system

Abstract

The system according to this embodiment aims to efficiently perform similarity checks of technical information and assessments of intellectual property infringement risks. [Solution] The system according to the embodiment comprises a reception unit, a collection unit, and an evaluation unit. The reception unit receives input of technical information relating to the technology. The collection unit collects technologies similar to the technical information received by the reception unit from an intellectual property database. The evaluation unit evaluates the similarity between the technical information and other intellectual property, or the risk of infringing other intellectual property, based on the information collected by the collection unit.

Description

Technical Field

[0004] ,

[0006] , , , ,

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[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, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that checking the similarity of technical information and evaluating the risk of intellectual property infringement require time and effort and are difficult to perform efficiently.

[0005] The system according to the embodiment aims to efficiently perform a similarity check of technical information and an evaluation of the risk of intellectual property infringement.

Means for Solving the Problems

[0007] The system according to this embodiment can efficiently perform similarity checks of technical information and assess intellectual property infringement risks. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI assistant, an intellectual property rights management support tool according to an embodiment of the present invention, is a system that searches databases of patents, copyrights, and trademarks to perform similarity checks and assess infringement risks. The AI ​​assistant accepts input of technical information relating to technology. For example, an overview of a new technology or the contents of a patent application may be entered. This information is entered into the AI ​​assistant. Next, the AI ​​assistant collects technologies similar to the entered technical information from intellectual property databases, including patents, copyrights, and trademarks. For example, it searches for similar patents in the patent database and similar copyrighted works in the copyright database. In this way, it collects intellectual property information similar to the technical information. Based on the collected information, the AI ​​assistant assesses the similarity between the technical information and other intellectual property, or the risk of infringing other intellectual property. For example, it compares the patent claim content with the technical information to assess similarity. It also compares the copyright content with the technical information to assess the risk of infringement. Based on this assessment result, it determines whether the technical information may infringe other intellectual property. Furthermore, the AI ​​assistant manages license expiration dates and renewal dates and automatically provides necessary notifications. For example, when a license is about to expire, a renewal notice is automatically sent. It also automatically manages newly acquired license information and tracks renewal dates. This streamlines license management and prevents missed renewals. This system streamlines intellectual property management, allowing for rapid similarity checks of technical information and assessment of infringement risks. Furthermore, the automation of license management and renewal notices reduces the burden of administrative tasks. For instance, technical information can be extracted and automatically received based on emails, messages, and meeting minutes received by users in the development department. This streamlines the collection of technical information and allows for rapid evaluation. As a result, the AI ​​assistant, an intellectual property management support tool, can quickly perform similarity checks of technical information and assess infringement risks.

[0029] The AI ​​assistant, an intellectual property rights management support tool according to this embodiment, comprises a reception unit, a collection unit, and an evaluation unit. The reception unit receives input of technical information relating to technology. Technical information includes, but is not limited to, an overview of a new technology or the contents of a patent application. The reception unit can extract and automatically receive technical information based, for example, on emails, messages, or meeting minutes received by users in the development department. The collection unit collects technologies similar to the technical information received by the reception unit from an intellectual property database, including patents, copyrights, and trademarks. The collection unit can, for example, search for similar patents from the patent database and similar copyrighted works from the copyright database. The collection unit can also search for similar trademarks from the trademark database. The evaluation unit evaluates the similarity between the technical information and other intellectual property, or the risk of infringing other intellectual property, based on the information collected by the collection unit. The evaluation unit can, for example, compare the claims of a patent with the technical information to evaluate the similarity. The evaluation unit can also compare the contents of a copyright with the technical information to evaluate the risk of infringement. Furthermore, the evaluation unit can compare the content of trademarks with technical information to assess similarity and infringement risk. This enables the AI ​​assistant, an intellectual property rights management support tool according to the embodiment, to efficiently input, collect, and evaluate technical information. Some or all of the above-described processes in the reception unit, collection unit, and evaluation unit may be performed using AI, for example, or without AI. For example, the reception unit can input technical information into the AI, which can analyze the technical information and collect similar technologies. The collection unit can use AI to search for similar technologies from an intellectual property database, and the evaluation unit can use AI to evaluate the similarity and infringement risk between the technical information and other intellectual property.

[0030] The reception desk accepts input of technical information related to technology. This technical information includes, but is not limited to, an overview of a new technology or the contents of a patent application. The reception desk can extract and automatically accept technical information based on, for example, emails and messages received by users in the development department, or meeting minutes. Specifically, the reception desk uses natural language processing (NLP) technology to analyze the content of emails and messages and extract technical information. For example, it can detect specific keywords or phrases from the body of an email and recognize them as technical information. Similarly, for meeting minutes, speech recognition technology can be used to convert the audio data into text and extract technical information. Furthermore, the reception desk also provides an interface for direct user input, allowing users to manually enter technical information. This enables the reception desk to accept technical information in a variety of ways, improving user convenience.

[0031] The collection unit collects technologies similar to the technical information received by the reception unit from intellectual property databases, including patents, copyrights, and trademarks. For example, the collection unit can search for similar patents in the patent database and similar copyrighted works in the copyright database. It can also search for similar trademarks in the trademark database. Specifically, the collection unit uses AI to perform keyword searches and full-text searches on the patent database to identify patents similar to the technical information. For example, based on keywords included in the technical information, it searches for patent titles, abstracts, and claims, and lists patents with a high degree of similarity. Similarly, it performs keyword searches based on the technical information on the copyright database to identify similar copyrighted works. For trademarks, it searches for trademarks related to the technical information on the trademark database and identifies trademarks with a high degree of similarity. In this way, the collection unit can efficiently collect information from various intellectual property databases and comprehensively grasp intellectual property similar to the technical information.

[0032] The evaluation unit assesses the similarity between technical information and other intellectual property, or the risk of infringement, based on the information collected by the collection unit. For example, the evaluation unit can compare the content of patent claims with technical information to assess similarity. It can also compare the content of copyrights with technical information to assess infringement risk. Furthermore, it can compare the content of trademarks with technical information to assess similarity and infringement risk. Specifically, the evaluation unit uses AI to analyze the text of patent claims and technical information and calculate similarity. For example, it uses natural language processing technology to evaluate the degree of agreement in claim structure and keywords and calculate a similarity score. Similarly, it performs text analysis on copyright content and technical information to assess infringement risk. For trademarks, it can use image recognition technology to evaluate the relationship between trademark design or logo and technical information. As a result, the evaluation unit can accurately assess the similarity and infringement risk between technical information and other intellectual property and provide users with specific risk assessment results. Furthermore, based on the assessment results, the evaluation unit can also provide users with specific countermeasures and advice. For example, the evaluation department can suggest points to be aware of when filing a patent application and methods to avoid copyright infringement. This allows the evaluation department to support users in properly managing their intellectual property rights and minimizing risks.

