users and updating the information set to improve the accuracy of future strategies.

[0834] "Example 2 of combining an emotion engine"

[0835] (Claim 1)

[0836] A means for collecting, organizing, and storing estimation documents related to past construction activities in an information storage device,

[0837] A means of having users enter new quotes,

[0838] A method for analyzing the entered quotation data, comparing it with past data, and identifying inappropriate items,

[0839] A means for generating adjustment strategies for items deemed inappropriate,

[0840] A means of performing sentiment analysis based on the user's input behavior and choices, and customizing strategies that take emotional states into consideration,

[0841] A means of notifying the user of the generated strategy and supporting the user in initiating negotiations,

[0842] By receiving feedback from users and updating the information storage system, we can improve the accuracy of future strategies.

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, comprising means for automatically listing inappropriate items based on the analysis results and adjusting the strategy taking sentiment analysis into consideration.

[0846] (Claim 3)

[0847] The system according to claim 1, which generates strategies that reflect the emotional state of the user and improves the strategies based on the user's feedback.

[0848] "Application example 2 when combining with an emotional engine"

[0849] (Claim 1)

[0850] A means of collecting, organizing, and storing past planning and estimation data in information storage,

[0851] A means of having users enter new quotes,

[0852] A means of analyzing the entered quotation information, comparing it with past information, and identifying any inappropriate parts,

[0853] A means for monitoring and analyzing the emotional state of users and generating negotiation strategies for price reductions based on that,

[0854] A means of notifying users of the generated strategy and assisting users in initiating negotiations with the planning company,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, comprising means for providing a strategy that automatically lists inappropriate parts based on analysis results and emotional state, and attaches reassuring explanations.

[0858] (Claim 3)

[0859] The system according to claim 1, comprising means for receiving user feedback and sentiment information and updating information storage to improve the accuracy of future strategies. [Explanation of symbols]

[0860] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting and organizing past transaction estimate data and storing it in an information set, A means of having the user enter a new quote, A means of analyzing the entered quotation data, comparing it with past information, and identifying inappropriate items, A means of generating a consensus-building negotiation strategy for items deemed inappropriate, A means of notifying users of the generated strategy and supporting negotiations in real time using mobile devices, A system that includes this.

2. The system according to claim 1, comprising means for automatically listing inappropriate items based on the analysis results and providing them via a mobile terminal.

3. The system according to claim 1, which includes means for receiving feedback from users and updating the information set to improve the accuracy of future strategies.

Images