[0033] The system further includes an extraction unit that extracts technical information being developed in the development department based on information from emails received by users in the development department, messages exchanged between users within the development department, and meeting minutes from meetings held within the development department. The receiving unit can automatically receive the technical information extracted by the extraction unit. For example, the extraction unit can extract technical information from emails received by users in the development department. It can also extract technical information from messages exchanged between users within the development department. Furthermore, it can extract technical information from meeting minutes from meetings held within the development department. This automates the extraction and reception of technical information in the development department. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the contents of emails, messages, and meeting minutes into the AI, which can extract the technical information. The receiving unit can input the technical information extracted by the extraction unit into the AI, which can analyze the technical information and collect similar technologies.

[0034] The collection unit periodically collects newly published intellectual property information, and the evaluation unit can perform evaluations based on the results periodically collected by the collection unit. For example, the collection unit can periodically collect newly published patent information from the Japan Patent Office database. The collection unit can also periodically collect newly published copyright information from the copyright database. Furthermore, the collection unit can also periodically collect newly published trademark information from the trademark database. The evaluation unit can evaluate the similarity and infringement risk between the technical information and other intellectual property based on the patent information periodically collected by the collection unit. Furthermore, the evaluation unit can also evaluate the similarity and infringement risk between the technical information and other intellectual property based on the copyright information periodically collected by the collection unit. Furthermore, the evaluation unit can also evaluate the similarity and infringement risk between the technical information and other intellectual property based on the trademark information periodically collected by the collection unit. This ensures that newly published intellectual property information is collected and evaluated periodically. Some or all of the above-described processes in the collection unit and evaluation unit may be performed using AI, for example, or without AI. For example, the collection unit inputs newly released patent information from the Japan Patent Office database into the AI, which then collects the patent information. The evaluation unit inputs the patent information collected by the collection unit into the AI, which then evaluates the similarity and infringement risk between the technical information and other intellectual property.

[0035] The system may also include a management unit that manages license expiration dates and renewal dates and automatically sends necessary notifications. For example, the management unit can manage license expiration dates using a calendar and automatically send renewal notifications using a reminder function. The management unit can also track license renewal dates and set notification timings to prompt renewal procedures. This automates license management and renewal notifications. Some or all of the above processes in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input license expiration dates and renewal dates into the AI, which can then automatically send renewal notifications using calendar management and reminder functions.

[0036] The management department can automatically manage newly acquired license information and track renewal dates. For example, the management department can register newly acquired license information in a database and track renewal dates using an automatic renewal function. This automates the management of new license information and tracking of renewal dates. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input newly acquired license information into the AI, the AI ​​can register it in a database, and the automatic renewal function can track renewal dates.

[0037] The reception desk can analyze the user's past technical information input history and select the optimal input method. For example, if the user has frequently used voice input in the past, the reception desk can prioritize suggesting voice input. Furthermore, if the user has preferred text input in the past, the reception desk can set text input as the default. In addition, if the reception desk has previously performed input during a specific time period, it can send a notification during that time period. This allows the reception desk to select the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's past input history data into an AI, which can then select the optimal input method.

[0038] The reception unit can filter technical information input based on the user's current projects and areas of interest. For example, the reception unit can display only technical information related to the user's current project on the input screen. It can also prioritize input of highly relevant technical information based on the user's areas of interest. Furthermore, the reception unit can narrow down input candidates based on technical fields the user has shown interest in in the past. This allows for filtering of technical information based on the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's project information and area of ​​interest data into an AI, which can then perform the filtering.

[0039] The reception desk can prioritize inputting highly relevant information when technical information is entered, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize inputting technical information related to that region. Furthermore, if the user is on a business trip, the reception desk can prioritize inputting technical information related to their destination. Additionally, if the user is at home, the reception desk can prioritize inputting technical information related to their home. This allows for prioritization of technical information input based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into the AI, which can then prioritize inputting highly relevant information.

[0040] The reception unit can analyze the user's social media activity and input relevant information when technical information is entered. For example, the reception unit can automatically input technical information that the user has shared on social media. The reception unit can also prioritize inputting information related to the technical fields that the user follows on social media. Furthermore, the reception unit can input information about the technical communities that the user participates in on social media. This allows relevant information to be entered based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into AI, and the AI ​​can input relevant information.

[0041] The data collection unit can adjust the level of detail collected based on the importance of the technical information during collection. For example, the data collection unit can collect detailed information for highly important technical information. It can also collect only an overview for less important technical information. Furthermore, the data collection unit can adjust the depth of information collected according to its importance. This allows for adjustment of the level of detail collected according to the importance of the technical information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input technical information importance data into the AI, which can then adjust the level of detail of the collection.

[0042] The collection unit can apply different collection algorithms depending on the category of technical information during collection. For example, for patent information, the collection unit can collect detailed information from a patent database. It can also collect relevant information for copyright information from a copyright database. Furthermore, for trademark information, the collection unit can collect necessary information from a trademark database. This allows the collection algorithm to be applied according to the category of technical information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input technical information category data into an AI, which can then apply different collection algorithms.

[0043] The data collection unit can determine the priority of data collection based on the publication date of the technical information. For example, the data collection unit can prioritize the collection of recently published technical information. The data collection unit can also collect older technical information as needed. Furthermore, the data collection unit can adjust the priority of the information to be collected based on the publication date. This allows the data collection priority to be determined based on the publication date of the technical information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input technical information publication date data into AI, and the AI ​​can determine the priority of data collection.

[0044] The collection unit can adjust the order of collection based on the relevance of the technical information during collection. For example, the collection unit can prioritize the collection of the most relevant technical information. It can also postpone the collection of less relevant technical information. Furthermore, the collection unit can adjust the order of information to be collected based on relevance. This allows the collection order to be adjusted based on the relevance of the technical information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the relevance data of the technical information into the AI, and the AI ​​can adjust the collection order.

[0045] The evaluation unit can improve the accuracy of its evaluation by considering the interrelationships of technical information during the evaluation process. For example, the evaluation unit can analyze the interrelationships of technical information and prioritize the evaluation of information with high relevance. The evaluation unit can also improve the accuracy of its evaluation based on the interrelationships of technical information. Furthermore, the evaluation unit can adjust the evaluation results by considering the interrelationships of technical information. This allows for improved evaluation accuracy by considering the interrelationships of technical information. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data on the interrelationships of technical information into AI, which can then improve the accuracy of the evaluation.

[0046] The evaluation unit can perform evaluations while considering the attribute information of the submitter of the technical information. For example, the evaluation unit can adjust the evaluation criteria based on the submitter's field of expertise. It can also adjust the strictness of the evaluation based on the submitter's years of experience. Furthermore, the evaluation unit can adjust the evaluation criteria based on the submitter's past evaluation results. This allows the evaluation to be performed while considering the attribute information of the submitter of the technical information. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input the submitter's attribute information data into the AI, and the AI ​​can adjust the evaluation criteria.

[0047] The evaluation unit can perform evaluations while considering the geographical distribution of technical information. For example, the evaluation unit can adjust the evaluation criteria based on the geographical distribution of technical information. The evaluation unit can also prioritize the evaluation of geographically close technical information. Furthermore, the evaluation unit can adjust the evaluation results while considering the geographical distribution. This allows evaluations to be performed while considering the geographical distribution of technical information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input geographical distribution data of technical information into AI, and the AI ​​can adjust the evaluation criteria.

[0048] The evaluation unit can improve the accuracy of its evaluation by referring to relevant technical information literature during the evaluation process. For example, the evaluation unit can improve the accuracy of its evaluation by referring to relevant technical information literature. Furthermore, the evaluation unit can adjust the evaluation criteria based on the relevant literature. In addition, the evaluation unit can adjust the evaluation results by considering the relevant literature. This allows for improved evaluation accuracy by referring to relevant technical information literature. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input technical information literature data into AI, which can then improve the accuracy of the evaluation.

[0049] The extraction unit can improve the accuracy of extraction by considering the interrelationships of technical information during the extraction process. For example, the extraction unit can analyze the interrelationships of technical information and prioritize the extraction of highly relevant information. The extraction unit can also improve the accuracy of extraction based on the interrelationships of technical information. Furthermore, the extraction unit can adjust the extraction results by considering the interrelationships of technical information. This allows for improved extraction accuracy by considering the interrelationships of technical information. Some or all of the above-described processes in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input data on the interrelationships of technical information into AI, which can then improve the accuracy of extraction.

[0050] The extraction unit can perform extraction while considering the geographical distribution of technical information. For example, the extraction unit can adjust the extraction criteria based on the geographical distribution of technical information. The extraction unit can also prioritize the extraction of geographically close technical information. Furthermore, the extraction unit can adjust the extraction results considering the geographical distribution. This allows extraction to be performed while considering the geographical distribution of technical information. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input geographical distribution data of technical information into AI, and the AI ​​can adjust the extraction criteria.

[0051] The management department can optimize its management algorithm by referring to past management data during management. For example, the management department can analyze past management data and select the optimal management algorithm. It can also adjust the management algorithm based on past management data. Furthermore, the management department can improve management efficiency by referring to past management data. This allows for the optimization of the management algorithm by referring to past management data. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input past management data into AI, and the AI ​​can optimize the management algorithm.

[0052] The management department can weight management data based on the submission date of technical information during the management process. For example, the management department can prioritize the management of recently submitted technical information. The management department can also manage older technical information as needed. Furthermore, the management department can adjust the weighting of management data based on the submission date. This allows for weighting of management data based on the submission date of technical information. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input technical information submission date data into AI, and the AI ​​can weight the management data.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The reception desk can analyze the user's past technical information input history and select the optimal input method. For example, if the user has frequently used voice input in the past, the reception desk can prioritize suggesting voice input. Furthermore, if the user has preferred text input in the past, the reception desk can set text input as the default. In addition, if the reception desk has previously performed input during a specific time period, it can send a notification during that time period. This allows the reception desk to select the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's past input history data into an AI, which can then select the optimal input method.

[0055] The reception unit can filter technical information input based on the user's current projects and areas of interest. For example, the reception unit can display only technical information related to the user's current project on the input screen. It can also prioritize input of highly relevant technical information based on the user's areas of interest. Furthermore, the reception unit can narrow down input candidates based on technical fields the user has shown interest in in the past. This allows for filtering of technical information based on the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's project information and area of ​​interest data into an AI, which can then perform the filtering.

[0056] The reception desk can prioritize inputting highly relevant information when technical information is entered, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize inputting technical information related to that region. Furthermore, if the user is on a business trip, the reception desk can prioritize inputting technical information related to their destination. Additionally, if the user is at home, the reception desk can prioritize inputting technical information related to their home. This allows for prioritization of technical information input based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into the AI, which can then prioritize inputting highly relevant information.

[0057] The data collection unit can adjust the level of detail collected based on the importance of the technical information during collection. For example, the data collection unit can collect detailed information for highly important technical information. It can also collect only an overview for less important technical information. Furthermore, the data collection unit can adjust the depth of information collected according to its importance. This allows for adjustment of the level of detail collected according to the importance of the technical information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input technical information importance data into the AI, which can then adjust the level of detail of the collection.

[0058] The collection unit can apply different collection algorithms depending on the category of technical information during collection. For example, for patent information, the collection unit can collect detailed information from a patent database. It can also collect relevant information for copyright information from a copyright database. Furthermore, for trademark information, the collection unit can collect necessary information from a trademark database. This allows the collection algorithm to be applied according to the category of technical information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input technical information category data into an AI, which can then apply different collection algorithms.

[0059] The data collection unit can determine the priority of data collection based on the publication date of the technical information. For example, the data collection unit can prioritize the collection of recently published technical information. The data collection unit can also collect older technical information as needed. Furthermore, the data collection unit can adjust the priority of the information to be collected based on the publication date. This allows the data collection priority to be determined based on the publication date of the technical information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input technical information publication date data into AI, and the AI ​​can determine the priority of data collection.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk accepts technical information related to the technology. This technical information includes an overview of the new technology and the contents of patent applications. The reception desk can extract and automatically accept technical information based on emails, messages, and meeting minutes received by users in the development department. Step 2: The collection unit collects technologies similar to the technical information received by the reception unit from intellectual property databases, including patents, copyrights, and trademarks. The collection unit can search for similar patents from the patent database, similar copyrighted works from the copyright database, and similar trademarks from the trademark database. Step 3: The evaluation unit assesses the similarity between the technical information and other intellectual property, or the risk of infringing other intellectual property, based on the information collected by the collection unit. The evaluation unit can compare the content of patent claims with the technical information to assess similarity. It can also compare the content of copyrights with the technical information to assess the risk of infringement. Furthermore, it can compare the content of trademarks with the technical information to assess similarity and the risk of infringement.

[0062] (Example of form 2) An AI assistant, an intellectual property rights management support tool according to an embodiment of the present invention, is a system that searches databases of patents, copyrights, and trademarks to perform similarity checks and assess infringement risks. The AI ​​assistant accepts input of technical information relating to technology. For example, an overview of a new technology or the contents of a patent application may be entered. This information is entered into the AI ​​assistant. Next, the AI ​​assistant collects technologies similar to the entered technical information from intellectual property databases, including patents, copyrights, and trademarks. For example, it searches for similar patents in the patent database and similar copyrighted works in the copyright database. In this way, it collects intellectual property information similar to the technical information. Based on the collected information, the AI ​​assistant assesses the similarity between the technical information and other intellectual property, or the risk of infringing other intellectual property. For example, it compares the patent claim content with the technical information to assess similarity. It also compares the copyright content with the technical information to assess the risk of infringement. Based on this assessment result, it determines whether the technical information may infringe other intellectual property. Furthermore, the AI ​​assistant manages license expiration dates and renewal dates and automatically provides necessary notifications. For example, when a license is about to expire, a renewal notice is automatically sent. It also automatically manages newly acquired license information and tracks renewal dates. This streamlines license management and prevents missed renewals. This system streamlines intellectual property management, allowing for rapid similarity checks of technical information and assessment of infringement risks. Furthermore, the automation of license management and renewal notices reduces the burden of administrative tasks. For instance, technical information can be extracted and automatically received based on emails, messages, and meeting minutes received by users in the development department. This streamlines the collection of technical information and allows for rapid evaluation. As a result, the AI ​​assistant, an intellectual property management support tool, can quickly perform similarity checks of technical information and assess infringement risks.

[0063] The AI ​​assistant, an intellectual property rights management support tool according to this embodiment, comprises a reception unit, a collection unit, and an evaluation unit. The reception unit receives input of technical information relating to technology. Technical information includes, but is not limited to, an overview of a new technology or the contents of a patent application. The reception unit can extract and automatically receive technical information based, for example, on emails, messages, or meeting minutes received by users in the development department. The collection unit collects technologies similar to the technical information received by the reception unit from an intellectual property database, including patents, copyrights, and trademarks. The collection unit can, for example, search for similar patents from the patent database and similar copyrighted works from the copyright database. The collection unit can also search for similar trademarks from the trademark database. The evaluation unit evaluates the similarity between the technical information and other intellectual property, or the risk of infringing other intellectual property, based on the information collected by the collection unit. The evaluation unit can, for example, compare the claims of a patent with the technical information to evaluate the similarity. The evaluation unit can also compare the contents of a copyright with the technical information to evaluate the risk of infringement. Furthermore, the evaluation unit can compare the content of trademarks with technical information to assess similarity and infringement risk. This enables the AI ​​assistant, an intellectual property rights management support tool according to the embodiment, to efficiently input, collect, and evaluate technical information. Some or all of the above-described processes in the reception unit, collection unit, and evaluation unit may be performed using AI, for example, or without AI. For example, the reception unit can input technical information into the AI, which can analyze the technical information and collect similar technologies. The collection unit can use AI to search for similar technologies from an intellectual property database, and the evaluation unit can use AI to evaluate the similarity and infringement risk between the technical information and other intellectual property.

[0064] The reception desk accepts input of technical information related to technology. This technical information includes, but is not limited to, an overview of a new technology or the contents of a patent application. The reception desk can extract and automatically accept technical information based on, for example, emails and messages received by users in the development department, or meeting minutes. Specifically, the reception desk uses natural language processing (NLP) technology to analyze the content of emails and messages and extract technical information. For example, it can detect specific keywords or phrases from the body of an email and recognize them as technical information. Similarly, for meeting minutes, speech recognition technology can be used to convert the audio data into text and extract technical information. Furthermore, the reception desk also provides an interface for direct user input, allowing users to manually enter technical information. This enables the reception desk to accept technical information in a variety of ways, improving user convenience.

[0065] The collection unit collects technologies similar to the technical information received by the reception unit from intellectual property databases, including patents, copyrights, and trademarks. For example, the collection unit can search for similar patents in the patent database and similar copyrighted works in the copyright database. It can also search for similar trademarks in the trademark database. Specifically, the collection unit uses AI to perform keyword searches and full-text searches on the patent database to identify patents similar to the technical information. For example, based on keywords included in the technical information, it searches for patent titles, abstracts, and claims, and lists patents with a high degree of similarity. Similarly, it performs keyword searches based on the technical information on the copyright database to identify similar copyrighted works. For trademarks, it searches for trademarks related to the technical information on the trademark database and identifies trademarks with a high degree of similarity. In this way, the collection unit can efficiently collect information from various intellectual property databases and comprehensively grasp intellectual property similar to the technical information.

[0066] The evaluation unit assesses the similarity between technical information and other intellectual property, or the risk of infringement, based on the information collected by the collection unit. For example, the evaluation unit can compare the content of patent claims with technical information to assess similarity. It can also compare the content of copyrights with technical information to assess infringement risk. Furthermore, it can compare the content of trademarks with technical information to assess similarity and infringement risk. Specifically, the evaluation unit uses AI to analyze the text of patent claims and technical information and calculate similarity. For example, it uses natural language processing technology to evaluate the degree of agreement in claim structure and keywords and calculate a similarity score. Similarly, it performs text analysis on copyright content and technical information to assess infringement risk. For trademarks, it can use image recognition technology to evaluate the relationship between trademark design or logo and technical information. As a result, the evaluation unit can accurately assess the similarity and infringement risk between technical information and other intellectual property and provide users with specific risk assessment results. Furthermore, based on the assessment results, the evaluation unit can also provide users with specific countermeasures and advice. For example, the evaluation department can suggest points to be aware of when filing a patent application and methods to avoid copyright infringement. This allows the evaluation department to support users in properly managing their intellectual property rights and minimizing risks.

[0067] The system further includes an extraction unit that extracts technical information being developed in the development department based on information from emails received by users in the development department, messages exchanged between users within the development department, and meeting minutes from meetings held within the development department. The receiving unit can automatically receive the technical information extracted by the extraction unit. For example, the extraction unit can extract technical information from emails received by users in the development department. It can also extract technical information from messages exchanged between users within the development department. Furthermore, it can extract technical information from meeting minutes from meetings held within the development department. This automates the extraction and reception of technical information in the development department. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the contents of emails, messages, and meeting minutes into the AI, which can extract the technical information. The receiving unit can input the technical information extracted by the extraction unit into the AI, which can analyze the technical information and collect similar technologies.

[0068] The collection unit periodically collects newly published intellectual property information, and the evaluation unit can perform evaluations based on the results periodically collected by the collection unit. For example, the collection unit can periodically collect newly published patent information from the Japan Patent Office database. The collection unit can also periodically collect newly published copyright information from the copyright database. Furthermore, the collection unit can also periodically collect newly published trademark information from the trademark database. The evaluation unit can evaluate the similarity and infringement risk between the technical information and other intellectual property based on the patent information periodically collected by the collection unit. Furthermore, the evaluation unit can also evaluate the similarity and infringement risk between the technical information and other intellectual property based on the copyright information periodically collected by the collection unit. Furthermore, the evaluation unit can also evaluate the similarity and infringement risk between the technical information and other intellectual property based on the trademark information periodically collected by the collection unit. This ensures that newly published intellectual property information is collected and evaluated periodically. Some or all of the above-described processes in the collection unit and evaluation unit may be performed using AI, for example, or without AI. For example, the collection unit inputs newly released patent information from the Japan Patent Office database into the AI, which then collects the patent information. The evaluation unit inputs the patent information collected by the collection unit into the AI, which then evaluates the similarity and infringement risk between the technical information and other intellectual property.

[0069] The system may also include a management unit that manages license expiration dates and renewal dates and automatically sends necessary notifications. For example, the management unit can manage license expiration dates using a calendar and automatically send renewal notifications using a reminder function. The management unit can also track license renewal dates and set notification timings to prompt renewal procedures. This automates license management and renewal notifications. Some or all of the above processes in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input license expiration dates and renewal dates into the AI, which can then automatically send renewal notifications using calendar management and reminder functions.

[0070] The management department can automatically manage newly acquired license information and track renewal dates. For example, the management department can register newly acquired license information in a database and track renewal dates using an automatic renewal function. This automates the management of new license information and tracking of renewal dates. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input newly acquired license information into the AI, the AI ​​can register it in a database, and the automatic renewal function can track renewal dates.

[0071] The reception desk can estimate the user's emotions and adjust the timing of technical information input based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can delay the input timing to provide time for relaxation. The reception desk can also send an immediate notification to encourage input if the user is concentrating. Furthermore, if the user is tired, the reception desk can suggest postponing the input until the next day. This allows the timing of technical information input to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI, which can estimate the user's emotions and adjust the input timing.

[0072] The reception desk can analyze the user's past technical information input history and select the optimal input method. For example, if the user has frequently used voice input in the past, the reception desk can prioritize suggesting voice input. Furthermore, if the user has preferred text input in the past, the reception desk can set text input as the default. In addition, if the reception desk has previously performed input during a specific time period, it can send a notification during that time period. This allows the reception desk to select the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's past input history data into an AI, which can then select the optimal input method.

[0073] The reception unit can filter technical information input based on the user's current projects and areas of interest. For example, the reception unit can display only technical information related to the user's current project on the input screen. It can also prioritize input of highly relevant technical information based on the user's areas of interest. Furthermore, the reception unit can narrow down input candidates based on technical fields the user has shown interest in in the past. This allows for filtering of technical information based on the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's project information and area of ​​interest data into an AI, which can then perform the filtering.

[0074] The reception desk can estimate the user's emotions and determine the priority of the technical information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk can postpone the input of less important technical information. Conversely, if the user is relaxed, the reception desk can prioritize the input of more important technical information. Furthermore, if the user is in a hurry, the reception desk can only input the most important technical information. This allows the priority of technical information input to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI, which can estimate the user's emotions and determine the priority of the technical information to be entered.

[0075] The reception desk can prioritize inputting highly relevant information when technical information is entered, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize inputting technical information related to that region. Furthermore, if the user is on a business trip, the reception desk can prioritize inputting technical information related to their destination. Additionally, if the user is at home, the reception desk can prioritize inputting technical information related to their home. This allows for prioritization of technical information input based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into the AI, which can then prioritize inputting highly relevant information.

[0076] The reception unit can analyze the user's social media activity and input relevant information when technical information is entered. For example, the reception unit can automatically input technical information that the user has shared on social media. The reception unit can also prioritize inputting information related to the technical fields that the user follows on social media. Furthermore, the reception unit can input information about the technical communities that the user participates in on social media. This allows relevant information to be entered based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into AI, and the AI ​​can input relevant information.

[0077] The data collection unit can estimate the user's emotions and adjust the scope of technical information collected based on the estimated emotions. For example, if the user is stressed, the data collection unit can narrow the scope and collect only important information. If the user is relaxed, the data collection unit can collect a wide range of technical information. Furthermore, if the user is in a hurry, the data collection unit can collect only the most relevant information. This allows the scope of technical information collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user facial expression data into a generative AI, which can estimate the user's emotions and adjust the scope of technical information to be collected.

[0078] The data collection unit can adjust the level of detail collected based on the importance of the technical information during collection. For example, the data collection unit can collect detailed information for highly important technical information. It can also collect only an overview for less important technical information. Furthermore, the data collection unit can adjust the depth of information collected according to its importance. This allows for adjustment of the level of detail collected according to the importance of the technical information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input technical information importance data into the AI, which can then adjust the level of detail of the collection.

[0079] The collection unit can apply different collection algorithms depending on the category of technical information during collection. For example, for patent information, the collection unit can collect detailed information from a patent database. It can also collect relevant information for copyright information from a copyright database. Furthermore, for trademark information, the collection unit can collect necessary information from a trademark database. This allows the collection algorithm to be applied according to the category of technical information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input technical information category data into an AI, which can then apply different collection algorithms.

[0080] The data collection unit can estimate the user's emotions and determine the priority of technical information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can postpone the collection of less important information. Conversely, if the user is relaxed, the data collection unit can prioritize the collection of highly important information. Furthermore, if the user is in a hurry, the data collection unit can collect only the most important information. This allows the priority of technical information collection to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user facial expression data into a generative AI, which can estimate the user's emotions and determine the priority of technical information to collect.

[0081] The data collection unit can determine the priority of data collection based on the publication date of the technical information. For example, the data collection unit can prioritize the collection of recently published technical information. The data collection unit can also collect older technical information as needed. Furthermore, the data collection unit can adjust the priority of the information to be collected based on the publication date. This allows the data collection priority to be determined based on the publication date of the technical information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input technical information publication date data into AI, and the AI ​​can determine the priority of data collection.

[0082] The collection unit can adjust the order of collection based on the relevance of the technical information during collection. For example, the collection unit can prioritize the collection of the most relevant technical information. It can also postpone the collection of less relevant technical information. Furthermore, the collection unit can adjust the order of information to be collected based on relevance. This allows the collection order to be adjusted based on the relevance of the technical information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the relevance data of the technical information into the AI, and the AI ​​can adjust the collection order.

[0083] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is stressed, the evaluation unit can relax the evaluation criteria. Conversely, if the user is relaxed, the evaluation unit can apply strict evaluation criteria. Furthermore, if the user is in a hurry, the evaluation unit can apply simplified evaluation criteria. This allows the evaluation criteria to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit can input user facial expression data into a generative AI, which can estimate the user's emotions and adjust the evaluation criteria.

[0084] The evaluation unit can improve the accuracy of its evaluation by considering the interrelationships of technical information during the evaluation process. For example, the evaluation unit can analyze the interrelationships of technical information and prioritize the evaluation of information with high relevance. The evaluation unit can also improve the accuracy of its evaluation based on the interrelationships of technical information. Furthermore, the evaluation unit can adjust the evaluation results by considering the interrelationships of technical information. This allows for improved evaluation accuracy by considering the interrelationships of technical information. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data on the interrelationships of technical information into AI, which can then improve the accuracy of the evaluation.

[0085] The evaluation unit can perform evaluations while considering the attribute information of the submitter of the technical information. For example, the evaluation unit can adjust the evaluation criteria based on the submitter's field of expertise. It can also adjust the strictness of the evaluation based on the submitter's years of experience. Furthermore, the evaluation unit can adjust the evaluation criteria based on the submitter's past evaluation results. This allows the evaluation to be performed while considering the attribute information of the submitter of the technical information. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input the submitter's attribute information data into the AI, and the AI ​​can adjust the evaluation criteria.

[0086] The evaluation unit can estimate the user's emotions and adjust the display order of evaluation results based on the estimated emotions. For example, if the user is stressed, the evaluation unit can postpone displaying less important evaluation results. If the user is relaxed, the evaluation unit can prioritize displaying more important evaluation results. Furthermore, if the user is in a hurry, the evaluation unit can display only the most important evaluation results. This allows the display order of evaluation results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit can input user facial expression data into a generative AI, which can estimate the user's emotions and adjust the display order of evaluation results.

[0087] The evaluation unit can perform evaluations while considering the geographical distribution of technical information. For example, the evaluation unit can adjust the evaluation criteria based on the geographical distribution of technical information. The evaluation unit can also prioritize the evaluation of geographically close technical information. Furthermore, the evaluation unit can adjust the evaluation results while considering the geographical distribution. This allows evaluations to be performed while considering the geographical distribution of technical information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input geographical distribution data of technical information into AI, and the AI ​​can adjust the evaluation criteria.

[0088] The evaluation unit can improve the accuracy of its evaluation by referring to relevant technical information literature during the evaluation process. For example, the evaluation unit can improve the accuracy of its evaluation by referring to relevant technical information literature. Furthermore, the evaluation unit can adjust the evaluation criteria based on the relevant literature. In addition, the evaluation unit can adjust the evaluation results by considering the relevant literature. This allows for improved evaluation accuracy by referring to relevant technical information literature. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input technical information literature data into AI, which can then improve the accuracy of the evaluation.

[0089] The extraction unit can estimate the user's emotions and determine the priority of technical information to extract based on the estimated user emotions. For example, if the user is stressed, the extraction unit can postpone the extraction of less important technical information. Conversely, if the user is relaxed, the extraction unit can prioritize the extraction of highly important technical information. Furthermore, if the user is in a hurry, the extraction unit can extract only the most important technical information. This allows the extraction priority of technical information to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the extraction unit may be performed using AI, or not using AI. For example, the extraction unit can input user facial expression data into a generative AI, which can estimate the user's emotions and determine the priority of technical information to extract.

[0090] The extraction unit can improve the accuracy of extraction by considering the interrelationships of technical information during the extraction process. For example, the extraction unit can analyze the interrelationships of technical information and prioritize the extraction of highly relevant information. The extraction unit can also improve the accuracy of extraction based on the interrelationships of technical information. Furthermore, the extraction unit can adjust the extraction results by considering the interrelationships of technical information. This allows for improved extraction accuracy by considering the interrelationships of technical information. Some or all of the above-described processes in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input data on the interrelationships of technical information into AI, which can then improve the accuracy of extraction.

[0091] The extraction unit can estimate the user's emotions and adjust the display method of the extracted technical information based on the estimated user emotions. For example, if the user is stressed, the extraction unit can provide a simple and highly visible display method. If the user is relaxed, the extraction unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the extraction unit can provide a display method that gets straight to the point. This allows the display method of technical information to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input user facial expression data into the generative AI, which can estimate the user's emotions and adjust the display method.

[0092] The extraction unit can perform extraction while considering the geographical distribution of technical information. For example, the extraction unit can adjust the extraction criteria based on the geographical distribution of technical information. The extraction unit can also prioritize the extraction of geographically close technical information. Furthermore, the extraction unit can adjust the extraction results considering the geographical distribution. This allows extraction to be performed while considering the geographical distribution of technical information. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input geographical distribution data of technical information into AI, and the AI ​​can adjust the extraction criteria.

[0093] The management department can estimate the user's emotions and select management data based on the estimated emotions. For example, if the user is stressed, the management department can postpone the selection of less important management data. Conversely, if the user is relaxed, the management department can prioritize the selection of highly important management data. Furthermore, if the user is in a hurry, the management department can select only the most important management data. This allows for the selection of management data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI, or not using AI. For example, the management department can input user facial expression data into a generative AI, which can estimate the user's emotions and select management data.

[0094] The management department can optimize its management algorithm by referring to past management data during management. For example, the management department can analyze past management data and select the optimal management algorithm. It can also adjust the management algorithm based on past management data. Furthermore, the management department can improve management efficiency by referring to past management data. This allows for the optimization of the management algorithm by referring to past management data. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input past management data into AI, and the AI ​​can optimize the management algorithm.

[0095] The management unit can estimate the user's emotions and adjust the frequency of management based on the estimated emotions. For example, if the user is stressed, the management unit can reduce the frequency of management to alleviate the burden. Conversely, if the user is relaxed, the management unit can increase the frequency of management to provide more detailed management. Furthermore, if the user is in a hurry, the management unit can minimize the frequency of management. This allows the management unit to adjust the frequency of management according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, or not using AI. For example, the management unit can input user facial expression data into a generative AI, which can estimate the user's emotions and adjust the frequency of management.

[0096] The management department can weight management data based on the submission date of technical information during the management process. For example, the management department can prioritize the management of recently submitted technical information. The management department can also manage older technical information as needed. Furthermore, the management department can adjust the weighting of management data based on the submission date. This allows for weighting of management data based on the submission date of technical information. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input technical information submission date data into AI, and the AI ​​can weight the management data.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The reception desk can estimate the user's emotions and adjust the timing of technical information input based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can delay the input timing to provide time for relaxation. The reception desk can also send an immediate notification to encourage input if the user is concentrating. Furthermore, if the user is tired, the reception desk can suggest postponing the input until the next day. This allows the timing of technical information input to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI, which can estimate the user's emotions and adjust the input timing.

[0099] The reception desk can analyze the user's past technical information input history and select the optimal input method. For example, if the user has frequently used voice input in the past, the reception desk can prioritize suggesting voice input. Furthermore, if the user has preferred text input in the past, the reception desk can set text input as the default. In addition, if the reception desk has previously performed input during a specific time period, it can send a notification during that time period. This allows the reception desk to select the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's past input history data into an AI, which can then select the optimal input method.

[0100] The reception unit can filter technical information input based on the user's current projects and areas of interest. For example, the reception unit can display only technical information related to the user's current project on the input screen. It can also prioritize input of highly relevant technical information based on the user's areas of interest. Furthermore, the reception unit can narrow down input candidates based on technical fields the user has shown interest in in the past. This allows for filtering of technical information based on the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's project information and area of ​​interest data into an AI, which can then perform the filtering.

[0101] The reception desk can estimate the user's emotions and determine the priority of the technical information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk can postpone the input of less important technical information. Conversely, if the user is relaxed, the reception desk can prioritize the input of more important technical information. Furthermore, if the user is in a hurry, the reception desk can only input the most important technical information. This allows the priority of technical information input to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI, which can estimate the user's emotions and determine the priority of the technical information to be entered.

[0102] The reception desk can prioritize inputting highly relevant information when technical information is entered, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize inputting technical information related to that region. Furthermore, if the user is on a business trip, the reception desk can prioritize inputting technical information related to their destination. Additionally, if the user is at home, the reception desk can prioritize inputting technical information related to their home. This allows for prioritization of technical information input based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into the AI, which can then prioritize inputting highly relevant information.

[0103] The data collection unit can estimate the user's emotions and adjust the scope of technical information collected based on the estimated emotions. For example, if the user is stressed, the data collection unit can narrow the scope and collect only important information. If the user is relaxed, the data collection unit can collect a wide range of technical information. Furthermore, if the user is in a hurry, the data collection unit can collect only the most relevant information. This allows the scope of technical information collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user facial expression data into a generative AI, which can estimate the user's emotions and adjust the scope of technical information to be collected.

[0104] The data collection unit can adjust the level of detail collected based on the importance of the technical information during collection. For example, the data collection unit can collect detailed information for highly important technical information. It can also collect only an overview for less important technical information. Furthermore, the data collection unit can adjust the depth of information collected according to its importance. This allows for adjustment of the level of detail collected according to the importance of the technical information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input technical information importance data into the AI, which can then adjust the level of detail of the collection.

[0105] The collection unit can apply different collection algorithms depending on the category of technical information during collection. For example, for patent information, the collection unit can collect detailed information from a patent database. It can also collect relevant information for copyright information from a copyright database. Furthermore, for trademark information, the collection unit can collect necessary information from a trademark database. This allows the collection algorithm to be applied according to the category of technical information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input technical information category data into an AI, which can then apply different collection algorithms.

[0106] The data collection unit can estimate the user's emotions and determine the priority of technical information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can postpone the collection of less important information. Conversely, if the user is relaxed, the data collection unit can prioritize the collection of highly important information. Furthermore, if the user is in a hurry, the data collection unit can collect only the most important information. This allows the priority of technical information collection to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user facial expression data into a generative AI, which can estimate the user's emotions and determine the priority of technical information to collect.

[0107] The data collection unit can determine the priority of data collection based on the publication date of the technical information. For example, the data collection unit can prioritize the collection of recently published technical information. The data collection unit can also collect older technical information as needed. Furthermore, the data collection unit can adjust the priority of the information to be collected based on the publication date. This allows the data collection priority to be determined based on the publication date of the technical information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input technical information publication date data into AI, and the AI ​​can determine the priority of data collection.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk accepts technical information related to the technology. This technical information includes an overview of the new technology and the contents of patent applications. The reception desk can extract and automatically accept technical information based on emails, messages, and meeting minutes received by users in the development department. Step 2: The collection unit collects technologies similar to the technical information received by the reception unit from intellectual property databases, including patents, copyrights, and trademarks. The collection unit can search for similar patents from the patent database, similar copyrighted works from the copyright database, and similar trademarks from the trademark database. Step 3: The evaluation unit assesses the similarity between the technical information and other intellectual property, or the risk of infringing other intellectual property, based on the information collected by the collection unit. The evaluation unit can compare the content of patent claims with the technical information to assess similarity. It can also compare the content of copyrights with the technical information to assess the risk of infringement. Furthermore, it can compare the content of trademarks with the technical information to assess similarity and the risk of infringement.

[0110] 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.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] For example, the reception unit can receive technical information using the reception device 38 of the smart device 14. The collection unit can collect similar technologies from the intellectual property database using the specific processing unit 290 of the data processing device 12. The evaluation unit can evaluate the similarity and infringement risk between the technical information and other intellectual property using the specific processing unit 290 of the data processing device 12. The management unit can manage the expiration date and renewal date of licenses and automatically send notifications using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] 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.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] 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.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] For example, the reception unit can receive technical information using the microphone 238 of the smart glasses 214. The collection unit can collect similar technologies from the intellectual property database using the specific processing unit 290 of the data processing device 12. The evaluation unit can evaluate the similarity and infringement risk between the technical information and other intellectual property using the specific processing unit 290 of the data processing device 12. The management unit can manage the license expiration date and renewal date and automatically send notifications using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] 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.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] 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.

[0137] 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.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] For example, the reception unit can receive technical information using the microphone 238 of the headset terminal 314. The collection unit can collect similar technologies from the intellectual property database using the specific processing unit 290 of the data processing device 12. The evaluation unit can evaluate the similarity and infringement risk between the technical information and other intellectual property using the specific processing unit 290 of the data processing device 12. The management unit can manage the expiration date and renewal date of licenses and automatically send notifications using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] 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.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] 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.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] 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.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] 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.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] For example, the reception unit can receive technical information using the microphone 238 of the robot 414. The collection unit can collect similar technologies from the intellectual property database using the specific processing unit 290 of the data processing device 12. The evaluation unit can evaluate the similarity and infringement risk between the technical information and other intellectual property using the specific processing unit 290 of the data processing device 12. The management unit can manage the expiration date and renewal date of licenses and automatically send notifications using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0163] 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.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] 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.

[0166] 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.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] 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."

[0169] 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.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] 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.

[0181] (Note 1) A system characterized by comprising: a reception unit that receives input of technical information relating to technology; a collection unit that collects technologies similar to the technical information received by the reception unit from an intellectual property database; and an evaluation unit that evaluates the similarity between the technical information and other intellectual property, or the risk of infringing other intellectual property, based on the information collected by the collection unit. (Note 2) The system according to Appendix 1, further comprising an extraction unit that extracts technical information being developed in the development department based on information regarding emails received by users in the development department, messages sent and received between users within the development department, and minutes of meetings held within the development department, wherein the receiving unit automatically receives the technical information extracted by the extraction unit. (Note 3) The aforementioned collection unit is We regularly collect newly released intellectual property information, The evaluation unit, The evaluation is performed based on the results collected periodically by the aforementioned collection unit. The system described in Appendix 1, characterized by the features described herein. (Note 4) The system described in Appendix 1 is further characterized by comprising a management unit that manages the expiration date and renewal period of licenses and automatically provides necessary notifications. (Note 5) The management unit is characterized by automatically managing newly acquired license information and tracking renewal dates, as described in Appendix 4. (Note 6) The system described in Appendix 1, wherein the reception unit estimates the user's emotions and adjusts the timing of inputting technical information based on the estimated user's emotions. (Note 7) The aforementioned reception unit is By analyzing the user's past technical information input history, Select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering technical information, Filter based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is To estimate the user's emotions, Prioritize the technical information to be entered based on the estimated user's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering technical information, The system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering technical information, Analyze users' social media activity, Enter relevant information The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is To estimate the user's emotions, Adjust the scope of technical information collected based on estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During collection, Adjust the level of detail in the collection based on the importance of the technical information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During collection, Apply different collection algorithms depending on the category of technical information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is To estimate the user's emotions, Prioritize the technical information to collect based on estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is During collection, Prioritize data collection based on the timing of the release of technical information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is During collection, Adjust the order of collection based on the relevance of the technical information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit, To estimate the user's emotions, Adjust evaluation criteria based on estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, During the evaluation, Improving evaluation accuracy by considering the interrelationships of technical information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, During the evaluation, The evaluation will take into account the attribute information of the submitter of the technical information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, To estimate the user's emotions, The display order of evaluation results is adjusted based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, During the evaluation, The evaluation will take into account the geographical distribution of technical information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, During the evaluation, Improve the accuracy of evaluations by referring to relevant technical information literature. The system described in Appendix 1, characterized by the features described herein. (Note 24) The extraction unit is To estimate the user's emotions, Prioritize the technical information to extract based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 25) The extraction unit is During extraction, Improve extraction accuracy by considering the interrelationships of technical information. The system described in Appendix 2, characterized by the features described herein. (Note 26) The extraction unit is To estimate the user's emotions, Adjust the way technical information is displayed based on estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 27) The extraction unit is During extraction, Extraction is performed considering the geographical distribution of technical information. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned management department, To estimate the user's emotions, Select management data based on estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 29) The aforementioned management department, During management, Optimize the management algorithm by referring to past management data. The system described in Appendix 4, characterized by the features described herein. (Note 30) The aforementioned management department, To estimate the user's emotions, Adjust the frequency of interventions based on estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 31) The aforementioned management department, During management, Weighting of management data based on the submission date of technical information. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts input of technical information related to technology, A collection unit collects technologies similar to the technical information received by the aforementioned reception unit from an intellectual property database. The system includes an evaluation unit that, based on the information collected by the collection unit, evaluates the similarity between the technical information and other intellectual property, or the risk of infringing other intellectual property. A system characterized by the following features.

2. The system further includes an extraction unit that extracts technical information being developed in the development department based on information from emails received by users in the development department, messages exchanged between users within the development department, and minutes of meetings held within the development department. The aforementioned reception unit is The system automatically receives the technical information extracted by the extraction unit. The system according to feature 1.

3. The aforementioned collection unit is We regularly collect newly released intellectual property information, The evaluation unit described above, The evaluation is performed based on the results collected periodically by the aforementioned collection unit. The system according to feature 1.

4. The system according to claim 1, further comprising a management unit that manages the expiration date and renewal period of licenses and automatically provides necessary notifications.

5. The system according to claim 4, characterized in that the management unit automatically manages newly acquired license information and tracks the renewal period.

6. The system according to claim 1, characterized in that the reception unit estimates the user's emotions and adjusts the timing of inputting technical information based on the estimated user's emotions.

7. The aforementioned reception unit is By analyzing the user's past technical information input history, Select the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When entering technical information, Filter based on the user's current projects and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is Estimate the user's emotions, Prioritize the technical information to be entered based on the estimated user's sentiment. The system according to feature 1.

10. The aforementioned reception unit is When entering technical information, The system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system according to feature 1.

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